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

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.

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.

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.

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.

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

MODIS Land Use

Regional Data Assimilation and Forecasting

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.

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.

Improvement in short-term forecast using our local assimilation system

WRF 4 km at same time

NWS NAM

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.

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

Annual Climatologies of MM5 4-km domain

2011-2012

2012-2013

Verification of Small-Scale Orographic Effects

Dungen Buck

Cascade Cumulative Precipitation West LIne East LIne

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).

The End