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Mesoscale Probabilistic Prediction over the Northwest: An Overview. Cliff Mass University of Washington. National Academy Report: Completing the Forecast. - PowerPoint PPT Presentation
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Mesoscale Probabilistic Prediction over the Northwest:
An Overview
Cliff Mass
University of Washington
National Academy Report: Completing the Forecast
• Uncertainty is a fundamental characteristic of weather, seasonal climate, and hydrological prediction, and no forecast is complete without a description of its uncertainty.
• Recommendation 1: The entire Enterprise should take responsibility for providing products that effectively communicate forecast uncertainty information. NWS should take a leadership role in this effort.
• Most forecast products from … the National Oceanic and Atmospheric Administration’s (NOAA’s) National Weather Service (NWS) continue this deterministic legacy.
• The NWS short-range system undergoes no post-processing and uses an ensemble generation method (breeding) that may not be appropriate for short-range prediction. In addition, the short-range model has insufficient resolution to generate useful uncertainty information at the regional level. For forecasts at all scales, comprehensive post-processing is needed to produce reliable (or calibrated) uncertainty information.
How can the NWS become the world leader in high-resolution mesoscale
probabilistic prediction?
• Far too little resources are going towards mesoscale ensembles and post-processing. This must change.
• There is extensive knowledge and experience in the university community that should be tapped.
• The NWS needs to understand how to effectively disseminate probabilistic information.
How can the UW help?
• The UW has an extensive high-resolution mesoscale ensemble effort, with two systems running operationally.
• It is an end-to-end effort, ranging from ensembles and post-processing to dissemination. This knowledge can be transferred.
• Currently, UW is working with NCAR to build a system for the Air Force. A move is being made for the first AF system to be over the U.S.
• Why can’t the NWS participate in this?
Brief History
• Local high-resolution mesoscale NWP in the Northwest began in the mid-1990s after a period of experimentation showed the substantial potential of small grid spacing (12 to 4 km) over terrain.
• At that time NCEP was running 32-48km grid spacing and the Eta model clearly had difficulties in terrain.
The Northwest Environmental Prediction System
•Beginning in 1995, a team at the University of Washington, with the help of colleagues at Washington State University and others have built the most extensive regional weather/environmental prediction system in the U.S.
•It represents a different model of how weather and environmental prediction can be accomplished.
Pacific Northwest Regional Prediction: Major Components
• Real-time, operational mesoscale environmental prediction– MM5/WRF atmospheric model– DHSVM distributed hydrological model– Calgrid Air Quality Model– A variety of application models (e.g., road surface)
• Real-time collection and quality control of regional observations.
WRF Domains: 36-12-4km
AIRPACT Output Products
U.S. Forest Service Smoke and Fire Management System
NorthwestNet: Over 70 networks collected in real-time
Mesoscale Probabilistic Prediction
• By the late 1990’s, we had a good idea of the benefits of high resolution.
• It was clear that initial condition and physics uncertainty was large.
• We were also sitting on an unusual asset due to our work evaluating major NWP centers: real-time initializations and forecasts from NWP centers around the world.
• Also, inexpensive UNIX clusters became available.
Resolution (~ @ 45 N ) ObjectiveAbbreviation/Model/Source Type Computational Distributed Analysis
avn, Global Forecast System (GFS), Spectral T254 / L64 1.0 / L14 SSINational Centers for Environmental Prediction ~55 km ~80 km 3D Var cmcg, Global Environmental Multi-scale (GEM), Finite 0.90.9/L28 1.25 / L11 3D VarCanadian Meteorological Centre Diff ~70 km ~100 km eta, limited-area mesoscale model, Finite 32 km / L45 90 km / L37 SSINational Centers for Environmental Prediction Diff. 3D Var gasp, Global AnalysiS and Prediction model, Spectral T239 / L29 1.0 / L11 3D VarAustralian Bureau of Meteorology ~60 km ~80 km
jma, Global Spectral Model (GSM), Spectral T106 / L21 1.25 / L13 OIJapan Meteorological Agency ~135 km ~100 km ngps, Navy Operational Global Atmos. Pred. System, Spectral T239 / L30 1.0 / L14 OIFleet Numerical Meteorological & Oceanographic Cntr. ~60 km ~80 km
tcwb, Global Forecast System, Spectral T79 / L18 1.0 / L11 OITaiwan Central Weather Bureau ~180 km ~80 km ukmo, Unified Model, Finite 5/65/9/L30 same / L12 3D VarUnited Kingdom Meteorological Office Diff. ~60 km
“Native” Models/Analyses Available
“Ensemblers”Eric Grimit (r ) andTony Eckel (l) are besides themselves over the acquisition of the new 20 processor athelon cluster
UWME– Core : 8 members, 00 and 12Z
• Each uses different synoptic scale initial and boundary conditions
• All use same physics– Physics : 8 members, 00Z only
• Each uses different synoptic scale initial and boundary conditions
• Each uses different physics• Each uses different SST
perturbations• Each uses different land surface
characteristic perturbations– Centroid, 00 and 12Z
• Average of 8 core members used for initial and boundary conditions
Ensemble-Based Probabilistic Products
The MURI Project
• In 2000, Statistic Professor Adrian Raftery came to me with a wild idea: submit a proposal to bring together a strong interdisciplinary team to deal with mesoscale probabilistic prediction.
• Include atmospheric sciences, psychologists, statisticians, web display and human factors experts.
The Muri
I didn’t think it had a chance.
I was wrong. It was funded and very successful.
The MURI• Over five years substantial progress was
made:– Successful development of Bayesian Model
Averaging (BMA) postprocessing for temperature and precipitation
– Development of both global and local BMA– Development of grid-based bias correction– Completion of several studies on how people use
probabilistic information– Development of new probabilistic icons.
Raw 12-h Forecast Bias-Corrected Forecast
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48
BSS
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48
*ACMEcoreACMEcore*ACMEcore+ACMEcore+Uncertainty
*UW Basic Ensemble with bias correction
UW Basic Ensemble, no bias correction
*UW Enhanced Ensemble with bias cor.
UW Enhanced Ensemble without bias cor
Skill forProbability of T2 < 0°C
BSS: Brier Skill Score
Calibration Example-Max 2-m Tempeature(all stations in 12 km domain)
Ensemble-Based Probabilistic Products
Probability Density Functionat one point
MURI
• Improvements and extensions of UWME ensembles to multi-physics
• Development of BMA and probcast web sites for communication of probabilistic information.
• Extensive verification and publication of a large collection of papers.
• And plenty more…
Before Probcast: The BMA Site
PROBCAST
ENSEMBLESAHEAD
The JEFS Phase
• Joint AF and Navy project (at least it was supposed to be this way). UW and NCAR main contractors.
• Provided support to continue development of basic parameters.
• Joint project with NCAR to build a complete mesoscale forecasting system for the Air Force.
• For the first few years was centered on North Korea, then SW Asia, and now the U.S.
JEFS Highlights
• Under JEFS the post-processed BMA fields has been extended to wind speed and direction. Local BMA for precipitation.
• Development of EMOS, a regression-based approach that produces results nearly as good as BMA.
• Next steps: derived parameters (e.g., ceiling, visibility)
NSF Project
• Currently supporting extensive series of human-subjects studies to determine how people interpret uncertainty information.
• Further work on icons
• Further work on probcast.
Ensemble Kalman Filter Project
• Much more this afternoon.• 80-member synoptic ensemble (36 km-12
km or 36 km)• Uses WRF model• Six-hour assimilation steps.• Experimenting with 12 and 4 km to
determine value for mesoscale data assimilation-AOR in 3D.
Big Picture
• The U.S. is not where it should be regarding probabilistic prediction on the mesoscale.
• Current NCEP SREF is inadequate and uncalibrated.
• Substantial challenges in data poor areas for calibration and for fields like visibility that the models don’t simulate at all or simulate poorly.
• A nationally organized effort to push rapidly to 4-D probabilistic capabilities is required.
Opinion
• Creating sharp, reliable PDFs is only half the battle.
• The hardest part is the human side, making the output accessible, useful, and compelling. We NEED the social scientists.
• Probabilistic forecast information has the potential for great societal economic benefit.
The END