1. University of Washington Modeling Infrastructure Available for Olympex
Cliff Mass
University of Washington

2. Goals The Local Modeling Effort at the UW
Provide high-resolution forecasts for mission planning.
Provide high-resolution simulations to drive hydrological modeling
Assimilate a wide-range of mesoscale and synoptic observational assets to produce the best possible description of the mesoscale structures over the region.

3. Goals The Local Modeling Effort at the UW
Evaluate model fidelity, particularly for cloud and precipitation fields. Work with partners to improve microphysics and other model deficiencies.
Demonstrate the value of combining the model with observations to produce skillful snowpack and water-related fields.

4. Important Points
The Olympex area offers substantial precipitation and terrain; ideal for a GPM testbed.
Terrain offers the potential to place assets in crucial locations, with certainty that you will catch the cloud/precipitation structures you want.
Models are very good in the dynamics of orographic flows, so you can get the winds right fairly easily.
Then you can tear the microphysics/PBL physics apart to find the flaws and fix them.
Rivers offer a wonderful integration of moist processes, both on a short-term and long-term basis.

5. NW Modeling Resources
High-resolution WRF ARW forecasts at 36, 12, 4, and 1.3 km grid spacing completed twice a day.
High-resolution (4-km) WRF-DART Ensemble Kalman Filter (EnKF) data assimilation system run on a three-hour cycle, with intermittent 24-h forecasts.
12-km mesoscale ensemble system based on the initializations and forecasts of major modeling centers.
Collection of all real-time data assets over the region.

12. Optimized Physics for the Region
Based on testing hundreds of physics combinations, domains, and numerical options.
Best performance plus reliability
Physics:
SAS Convection on 4, 12, and 36 km
YSU PBL
Thompson Microphysics
RRTM IR, RRTMG solar radiation
NOAH LSM
MODIS land use

14. MODIS Land Use

15. Regional Data Assimilation and Forecasting

16. EnKF System
Based on a large (64 member) ensemble of forecasts at 36 and 4 km grid spacing. WRF model and DART Ensemble Kalman Filter (EnKF) System
Every three hours assimilate a wide range of observations to create 64 different analyses.
Then we forecast forward for 3 hours and then assimilate new observations.
Thus, we have a continuous cycle of probabilistic analyses.

17. EnKF Ensemble Forecasting System
We can run ensemble of forecasts forward to give us probabilistic forecasts for any period we want. Now doing 24h ahead, four times a day.

18. Improvement in short-term forecast using our local assimilation system

19. WRF 4 km at same time

20. NWS NAM

23. There is Room for Improvement
Using the data from the IMPROVE-2 experiment, UW, NCAR, Stony Brook and others put a lot of effort in improving moist physics.
In general, we do an excellent job on the windward side of barriers but often overpredict in the lee.
Probably a microphysical explanation, but PBL problems could also be involved.
OLYMPEX will provide a comprehensive data set for the next round of improvements of model moist physics.

24. Small-Scale Spatial Gradients in Climatological Precipitation on the Olympic Peninsula
Alison M. Anders, Gerard H. Roe, Dale R. Durran, and Justin R. Minder
Journal of Hydrometeorology
Volume 8, Issue 5 (October 2007) pp. 1068–1081

25. Annual Climatologies of MM5 4-km domain

30. Verification of Small-Scale Orographic Effects

31. Dungen
Buck

33. Cascade Cumulative Precipitation
West LIne
East LIne

34. Work for the Next Year
Provide model data sets to Jessica Lundquist and colleagues to test ability to determine snowpack from model output.
Improve model precipitation/cloud physics
Intensive model verification year. Use gauges, snow measurement, and hydrological verification
Enhance local data assimilation to include all regional radars and additional observational assets (e.g., TAMBAR aircraft).

35. The End