improving very-short-term storm predictions by assimilating radar and satellite data into a...
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IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND
SATELLITE DATA INTO A MESOSCALE NWP MODEL
Allen Zhao1, John Cook1, Qin Xu2, and Paul Harasti3
1 Naval Research Laboratory, Monterey, California, USA
2 National Severe Storms Laboratory, Norman. Oklahoma, USA.
3 University Corporation for Atmospheric Research, Boulder, Colorado, USA.
Phone: (831) 656-4700 Fax: (831) 656-4769 [email protected]
Nowcasting and Data Assimilation
Mesoscale NWP models provide a practical means for nowcasting
• A physical-based approach
• Provide all atmospheric parameters for nowcasting convective storms and other hazardous atmospheric conditions (e.g., low ceiling & visibility)
• Smooth transition from nowcasting (0-6h) to forecasting (6-72h)
0-6 hour represents a hard period for mesoscale NWP models
• Inaccurate initial conditions due to the lack of (or poor) observational data and inadequate data assimilation procedures
• Imperfectness in model dynamics & physical parameterization
Recent developments in high-resolution data assimilation pave the way to use NWP models for nowcasting
• More and more high-resolution data are available from radars, satellites and other sensors
• New techniques, such as variational methods and ensemble-based approaches, have been developed for mesoscale data assimilation
Objective: To study the opportunity and capability of improving 0-6 hour NWP forecasts by assimilation of high-resolution observational data
COAMPS
is a registered trademark of the Naval Research Laboratory
NAVDAS
ConventionalObservations
COAMPS Forecast
T, P, Z, U, V, qv
COAMPS®
Forecast
3D Cloud Analysis
Radarreflectivity
qv, qc, qi, qr, qs, qg
Satellitedata
Blending
3D Wind Analysis
Radar radial velocity
U, V, W, T, P
or
Data Assimilation Procedures
23:08 UTC May 09, 2003 Radar Radius = 150 km
Morehead City, NC(KMHX)
Norfolk, VA(KAKQ)
Raleigh, NC(KRAX)
Model domain (100x100, 6km)
3-D radar reflectivity on COAMPS® grid(Isosurface = 20 dBZ)
1816141210 8 6 4 2
Hei
ght
(km
)
0
20
10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0
South – North (600 km)
A Convective Storm Case
A strong convective storm system on 9 May 2003 was moving southward along the east coast of the United States
The storm system entered the study area at about 1800 UTC and reached its mature stage at about 2300 UTC
Data from three WSR-88D radars in that area were collected every 5-minutes
GOES-12 IR and vis data were also collected every 30 minutes
COAMPS
is a registered trademark of the Naval Research Laboratory
ForecastCNTL
No DataAssimilation
Forecast from 12 UTC 9 May
1-hourforecast
1-hourforecast
1-hourforecast Forecast
ALL
Satellite IR and vis, Radar reflectivity and radial velocity
Forecast from 12 UTC 9 May
22 UTC21 UTC20 UTC19 UTC
1-hourforecast
1-hourforecast
1-hourforecast Forecast
CLD
Satellite IR and vis data
Forecast from 12 UTC 9 May
22 UTC21 UTC20 UTC19 UTC
1-hourforecast
1-hourforecast
1-hourforecast Forecast
CLD+PR
Satellite IR and vis data, Radar Reflectivity
Forecast from 12 UTC 9 May
22 UTC21 UTC20 UTC19 UTC
1-hourforecast
1-hourforecast
1-hourforecast Forecast
WIND
Radar radial velocity
Forecast from 12 UTC 9 May
22 UTC21 UTC20 UTC19 UTC
Five experiments have been conducted:• CNTL: no radar data assimilation
• CLD: Cloud fields from satellite observations are assimilated hourly
• CLD+PR: Cloud fields from satellite observations and precipitations from radar reflectivity data are assimilated hourly
• WIND: Radar radial velocity data are assimilated hourly
• ALL: All these fields are assimilated hourly
12-hour forecasts were made starting at 22 UTC 9 May 2003 in all five experiments
Experiment Design
4
5
6
0.48 1.49 2.37 3.38 4.26 5.31 6.24 7.47 8.65 9.97 13.9716.6919.46
Radar Elevation Angle (degree)
RM
S E
rro
rs (
m/s
)
CNTL CLDCLD+PR WINDALL
0.79
0.84
0.89
0.48 1.49 2.37 3.38 4.26 5.31 6.24 7.47 8.65 9.97 13.9716.6919.46
Radar Elevation Angles (degree)
Co
rre
lati
on
Sc
ore
s
CNTL CLDCLD+PR WINDALL
Correlation coefficients and RMS errors of 1-hour forecast radial velocity (Vrf) verified
against radar observations of all scans
(Raleigh radar station, 23:00 UTC 9 May 2003)
Wind Forecast Improvements with Forecast Time
0.55
0.65
0.75
0.85
0.95
1 2 3 4 5
CNTL CLDCLD+PR WINDALL
5
7
9
1 2 3 4 5
CNTL CLDCLD+PR WINDALL
Forecast Hour
5
7
9
11
1 2 3 4 5
CNTL CLD
CLD+PR WIND
ALL
Forecast Hour
0.5
0.65
0.8
0.95
1 2 3 4 5
CNTL CLD
CLD+PR WIND
ALL
Ele. Angle
= 2.37o
RMS Error (m1s-1)Correlation Coefficient
Ele. Angle
= 1.49o
The data assimilations affected all dynamical and hydrological fields.
The effects of the implicit latent heat from the assimilated satellite and radar reflectivity data were seen in the temperature changes and affected the wind fields significantly.
The data assimilation impacts remained in the forecasts of winds, temperature and water vapor for several hours, but decreased rapidly in the precipitation fields as the storm system weakened.
Radar radial velocity assimilation led to the biggest improvement in wind forecast, while reflectivity assimilation was the major cause of the improvement in storm location and strength prediction.
The combined data assimilation did not have the best results in each individual field forecast, but was the best in overall improvement.
Conclusions