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ASSIMILATING DENSE PRESSURE OBSERVATIONS—A PREVIEW OF HOW THIS MAY IMPACT ANALYSIS AND NOWCASTING
Luke Madaus -- Wed., Sept. 21, 2011
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
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)
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?
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
Data sources
ASOS -- 103
All potential obs -- 1850
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)
Oct. 24, 2011 Convergence Zone
An “unforecast” convergence zone forms around 14Z (6AM PDT) and moves south across north Seattle during the morning commute
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
Oct. 24, 2011 Convergence Zone
Control Run – No assimilationCurrent EnKF System—3hr cycle
Oct. 24, 2011 Convergence Zone
Just Pressure Assim.—3hr cycleCurrent EnKF System—3hr cycle
Oct. 24, 2011 Convergence Zone
Just Pressure Assim.—3hr cycleCurrent EnKF System—3hr cycle
Oct. 24, 2011 Convergence Zone
Just Pressure Assim.—1hr cycleCurrent EnKF System—3hr cycle
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…
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
Acknowledgements
Advisors – Cliff Mass and Greg Hakim Phil Regulski Rahul Mahajan Mark Albright Jeff Anderson and Nancy Collins at NCAR Northwest Modeling Consortium
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.
Oct. 24, 2011 Convergence Zone
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
Different Parameterization
WSM-3 microphysics WSM-5 microphysics
How bad is bias?
Before Bias Correction535/1100 error >1.5mb
After Bias Correction50/1100 error > 1.5mb
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
EnKF assimilation
Pressure (hPa)1010
hPa
1009 hPa
EnsembleObservation
New Estimate