1. ASSIMILATING DENSE PRESSURE OBSERVATIONS— A PREVIEW OF HOW THIS MAY IMPACT ANALYSIS AND NOWCASTING Luke Madaus -- Wed., Sept. 21, 2011

2. 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

3. 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)

4. 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?

5. 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

6. Data sources ASOS -- 103All potential obs -- 1850

7. 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)

8. Oct. 24, 2011 Convergence Zone  An “unforecast” convergence zone forms around 14Z (6AM PDT) and moves south across north Seattle during the morning commute

9. 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

10. Oct. 24, 2011 Convergence Zone Control Run – No assimilationCurrent EnKF System—3hr cycle

11. Oct. 24, 2011 Convergence Zone Just Pressure Assim.—3hr cycleCurrent EnKF System—3hr cycle

12. Oct. 24, 2011 Convergence Zone Just Pressure Assim.—3hr cycleCurrent EnKF System—3hr cycle

13. Oct. 24, 2011 Convergence Zone Just Pressure Assim.—1hr cycleCurrent EnKF System—3hr cycle

14. Conclusion alone  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…

15. 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

16. Acknowledgements  Advisors – Cliff Mass and Greg Hakim  Phil Regulski  Rahul Mahajan  Mark Albright  Jeff Anderson and Nancy Collins at NCAR  Northwest Modeling Consortium

17. 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.

18. Oct. 24, 2011 Convergence Zone

19. 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

20. Different Parameterization WSM-3 microphysicsWSM-5 microphysics

21. How bad is bias? Before Bias Correction 535/1100  error >1.5mb After Bias Correction 50/1100  error > 1.5mb

22. Pressure tendency  What about pressure tendency as a way to avoid bias? Bias not present in this representation of pressure obs

23. EnKF assimilation Pressure (hPa) 1010 hPa 1009 hPa Ensemble Observation New Estimate