Download - PERSPECTIVE A THESIS
FORECAST VERIFICATION: A DISPERSION MODELING
PERSPECTIVE
by
RACHEL ROGERS-VAN NICE, B.S.
A THESIS
IN
ATMOSPHERIC SCIENCE
Submitted to the Graduate Faculty of Texas Tech University in
Partial Fulfillment of the Requirements for
the Degree of
MASTER OF SCIENCES
Approved
Dr. Sukanta Basu Chairperson of the Committee
Dr. Kevin Mulligan
Dr. John Schroeder
Fred Hartmeister Dean of the Graduate School
May, 2008
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ACKNOWLEDGMENTS
I first must thank Dr. Sukanta Basu, the chair of my thesis advisory committee.
His patience and unending explanations have taught me more than I ever anticipated. He
has challenged and encouraged me during every step of my graduate career. I also want
to thank Dr. John Schroeder and Wes Burgett for all their help and information regarding
the West Texas Mesonet. I also thank Dr. Kevin Mulligan for allowing me to spend
countless hours in the GIS lab in addition to his input on the maps in the document. I
also need to thank Joe Touma of the NOAA Air Resources Laboratory for identifying the
need for research in this field. This work was partially funded by the Texas Advanced
Research Program (003644-0003-2006) grant.
I offer much thanks Brandon Storm, my L.C., for the insight and knowledge he
has shared with me along the way. Also, many thanks to my friends, Jennifer Huckabee
and Angela Montoya for the wise words only girlfriends can truly share.
I cannot express my thanks enough to my family for the sacrifices they have made
to allow me to attend graduate school. My husband, Chris for his loving words, support
and encouragement throughout the entire process, and my son Cameron, for his
understanding and help offered whenever needed.
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TABLE OF CONTENTS
ACKNOWLEDGMENTS .................................................................................................. ii
ABSTRACT...................................................................................................................... vii
LIST OF TABLES........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
1 INTRODUCTION .......................................................................................................... 1
1.1 Background.............................................................................................................. 1
1.2 Coupling of Mesoscale and Dispersion Models ...................................................... 2
1.3 Limitations of Present Day PBL Parameterizations ................................................ 5
1.4 Problem Statement ................................................................................................... 7
2 DESCRIPTION OF OBSERVATIONAL DATA........................................................ 18
2.1 Description of the West Texas Mesonet ................................................................ 18
2.1.1 Station Site Information.................................................................................. 18
2.1.2 Instrumentation ............................................................................................... 18
2.1.3 Quality Assurance and Control....................................................................... 20
2.1.4 Data Transfer .................................................................................................. 20
2.2 Summary ................................................................................................................ 21
3 WTM DERIVED SURFACE LAYER CHARATERISTICS...................................... 25
3.1 Location Description.............................................................................................. 25
3.2 West Texas Mesonet Data ..................................................................................... 26
3.3 Methodology.......................................................................................................... 26
3.4 ONC Observations ................................................................................................. 26
3.4.1 June 2006 Observations of Wind Speed and Direction ONC......................... 27
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3.4.2 December 2006 Observations of Wind Speed and Direction ONC................ 27
3.5 OFC Observations of Wind Speed and Wind Direction........................................ 27
3.5.1 June 2006 Observations of Wind Speed and Direction OFC ......................... 27
3.5.2 December 2006 Observations of Wind Speed and Direction OFC ................ 28
3.6 RTC Observations of Wind Speed and Wind Direction........................................ 28
3.6.1 June 2006 Observations of Wind Speed and Direction at RTC...................... 28
3.6.2 December 2006 Observations of Wind Speed and Direction at RTC ............ 28
3.7 2-m Temperature Observations.............................................................................. 28
3.8 Summary ................................................................................................................ 29
4 DESCRIPTION OF MODEL DATA........................................................................... 36
4.1 Description of the Weather and Research Forecast Model.................................... 36
4.1.1 Model Domain and Physics ............................................................................ 36
4.1.2 Planetary Boundary Layer Parameterizations in WRF................................... 37
4.2 Static Fields............................................................................................................ 38
4.3 Summary ................................................................................................................ 38
5 WRF SURFACE LAYER CHARACTERISTICS....................................................... 46
5.1 WRF Model Forecast............................................................................................. 46
5.2 Methodology.......................................................................................................... 46
5.3 ONC WRF Model Forecast ................................................................................... 47
5.3.1 June 2006 Model Forecast of Wind Speed and Direction ONC..................... 47
5.3.2 December 2006 Model Forecast of Wind Speed and Direction ONC............ 47
5.3.3 Monthly Statistics of Wind Speed and Direction ONC.................................. 47
5.3.4 Diurnal Statistics of Wind Speed and Direction ONC.................................... 48
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5.4 OFC Observations of Wind Speed and Wind Direction........................................ 48
5.4.1 June 2006 Model Forecast Wind Speed and Direction OFC.......................... 48
5.4.2 December 2006 Model Forecast Wind Speed and Direction OFC................. 48
5.4.3 Monthly Statistics of Wind Speed and Direction OFC................................... 49
5.4.4 Diurnal Statistics of Wind Speed and Direction OFC .................................... 49
5.5 RTC Observations of Wind Speed and Wind Direction........................................ 49
5.5.1 June 2006 Model Forecast Wind Speed and Direction at RTC...................... 49
5.5.2 December 2006 Model Forecast Wind Speed and Direction at RTC............. 50
5.5.3 Monthly Statistics of Wind Speed and Direction at RTC............................... 50
5.5.4 Diurnal Statistics of Wind Speed and Direction at RTC ................................ 50
5.6 2-m Temperature Observations.............................................................................. 50
5.6.1 2-m Temperature Statistics ............................................................................. 51
5.7 Summary ................................................................................................................ 52
6 WIND ATLAS ANALYSIS AND APPLICATION PROGRAM ............................... 81
6.1 Description............................................................................................................. 81
6.2 Main Calculation Blocks........................................................................................ 81
6.3 Summary ................................................................................................................ 83
7 WRF-WAsP COUPLING............................................................................................. 87
7.1 Purpose................................................................................................................... 87
7.2 Macy Ranch ........................................................................................................... 88
7.3 Fluvanna................................................................................................................. 90
7.4 Reese Technology Center ...................................................................................... 90
7.5 Summary ................................................................................................................ 91
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8 SUMMARY AND CONCLUSIONS ......................................................................... 112
8.1 Summary .............................................................................................................. 112
8.2 Conclusions.......................................................................................................... 112
REFERENCES ............................................................................................................... 115
A WTM OBSERVATIONAL WIND ROSES.............................................................. 117
B WRF FORECAST WIND ROSES ............................................................................ 151
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ABSTRACT
The Environmental Protection Agency currently uses AERMOD, an air quality
dispersion model to aid in the forecasting of transport and dispersion of air pollution for
the U.S. Typically, NWS-ASOS observations (post-processed by EPA-AERMET model)
are used as input to the AERMOD model. This traditional framework of running a
dispersion model based on point observations is quite problematic from a variety of
theoretical standpoints (e.g., lack of representativeness of meteorological data). An
alternative viable framework would be to use prognostic meteorological models in
conjunction with AERMOD. Indeed, contemporary research shows that the use of
prognostic models as a substitute for NWS-ASOS observations alleviates some of the
longstanding dispersion modeling problems, but at the same time creates new concerns.
I will elaborate on several questions that need to be adequately addressed before
prognostic models can be reliably utilized in operational dispersion applications. Most of
these questions are rooted in prognostic models’ (in) ability to accurately represent the
boundary layer variables of interest to the dispersion modeling community (e.g., wind
speed, wind direction, temperature). I will compare the potential of a new generation
prognostic meteorological model called the Weather Research and Forecasting (WRF)
model in capturing wind speed variable versus data from the West Texas Mesonet by
statistical analysis for verification. One year of ARW WRF output is analyzed. The WRF
is a 36/12 km two-way nested run using the YSU PBL scheme. With use of innovative
strategies for verification of complex spatio-temporal forecast fields and novel
verification measures will make this study distinct.
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LIST OF TABLES
1 1: Model performance measures for surface wind speed and direction for MM5 for the 4-km grid in the Central California SARMAP domain.. ................................ 14
3.1: Air temperature lapse rate variations as a function of month. .................................. 31 7.1: Comparison of Wind Speed Observations, Forecast, WRF-WAsP Forecast, and
WRF-WAsP Forecast with roughness calculated for each sector. ............... 110 7.2: Roughness lengths for Fluvanna, Macy Ranch, and RTC...................................... 111
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LIST OF FIGURES
1.1: AERMOD modeling system structure........................................................................ 9 1.2: ASOS Station............................................................................................................ 10 1.3: Wind roses of simulated winds derived from NWS observations ............................ 11 1.4: Comparison of ranked hourly concentrations from AERMOD-NWS and
AERMOD-MM5...................................................................................................... 12 1.5: Average angular distribution of wind direction prevailed over Pune....................... 13 1.6: (top) Bias, (middle) RMSE, and (bottom) error standard deviation........................ 15 1.7: Time series of area-averaged horizontal wind speeds (ms-1).................................... 16 1.8: Time series of aerial averaged 2-m temperature....................................................... 17 2.1: Stations within the West Texas Mesonet as of April 2008....................................... 22 2.2: Lubbock West Texas Mesonet Station. .................................................................... 23 2.3: Elevation view of a typical West Texas Mesonet station ......................................... 24 3.1: 2-m Temperature Histogram as a Function of Month .............................................. 32 3.2: 2-m Temperature Histogram as a Function of Time................................................. 33 3.3: 10-m Wind Speed Histogram as a Function of Month ............................................. 34 3.4: 10-m Wind Speed Histogram as a Function of Time ............................................... 35 4.1: WRF system components. ........................................................................................ 39
4.2: Operational ARW domain ........................................................................................ 40
4.3: WRF Domain............................................................................................................ 41
4.4: Illustration of PBL Processes.................................................................................... 42
4.5: WRF 12-km Digital Elevation Model ...................................................................... 43
4.6: WRF 12-km Land Use.............................................................................................. 44
4.7: 30-m National Land Cover Data (2001). .................................................................. 45
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5.1: ONC monthly mean bias of wind speed. .................................................................. 53
5.2: ONC monthly RMSE for wind speed. ...................................................................... 54
5.3: ONC diurnal mean bias for wind speed.................................................................... 55
5.4: ONC diurnal RMSE of wind speed. ........................................................................ 56
5.5: OFC monthly mean bias of wind speed.................................................................... 57
5.6: OFC monthly RMSE for wind speed........................................................................ 58
5.7: OFC diurnal mean bias of wind speed...................................................................... 59
5.8: OFC diurnal RMSE for wind speed.......................................................................... 60
5.9: RTC monthly mean bias of wind speed.................................................................... 61
5.10: RTC monthly RMSE for wind speed...................................................................... 62
5.11: RTC diurnal mean bias of wind speed.................................................................... 63
5.12: RTC diurnal RMSE for wind speed........................................................................ 64
5.13: 2-m WRF Forecast Temperature Histogram as a Function of Month .................... 65
5.14: OFC diurnal 2-m temperature bias ......................................................................... 66
5.15: ONC diurnal 2-m temperature bias......................................................................... 67
5.16: RTC diurnal 2-m temperature bias ......................................................................... 68
5.17: 2-m WRF Forecast Temperature Histogram as a Function of Time ...................... 69
5.18: OFC monthly 2-m temperature bias. ...................................................................... 70
5.19: ONC monthly 2-m temperature bias....................................................................... 71
5.20: RTC monthly 2-m temperature bias. ...................................................................... 72
5.21: OFC diurnal 2-m temperature RMSE..................................................................... 73
5.22: ONC diurnal 2-m temperature RMSE. ................................................................... 74
5.23: RTC diurnal 2-m temperature RMSE..................................................................... 75
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5.24: OFC monthly 2-m temperature RMSE................................................................... 76
5.25: ONC monthly 2-m temperature RMSE. ................................................................. 77
5.26: RTC monthly 2-m temperature RMSE................................................................... 78
5.27: 10-m WRF Forecast Wind Speed Histogram as a Function of Month................... 79
5.28: 10-m WRF Forecast Wind Speed Histogram as a Function of Time ..................... 80
6.1: Example of terrain corresponding to roughness class 0.. ......................................... 85 6.2: Example of terrain corresponding to roughness class 1.. ......................................... 85 6.3: Example of terrain corresponding to roughness class 2.. ......................................... 86 6.4: Example of terrain corresponding to roughness class 3.. ......................................... 86 6.5: The wind atlas method of WAsP. ............................................................................. 84 7.1: WRF-WAsP Framework........................................................................................... 93
7.2: Photo of Macy Ranch mesonet station ..................................................................... 94
7.3: April 2006 – March 2007 Macy Ranch Wind Rose ................................................. 95
7.4: Macy Ranch Wind Rose from January 2002 to Present. .......................................... 96
7.5: Annual Averaged 12-km grid spacing wind speed at Macy Ranch.......................... 97
7.6: 100-m Digital Elevation Model of Macy Ranch derived from 30-m NED.............. 98
7.7: Annual Averaged 100-m grid spacing Wind Speed at Macy Ranch. ....................... 99
7.8: April 2006 – March 2007 Fluvanna Wind Rose..................................................... 100
7.9: Fluvanna Wind Rose from July 2002 to Present. ................................................... 101
7.10: Annual Averaged 12-km grid spacing wind speed at Fluvanna. .......................... 102
7.11: 100-m Digital Elevation Model of Fluvanna derived from 30-m NED ................ 103
7.12: Annual Averaged 100-m grid spacing Wind Speed at Fluvanna........................... 104
7.13: April 2006 – March 2007 RTC Wind Rose........................................................... 105
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7.14: Reese Technology Center Wind Rose from June 2000 to Present. ....................... 106
7.15: Annual Averaged 12-km grid spacing wind speed at RTC .................................. 107
7.16: 100-m Digital Elevation Model of RTC derived from 30-m NED....................... 108
7.17: Annual Averaged 100-m grid spacing Wind Speed at RTC................................. 109
A.1: Wind Direction and Wind Speed for April 2006: ONC ........................................ 121
A.2: Wind Direction and Wind Speed for May 2006: ONC.......................................... 121
A.3: Wind Direction and Wind Speed for June 2006: ONC.......................................... 122
A.4: Wind Direction and Wind Speed for July 2006: ONC .......................................... 122
A.5: Wind Direction and Wind Speed for August 2006: ONC ..................................... 123
A.6: Wind Direction and Wind Speed for September 2006: ONC................................ 123
A.7: Wind Direction and Wind Speed for October 2006: ONC.................................... 124
A.8: Wind Direction and Wind Speed for November 2006: ONC ................................ 124
A.9: Wind Direction and Wind Speed for December 2006: ONC ................................ 125
A.10: Wind Direction and Wind Speed for January 2007: ONC .................................. 125
A.11: Wind Direction and Wind Speed for February 2007: ONC ................................ 126
A.12: Wind Direction and Wind Speed for March 2007: ONC .................................... 126
A.13: Wind Direction and Wind Speed for 0Z: ONC ................................................... 127
A.14: Wind Direction and Wind Speed for 3Z: ONC ................................................... 127
A.15: Wind Direction and Wind Speed for 6Z: ONC ................................................... 128
A.16: Wind Direction and Wind Speed for 9Z: ONC ................................................... 128
A.17: Wind Direction and Wind Speed for 12Z: ONC ................................................. 129
A.18: Wind Direction and Wind Speed for 15Z: ONC ................................................. 129
A.19: Wind Direction and Wind Speed for 18Z: ONC ................................................. 130
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A.20: Wind Direction and Wind Speed for 21Z: ONC ................................................. 130
A.21: Wind Direction and Wind Speed for April 2006: OFC ....................................... 131
A.22: Wind Direction and Wind Speed for May 2006: OFC ........................................ 131
A.23: Wind Direction and Wind Speed for June 2006: OFC ........................................ 132
A.24: Wind Direction and Wind Speed for July 2006: OFC......................................... 132
A.25: Wind Direction and Wind Speed for August 2006: OFC.................................... 133
A.26: Wind Direction and Wind Speed for September 2006: OFC .............................. 133
A.27: Wind Direction and Wind Speed for October 2006: OFC................................... 134
A.28: Wind Direction and Wind Speed for November 2006: OFC............................... 134
A.29: Wind Direction and Wind Speed for December 2007: OFC ............................... 135
A.30: Wind Direction and Wind Speed for January 2007: OFC ................................... 135
A.31: Wind Direction and Wind Speed for February 2007: OFC ................................. 136
A.32: Wind Direction and Wind Speed for March 2007: OFC ..................................... 136
A.33: Wind Direction and Wind Speed for 0Z: OFC .................................................... 137
A.34: Wind Direction and Wind Speed for 3Z: OFC .................................................... 137
A.35: Wind Direction and Wind Speed for 6Z: OFC .................................................... 138
A.36: Wind Direction and Wind Speed for 9Z: OFC .................................................... 138
A.37: Wind Direction and Wind Speed for 12Z: OFC .................................................. 139
A.38: Wind Direction and Wind Speed for 15Z: OFC .................................................. 139
A.39: Wind Direction and Wind Speed for 18Z: OFC .................................................. 140
A.40: Wind Direction and Wind Speed for 21Z: OFC .................................................. 140
A.41: Wind Direction and Wind Speed for April 2006: RTC ....................................... 141
A.42: Wind Direction and Wind Speed for May 2006: RTC ........................................ 141
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A.43: Wind Direction and Wind Speed for June 2006: RTC ........................................ 142
A.44: Wind Direction and Wind Speed for July 2006: RTC......................................... 142
A.45: Wind Direction and Wind Speed for August 2006: RTC.................................... 143
A.46: Wind Direction and Wind Speed for September 2006: RTC .............................. 143
A.47: Wind Direction and Wind Speed for October 2006: RTC................................... 144
A.48: Wind Direction and Wind Speed for November 2006: RTC............................... 144
A.49: Wind Direction and Wind Speed for December 2006: RTC ............................... 145
A.50: Wind Direction and Wind Speed for January 2007: RTC ................................... 145
A.51: Wind Direction and Wind Speed for February 2007: RTC ................................. 146
A.52: Wind Direction and Wind Speed for March 2007: RTC ..................................... 146
A.53: Wind Direction and Wind Speed for 0Z: RTC .................................................... 147
A.54: Wind Direction and Wind Speed for 3Z: RTC .................................................... 147
A.55: Wind Direction and Wind Speed for 6Z: RTC .................................................... 148
A.56: Wind Direction and Wind Speed for 9Z: RTC .................................................... 148
A.57: Wind Direction and Wind Speed for 12Z: RTC .................................................. 149
A.58: Wind Direction and Wind Speed for 15Z: RTC .................................................. 149
A.59: Wind Direction and Wind Speed for 18Z: RTC .................................................. 150
A.60: Wind Direction and Wind Speed for 21Z: RTC .................................................. 150
B.1: Wind Direction and Wind Speed for April 2006: WRF Forecast ONC ................ 156
B.2: Wind Direction and Wind Speed for May 2006: WRF Forecast ONC.................. 156
B.3: Wind Direction and Wind Speed for June 2006: WRF Forecast ONC.................. 157
B.4: Wind Direction and Wind Speed for July 2006: WRF Forecast ONC .................. 157
B.5: Wind Direction and Wind Speed for August 2006: WRF Forecast ONC ............. 158
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B.6: Wind Direction and Wind Speed for September 2006: WRF Forecast ONC........ 158
B.7: Wind Direction and Wind Speed for October 2006: WRF Forecast ONC............ 159
B.8: Wind Direction and Wind Speed for November 2006: WRF Forecast ONC........ 159
B.9: Wind Direction and Wind Speed for December 2006: WRF Forecast ONC ........ 160
B.10: Wind Direction and Wind Speed for January 2007: WRF Forecast ONC .......... 160
B.11: Wind Direction and Wind Speed for February 2007: WRF Forecast ONC ........ 161
B.12: Wind Direction and Wind Speed for March 2007: WRF Forecast ONC ............ 161
B.13: Wind Direction and Wind Speed for 0Z: WRF Forecast ONC ........................... 162
B.14: Wind Direction and Wind Speed for 3Z: WRF Forecast ONC ........................... 162
B.15: Wind Direction and Wind Speed for 6Z: WRF Forecast ONC ........................... 163
B.16: Wind Direction and Wind Speed for 9Z: WRF Forecast ONC ........................... 163
B.17: Wind Direction and Wind Speed for 12Z: WRF Forecast ONC ......................... 164
B.18: Wind Direction and Wind Speed for 15Z: WRF Forecast ONC ......................... 164
B.19: Wind Direction and Wind Speed for 18Z: WRF Forecast ONC ......................... 165
B.20: Wind Direction and Wind Speed for 21Z: WRF Forecast ONC ......................... 165
B.21: Wind Direction and Wind Speed for April 2006: WRF Forecast OFC ............... 166
B.22: Wind Direction and Wind Speed for May 2006: WRF Forecast OFC ................ 166
B.23: Wind Direction and Wind Speed for June 2006: WRF Forecast OFC ................ 167
B.24: Wind Direction and Wind Speed for July 2006: WRF Forecast OFC................. 167
B.25: Wind Direction and Wind Speed for August 2006: WRF Forecast OFC............ 168
B.26: Wind Direction and Wind Speed for September 2006: WRF Forecast OFC ...... 168
B.27: Wind Direction and Wind Speed for October 2006: WRF Forecast OFC........... 169
B.28: Wind Direction and Wind Speed for November 2006: WRF Forecast OFC....... 169
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B.29: Wind Direction and Wind Speed for December 2006: WRF Forecast OFC ....... 170
B.30: Wind Direction and Wind Speed for January 2007: WRF Forecast OFC ........... 170
B.31: Wind Direction and Wind Speed for February 2007: WRF Forecast OFC ......... 171
B.32: Wind Direction and Wind Speed for March 2007: WRF Forecast OFC ............. 171
B.33: Wind Direction and Wind Speed for 0Z: WRF Forecast OFC ............................ 172
B.34: Wind Direction and Wind Speed for 3Z: WRF Forecast OFC ............................ 172
B.35: Wind Direction and Wind Speed for 6Z: WRF Forecast OFC ............................ 173
B.36: Wind Direction and Wind Speed for 9Z: WRF Forecast OFC ............................ 173
B.37: Wind Direction and Wind Speed for 12Z: WRF Forecast OFC .......................... 174
B.38: Wind Direction and Wind Speed for 15Z: WRF Forecast OFC .......................... 174
B.39: Wind Direction and Wind Speed for 18Z: WRF Forecast OFC .......................... 175
B.40: Wind Direction and Wind Speed for 21Z: WRF Forecast OFC .......................... 175
B.41: Wind Direction and Wind Speed for April 2006: WRF Forecast RTC ............... 176
B.42: Wind Direction and Wind Speed for May 2006: WRF Forecast RTC ................ 176
B.43: Wind Direction and Wind Speed for June 2006: WRF Forecast RTC ................ 177
B.44: Wind Direction and Wind Speed for July 2006: WRF Forecast RTC................. 177
B.45: Wind Direction and Wind Speed for August 2006: WRF Forecast RTC............ 178
B.46: Wind Direction and Wind Speed for September 2006: WRF Forecast RTC ...... 178
B.47: Wind Direction and Wind Speed for October 2006: WRF Forecast RTC........... 179
B.48: Wind Direction and Wind Speed for November 2006: WRF Forecast RTC....... 179
B.49: Wind Direction and Wind Speed for December 2006: WRF Forecast RTC ....... 180
B.50: Wind Direction and Wind Speed for January 2007: WRF Forecast RTC ........... 180
B.51: Wind Direction and Wind Speed for February 2007: WRF Forecast RTC ......... 181
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B.52: Wind Direction and Wind Speed for March 2007: WRF Forecast RTC ............. 181
B.53: Wind Direction and Wind Speed for 0Z: WRF Forecast RTC ............................ 182
B.54: Wind Direction and Wind Speed for 3Z: WRF Forecast RTC ............................ 182
B.55: Wind Direction and Wind Speed for 6Z: WRF Forecast RTC ............................ 183
B.56: Wind Direction and Wind Speed for 9Z: WRF Forecast RTC ............................ 183
B.57: Wind Direction and Wind Speed for 12Z: WRF Forecast RTC .......................... 184
B.58: Wind Direction and Wind Speed for 15Z: WRF Forecast RTC .......................... 184
B.59: Wind Direction and Wind Speed for 18Z: WRF Forecast RTC .......................... 185
B.60: Wind Direction and Wind Speed for 21Z: WRF Forecast RTC .......................... 185
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CHAPTER 1
INTRODUCTION
1.1 Background
The Environmental Protection Agency (EPA) currently uses the American
Meteorological Society (AMS) and EPA Regulatory Model (AERMOD; Figure 1.1), an
air quality dispersion model, to aid transport and dispersion forecasting of air pollution
within the United States. Typically, observations from the National Weather Service-
Automated Surface Observing Systems (NWS-ASOS; Figure 1.2) are primed by the
AERMOD meteorological preprocessor, AERMET, before input into AERMOD. This
traditional framework of running a dispersion model based on point observations is quite
problematic from a variety of theoretical and practical standpoints, and contains many
limitations (Touma et al. 2007), such as:
• NWS-ASOS data represents a single point measurement that for many situations is representative of a relatively small area around the observation site;
• observations at NWS sites are taken at a single height that is generally 10 m
above ground level;
• atmospheric turbulence measurements are not available from NWS sites; which AERMOD could directly use;
• timeliness of data is inadequate because of the need to execute time consuming
quality control programs; • and the meteorological data may not be representative due to the fact:
a) surface observations collected some distance away from emissions source, or b) upper air soundings needed to estimate the mixing height are usually not collocated with surface observation sites An alternative viable framework to reduce the limitations above would be to use
prognostic meteorological models, such as the Weather Research and Forecasting (WRF)
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model as input for AERMOD. Contemporary research shows that the use of prognostic
models as a substitute for NWS-ASOS point observations alleviates some of the
longstanding dispersion modeling problems, but at the same time creates new concerns.
1.2 Coupling of Mesoscale and Dispersion Models
Dispersion models require turbulent characteristics of the atmosphere that,
although not a direct output, can be derived from the temperature and wind fields in
prognostic models. The use of prognostic model forecasts would also ensure
meteorological data consistency, and complete spatial coverage for the entire United
States. In this subsection, results from a few relevant works from this research arena are
summarized.
The use of prognostic mesoscale models to provide the meteorological data
necessary for air quality models in British Columbia was explored by Rowen Williams
Davies & Irwin Inc. (RWDI) in 2002. RWDI found that by combining a coarse
resolution prognostic model run with a finer scale diagnostic model run did not improve
simulation results when complex terrain was present without a large number of
observation sites incorporated into the simulation. They recommended that in the
common scenario of sparse observations, fine horizontal resolutions (less than 3-km) in
the prognostic model runs are needed in order to capture the effects of complex
topography.
Recently, Isakov et al. (2007) investigated whether or not the meteorological
inputs needed for dispersion models could be obtained from gridded outputs of mesoscale
models. The models compared in this study were the Penn State Mesoscale Model
Version 5 (MM5) and the National Centers for Environmental Prediction’s (NCEP)
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Meso-Eta model. The model outputs were first compared to onsite measurements and
then input into a dispersion model to compare their performance with a Tracer Field
Study conducted in Wilmington, California during 2004. The MM5 contained nested
grids of 27 km, 9 km and 3 km with 36 full-sigma levels. The Meso-Eta used a 12 km
grid spacing. The surface flows in the area were largely from the south, south-east and
northwest according to NWS observations. The southerly component was attributed to a
sea breeze that occurred daily from approximately 6am to 1pm. Overall, both models had
trouble representing the wind direction (Figure 1.3), although the MM5 with a grid
resolution of 3 km outperformed the 12 km resolution of the Meso-Eta in capturing the
sea breeze patterns at Wilmington. Generally, the MM5 did fairly well in estimating the
maximum mixed layer heights at the observed site, while the Meso-Eta tended to
underestimate mixed layer heights. Encouragingly, a dispersion model (similar to
AERMOD) when coupled with either MM5 or Eta provided concentration estimates quite
comparable to the corresponding observed values.
The feasibility of using prognostic model forecasts in place of NWS-ASOS data
in the AERMOD air quality model is currently under evaluation by the EPA (Atkinson
2005, Touma et al. 2007). The use of prognostic model forecasts would provide
meteorological data consistency, complete spatial coverage for the entire U.S., and obtain
turbulence characteristics for AERMOD. AERMOD was applied using MM5 simulation
results and NWS observations to compare the hourly and annual average benzene
concentrations around the airport in Philadelphia, PA for 2001 (Touma et al. 2007). They
found that AERMOD-MM5 predicted higher concentrations of benzene by a factor of
two to three, versus those from AERMOD-NWS (Figure 1.4). This difference could be
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attributed to the abundance of calm wind hours in the NWS data or the approach
implemented using MM5 data in AERMOD for the study. The response of AERMOD to
the different outputs available from prognostic models needs to be explored further
before comprehensive assessment of the AERMOD-MM5 modeling framework can be
made (Touma et al. 2007).
Kesarkar et al. (2007) recently compared the simulated and observed temperature
and winds fields over Pune, India by coupling the WRF with AERMOD. They found the
WRF tended to overestimate the wind speeds (mean speed 3.65 ms-1), when compared to
the observed (mean speed difference 0.65ms-1) and found the simulated wind directions
contained a bias distributed between the West and North directions when compared to
observations (Figure 1.5). The simulated temperatures showed high correlation and a
standard deviation of 2.46 °C. Most importantly, the AERMOD-WRF modeling
framework generally underestimated the PM10 concentrations over the city.
Based on these recent studies, it is perhaps fair to conclude that several questions
need to be adequately addressed before prognostic models can be reliably utilized in
operational dispersion applications. Most of these questions are rooted in prognostic
models ability to accurately represent the boundary layer variables of interest to the
dispersion modeling community (e.g., wind speed, wind direction, temperature). In the
past, a handful of modeling studies have attempted to shed some light on the strengths
and weaknesses of planetary boundary layer (PBL) parameterizations in mesoscale
models. In the next subsection, succinct summaries of their results are provided.
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1.3 Limitations of Present Day PBL Parameterizations
Four mesoscale meteorological model simulations (MM5, Regional Atmospheric
Modeling System -RAMS, Coupled Ocean/Atmospheric Mesoscale Prediction System-
COAMPS, and the Operational Multiscale Environment Model with Grid Adaptivity-
OMEGA) over four geographic domains (Northeast U.S., Lake Michigan area, central
California, and Iraq) were compared to boundary layer observations by Hanna and Yang
(2001). Over the United States the modeled surface wind speed and direction biases were
found to be 1 ms-1 and 10° or less respectively. The calculated root mean square error
(RMSE) for wind speed was around 2 ms-1, and 60° for wind direction (Table 1. 1). In
Iraq, the bias for mean wind speed was found to be as high as a factor of 2 with RMSE as
much as 6 ms-1. The authors contributed these disparities to the complex terrain and
limited meteorological data for assimilation.
In 2003, Zhong and Fast were the first to compare the results of regional
simulations with grid spacing smaller than 1 km from the fifth-generation MM5, RAMS,
and Meso-Eta. The purpose was to determine how well each model reproduced terrain-
induced flows as well as boundary layer structure within the Salt Lake valley. It was
found that in the valley environment all three models contained the largest wind speed
and direction errors (Figure 1.6). This result could be due to the models’ inability to
accurately simulate the ambient flow and pressure gradients that occur because of the
dynamic and thermodynamic effects of the mountains. A nighttime cold bias was found
in all three models that began as cold biases in the simulated daytime boundary layer
temperatures. All three models performed relatively well in capturing the general day and
nighttime valley flows as well as near surface convergence and divergence over the
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valley floor. Unfortunately, large timing and magnitude errors occurred in the
circulation, boundary layer temperature, and temperature gradients during the
simulations.
The ability of five boundary layer parameterization schemes of MM5 to capture
the diurnal cycles of surface winds and temperatures were compared by Zhang and Zheng
(2004). It was found that all five schemes were able to reproduce the diurnal phase of
surface temperature while they underestimated the magnitude and contained phase errors
of surface wind speed (Figure 1.7).
Berg and Zhong completed another study in 2005 of the performance of the MM5
using several turbulence parameterizations at two locations of varying topography and
climate, the southern Great Plains and the Salt Lake valley. The turbulence
parameterizations used were the Blackadar (BK), Gayno-Seaman (GS), and Medium
Range Forecast (MRF) schemes. They found that similar skills were exhibited in all
three schemes when predicting the near-surface temperature, winds and other surface
variables.
Michelson and Bao (2006), during their simulation comparison of MM5 (version
3.7) and WRF (version 2.1) over the Central California Ozone Study (CCOS) field study
area, found that both MM5 and WRF contained a warm bias for 2-m air temperature.
This bias was particularly noticeable during the night in the San Joaquin Valley, with the
WRF simulation exhibiting a greater warm bias than MM5 (Figure 1.8). The
incongruities in land use, terrain height, vegetation coverage, and soil moisture and
temperature, as well as net radiation amounts could be the cause of the larger warm bias
in the WRF simulation. The 10-m wind speeds from the WRF simulation were generally
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faster than that of the MM5, while the diurnal phase of both model simulations differed
from the observations. A wind direction bias was also evident in simulations from both
the WRF and MM5.
1.4 Problem Statement
In summary, all mesoscale models have historically struggled to represent the
atmospheric boundary layers with any fidelity, and the traditional approach of mesoscale
model forecast verification (i.e., comparison of meteorological point observations with
spatial-averaged model output), is fundamentally flawed to some extent. The spatial
resolution of the prognostic mesoscale model can influence how realistically
meteorological variables and transport are represented. If the grid spacing is too large,
important local topographical and meteorological features can be overlooked by the
model.
In order to address these issues, a new framework to generate high-resolution
spatial wind data by coupling the WRF model with a diagnostic model (WAsP) is
proposed. This new WRF-WAsP framework makes comparison of model output and
observations more statistically meaningful. In order to establish the potential of the new
framework, WRF generated surface layer data (wind speeds, wind direction, and
temperature) is compared with high spatial resolution observations from the West Texas
Mesonet utilizing traditional forecast verification tools. The organization of this thesis is
as follows: Chapter 2 describes the West Texas Mesonet followed by the analysis of the
data in Chapter 3. The WRF model is explained in Chapter 4. The WRF model data is
analyzed in Chapter 5. Chapters 6 and 7 introduce the new framework for comparison of
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the model output with the observations. Chapter 8 contains the summary and conclusions
of this thesis work.
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Figure 1.1: AERMOD modeling system structure. Adapted from Touma et al (2007).
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Figure 1.2: ASOS Station (http://www.weather.gov/om/cm/asos.html)
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Figure 1.3: Wind roses of simulated winds derived from NWS observations (a) from the Eta Model (c) from MM5 (e) for all hours from August 26th to September 10th, and only during hours of tracer experiments (b, d, f). Adapted from Isakov et al (2006).
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Figure 1.4: Comparison of ranked hourly concentrations from AERMOD-NWS and AERMOD-MM5. Adapted from Touma et al. (2007).
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Figure 1.5: Average angular distribution of wind direction prevailed over Pune during the period of field campaign (12-17 April 2005): (a) as simulated by WRF (b) as observed in NCEP ADP dataset. Adapted from Kesarkar et al. (2007).
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Table 1. 1: Model performance measures for surface wind speed and direction for MM5 for the 4-km grid in the Central California SARMAP domain. Bias is defined as simulated minus observed. Adapted from Hanna and Yang (2001).
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Figure 1.6: (top) Bias, (middle) RMSE, and (bottom) error standard deviation of the (left) simulated temperature, (center) wind speed, and (right) wind direction at 36 stations in the Salt Lake valley and over the foothills during IHOPs 6 and 7. Gray shading denotes nighttime periods. Adapted from Zhong and Fast (2003).
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Figure 1.7: Time series of area-averaged horizontal wind speeds (m s-1) as simulated with the BLK, BT, GS, MRF, and MYJ, PBL schemes during the 3-day period of 1200 UTC 12 Jul-1200 UTC 15 July 1997. They are taken at z = (a) 50 and (b) 243 m. The observed VSFC (solid) is superposed to help to understand the relationship between the surface and boundary layer winds. Adapted from Zhang and Zheng (2004).
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Figure 1.8: Time series of aerial averaged 2-m temperature for the Southern San Joaquin Valley. The black line is observations, the red line is MM5 and the blue line is WRF. Adapted from Michelson and Bao (2006).
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CHAPTER 2
DESCRIPTION OF OBSERVATIONAL DATA
2.1 Description of the West Texas Mesonet
The West Texas Mesonet (WTM) is a network of 53 automated meteorological
stations, two atmospheric profilers, and one upper-air sounding system (at Reese
Technology Center) located in the Panhandle of West Texas and New Mexico (Figure
2.1, www.mesonet.ttu.edu). Fifteen meteorological variables (e.g. temperature and wind
speed) are reported every 5minutes. Ten agricultural variables are also measured (e.g.
soil moisture, and soil temperature) and reported every 15 minutes (Schroeder et al.
2005).
2.1.1 Station Site Information
Each station is surrounded by a 10-m by 10-m enclosure fence and contains a 10-
m tall, guyed aluminum tower (Figure 2.2). All of the sensors are mounted to boom arms
at various levels of the tower, with the exception of the soil temperature and moisture
content sensors and rain gauges (Figure 2.3).
2.1.2 Instrumentation
Each mesonet station contains various meteorological instruments to measure
temperature, barometric pressure, wind speed, wind direction, solar radiation, rainfall,
barometric pressure, soil temperature and moisture, and leaf wetness. A list of the
instrumentation can be found at www.mesonet.ttu.edu. This research is focused
primarily on 10-m wind speed and direction and 2-m temperature. Detailed descriptions
of the instruments pertaining to these variables are discussed in the following.
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a.) ANEMOMETERS
A R.M. Young 05103 Wind Monitor is located 10-m above ground level (AGL)
at each station. This instrument is a propeller type anemometer that measures both wind
speed and direction. The daily average wind speed and direction, peak gust, and standard
deviations of the wind speed and direction are calculated using data from this
anemometer. To provide information to the agricultural community about low-level wind
data necessary for crop spraying, an additional anemometer has been placed at the 2-m
level. This 2-m level anemometer is a R. M. Young 030103 Wind Sentry, but was not
used in this study.
b.) TEMPERATURE AND RELATIVE HUMIDITY
The Vaisala HMP45C (modified by Campbell Scientific) located at the 1.5-m
AGL and is mounted in a nonaspirated 12-plate radiation shield that is positioned 0.9-m
away from the tower, is the primary temperature and relative humidity sensor. Campbell
Scientific 107 temperature probes are also installed at the 9-m and 2-m levels. This
sensor is located 0.6-m away from the tower and mounted in a nonaspirated 6-plate
radiation shield.
c.) DATA LOGGERS
Campbell Scientific CR23X data loggers with one megabyte of extended memory
are used by the WTM. The 5-min observations from each meteorological sensor and 15-
min observations from the agricultural sensors are recorded by a data logger at each site.
Each data logger is equipped with two 6-V batteries as a backup in the event that power
is lost from the main marine battery. Each data logger can store 78 days of observations
before it is overwritten by new data.
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2.1.3 Quality Assurance and Control
The WTM performs quality assurance/quality control (QA/QC) procedures in two
steps. First, a FORTRAN application was developed to test the incoming data to identify
suspicious or potentially bad data. This process is completed using five tests: the range
test, step test, persistence test, like instrument test and a spatial test. Each observation is
flagged either as: good (“0”), suspect (“1”), warning ( “2”), or as a failure (“3”) based on
the confidence level of the observation as a result of these tests. In the second step, this
data is reviewed by a “decision maker” to interpret the results of the first step. To further
identify failing instruments, communication problems, and other issues related to the data
quality, the WTM staff employs further quality checks as well. All data employed in this
research has been visually verified.
2.1.4 Data Transfer
The observations are made available in real-time by several different methods of
data communications. Recurring costs were not initially part of the WTM budget. As a
result the data transfer methods are limited. A reliable two-way communications system
was chosen using the following methods: Extended Line of Sight Radio (ELOS), cellular
phone, landline phone, and the internet.
A 73-m tower was erected at Reese Technology Center by the Texas Tech
University’s Wind Science and Engineering Research Center to accommodate the need of
a high antenna for the ELOS system. Two additional antennas were installed to serve as
the base radio station. One radio is located at each station within the radio
communication network. Because of the line-of-site requirements several other stations
serve as repeaters within the network. Due to the remote location of several station sites
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that are beyond the radio communication network, cellular phone technology was
employed. In conjunction with the NWS Office in Lubbock, TX and Southern Regional
Headquarters of the NWS, three landline phone connections have been provided.
Because of the high cost of connecting to each station containing a landline, these
stations are called only once or twice an hour, unless a severe weather event is occurring.
An internet connection was provided free of charge from a municipality in the Texas
Panhandle along with allowing a radio antenna to be set up on the roof of their building.
This method allows for the 5-min data to be made available on a real-time basis
(Schroeder et al. 2005).
2.2 Summary
The WTM currently contains 53 surface stations located over eastern New
Mexico and the majority of the Texas Panhandle. Each station records 5-min
meteorological data and 15-min agricultural data that are relayed back to RTC in
Lubbock, Texas by radio, cell phone, land phone or internet connection. All data is
archived and available for purchase through the WTM office.
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Figure 2.1: Stations within the West Texas Mesonet as of April 2008
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Figure 2.2: Lubbock West Texas Mesonet Station (www.mesonet.ttu.edu).
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Figure 2.3: Elevation view of a typical West Texas Mesonet station from Schroeder et al. 2005. The
2-m temperature probe is not easily depicted given its location and has been omitted.
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CHAPTER 3
WTM DERIVED SUFACE LAYER CHARACTERISTICS In this Chapter the near surface meteorological variables, such as 2-m
temperature, 10-m wind speed, and 10-m wind direction are studied by utilizing West
Texas Mesonet observations.
3.1 Location Description
The study area contains an interesting topographic feature, the Llano Estacado,
known in the region as the Caprock. The Llano is a very flat, semiarid plateau that gently
slopes from the northwest to southeast at a generally uniform rate of 1.89 m km-1(10
feet/mile; Calvert 2008). The Llano straddles the New Mexico-Texas border between
Interstate 40 to the north and Interstate 20 to the south encompassing approximately half
of the study area (Figure 2.1). The Llano is dry and void of large trees, with the
prevailing wind from the southwest. This wind direction is a result of zonal flow over the
Rocky Mountains to the west of the study area. This flow induces a region of vorticity
east of the mountain range that sets up a lee trough located in eastern Colorado. The
distinguishing characteristic of the Llano is the Caprock Escarpment, a precipitous cliff,
with an average height of 91.44 m (300 ft), most prominent on the north and east sides of
the plateau. As a result of this abrupt change in topography the WTM data has been
divided into two categories: on the Caprock (ONC) and off the Caprock (OFC). These
divisions will help facilitate the evaluation of WRF performance in flat regions versus
complex terrain in a later section. ONC refers to locations west of the Caprock
Escarpment, OFC refers to locations east of the Caprock Escarpment. In addition, the
WTM station located at Reese Technology Center (RTC) was evaluated separately due to
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its smooth and flat terrain surrounding the station site in addition to containing the most
complete dataset as a result of its location at the WTM office.
3.2 West Texas Mesonet Data
Of the 53 WTM stations currently in operation, forty-eight of them were used in
this study. The five stations that were not evaluated came online during the study period
or later, and did not contain the full year of data. All data were obtained from the WTM
archives. The data analyzed are from April 2006 through March 2007. This date range
was chosen based upon the availability of corresponding WRF model data for
comparison in Chapter 5. The surface layer characteristics considered in this study are
10-m wind speed, 10-m wind direction, and 2-m temperature. Each characteristic was
analyzed separately for the two categories ONC and OFC, as well as for RTC.
3.3 Methodology
The WTM averaged meteorological observations are recorded every five minutes
(Schroeder et al. 2005). To later compare WTM data to the WRF model outputs, the
WTM data was first averaged hourly and then down sampled every three hours. The
three hourly data were utilized to study the monthly and diurnal variations of 10-m wind
speed, 10-m wind directions, and 2-m temperature.
3.4 ONC Observations
The 10-m wind speeds and directions were analyzed as a function of month as
well as a function of time of the day for 32 WTM stations located on the Caprock. The
months of June and December are analyzed in detail in this section. The complete year
and diurnal observational data are available in Appendix A.
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3.4.1 June 2006 Observations of Wind Speed and Direction ONC
During the month of June at locations ONC the wind was primarily from the
south and south/southeast reaching speeds of 4-6 ms-1. The south/southwest direction
also experienced wind speeds topping 4-6 ms-1, but with less frequency. The wind
directions of the north, north/northwest, west/northwest and west were virtually
nonexistent during the month of June.
3.4.2 December 2006 Observations of Wind Speed and Direction ONC
The wind direction is most prominently from the west/southwest direction in
December, with all directions containing a westerly component showing strong. The
wind speed exceeds 4-6 ms-1 in most directions. The east, east/southeast, south/southeast
and south directions are not prominent during this winter month.
3.5 OFC Observations of Wind Speed and Wind Direction
Sixteen WTM stations are located off the Caprock. The 10-m wind speeds and
directions were also broken down as a function of month and time of the day to compare
the monthly and diurnal cycles of wind observations to WRF forecasts. In this section
the months of June and December 2006 are discussed in detail. All other months and
times can be found in Appendix A.
3.5.1 June 2006 Observations of Wind Speed and Direction OFC
For locations OFC in June the predominant wind direction is from the
south/southeast with speeds of 4-6 ms-1. The southerly wind direction is also present,
though occurring less often with wind speed topping 4-6 ms-1 as well. The weakest
components of the wind in June are the north, north/northwest, west, and west/southwest
directions.
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3.5.2 December 2006 Observations of Wind Speed and Direction OFC
In December the strongest component of the wind is from the south/southwest
direction with wind speeds predominantly of 2-4 ms-1, but wind speeds of 4-6 ms-1
occurring occasionally. The north/northeast and west/southwest directions contain the
highest occurrence of 4-6 ms-1 wind speeds.
3.6 RTC Observations of Wind Speed and Wind Direction
Reese Technology Center is located northwest of Lubbock (33° 36’ 25.75” N,
102° 02’ 54.99” W) in the center of the study area. The 10-m wind speed, 10-m wind
direction and 2-m temperature were examined for this location separately. Detailed
descriptions of the wind speeds and directions for each month, as well as time, can be
found in Appendix A.
3.6.1 June 2006 Observations of Wind Speed and Direction at RTC
The observations at RTC contain the fastest wind speeds for the month of June.
The south wind is dominant, reaching 6-8 ms-1. The south/southwest direction is the
second most often occurring wind direction with wind speeds also topping 6-8 ms-1.
3.6.2 December 2006 Observations of Wind Speed and Direction at RTC
The least common wind directions in December at RTC are the east,
east/southeast, and south/southeast. The wind is mainly from the west direction topping
6-8 ms-1. The north and north/northwest directions are strong as well at 6-8 ms-1, though
occurring less frequently.
3.7 2-m Temperature Observations
The monthly averaged 2-m temperature was found to be higher in locations OFC
when compared to stations ONC. One reason for this is the elevation difference between
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the categories. The strong temperature dependence on elevation has been reported by
Liston and Elder (2006). Following their approach, I came up with:
Tavg(ONC) = Tavg(OFC) – Γ(zONC – zOFC) (3.1)
The average temperature (Tavg) is a function of Γ (C/m), which is the lapse rate (Table
3.1), z (m) is the elevation, and Tavg(OFC) (C) is the average temperature OFC. The
average temperature from WTM observations ONC in January is 0.69 °C, using the lapse
rate, elevation difference between ONC, OFC, and the TOFC, the average temperature
ONC was calculated as 0.98 °C. Similarly, the observed average temperature in July for
ONC is 26.73 °C, the calculated Tavg(ONC) came out to be 26.56 °C. Thus, the elevation
difference between ONC and OFC can simply explain the temperature anomaly. The
highest temperatures for all three locations can be found in the summer months of June,
July and August. The temperatures at RTC are comparable to the averaged temperature
at locations ONC (Figure 3.1).
The diurnal cycle of temperature is evident when averaging the 2-m temperature
as a function of time. Locations off the Caprock are still higher than locations on the
Caprock at all hours of the day. Reese Center and stations on the Caprock are cooler than
the stations further east, and Reese Center is continuing to be a few degrees warmer than
the averaged station on the Caprock (Figure 3.2). This difference might be attributed to
the station’s proximity to the airport runway and the urban area of Lubbock.
3.8 Summary
The WTM meteorological stations were divided into two categories: ONC, and
OFC as well as the RTC station because of the data dependability and other factors for
analysis and later comparison with the WRF model data in Chapter 5. The fastest 10-m
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wind speeds occur in April for both categories (Figure 3.3). RTC contains higher wind
speeds at all times (Figure 3.4) and all seasons when compared to the stations averaged
ONC and OFC. The 2-m temperature average for locations OFC exceeds the 2-m
temperature averages for both RTC and stations ONC.
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Table 3.1: Air temperature lapse rate variations as a function of month. Adapted from Liston and Elder (2006).
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Figure 3.1: 2-m Temperature Histogram as a Function of Month
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Figure 3.2: 2-m Temperature Histogram as a Function of Time
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Figure 3.3: 10-m Wind Speed Histogram as a Function of Month
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Figure 3.4: 10-m Wind Speed Histogram as a Function of Time
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CHAPTER 4
DESCRIPTION OF MODEL DATA
4.1 Description of the Weather and Research Forecast Model The WRF model, a next generation numerical weather prediction (NWP) model,
was recently developed as a collaborative effort among the National Center for
Atmospheric Research (NCAR) Mesoscale and Microscale Meteorology (MMM)
Division, the National Oceanic and Atmospheric Administration’s (NOAA) NCEP, Earth
System Research Laboratory (ESRL), the Department of Defense Air Force Weather
Agency (AFWA) and Navel Research Laboratory (NRL), the Center for Analysis and
Prediction of Storms (CAPS) at the University of Oklahoma, the Federal Aviation
Administration (FAA), along with the participation of a number of university scientists
(Skamarock et al. 2005).
The software framework of the WRF (Figure 4.1) provides the option for two
dynamical cores to be used, the Advance Research WRF (ARW) and the Nonhydrostatic
Mesoscale Model (NMM) WRF. For this research, the ARW WRF, which was
developed primarily at NCAR, was used.
4.1.1 Model Domain and Physics
The WRF domain consists of a 36/12 km two-way nested grid (operationally ran
by NCAR; Figure 4.2). The 48-hr forecast is initialized at 0000 UTC from 40-km Eta
model forecasts. The WRF forecast is output every three hours. The Eta model is also
used as boundary conditions for the WRF. There are nine vertical grids in the lowest 2-
km of the atmosphere. The domain chosen for this research encompasses the area
covered by the West Texas Mesonet (Figure 4.3).
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The ARW contains many physics options. The physics categories are (1)
microphysics, (2) cumulus parameterization, (3) planetary boundary layer (PBL), (4)
land-surface model, and (5) radiation (Skamarock et al. 2005). The ARW operational
forecast utilizes theYonsei University (YSU) PBL scheme, the Kain-Fritsch (KF)
cumulus parameterization, WRF Single-Moment 3-class (WSM3) scheme, Noah Land
Surface Model (LSM), Longwave Rapid Radiative Transfer Model (RRTM), and
Mesoscale Model Version 5 (MM5) (Duhdia) Shortwave. A detailed description of these
schemes can be found in Skamarock et al. (2005).
4.1.2 Planetary Boundary Layer Parameterizations in WRF
The transport of emission sources is primarily focused on the lowest 1-2-km of
the atmosphere, the PBL. Turbulence flux properties within the PBL are determined by
the PBL parameterization of the model. Three PBL parameterizations are supported in
WRF, the Yonsei University (YSU) PBL, Mellor-Yamada-Janjic (MYF) PBL and the
Medium Range Forecast Model (MRF) PBL. The YSU PBL scheme is latest version of
the MRF PBL.
To represent fluxes as a result of non-gradient terms the YSU PBL uses counter-
gradient terms. A schematic of the PBL processes represented by the YSU is available in
Figure 4.4. The YSU employs non-local mixing during the daytime and local mixing at
night. The diagram further illustrates that the surface is influenced by the strength of the
PBL scheme. A critical bulk Richardson number of zero is used to define the top of the
PBL and is only dependent on the buoyancy profile, which generally lowers the top of the
PBL compared to that of the MRF PBL which sets the bulk Richardson number to 0.5 or
1 (Skamarock et al. 2005).
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4.2 Static Fields
For near surface flows, the two static fields of interest are terrain and land cover.
The WRF incorporates 2-min resolution United States Geological Survey (USGS)
National Elevation Datasets to represent the terrain elevation. The coarseness of this
layer at 12-km grid spacing creates a smoothing of topography in WRF (Figure 4.5) when
compared to the 30-m resolution of available National Elevation Dataset (NED)
topography. Land cover is important because surface heterogeneity and roughness affect
the variables of interest (wind speed, wind direction, and temperature). WRF’s land use
coverage is broken into 24 categories (Figure 4.6) at 12-km resolution, four of which
occur in West Texas. WRF only considers two values of roughness length for each land
use category, one for summer, another for winter. This simplification should have an
effect on all variables of interest for comparison. A finer scale (30-m resolution) land use
coverage, available from the National Land Cover Data (NLCD) is shown in Figure 4.7.
4.3 Summary
The Weather and Research Forecasting model has been developed as a
collaborative effort of many agencies and university scientists. The goal is to provide a
next generation mesoscale forecast model and data assimilation system that advances
both the understanding and prediction of mesoscale weather and accelerates the transfer
of research advances into operation (Skamarock et al. 2005). The objective in utilizing
this data is to explore the possibility of inputting the forecast into AERMOD in place of
NWS observations.
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Figure 4.1: WRF system components. Adapted from Skamarock et al. 2005.
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Figure 4.2: Operational ARW domain from http://wrf-model.org/plots/realtime_nest.php
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Figure 4.3: WRF Domain
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Figure 4.4: Illustration of PBL Processes. Adapted from Duhdia (2008).
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Figure 4.5: WRF 12-km Digital Elevation Model
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Figure 4.6: WRF 12-km Land Use
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Figure 4.7: 30-m National Land Cover Data (2001).
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CHAPTER 5
WRF MODEL DERIVED SUFACE LAYER CHARACTERISTICS
The WRF’s ability to represent surface layer characteristics is explored to assess
the accuracy of the model forecasts and the possibility of incorporating model data into
AERMOD for dispersion modeling.
5.1 WRF Model Forecast
The operational forecast data analyzed are from April 2006 through March 2007.
This date range was chosen based upon the availability of a complete year’s worth of
data. The surface layer characteristics considered in this study are 10-m wind speed, 10-
m wind direction, and 2-m temperature. Each characteristic was analyzed separately for
the two categories (ONC and OFC) and RTC.
5.2 Methodology
Each WRF grid point was paired with a corresponding WTM station. The criteria
necessary for the coupling was based upon proximity to a WTM station and elevation of
WRF grid point. The 10-m wind speed, 10-m wind direction and temperature were then
averaged for grid points ONC, OFC and RTC using the same method as previously
discussed for the WTM observations.
The mean bias and root mean square error (RMSE) were calculated as a function
of month and time for each category to further explore the performance of the model.
RMSE was calculated the following way:
( )∑ −∗ xx ofN1 2 (5.1)
The mean bias formula implemented was:
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∑ −∗ )(1 xx ofN (5.2)
where xf and xo is WRF’s forecast and observed, respectively.
5.3 ONC WRF Model Forecast The 10-m wind speed and wind direction were analyzed as a function of month
and time for 32 WRF grid points which correspond to the WTM stations located on the
Caprock. The months of June and December 2006 are described in this section. A
complete description of the entire study period by month as well as diurnal cycle can be
found in the Appendix B.
5.3.1 June 2006 Model Forecast of Wind Speed and Direction ONC
The forecasted wind speeds in the month of June were generally underestimated
by WRF when compared to the observations. The model forecast does represent the
south wind well, but underestimates the south/southwest component.
5.3.2 December 2006 Model Forecast of Wind Speed and Direction ONC
The WRF does extremely well in forecasting the wind speeds and wind directions
in December 2006. The occurrence of the west wind is overestimated, as well as an
underestimation of the west/northwest component but the speeds are correctly forecasted
at a maximum of 4-6 ms-1.
5.3.3 Monthly Statistics of Wind Speed and Direction ONC
The monthly mean bias for wind speed is always negative for locations ONC,
implying that WRF consistently underestimates the wind speed throughout the year
(Figure 5.1). The monthly RMSE for wind speed is lower during the summer months.
Since the YSU PBL scheme is believed to perform well under unstable conditions, one
would expect the WRF model to perform better during the summer months (Figure 5.2).
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5.3.4 Diurnal Statistics of Wind Speed and Direction ONC
The diurnal mean bias has the greatest value during the hours of 0000 and 1500
UTC, times during which the morning and evening transitions are common. The mean
bias is lowest during daytime hours, but still is negative (Figure 5.3). The RMSE is
largest during the hours of 0000, and 1500 UTC. The evening and morning transitions
occur during these hours in addition to WRF consistently underestimating the wind speed
at night (Figure 5.4).
5.4 OFC Observations of Wind Speed and Wind Direction
Sixteen of the WRF grid points that correspond to WTM stations are located
OFC. The 10-m wind speed and wind direction were also broken down as a function of
month and time to observe the seasonal and diurnal variations represented by the model.
The months of June and December are presented in this section, all other months as well
as times can be found in Appendix B.
5.4.1 June 2006 Model Forecast Wind Speed and Direction OFC
During the month of June WRF overestimates how often the wind is from the
southerly direction and underestimates the south/southeast direction. The other directions
are well reproduced in the forecast. The wind speeds are also correct at maximums of 4-
6 ms-1.
5.4.2 December 2006 Model Forecast Wind Speed and Direction OFC
The dominant wind direction in month of December is forecasted to come from
the west, unfortunately this does not appear in the observational data. The westerly wind
is also overestimated by 2-4 ms-1. WRF also underestimates the occurrence of the south
wind, but captures the wind speed of 2-4 ms-1.
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5.4.3 Monthly Statistics of Wind Speed and Direction OFC
During January, when WRF has the most trouble capturing the true observed
value of the wind, the monthly mean bias is the highest. There is an overall positive bias
during the year for the averaged monthly data for all averaged locations OFC, but the
wind directions are consistently incorrect when compared to the observations (Figure
5.5). The RMSE is the largest in January and March. It seems the varying synoptic
conditions and thunderstorm activity in the region during the latter part of March, were
not properly captured by the model, which has lead to the increased error. The locations
OFC, during summer months are exhibiting the lowest RMSE, as previously seen in the
ONC data (Figure 5.6).
5.4.4 Diurnal Statistics of Wind Speed and Direction OFC
WRF underestimates the wind speed during the nighttime hours while
overestimates it during the day. This can be observed in the mean bias calculations
(Figure 5.7). The RMSE is constant throughout the diurnal cycle (Figure 5.8).
5.5 RTC Observations of Wind Speed and Wind Direction
RTC, is located northwest of Lubbock at latitude 33° 36’ 25.75” N, longitude
102° 02’ 54.99” W in an area of very flat, homogenous terrain and classified as grassland
in the WRF land use coverage.
5.5.1 June 2006 Model Forecast Wind Speed and Direction at RTC
During the month of June, WRF greatly underestimated the wind speeds at RTC.
The maximum wind speeds in the forecast are 4-6ms-1, while the observations reach
speeds of 6-8 ms-1. The overall wind directions are well forecasted by WRF.
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5.5.2 December 2006 Model Forecast Wind Speed and Direction at RTC
December wind speeds are underestimated as well. The observations contain a
maximum of 6-8 ms-1 westerly wind, while WRF forecasts 4-6 ms-1 from the same
direction. WRF does manage to represent all of the directions correctly.
5.5.3 Monthly Statistics of Wind Speed and Direction at RTC
RTC contains the greatest negative mean bias and RMSE calculations compared
to those of ONC and OFC. This is due to the fact that RTC is one single station, while
ONC and OFC are averaged ensembles. The average wind speed is under predicted at
RTC during every month of the year (Figure 5.9). The RMSE is once again highest in
March due to the models inability to capture synoptic conditions which occurred during
the last week of the month (Figure 5.10).
5.5.4 Diurnal Statistics of Wind Speed and Direction at RTC
The diurnal mean bias is the most negative during the hours of 0000 and 1500
UTC, the typical hours of the morning and evening transitions (Figure 5.11). The highest
RMSE is also found during the same hours (Figure 5.12). This result is another example
of WRF’s inability to represent the diurnal transitions well.
5.6 2-m Temperature Observations
The monthly averaged 2-m temperature was found to be higher at RTC when
compared to WRF grid points ONC and OFC. The forecast temperatures for the grid
points located ONC and OFC are relatively even (Figure 5.13). In reality there should be
a difference in the temperatures due to elevation differences between ONC and OFC.
WRF fails to capture the effects of topography as a result of the large grid spacing.
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5.6.1 2-m Temperature Statistics
The diurnal cycle of temperature is still evident in the WRF forecast when
averaging the 2-m temperature as a function of time, although WRF exhibits a warm bias
at night in the temperature forecasts for locations OFC, ONC and RTC (Figure 5.14,
Figure 5.15, and Figure 5.16). This bias is seen in all locations and does not appear to be
topography related. It has been seen in other studies that WRF has a warm bias at night,
possibly, as a result of the PBL parameterization. RTC temperatures are higher than
locations OFC and ONC at all hours of the day (Figure 5.17). The monthly bias
calculations are comparable at all locations. March contains a very low bias in locations
ONC and at RTC, even negative for OFC. After further research into the data, it was
determined that WRF showed little to no skill in forecasting temperatures at the end of
March in the region. The observations contain little diurnal cycle due to synoptic
conditions in the region, while WRF continued to forecast a large diurnal cycle in
temperature (Figure 5.18, Figure 5.19, and Figure 5.20).
The RMSE was also calculated for each category OFC, ONC and RTC. The
lowest RMSE can be found during the hours of 1500 and 1800 UTC for all locations
(Figure 5.21, Figure 5.22, and Figure 5.23). This could be attributed to the hours
identified generally being the most unstable hours of the day. The YSU parameterization
scheme is tuned for convective boundary layer conditions and performs better during
these hours (Hong et al. 2006). This thinking can also be applied when considering the
monthly RMSE for OFC, ONC and RTC. During the summer months there are a greater
number of unstable hours in each day, which leads to a lower RMSE calculation (Figure
5.24, Figure 5.25, and Figure 5.26). RMSE is not a good statistic to use because is does
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not take into account location or timing errors associated with forecasts. This becomes
evident in the month of March, where the RMSE value is no worse than other months,
but there were extensive errors in the forecasts.
5.7 Summary
The WRF meteorological forecast was divided into two categories for analysis:
ONC and OFC for comparison with WTM observations along with the RTC station
separately. For the WRF data, the fastest 10-m wind speeds occur in April for both
categories which does agree with the observational data (Figure 5.27). The highest wind
speeds are found in locations ONC and at RTC at varying times (Figure 5.28). The 2-m
temperature average at RTC exceeds the 2-m temperature averages for both OFC and
ONC. This is contradictory to the WTM observations. The WRF model contains a warm
temperature bias. The performance of the WRF in forecasting wind speed, wind
direction, and temperature needs to be improved. In Chapter 7, a new framework is
proposed to improve the wind speed forecasts and be used as an input into AERMOD.
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Figure 5.1: ONC monthly mean bias of wind speed.
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Figure 5.2: ONC monthly RMSE for wind speed.
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Figure 5.3: ONC diurnal mean bias for wind speed.
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Figure 5.4: ONC diurnal RMSE of wind speed.
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Figure 5.5: OFC monthly mean bias of wind speed.
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Figure 5.6: OFC monthly RMSE for wind speed.
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Figure 5.7: OFC diurnal mean bias of wind speed.
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Figure 5.8: OFC diurnal RMSE for wind speed.
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Figure 5.9: RTC monthly mean bias of wind speed.
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Figure 5.10: RTC monthly RMSE for wind speed.
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Figure 5.11: RTC diurnal mean bias of wind speed.
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Figure 5.12: RTC diurnal RMSE for wind speed.
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Figure 5.13: 2-m WRF Forecast Temperature Histogram as a Function of Month
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Figure 5.14: OFC diurnal 2-m temperature bias
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Figure 5.15: ONC diurnal 2-m temperature bias
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Figure 5.16: RTC diurnal 2-m temperature bias
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Figure 5.17: 2-m WRF Forecast Temperature Histogram as a Function of Time
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Figure 5.18: OFC monthly 2-m temperature bias.
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Figure 5.19: ONC monthly 2-m temperature bias.
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Figure 5.20: RTC monthly 2-m temperature bias.
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Figure 5.21: OFC diurnal 2-m temperature RMSE.
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Figure 5.22: ONC diurnal 2-m temperature RMSE.
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Figure 5.23: RTC diurnal 2-m temperature RMSE.
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Figure 5.24: OFC monthly 2-m temperature RMSE.
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Figure 5.25: ONC monthly 2-m temperature RMSE.
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Figure 5.26: RTC monthly 2-m temperature RMSE.
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Figure 5.27: 10-m WRF Forecast Wind Speed Histogram as a Function of Month
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Figure 5.28: 10-m WRF Forecast Wind Speed Histogram as a Function of Time
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CHAPTER 6
WIND ATLAS ANALYSIS AND APPLICATION PROGRAM
6.1 Description By comparing the WRF forecast data to the WTM meteorological observations, it
is easy to see that the WRF does not perform so well when complex topography
(locations east of the Caprock Escarpment) is present in forecasting wind speed and wind
direction. This can be attributed to the generalization of the land use, topography, and
large grid spacing of the domain, when comparing the WTM station data to the nearest
WRF grid point. To further explore the possibly of improving the WRF forecast, the
Wind Atlas Analysis and Application Program (WAsP) was explored to account for the
terrain differences across the domain as a correction factor. The WAsP is mainly used
for wind data analysis, wind atlas generation, wind climate estimation, and siting of wind
turbines (Mortensen 2004). The program was first developed at the Riso National
Laboratory in Denmark and released in 1987. The model is based on Jackson and Hunt
theory (J-H theory). Models that are based on this theory are different from other mass-
consistent models. The biggest difference is the role of momentum conservation. In the
WAsP model, mass is conserved, but also attempts to solve the Navier-Stokes momentum
equations, which are ignored in other mass-consistent models (Rohatgi and Nelson 1994).
6.2 Main Calculation Blocks
WAsP contains five main calculation blocks. The first is the analysis of raw data.
This option allows the user to receive a statistical summary of the observed wind climate,
at a specific location by analyzing any time series of wind measurements. The next
calculation block produces the generation of a numerical wind atlas of the domain. A
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regional wind climate or wind atlas data set can be produced from analyzed wind data. A
schematic diagram of the process is represented in Figure 6.1. In this step the wind
observations have been ‘cleaned’ with respect to site-specific conditions, by removing the
effects on wind by obstacles and terrain. The wind atlas data are now site independent
and standard conditions have been applied to the wind distributions. The third step
involves an estimation of the wind climate. The program estimates the wind climate at a
specific point using the wind atlas data set. This estimate is accomplished by performing
the inverse calculation as was used to generate the wind atlas. A more accurate
prediction of the wind climate at a specific location is obtained by introducing
descriptions of terrain around the location. The WAsP code considers surface roughness
and obstacles upstream during the evaluation of locations. The roughness of a surface is
determined by the size and distribution of the roughness elements it contains.
Vegetation, infrastructure, and soil surfaces are typically considered for land surfaces.
The surface roughness is divided into four classes of terrain in the wind atlas (Figure 6.2,
Figure 6.3, Figure 6.4, and Figure 6.5):
Class 0: Water areas such as seas and lakes Class 1: Open areas, usually flat or rolling with scattered windbreaks or shelters (single farms, low bushes). Class 2: Farmlands with windbreaks of mean separation distance in excess of 1000m, and some isolated trees and buildings. The terrain may be flat or strongly undulating. Class 3: Urban districts, forests, and farmland with closely spaced windbreaks, the average separation being a few hundred meters. Forest and urban areas also belong to this class (Rohatgi and Nelson 1994). The wind power potential is estimated in the next block. By providing WAsP
with the power curve of specific wind turbine, the annual mean energy production of the
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turbine can be calculated. In the final block, an estimation of the wake losses for each
turbine in a wind farm can be calculated and the net annual energy production of each
wind turbine and the entire farm (Mortensen 2004).
6.3 Summary
The WAsP model takes into account the effects of terrain, surface roughness and
obstacles on wind speeds and directions, which are not represented in other course
models. This step is accomplished by first removing the effects of topography in a data
set to produce a wind atlas. Next the topographic effects on a specific site are calculated
and reintroduced into the data set to create a wind climate of a region. The final steps are
the estimation of wind power potential and wind farm production. Through this process
the estimated wind speed and direction of a location can be greatly improved. By
implementing the use of this program a correction factor is introduced to account for the
effects terrain and exposure have on wind variables.
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Figure 6.1: The wind atlas method of WAsP. Meteorological models are used to calculate the regional wind climatologies from the raw data. In the reverse process – the application of wind atlas data – the wind climate at any specific site may be calculated from the regional climatology (Mortensen et al. 2005).
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Figure 6.2: Example of terrain corresponding to roughness class 0. Adapted from Troen and Petersen (1989).
Figure 6.3: Example of terrain corresponding to roughness class 1. Adapted from Troen and Petersen (1989).
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Figure 6.4: Example of terrain corresponding to roughness class 2. Adapted from Troen and Petersen (1989).
Figure 6.5: Example of terrain corresponding to roughness class 3. Adapted from Troen and Petersen (1989).
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CHAPTER 7
WRF-WAsP COUPLING
7.1 Purpose WRF-based average wind forecasts can be significantly improved by utilizing
WAsP in conjunction with high-resolution local topography (digital elevation). A wind
field is influenced when a hill, obstacle or other topographic feature is present. When the
wind encounters height change in terrain, it can affectively increase the mean wind speed
as much as 5% at the top of the topographic feature. Given a 5% height increase, the
increase in wind speed can result in a 15% increase in available power (Troen and
Peterson 1989). Terrain becomes a very important variable when considering this effect.
In this work a new framework is proposed for improving the forecast of the local wind by
implementing a correction factor to account for the terrain with WAsP (Figure 7.1):
Step 1: Input the combination of the operational WRF forecasts and the 12-km WRF grid into WAsP to remove the terrain effects on the wind to obtain a numerical wind atlas of the domain.
Step 2: Next, run the numerical wind atlas with 30-m National Elevation Data
(NED) through WAsP again to add in the updated topography and obtain the local wind.
The average wind speed and power density were calculated to see if by including
the use of WAsP the model averages were improved. Three specific WTM station
locations have been chosen to compare the observational data, WRF forecast data, and
the coupling of WRF data with WAsP. The three sites chosen were Macy Ranch,
Fluvanna, and Reese Technology Center mesonet locations.
Although WAsP can account for obstacles, they were not taken into consideration
in this study. Several of the mesonet stations are affected by obstacles in the path of the
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sensors. The stations at Plainview, Lubbock, Ralls and Memphis are affected by urban
areas. All of the stations still meet the 20 to 1 rule for distance from obstructions, but the
urban-type surroundings do have a significant impact on average wind speed readings.
The Plainview station is located one mile south of the downtown area, just off business I-
27. The station is located in an area of open exposure, but when compared to
surrounding stations the buildings and structures in the distance do impact the average
wind speeds. The Lubbock mesonet station was moved several years ago to range land
northwest of Texas Tech University, but the average wind speed is still impacted by the
urban areas of Lubbock. The town of Ralls does impact the Ralls station. Wind speeds
are higher at locations surrounding the area in the northwest wind direction. The
Memphis station is located in a broad river valley. It is located on the edge of town near
a cemetery. Urban surroundings do have some impact on wind speeds, but the
predominate influence is the temperature inversions that form at night in this region
(personal communication with Wes Burgett, WTM).
7.2 Macy Ranch
The WTM station at Macy Ranch is located approximately 10-m from the edge of
the Caprock Escarpment (Figure 7.2). As a result of its location in complex topography
the WRF model has preformed poorly when compared to the WTM observations. The
average wind speed at Macy Ranch from WTM observations for the time period April
2006 – March 2007 was 4.98 ms-1. This average for the year of data analyzed is not
unlike the overall average wind speed since January 2002, when the station came online
(Figure 7.3). The wind direction for the year analyzed is also in agreement with the
entire life period of the station as well (Figure 7.4). The WRF forecast under predicts the
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average wind speed by 0.61ms-1. One reason for this under prediction is the averaging of
the wind speed over the 12-km grid (Figure 7.5). WRF also generalizes the terrain and
fails to capture its effects on the wind speed and wind direction (Figure 4.5). The terrain
integrated into the WAsP model is a finer resolution, a 100-m National Elevation Dataset
(Figure 7.6). Power density, a measure of the energy available from the wind is
calculated by the formula:
3
21 uE ρ= (7.1)
where ρ is the mean air density and u is the mean wind velocity (Troen and Peterson
1989). The power density at 10-m for the WTM observations was calculated by WAsP to
be 136 Wm-2, while the power density for the WRF forecast is only 86 Wm-2. By
inputting the WRF forecast into WAsP the average wind speed increases to 5.28 ms-1
with the power density increasing to 152 Wm-2 (Table 7.1). WAsP creates a numerical
wind atlas and then by adding in the terrain effects of the area, a more accurate
representation of the local wind is generated (Figure 7.7). This improves the WRF
forecast because WAsP computes power for the individual wind sectors and then takes
the average. This results in WAsP doing better in each wind sector and is responsible for
a larger power calculation.
The roughness lengths can also be manipulated in WAsP. Vega and Letchford
(2007) calculated the roughness for 16 sectors for each WTM station. The WRF-WAsP
coupling was repeated with accounting for the average annual roughness for 12 wind
sectors by following their method. The roughness lengths for each station with
corresponding sectors can be found in Table 7.2. As a result, the forecasted wind speed
at Macy Ranch increased to a greater over prediction as well as the mean power density.
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7.3 Fluvanna
The Fluvanna WTM station is located in an area of less dramatic terrain when
compared to Macy Ranch but, is still not homogenous when compared to the area
surrounding Reese Technology Center. The average wind speed from the WTM
observations for the year is 4.43 ms-1. This average wind speed for the data range is
consistent with the average wind speed calculated for the life span of the Fluvanna
mesonet station at 4.46 ms-1 as well as the overall wind direction (Figure 7.8 and Figure
7.9). The WRF model performs well with an average wind speed of 4.42 ms-1 (Figure
7.10). The calculated power density from the observational data is 98 Wm-2, while from
the WRF forecast the power density is low at 89 Wm-2. When combing the WRF
forecast with WAsP the average wind speed is calculated to 4.44 ms-1 with the power
density increasing to 91 Wm-2 (Table 7.1). By increasing the resolution of the
surrounding terrain the wind speed forecast can be improved (Figure 7.11). Figure 7.12
represents the averaged wind speed for Fluvanna when WAsP is combined with the WRF
forecast. By repeating the process, but including the roughness lengths the average wind
speed increases to 5.15 ms-1, with the mean power density calculated at 145 Wm-2 (Table
7.2). Both of these values are over estimated like in the previous case.
7.4 Reese Technology Center
RTC is located near the center of the domain in an area of homogenous terrain on
the Llano Estacado. The average wind speed for RTC was 4.97 ms-1. The data analyzed
for the time period from April 2006 to March 2007 is representative of the overall climate
of the area (Figure 7.13). The average wind speed for the life span of the station is 5.13
ms-1 with the dominant wind direction from the south (Figure 7.14). WRF
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underestimated the wind speed to be 4.21 ms-1 for the year (Figure 7.15). When
combining the WRF forecast data barely increases with WAsP. The average wind speed
calculation increases to 4.23 ms-1. The power density for RTC observations is 148 Wm-2
with WRF forecast power density calculated at 78 Wm-2. The WRF-WAsP power
density only increases to 79 Wm-2 (Table 7.1). Although there is a small improvement in
the forecast when WAsP is employed it is not as dramatic of an improvement as in the
Macy Ranch or Fluvanna cases. This result is likely due to the homogenous terrain in the
area (Figure 7.16). The terrain effects are minimal on the wind speed and direction at
RTC (Figure 7.17). This is a limitation of the correction factor employed, only
substantial improvements have been seen in complex terrain. By including specific
surface roughnesses for each sector at RTC the average wind speed increased to 5.31 ms-
1, with the mean power density calculated at 158 Wm-2. It seems that by taking
roughness into account for homogenous terrain the forecast is improved.
7.5 Summary
The average wind speed at three locations in the domain was calculated to
compare with each of the location’s WRF average wind speed forecast when combined
with WAsP. The locations chosen were Macy Ranch, Fluvanna and Reese Technology
Center. The forecast for Macy Ranch greatly improved when WAsP was utilized to
account for the complex topography in the area. Fluvanna’s forecast also improved but
the average wind speed forecast at Reese Technology Center showed little change due to
the homogenous terrain of the area surrounding the mesonet site.
Next, the WRF-WAsP process was repeated with the inclusion of specific surface
roughness calculations for each station’s 12 wind sectors. The yearly average surface
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roughness lengths were used as input into WAsP. The forecasts were improved for the
RTC location, but worsened at Fluvanna and Macy Ranch. There is some controversy on
how to calculate the roughness lengths for a specific location (Vega and Letchford 2007).
This issue needs to be explored further in future work.
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Figure 7.1: WRF-WAsP Framework
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Figure 7.2: Photo of Macy Ranch mesonet station (www.mesonet.ttu.edu).
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Figure 7.3: April 2006 – March 2007 Macy Ranch Wind Rose
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Figure 7.4: Macy Ranch Wind Rose from January 2002 to Present (www.mesonet.ttu.edu).
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Figure 7.5: Annual Averaged 12-km grid spacing wind speed at Macy Ranch.
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Figure 7.6: 100-m Digital Elevation Model of Macy Ranch derived from 30-m NED
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Figure 7.7: Annual Averaged 100-m grid spacing Wind Speed at Macy Ranch.
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Figure 7.8: April 2006 – March 2007 Fluvanna Wind Rose
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Figure 7.9: Fluvanna Wind Rose from July 2002 to Present (www.mesonet.ttu.edu).
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Figure 7.10: Annual Averaged 12-km grid spacing wind speed at Fluvanna.
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Figure 7.11: 100-m Digital Elevation Model of Fluvanna derived from 30-m NED
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Figure 7.12: Annual Averaged 100-m grid spacing Wind Speed at Fluvanna.
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Figure 7.13: April 2006 – March 2007 Reese Technology Center Wind Rose
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Figure 7.14: Reese Technology Center Wind Rose from June 2000 to Present (www.mesonet.ttu.edu).
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Figure 7.15: Annual Averaged 12-km grid spacing wind speed at Reese Technology Center.
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Figure 7.16: 100-m Digital Elevation Model of Reese Technology Center derived from 30-m NED
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Figure 7.17: Annual Averaged 100-m grid spacing Wind Speed at Reese Technology Center.
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Table 7. 1: Comparison of Wind Speed Observations, Forecast, WRF-WAsP Forecast, and WRF-WAsP Forecast with roughness calculated for each sector with Mean Power Density Calculations for Macy Ranch, Fluvanna, and Reese Technology Center.
WTM WRF WRF-WAsP WRF-WAsP
(roughness)
MACY 4.98 ms-1 4.37 ms-1 5.28 ms-1 6.09ms-1
136 Wm-2 86 Wm-2 152 Wm-2 234 Wm-2
FLUV 4.43 ms-1 4.42 ms-2 4.44 ms-1 5.15 ms-1
98 Wm-2 89 Wm-2 91 Wm-2 145 Wm-2
REES 4.97 ms-1 4.21 ms-1 4.23 ms-1 5.31 ms-1
148 Wm-2 78 Wm-2 79 Wm-2 158Wm-2
:
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Table 7.2: Roughness lengths for the 12 wind sectors of Fluvanna, Macy Ranch, and RTC Mesonet Station
Aerodynamic roughness Sector Angle FLUV MACY REES z01 N 0 0.022 0.017 0.010 z02 NNE 30 0.044 0.015 0.009 z03 ENE 60 0.060 0.027 0.009 z04 E 90 0.107 0.031 0.009 z05 ESE 120 0.062 0.026 0.008 z06 SSE 150 0.029 0.021 0.008 z07 S 180 0.019 0.023 0.007 z08 SSW 210 0.023 0.050 0.011 z09 WSW 240 0.024 0.085 0.014 z010 WSW 270 0.021 0.039 0.011 z011 WNW 300 0.018 0.022 0.009 z012 NNW 330 0.020 0.015 0.009
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CHAPTER 8
SUMMARY AND CONCLUSIONS
8.1 Summary
The purpose of this research was to assess the performance of a new-generation
weather prediction model (WRF) in capturing surface layer characteristics in the hopes of
one day implementing the forecasts in dispersion modeling. West Texas Mesonet
meteorological observations for the time period April 2006 – March 2007 were compared
to WRF 12-km grid operational forecasts of the same period. The nearest WRF grid
point to a corresponding mesonet station was identified. The data were divided into two
categories, on the Caprock and off the Caprock to assess the spatial variability on wind
speed, wind direction and temperature. Observations at Reese Technology Center were
also analyzed due to the reliability of the data. The data from each category were then
analyzed as a function of time and month to further test the ability of the model in
representing the diurnal and seasonal cycles of temperature, wind speeds, and wind
direction.
WAsP was utilized to remove the effects of terrain and exposure in the wind
forecast to create a numerical wind atlas of the domain. The atlas was then coupled with
30-m NED data to determine the local wind with hopes of improving the WRF
operational forecast of wind speed and wind direction. The 2-m temperature data from
the observations and model were also compared to assess the performance of WRF.
8.2 Conclusions
By comparing the observational data to the WRF model forecast it was very
obvious that the model struggled in producing an accurate forecast of wind speed and
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wind direction. As a result, three individual WTM stations were examined to resolve the
sub-grid orography not properly represented in WRF. By utilizing WAsP to include the
effects on wind speed and wind direction by complex terrain the model forecast was
improved. Little improvement occurred in locations of flat homogeneous topography.
This could be a result of WRF’s treatment of roughness lengths. WRF only breaks the
roughness calculations into two seasons: summer and winter. A second forecast was
made by including a roughness length in WAsP that was more representative of the
locations. This did improve the wind speed and mean power density calculations at RTC,
but only proved to worsen the Fluvanna and Macy Ranch forecasts.
The 12-km grid size is also too coarse to capture the correct land use of the area.
Specifically, Lubbock is classified as grassland which has a surface roughness of 12 cm
in the summer, and 10 cm in the winter. Lubbock should contain a higher roughness
value as a result of the urban land use that is not represented in the model. Reese
Technology Center is also categorized as grassland in the land use, but the area is very
smooth and flat. The roughness calculation should be lower for RTC. This difference
would help to increase the forecast wind speeds in the region which are presently too low.
Several steps can be taken to improve the WRF forecasts. First, the terrain
representation in the WRF model needs to be improved to account for the effects of
topography on wind variables. A solution to this problem would be to use a smaller grid
spacing. Satellite derived land use patterns at 30-m resolution, derived more frequently
throughout the year, and would result in more representative roughness length
calculations for a given area. The physical parameterizations of the PBL scheme need
improvement as well. Unfortunately this is the case with all the contemporary mesoscale
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meteorological models. The significant temperature bias found in the model data is most
likely due to a combination of problems in the land surface and PBL parameterizations to
represent the surface and boundary layer flux values correctly.
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REFERENCES
Atkinson, D., 2005: Assimilated Meteorological Data. 8th Modeling Conf. on Air Quality Models. Research Triangle Park, NC 22-23 September.
Berg, L.K., S. Zhong, 2005: Sensitivity of MM5-simulated boundary layer characteristics
to turbulence parameterizations. J. Appl. Meteor., 44, 1467-1483. Calvert, J.B., cited 2008: The Llano Estacado. [Available online at
http://mysite.du.edu/~jcalvert/geol/llano.htm] Dabbert, W.F., and Coauthors, 2004: Meteorological research needs for improved air
quality forecasting. Bull. Amer. Meteor. Soc., 85, 563-586. Duhdia, J., 2008: WRF Physics Options. Unpublished. Hanna, S.R., R. Yang, 2001: Evaluation of mesoscale models’ simulation of near-surface
winds, temperature gradients and mixing depths. J. Appl. Meteor., 40, 1095-1104. Hong, S., Y. Noh, J. Dudhia, 2006: A New Vertical Diffusion Package with an Explicit
Treatment of Entrainment Processes. Mon. Wea. Rev., 134, 2318-2341. Isakov, V., A. Venkatram, J.S. Touma, D. Koračin, T.L. Otte, 2007: Evaluating the use of
outputs from comprehensive meteorological models in air quality modeling applications. Atmos. Environ., 41, 1689-1705.
Kesarkar, A.M., M. Dalvi, A. Kaginalkar, A. Ojha, 2007: Coupling of the Weather
Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune, India. Atmos. Environ., 41, 1976-1988.
Michelson, S.A., Jian-Wen Bao, 2006: Comparison of WRF and MM5 simulations for
air-quality applications. 14tn Joint Conf. on Applications of Air Pollution Meteorology and the Air and Waste Management Association, Atlanta, GA, Amer. Meteor. Soc., CD-ROM, J2.7.
Mortensen, N.G., D. Heathfield, L. Myllerup, L. Landberg, O. Rathmann, 2004: Getting
Started with WAsP 8. Riso-I-1950(ed.2)(EN). NWS, cited 2008: Change Management: ASOS [Available online at
http://www.weather.gov/om/cm/asos.html] Pielke, R.A., M. Uliasz, 1998: Use of meteorological models as input to regional and
mesoscale air quality models-limitations and strengths. Atmos. Environ., 32, 1455-1466.
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Rohatgi, J.S., V. Nelson, 1994: Wind Characteristics An Analysis for the Generation of Wind Power. Alternative Energy Institute. 239 pp.
Rowen Williams Davies & Irwin Inc., 2002: Using Mesoscale Models to Support
Regulatory Dispersion Modeling. Project #02-7041 Mesoscale Model Review. Victoria, B.C.19 pp.
Schroeder, J.L., W. Burgett, K. Haynie, I. Sonmez, G. Skwira, A. Doggett, J. Lipe, 2005:
The West Texas Mesonet: A Technical Overview. J. Atmos. Oceanic Technol., 22, 211-222.
Skamarock, W.C., J. Klemp, J. Duhdia, D. Gill, D. Barker, W. Wang, J. Powers, 2005: A
Description of the Advanced Research WRF Version 2. NCAR/TN-468+STR, 88PP. Touma, J.S., V. Isakov, A.J. Cimorelli, R.W. Brode, B. Anderson, 2007: Using
prognostic model-generated meteorological output in the AERMOD dispersion model: An illustrative application in Philadelphia, PA. J. Air & Waste Manage. Assoc., 57, 586-595.
Troen, I., E. Petersen, 1989: European Wind Atlas. Riso National Lab, 656pp. Vega, R.E., and C.W. Letchford, 2007: On the directional aerodynamic roughness
estimation for exposure corrections. In review. Zhang, D.-L., Zheng, W.-Z., 2004: Diurnal cycles of surface winds and temperatures as
simulated by five boundary layer parameterizations. J. Appl. Meteor., 43, 157-169. Zhong, S., J. Fast, 2003: An evaluation of the MM5, RAMS, and Meso-Eta models at
subkilometer resolution using VTMX field campaign data in the Salt Lake Valley. Mon. Wea. Rev., 131, 1301-1322.
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APPENDIX A
WTM OBSERVATIONAL WIND ROSES
This section describes the monthly and diurnal variations of wind speed and wind
directions at location ONC, OFC and RTC.
A.1 Monthly Variation of Wind Speed and Direction ONC
The ONC data from the month of April 2006 indicates predominately south and
west southwest wind directions with the west and west/southwest directions exhibiting
the strongest average wind at 6-8 ms-1 (Figure A.1). In May and June, the average wind
speed slows to 4-6 ms-1 and shifts the strongest component to the south (Figure A.2 and
Figure A.3). The wind direction in July is mainly from the south/southeast, but the
strongest winds can be found coming from the south and south/southwest (Figure A.4).
In August, September, and October the wind continues to be from the south, but the
average wind speed picks up to 4-6 ms-1 (Figure A.5, Figure A.6, and Figure A.7). In
November, the wind direction shifts to a prevailing south/southwest direction but the
fastest speeds are located the north/northwest and north directions (Figure A.8). Moving
on into December, the wind direction changes again to a predominantly west/southwest
direction with an additional strong westerly component (Figure A.9). In January 2007,
the wind direction is divided between the north and the south/southwest directions
(Figure A.10). The spring windy season begins in February, with average wind speeds
toping 6-8 ms-1 from the prevailing west/southwest direction and a peak of 8-10 ms-1
sneaking in from the west/northwest (Figure A.11). March winds are weighted towards
the south once again with speeds ranging from 4-6 ms-1 (Figure A.12).
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A.2 Diurnal Variation of Wind Speed and Direction ONC
The three hourly wind speeds and direction for the year are analyzed to assess the
timing of WRF in Chapter 5. The wind direction at 0000, 0300, and 0600 UTC are
almost identical with southerly and southeasterly components, the only difference being
the higher wind speeds at 0000 UTC (Figure A.13, Figure A.14, and Figure A.15). The
wind obtains a stronger south/southwesterly flow by 0900 UTC (Figure A.16), and
continues to gain more prominence in the westerly directions at 1200 UTC (Figure A.17).
At 1500 UTC, the averaged wind speeds have increased in strength to the south,
south/southwest, and west/southwest directions, with the strongest speed occurring from
the west at 6-8 ms-1 (Figure A.18). The 1800 and 2100 UTC wind directions agree with
the overall flow in the area. Winds are predominantly from the south, south/southwest
and west/southwest with wind speeds peaking at 6-8 ms-1 (Figure A.19 and Figure A.20).
A.3 Monthly Variation of Wind Speed and Direction OFC
Wind directions in April 2006 at locations OFC are predominately from the south.
The west and west/southwest directions feature the fastest wind speeds at 6-8 ms-1
(Figure A.21). During May and June, the prevailing wind is from the south and
south/southeast, at times reaching 4-6 ms-1 (Figure A.22 and Figure A.23). In July,
August, and September the overall speeds slow and continue to blow from the south and
south/southeast directions (Figure A.24, Figure A.25, and Figure A.26). In October, the
south/southwest direction occurs most frequently, but the strongest winds can be found
less often from directions containing a northerly component (Figure A.27). The wind
speed further increases in November from the north/northwest, to speeds as high as 6-8
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ms-1 (Figure A.28). In December, locations OFC receive wind blowing mainly from the
south/southwest, with maximum speeds slowing to 4-6 ms-1 (Figure A.29). Wind
direction and speeds are divided between the north and south/southwest during the month
of January (Figure A.30). As the season progresses into February, maximum wind
speeds increase to 8-10 ms-1 from the west, while the prevailing wind direction is from
the south/southwest (Figure A.31). March brings winds mainly from the south and
southeast directions with speeds topping 4-6 ms-1 at times (Figure A.32).
A.4 Diurnal Variation of Wind Speed and Direction OFC
The hours of 0000, 0300, and 0600 UTC exhibit wind directions from the south
and southeast directions (Figure A.33, Figure A.34, and Figure A.35). South southwest
winds are most common at 0900 UTC, but the fastest wind speeds can be located in the
north/northwest quadrant (Figure A.36). The wind direction varies at 1200 UTC with
overall slower speeds (Figure A.37). The wind speed begins to increase at 1500 UTC,
with maximum speeds located in the west/southwest and north/northwest directions
(Figure A.38). 1800 and 2100 UTC are comparable with the prevailing wind direction
from the south and fastest speeds occurring in the west directions (Figure A.39 and
Figure A.40). The diurnal cycle of the wind speed is consistent with the stabilizing of the
surface layer at nighttime hours, as a result the 10-m wind dies down, but the upper level
wind actually speeds up at this time because the surface is decoupled.
A.5 Monthly Variation of Wind Speed and Direction at RTC
In April 2006 the wind direction is primarily from the west/southwest containing wind
speeds as high as 6-8 ms-1. The overall strongest wind speeds are from the west at 8-10
ms-1 (Figure A.41). In May the prevailing wind is from the south and south/southeast,
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with winds in excess of 6-8 ms-1 from the north (Figure A.42). A southerly wind is
dominant in June with speeds in excess of 6-8 ms-1 (Figure A.43). The southerly wind
slows to peak averages of 4-6 ms-1 in July, August, and September 2006 (Figure A.44,
Figure A.45, and Figure A.46). In October, November, and December the wind
maximums can be found in the west and west/northwest directions at 6-8 ms-1 (Figure
A.47, Figure A.48, and Figure A.49). January consists of strong northerly winds peaking
at 6-8 ms-1 but also has a reoccurring wind from the south/southwest (Figure A.50). In
February, the wind gains momentum in the west and west/southwest directions with
speeds from 8-10 ms-1 (Figure A.51). March once again shows a dominant south wind
but winds maximums in all directions of 6-8 ms-1 (Figure A.52).
A.6 Diurnal Variation of Wind Speed and Direction at RTC
During the hours of 0000, 0300, 0600, and 0900 UTC at RTC the wind
predominately blows from the south, with the fastest speeds coming from directions
containing a northerly component (Figure A.53, Figure A.54, Figure A.55, and Figure
A.56). At 1200 UTC the wind is predominately slower from 2-4 ms-1, except when it is
from the west and north/northwest directions, where it reaches 6-8 ms-1 (Figure A.57).
The times of 1500 and 1800 UTC show further intensifying wind speeds from the west to
8-10 ms-1, with strong northerly wind speeds as well (Figure A.58 and Figure A.59). By
2100 UTC the prevailing wind is from the south with wind directions from the west,
reaching maximums of 8-10 ms-1 (Figure A.60).
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Figure A.1: Wind Direction and Wind Speed for April 2006: ONC
Figure A.2: Wind Direction and Wind Speed for May 2006: ONC
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Figure A.3: Wind Direction and Wind Speed for June 2006: ONC
Figure A.4: Wind Direction and Wind Speed for July 2006: ONC
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Figure A.5: Wind Direction and Wind Speed for August 2006: ONC
Figure A.6: Wind Direction and Wind Speed for September 2006: ONC
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Figure A.7: Wind Direction and Wind Speed for October 2006: ONC
Figure A.8: Wind Direction and Wind Speed for November 2006: ONC
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Figure A.9: Wind Direction and Wind Speed for December 2006: ONC
Figure A.10: Wind Direction and Wind Speed for January 2007: ONC
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Figure A.11: Wind Direction and Wind Speed for February 2007: ONC
Figure A.12: Wind Direction and Wind Speed for March 2007: ONC
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Figure A.13: Wind Direction and Wind Speed for 0Z: ONC
Figure A.14: Wind Direction and Wind Speed for 3Z: ONC
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Figure A.15: Wind Direction and Wind Speed for 6Z: ONC
Figure A.16: Wind Direction and Wind Speed for 9Z: ONC
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Figure A.17: Wind Direction and Wind Speed for 12Z: ONC
Figure A.18: Wind Direction and Wind Speed for 15Z: ONC
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Figure A.19: Wind Direction and Wind Speed for 18Z: ONC
Figure A.20: Wind Direction and Wind Speed for 21Z: ONC
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Figure A.21: Wind Direction and Wind Speed for April 2006: OFC
Figure A.22: Wind Direction and Wind Speed for May 2006: OFC
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Figure A.23: Wind Direction and Wind Speed for June 2006: OFC
Figure A.24: Wind Direction and Wind Speed for July 2006: OFC
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Figure A.25: Wind Direction and Wind Speed for August 2006: OFC
Figure A.26: Wind Direction and Wind Speed for September 2006: OFC
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Figure A.27: Wind Direction and Wind Speed for October 2006: OFC
Figure A.28: Wind Direction and Wind Speed for November 2006: OFC
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Figure A.29: Wind Direction and Wind Speed for December 2007: OFC
Figure A.30: Wind Direction and Wind Speed for January 2007: OFC
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Figure A.31: Wind Direction and Wind Speed for February 2007: OFC
Figure A.32: Wind Direction and Wind Speed for March 2007: OFC
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Figure A.33: Wind Direction and Wind Speed for 0Z: OFC
Figure A.34: Wind Direction and Wind Speed for 3Z: OFC
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Figure A.35: Wind Direction and Wind Speed for 6Z: OFC
Figure A.36: Wind Direction and Wind Speed for 9Z: OFC
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Figure A.37: Wind Direction and Wind Speed for 12Z: OFC
Figure A.38: Wind Direction and Wind Speed for 15Z: OFC
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Figure A.39: Wind Direction and Wind Speed for 18Z: OFC
Figure A.40: Wind Direction and Wind Speed for 21Z: OFC
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Figure A.41: Wind Direction and Wind Speed for April 2006: RTC
Figure A.42: Wind Direction and Wind Speed for May 2006: RTC
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Figure A.43: Wind Direction and Wind Speed for June 2006: RTC
Figure A.44: Wind Direction and Wind Speed for July 2006: RTC
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Figure A.45: Wind Direction and Wind Speed for August 2006: RTC
Figure A.46: Wind Direction and Wind Speed for September 2006: RTC
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Figure A.47: Wind Direction and Wind Speed for October 2006: RTC
Figure A.48: Wind Direction and Wind Speed for November 2006: RTC
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Figure A.49: Wind Direction and Wind Speed for December 2006: RTC
Figure A.50: Wind Direction and Wind Speed for January 2007: RTC
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Figure A.51: Wind Direction and Wind Speed for February 2007: RTC
Figure A.52: Wind Direction and Wind Speed for March 2007: RTC
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Figure A.53: Wind Direction and Wind Speed for 0Z: RTC
Figure A.54: Wind Direction and Wind Speed for 3Z: RTC
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Figure A.55: Wind Direction and Wind Speed for 6Z: RTC
Figure A.56: Wind Direction and Wind Speed for 9Z: RTC
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Figure A.57: Wind Direction and Wind Speed for 12Z: RTC
Figure A.58: Wind Direction and Wind Speed for 15Z: RTC
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Figure A.59: Wind Direction and Wind Speed for 18Z: RTC
Figure A.60: Wind Direction and Wind Speed for 21Z: RTC
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APPENDIX B
WRF FORECAST WIND ROSES
B.1 WRF Monthly Variation of Wind Speed and Direction ONC The month of April 2006 shows predominately south and west wind directions,
with the west direction exhibiting the strongest average wind speed at 6-8 ms-1. The
model fails to capture the strong west/southwest wind as seen in the observed data. It
also overestimates the frequency of the west wind (Figure B.1). In May and June the
average wind speed slows to 4-6 ms-1 and shifts the strongest component to the south in
the observations. However, WRF again underestimates the wind speed and fails to
capture the magnitude of the wind in the south/southwest and south/southeast directions
(Figure B.2 and Figure B.3). The wind direction in July is mainly from the
south/southeast, but WRF places the highest average wind in the south/southwest
direction. The July averages are underestimated in the model as well (Figure B.4). In
August, the observed wind continues to be from the south, while WRF shifts the direction
to south/southwest (Figure B.5). During September and October, WRF captures the
overall wind direction but underestimates the average wind speed (Figure B.6 and Figure
B.7). In November, the wind direction shifts to a prevailing south/southwest direction
but the fastest speeds are located the north/northwest and north directions in the
observations. During the same time period, WRF overestimates the wind coming from
the north, as well as under predicting the wind in the south/southwest direction (Figure
B.8). The December observed wind direction is predominantly west/southwest in
direction, with an additional strong westerly component. The model forecasts capture the
correct overall direction of the wind, but the magnitude of the wind is incorrect in both
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the west and west/southwest directions (Figure B.9). The January 2007 model forecast
shows promise with the wind direction divided between the north and the
south/southwest directions as in the observational data (Figure B.10). When the spring
windy season begins in February, the WRF wind speeds are underestimated, with average
wind speeds only reaching 4-8 ms-1 from the prevailing west/southwest direction. The
maximum observed wind of 8-10 ms-1 from the west/northwest direction is missed
completely (Figure B.11). The March observational winds are weighted towards the
south with speeds ranging from 4-6 ms-1. However, WRF places the strongest winds in
the west/southwest direction at 6-8 ms-1 which is not present in the observations. WRF
has difficulty capturing the correct wind direction (Figure B.12).
B.2 WRF Diurnal Variation of Wind Speed and Direction ONC
The wind direction from the WRF model at 0000, 0300 and 0600 UTC agree with
the observations, as both have wind directions with the south and southeasterly
components. The only difference being the model underestimates the wind speed (Figure
B.13, Figure B.14, and Figure B.15). WRF fails to capture the magnitude of the wind
speed when compared to the observations at 0900, 1200, and 1500 UTC (Figure B.16,
Figure B.17, and Figure B.18). The 1800 and 2100 UTC WRF forecasts do capture the
array wind directions, but once again WRF underestimates the wind speed (Figure B.19
and Figure B.20).
B.3 WRF Monthly Variation of Wind Speed and Direction OFC
Wind directions in the observational data during April 2006 at locations OFC are
predominately from the south. WRF splits the wind direction during this month between
the south and west directions, and fails to capture the magnitude of the wind in the
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west/southwest direction (Figure B.21). During May and June, the observed wind is
mainly from the south and south/southeast, while the model places the wind direction
predominantly from the south/southeast direction, and again underestimates the
magnitude in those directions (Figure B.22 and Figure B.23). In July and August, WRF
underestimates the wind speed and exhibits an overestimation of the wind coming from
the south/southwest direction (Figure B.24 and Figure B.25). During September WRF
fails to represent the correct magnitude and direction of the wind (Figure B.26). In
October the observational data contains winds from the south/southwest direction, most
frequently but the model forecasts fail again to capture accurate magnitudes (Figure
B.27). The increase in the wind speed during November from the north/northwest is
underestimated in WRF. WRF not only overestimates the north wind speed in
November, but it also underestimates the frequency (Figure B.28). In December,
locations OFC receive wind blowing mainly from the south/southwest, which WRF is
able to capture, but the model overestimates the wind speed for the westerly wind (Figure
B.29). The divisions of wind direction and speeds between the north, and
south/southwest during the month of January are represented poorly in WRF (Figure
B.30). As the season progresses into February, maximum observed wind speed increases
to 8-10 ms-1 from the west while the prevailing wind direction is from the
south/southwest. WRF is unable to represent either of these conditions (Figure B.31).
WRF shows even less skill in March in capturing the wind direction or speed present in
the observational data (Figure B.32).
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B.4 WRF Diurnal Variation of Wind Speed and Direction OFC
The hours of 0000, 0300, and 0600 UTC exhibit wind directions from the south
and southeast directions, which is in agreement with the WTM observations (Figure
B.33, Figure B.34, and Figure B.35). At 0900 UTC, the model forecast lacks skill in
determining the correct wind speed and magnitude once again (Figure B.36). The wind
direction is represented incorrectly, although the correct magnitude is being shown by
WRF at 1200 and 1500 UTC (Figure B.37 and Figure B.38). The 1800 and 2100 UTC
WRF forecasts have the predominant wind direction from the south, which is present in
the observations, but the magnitude to too low (Figure B.39 and Figure B.40).
B.5 WRF Monthly Variation of Wind Speed and Direction at RTC
In April, May, June, and July of 2006, WRF shows no skill in capturing the speed
of the wind, although the wind directions represented in the data appear to be correct
when compared to the observations (Figure B.41, Figure B.42, Figure B.43, and Figure
B.44). WRF underestimates the wind speed in August, September, October, and
November of 2006 (Figure B.45, Figure B.46, Figure B.47, and Figure B. 48). During
the months of December and January, WRF fails to capture the strong northerly winds, in
addition to underestimating the strength of the west wind in December (Figure B.49 and
Figure B.50). In February, WRF underestimates the wind speeds in the west and
west/southwest directions (Figure B.51). March once again shows a dominant south
wind in the observational data, but WRF struggles, and overestimates the wind speed in
the west/southwest as well as forecasting the wrong wind directions (Figure B.52).
B.6 WRF Diurnal Variation of Wind Speed and Direction at RTC
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During the hours of 0000, 0300, 0600, and 0900 UTC at RTC the model forecast
does well in representing the wind directions from the south, but underestimates the wind
speed at all times (Figure B.53, Figure B.54, Figure B.55, and Figure B.56). At 1200
UTC, WRF overestimates the occurrences of the wind from the south/southwest direction
(Figure B.57). The times of 1500 and 1800 UTC show further intensifying wind speeds
from the west to 8-10 ms-1 with strong northerly wind speeds as well which is
underestimated in the model forecast (Figure B.58 and Figure B.59). At 2100 UTC, the
prevailing wind from the south is underestimated with wind directions from the west,
reaching maximums of 8-10 ms-1 in the WRF forecasts (Figure B.60).
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Figure B.1: Wind Direction and Wind Speed for April 2006: WRF Forecast ONC
Figure B.2: Wind Direction and Wind Speed for May 2006: WRF Forecast ONC
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Figure B.3: Wind Direction and Wind Speed for June 2006: WRF Forecast ONC
Figure B.4: Wind Direction and Wind Speed for July 2006: WRF Forecast ONC
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Figure B.5: Wind Direction and Wind Speed for August 2006: WRF Forecast ONC
Figure B.6: Wind Direction and Wind Speed for September 2006: WRF Forecast ONC
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Figure B.7: Wind Direction and Wind Speed for October 2006: WRF Forecast ONC
Figure B.8: Wind Direction and Wind Speed for November 2006: WRF Forecast ONC
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Figure B.9: Wind Direction and Wind Speed for December 2006: WRF Forecast ONC
Figure B.10: Wind Direction and Wind Speed for January 2007: WRF Forecast ONC
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Figure B.11: Wind Direction and Wind Speed for February 2007: WRF Forecast ONC
Figure B.12: Wind Direction and Wind Speed for March 2007: WRF Forecast ONC
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Figure B.13: Wind Direction and Wind Speed for 0Z: WRF Forecast ONC
Figure B.14: Wind Direction and Wind Speed for 3Z: WRF Forecast ONC
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Figure B.15: Wind Direction and Wind Speed for 6Z: WRF Forecast ONC
Figure B.16: Wind Direction and Wind Speed for 9Z: WRF Forecast ONC
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Figure B.17: Wind Direction and Wind Speed for 12Z: WRF Forecast ONC
Figure B.18: Wind Direction and Wind Speed for 15Z: WRF Forecast ONC
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Figure B.19: Wind Direction and Wind Speed for 18Z: WRF Forecast ONC
Figure B.20: Wind Direction and Wind Speed for 21Z: WRF Forecast ONC
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Figure B.21: Wind Direction and Wind Speed for April 2006: WRF Forecast OFC
Figure B.22: Wind Direction and Wind Speed for May 2006: WRF Forecast OFC
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Figure B.23: Wind Direction and Wind Speed for June 2006: WRF Forecast OFC
Figure B.24: Wind Direction and Wind Speed for July 2006: WRF Forecast OFC
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Figure B.25: Wind Direction and Wind Speed for August 2006: WRF Forecast OFC
Figure B.26: Wind Direction and Wind Speed for September 2006: WRF Forecast OFC
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Figure B.27: Wind Direction and Wind Speed for October 2006: WRF Forecast OFC
Figure B.28: Wind Direction and Wind Speed for November 2006: WRF Forecast OFC
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Figure B.29: Wind Direction and Wind Speed for December 2006: WRF Forecast OFC
Figure B.30: Wind Direction and Wind Speed for January 2007: WRF Forecast OFC
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Figure B.31: Wind Direction and Wind Speed for February 2007: WRF Forecast OFC
Figure B.32: Wind Direction and Wind Speed for March 2007: WRF Forecast OFC
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Figure B.33: Wind Direction and Wind Speed for 0Z: WRF Forecast OFC
Figure B.34: Wind Direction and Wind Speed for 3Z: WRF Forecast OFC
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Figure B.35: Wind Direction and Wind Speed for 6Z: WRF Forecast OFC
Figure B.36: Wind Direction and Wind Speed for 9Z: WRF Forecast OFC
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Figure B.37: Wind Direction and Wind Speed for 12Z: WRF Forecast OFC
Figure B.38: Wind Direction and Wind Speed for 15Z: WRF Forecast OFC
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Figure B.39: Wind Direction and Wind Speed for 18Z: WRF Forecast OFC
Figure B.40: Wind Direction and Wind Speed for 21Z: WRF Forecast OFC
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Figure B.41: Wind Direction and Wind Speed for April 2006: WRF Forecast RTC
Figure B.42: Wind Direction and Wind Speed for May 2006: WRF Forecast RTC
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Figure B.43: Wind Direction and Wind Speed for June 2006: WRF Forecast RTC
Figure B.44: Wind Direction and Wind Speed for July 2006: WRF Forecast RTC
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Figure B.45: Wind Direction and Wind Speed for August 2006: WRF Forecast RTC
Figure B.46: Wind Direction and Wind Speed for September 2006: WRF Forecast RTC
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Figure B.47: Wind Direction and Wind Speed for October 2006: WRF Forecast RTC
Figure B. 48: Wind Direction and Wind Speed for November 2006: WRF Forecast RTC
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Figure B.49: Wind Direction and Wind Speed for December 2006: WRF Forecast RTC
Figure B.50: Wind Direction and Wind Speed for January 2007: WRF Forecast RTC
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Figure B.51: Wind Direction and Wind Speed for February 2007: WRF Forecast RTC
Figure B.52: Wind Direction and Wind Speed for March 2007: WRF Forecast RTC
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Figure B.53: Wind Direction and Wind Speed for 0Z: WRF Forecast RTC
Figure B.54: Wind Direction and Wind Speed for 3Z: WRF Forecast RTC
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Figure B.55: Wind Direction and Wind Speed for 6Z: WRF Forecast RTC
Figure B.56: Wind Direction and Wind Speed for 9Z: WRF Forecast RTC
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Figure B.57: Wind Direction and Wind Speed for 12Z: WRF Forecast RTC
Figure B.58: Wind Direction and Wind Speed for 15Z: WRF Forecast RTC
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Figure B.59: Wind Direction and Wind Speed for 18Z: WRF Forecast RTC
Figure B.60: Wind Direction and Wind Speed for 21Z: WRF Forecast RTC
PERMISSION TO COPY
In presenting this thesis in partial fulfillment of the requirements for a master’s
degree at Texas Tech University or Texas Tech University Health Sciences Center, I
agree that the Library and my major department shall make it freely available for
research purposes. Permission to copy this thesis for scholarly purposes may be granted
by the Director of the Library or my major professor. It is understood that any copying
or publication of this thesis for financial gain shall not be allowed without my further
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Agree (Permission is granted.)
________Rachel Gwynne Rogers-Van Nice________________ _04/02/08_______ Student Signature Date Disagree (Permission is not granted.) _______________________________________________ _________________ Student Signature Date