assimilating dense pressure observations— a preview of how this may impact analysis and nowcasting...
TRANSCRIPT
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ASSIMILATING DENSE PRESSURE OBSERVATIONS—A PREVIEW OF HOW THIS MAY IMPACT ANALYSIS AND NOWCASTING
Luke Madaus -- Wed., Sept. 21, 2011
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Past problems
Weather models still poorly predict the timing and intensity of significant weather events
Images from Phil Regulski
For short-range forecasts, important to capture variability at small scales using very high resolution Eckel and Mass (2005)
Data assimilation can try to introduce small-scale features – if variables assimilated are chosen judiciously
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Why pressure?
Less sensitive to representativeness error
Widely available observations Has far-reaching meso- and synoptic-
scale relevance Also can provide information in the
vertical (Dirren et al 2007)
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Fundamental question
To investigate: Use a large ensemble capable of resolving
mesoscale features Need observations at a density sufficient to
represent the same scales of variability we are trying to model
To what extent can pressure observations be used to describe phenomena on the mesoscale?
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High-resolution
Current Setup
• 4 km grid spacing• 80 member ensemble• Quasi-explicit resolution of:• Some convective
processes• Small-scale boundaries• Some localized orographic effects
• Need observed data to match!
Weisman et al. 2008
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Data sources
ASOS -- 103
All potential obs -- 1850
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Data sources
TOTAL – 1000-1600 observations hourly across Pac. NW
ASOS – Canada and US (100) Weather Underground (650) AWS Schoolnet (80) CWOP (250) RAWS (5) Oregon RWIS (10) Pendleton WFO Network (15) Land/Sea Synop (30) Other (50)
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Oct. 24, 2011 Convergence Zone
An “unforecast” convergence zone forms around 14Z (6AM PDT) and moves south across north Seattle during the morning commute
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Oct. 24, 2011 Convergence Zone Started 4km domain at 6Z and assimilated
data through 15Z Control – No assimilation Real-Time EnKF all observation types Pressure-only assimilation every 3 hours
without bias removal Pressure-only assimilation every 3 hours with
bias removal Real-Time EnKF + additional pressure
observations every 3 hours Pressure-only assimilation every 1 hour with
bias removal
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Oct. 24, 2011 Convergence Zone
Control Run – No assimilationCurrent EnKF System—3hr cycle
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Oct. 24, 2011 Convergence Zone
Just Pressure Assim.—3hr cycleCurrent EnKF System—3hr cycle
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Oct. 24, 2011 Convergence Zone
Just Pressure Assim.—3hr cycleCurrent EnKF System—3hr cycle
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Oct. 24, 2011 Convergence Zone
Just Pressure Assim.—1hr cycleCurrent EnKF System—3hr cycle
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Conclusion
Pressure observations alone seem to be able to capture much of small-scale variability
Pressure observation adjustments affect analysis of dynamic fields (pressure,winds) in a positive way Better precipitation development forecasts
as a result Hourly assimilation looks like it could do
wonderful things…
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Future work
Running long-term case (April 10-30, 2011) for more robust statistics Focus on reducing errors in pressure and
wind analyses Subsequently improved wind and
precipitation forecasts. Looking to get more from pressure
observations through assimilating pressure tendency
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Acknowledgements
Advisors – Cliff Mass and Greg Hakim Phil Regulski Rahul Mahajan Mark Albright Jeff Anderson and Nancy Collins at NCAR Northwest Modeling Consortium
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References
Anderson, J., B. Wyman, S. Zhang, T. Hoar, 2005: Assimilation of surface pressure observations using an ensemble filter in an idealized global atmospheric prediction system. J. Atmos. Sci., 62, 2925-2938.
Dirren, S., R. Torn, G. Hakim, 2007: A data assimilation case study using a limited-area ensemble filter. Mon. Wea. Rev., 135, 1455-1473.
Eckel, F. A., C. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Weather and Forecasting, 20, 328-350.
Mass, C. and G. Ferber, 1990: Surface pressure perturbations produced by an isolated mesoscale topographic barrier, part 1: general characteristics and dynamics. Mon. Wea. Rev., 118, 2579-2596.
McMurdie, L., C. Mass, 2004: Major numerical forecast failures over the northeast Pacific. Weather and Forecasting, 19, 338-356.
Miller, P. and M. Barth, 2002: RSAS Technical Procedures Bulletin. MSAS/RSAS. Web. Accessed: Sept. 12, 2011.
Weisman, M., C. Davis, W. Wang, K. Manning, J. Klemp, 2008: Experiences with 0-36-h explicit convective forecasts with the WRF-ARW model. Weather and Forecasting, 23, 407-437
Wheatley, D. and D. Stensrud, 2010: The impact of assimilating surface pressure observations on severe weather events in a WRF mesoscale ensemble system. Mon. Wea. Rev., 138, 1673-1694.
Whitaker, J., G. Compo, X. Wei, T. Hamill, 2001: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132, 1190-1200.
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Oct. 24, 2011 Convergence Zone
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Pressure tendency
Covariances not as strong—less impact than raw pressure (Wheatley and Stensrud 2009)
Pressure tendency requires continuity of observation
Not currently supported in the DART EnKF assimilation framework
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Different Parameterization
WSM-3 microphysics WSM-5 microphysics
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How bad is bias?
Before Bias Correction535/1100 error >1.5mb
After Bias Correction50/1100 error > 1.5mb
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Pressure tendency
What about pressure tendency as a way to avoid bias?
biasobs ptptp )()(
])([])([)()( biasbiasobsobs pttpptpttptp
)()( ttptpptend
truthtend
obstend pttptptp )()()(
Bias not present in this representation of pressure obs
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EnKF assimilation
Pressure (hPa)1010
hPa
1009 hPa
EnsembleObservation
New Estimate