Transcript UWEnsembleSystem.ppt

University of Washington
Ensemble Systems
for Probabilistic Analysis
and Forecasting
Cliff Mass, Atmospheric Sciences
University of Washington
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.
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.
UWME Parent Modeling Systems
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
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.
Application of BMA-Max 2-m Temperature
(all stations in 12 km domain)
Improves reliability and sharpness
The BMA Site
The Next Challenge: Making
Probabilistic Forecasts
Accessible to Users
• Creating good probabilistic information
is only half the challenge—and probably
the easier half.
PROBCAST
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.
UW EnKF System
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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
Mesoscale Covariances
12 Z January 24, 2004
Camano Island Radar
|V950|-qr covariance
36-km
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.
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.
The End