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 postprocessing 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: realtime initializations and forecasts from NWP
centers around the world.
• Also, inexpensive UNIX clusters became
available.
“Native” Models/Analyses Available
Resolution (~ @ 45 N )
Abbreviation/Model/Source
Type
avn, Global Forecast System (GFS),
Spectral T254 / L64
~55 km
National Centers for Environmental Prediction
cmcg, Global Environmental Multi-scale (GEM),
Computational
Distributed
1.0 / L14
~80 km
Objective
Analysis
SSI
3D Var
Finite
Diff
0.90.9/L28 1.25 / L11
~70 km
~100 km
3D Var
Finite
Diff.
32 km / L45
90 km / L37
SSI
3D Var
Spectral T239 / L29
~60 km
1.0 / L11
~80 km
3D Var
Spectral T106 / L21
~135 km
1.25 / L13
~100 km
OI
Spectral T239 / L30
Fleet Numerical Meteorological & Oceanographic Cntr.
~60 km
1.0 / L14
~80 km
OI
tcwb, Global Forecast System,
1.0 / L11
~80 km
OI
Canadian Meteorological Centre
eta, limited-area mesoscale model,
National Centers for Environmental Prediction
gasp, Global AnalysiS and Prediction model,
Australian Bureau of Meteorology
jma, Global Spectral Model (GSM),
Japan Meteorological Agency
ngps, Navy Operational Global Atmos. Pred. System,
Taiwan Central Weather Bureau
ukmo, Unified Model,
United Kingdom Meteorological Office
Spectral T79 / L18
~180 km
Finite
Diff.
5/65/9/L30 same / L12
~60 km
3D Var
“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 Forecast
Bias-Corrected Forecast
B
Skill for
Probability of T2 < 0°C
0.2
*ACMEcore
*UW Basic Ensemble with bias correction
ACMEcore
UW Basic Ensemble, no bias correction
*ACMEcore+
*UW Enhanced Ensemble with bias cor.
ACMEcore+
UW Enhanced Ensemble without bias cor
Uncertainty
0.1
0.0
-0.1
00
0.6
03
06
09
12
15
18
21
24
42
45
48
0.5
BSS
0.4
0.3
0.2
0.1
0.0
-0.1
00
03
06
09
12
15
18
21
24
27
30
33
36
BSS: Brier Skill Score
39
27
30
33
36
39
42
Calibration Example-Max 2-m Tempeature
(all stations in 12 km domain)
Probability Density Functio
at one point
Ensemble-Based
Probabilistic Products
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 4D 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