Mesoscale Probabilistic Prediction over the Northwest: An Overview

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Mesoscale Probabilistic Prediction over the
Northwest An Overview

• Cliff Mass
• University of Washington

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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.

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• Most forecast products from the National
  Oceanic and Atmospheric Administrations (NOAAs)
  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.

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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.

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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 cant the NWS participate in this?

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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.

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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.

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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.

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WRF Domains 36-12-4km
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AIRPACT Output Products
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U.S. Forest Service Smoke and Fire Management
System
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NorthwestNet Over 70 networks collected in
real-time
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Mesoscale Probabilistic Prediction

• By the late 1990s, 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.

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Native Models/Analyses Available
Resolution ( _at_ 45 ?N )
Objective Abbreviation/Model/Source
Type Computational Distributed Analysis
avn, Global Forecast System (GFS),
Spectral T254 / L64 1.0? / L14 SSI National
Centers for Environmental Prediction 55 km 80
km 3D Var   cmcg, Global Environmental
Multi-scale (GEM), Finite 0.9??0.9?/L28 1.25? /
L11 3D Var Canadian Meteorological Centre Diff
70 km 100 km   eta, limited-area mesoscale
model, Finite 32 km / L45 90 km /
L37 SSI National Centers for Environmental
Prediction Diff. 3D Var   gasp, Global
AnalysiS and Prediction model, Spectral T239 /
L29 1.0? / L11 3D Var Australian Bureau of
Meteorology 60 km 80 km jma, Global Spectral
Model (GSM), Spectral T106 / L21 1.25? /
L13 OI Japan Meteorological Agency 135 km 100
km   ngps, Navy Operational Global Atmos. Pred.
System, Spectral T239 / L30 1.0? / L14 OI Fleet
Numerical Meteorological Oceanographic Cntr.
60 km 80 km tcwb, Global Forecast
System, Spectral T79 / L18 1.0? / L11 OI Taiwan
Central Weather Bureau 180 km 80 km   ukmo,
Unified Model, Finite 5/6??5/9?/L30 same /
L12 3D Var United Kingdom Meteorological Office
Diff. 60 km
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Ensemblers Eric Grimit (r ) and Tony Eckel (l)
are besides themselves over the acquisition of
the new 20 processor athelon cluster
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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

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Ensemble-Based Probabilistic Products
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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.

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The Muri

• I didnt think it had a chance.

I was wrong. It was funded and very successful.
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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.

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Raw 12-h Forecast
Bias-Corrected Forecast
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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 T2 lt 0C
BSS Brier Skill Score
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Calibration Example-Max 2-m Tempeature(all
stations in 12 km domain)
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Probability Density Function at one point
Ensemble-Based Probabilistic Products
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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

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Before Probcast The BMA Site
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PROBCAST
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ENSEMBLES AHEAD
JEFS
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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.

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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)

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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.

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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.

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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 dont simulate at all or simulate
  poorly.
• A nationally organized effort to push rapidly to
  4-D probabilistic capabilities is required.

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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.

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The END