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

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00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48

BSS

-0.1

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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