Transcript OlympexAug2014CliffMass.ppt

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:
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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 4km 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