1. University of Washington Ensemble Systems for Probabilistic Analysis and Forecasting
Cliff Mass, Atmospheric Sciences
University of Washington

2. UW Mesoscale Ensemble Systems
An attempt to create end-to-end mesoscale probabilistic guidance.
Two major ensemble systems exploring different approaches to generating initial conditions.
Based on high-resolution (12-km or 4-km grid spacing) ensembles.

3. Two UW Ensemble Systems
UWME: eight members with initializations and boundary conditions from major operational NWP systems. 72 hr, 36 and 12-km grid spacing. WRF model.
UW EnKF: 60 members, 36 and 4-km grid spacing. 3-hr cycling, with 24-h forecasts once a day. WRF model and DART infrastructure.

4. UWME Parent Modeling Systems

5. UWME
Originally MM5 based, but last year switched to WRF with improved physics options.
Initially applied physics diversity, but not using that now due to computer limitations.
Kain-Fritsch CU, YSU PBL, Thompson microphysics, RRTM LW, Dudhia SW. Noah LSM

10. Bayesian Model Averaging
Assumes a Gaussian (or other) PDF for each ensemble member.
Assumes the variance of each member is the same (in current version).
Includes a simple bias correction for each member.
Weights each member by its performance during a training period (we are using 25 days)
Adds the pdfs from each member to get a total pdf.

12. Application of BMA-Max 2-m Temperature (all stations in 12 km domain)
Improves reliability and sharpness

13. The BMA Site

14. The Next Challenge: Making Probabilistic Forecasts Accessible to Users
Creating good probabilistic information is only half the challenge—and probably the easier half.

15. PROBCAST

19. UW EnKF System
To build a larger, high-resolution ensemble system directed towards data assimilation and short-term forecasting.
Both probabilistic analyses and forecasts.
Originally based on the Torn-Hakim infrastructure, but now uses the NCAR DART system.

20. UW EnKF System 36 km and 4 km domains Now a 3-hr analysis cycle.
60 members using the WRF model.
Runs out 24-h once a day.
Completely operational and reliable

21. Mesoscale Covariances
12 Z January 24, 2004
Camano Island Radar
|V950|-qr covariance

23. 36-km

25. UW EnKF
Assimilates a variety of data types: sat winds, surface obs, acars, radiosondes.
Tests with radars completed (winds) and will make use of current radars and the new coastal radar.
Major innovations in data selection and bias removal.
Moving to a one-hour analysis cycle. Add physics diversity.
Research needed during next year on vertical localization, improved bias removal, and other issues
Extensive verification, which will be expanded.

26. UW As a Regional Mesoscale Testbed for Probabilistic Prediction
A fairly large interdisciplinary effort, previously supported by large MURI project, and recently ending AF JEFS and NWS CSTAR funding.
Lack of support threatens the continued viability of our efforts.
Need for better pathways of research from groups such as ours to operations.

27. The End