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: km

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 ) Objective Abbreviation/Model/Source Type Computational Distributed Analysis avn, Global Forecast System (GFS), SpectralT254 / L641.0  / L14 SSI National Centers for Environmental Prediction~55 km~80 km3D Var cmcg, Global Environmental Multi-scale (GEM), Finite0.9  0.9  /L  / L113D Var Canadian Meteorological Centre Diff ~70 km ~100 km eta, limited-area mesoscale model, Finite32 km / L45 90 km / L37SSI National Centers for Environmental Prediction Diff.3D Var gasp, Global AnalysiS and Prediction model,SpectralT239 / L291.0  / L11 3D Var Australian Bureau of Meteorology~60 km~80 km jma, Global Spectral Model (GSM),SpectralT106 / L  / L13OI Japan Meteorological Agency~135 km~100 km ngps, Navy Operational Global Atmos. Pred. System,SpectralT239 / L301.0  / L14OI Fleet Numerical Meteorological & Oceanographic Cntr. ~60 km~80 km tcwb, Global Forecast System,SpectralT79 / L181.0  / L11 OI Taiwan Central Weather Bureau~180 km~80 km ukmo, Unified Model, Finite5/6  5/9  /L30same / L123D Var United Kingdom Meteorological Office Diff.~60 km “Native” Models/Analyses Available

“Ensemblers” Eric Grimit (r ) and Tony 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 ForecastBias-Corrected Forecast

*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 for Probability of T 2 < 0°C BSS: Brier Skill Score

Calibration Example-Max 2-m Tempeature (all stations in 12 km domain)

Ensemble-Based Probabilistic Products Probability Density Function at 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

ENSEMBLES AHEAD

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