Transcript forecast - Atmospheric Sciences

JEFS Project Update
And its Implications for the UW MURI
Effort
Cliff Mass
Atmospheric Sciences
University of Washington
ENSEMBLES
AHEAD
Joint Ensemble Forecast System
(JEFS)
NCAR
JEFS’ Goal
Prove the value, utility, and operational feasibility
of ensemble forecasting to DoD operations.
Deterministic
Forecasting
?
• Ignores forecast uncertainty
• Potentially very misleading
• Oversells forecast capability
Ensemble
Forecasting
…etc
• Reveals forecast uncertainty
• Yields probabilistic information
• Enables optimal decision making
Organization
AFWA
J
E
F
S
T
E
A
M
FNMOC
HPCMP
NRL
Contribution
- JEFS integration
- FY05-FY07 Funding
- JEFS integration
- NOGAPS members for JGE
- Primary Hardware Funding
- Programming Environment and Training (PET)
onsite at AFWA
- JGE and JME initial conditions
- COAMPS model perturbations
ARL
- Uncertainty visualization tool: Weather Risk Analysis
and Portrayal (WRAP)
DTRA
- FY05-FY09 Funding
NCAR
- WRF model perturbations
UW
20 OWS
- Calibration (bias correction and BMA)
- Product Design/Development
- JEFS operational testing and evaluation
17 OWS
- JEFS operational testing and evaluation
Yokosuka
NPMOC
NPS & AFIT
- JEFS operational testing and evaluation
ONR
- Consultation
- Research project(s)
Players
Maj Tony Eckel
Dr. Jerry Wegiel
Mr. Norm Mandy
Dr. Mike Sestack
Mr. John Boisseau
Dr. Steve Klotz (at AFWA)
Dr. Craig Bishop
Dr. Jim Doyle
Dr. Carolyn Reynolds
Ms. Sue Chen
Mr. Justin McLay
Mr. Dave Knapp
Ms. Barb Sauter
Mr. Hyam Singer (Next Century)
Mr. Allen Hill (Next Century)
CDR Stephanie Hamilton
Mr. Pat Hayes
Dr. Jordan Powers
Dr. Chris Snyder
Dr. Cliff Mass
Dr. Eric Grimit
Lt Col Mike Farrar
Maj David Andrus
Maj Christopher Finta
1Lt Perry Sweat
?
Dr. Russ Elsberry
Maj Bob Stenger
Dr. Steve Tracton
Joint Global Ensemble (JGE)
• Description: Combination of current GFS and NOGAPS global, medium-range
ensemble data. Possible expansion to include ensembles from CMC,
UKMET, JMA, etc.
• Initial Conditions: Breeding of Growing Modes 1
• Model Variations/Perturbations: Two unique models, but no model perturbations
• Model Window: Global
• Grid Spacing: 1.0 1.0 (~80 km)
• Number of Members: 40 at 00Z
30 at 12Z
• Forecast Length/Interval: 10 days/12 hours
• Timing
• Cycle Times: 00Z and 12Z
• Products by: 07Z and 19Z
1 Toth, Zoltan, and Eugenia Kalnay, 1997: Ensemble Forecasting at NCEP and the Breeding Method. Monthly Weather
Review: Vol. 125, No. 12, pp. 3297–3319.
Joint Mesoscale Ensemble (JME)
• Description: Multiple high resolution, mesoscale model runs generated at FNMOC
and AFWA
• Initial Conditions: Ensemble Transform Filter 2 run on short-range (6-h),
mesoscale data assimilation cycle driven by GFS and NOGAPS
ensemble members
• Model variations/perturbations:
• Multimodel: WRF-ARW, COAMPS
• Varied-model: various configurations of physics packages
• Perturbed-model: randomly perturbed sfc boundary conditions (e.g., SST)
• Model Window: East Asia
• Grid Spacing: 15 km for baseline JME
•
5 km nest later in project
• Number of Members: 30 (15 run at each DC site)
• Forecast Length/Interval: 60 hours/3 hours
• Timing
• Cycle Times: 06Z and 18Z
• Products by: 14Z and 02Z
5km
~7 h production
/cycle
15km
2 Wang, Xuguang, and Craig H. Bishop, 2003: A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble
Forecast Schemes. Journal of the Atmospheric Sciences: Vol. 60, No. 9, pp. 1140–1158.
UW MURI Contributions
UW team making major contributions
to the JEFS mesoscale system
including:
•
Observation-based bias correction on
a grid
•
Localized BMA
•
Work on a variety of output products
NCAR Contributions

Ensemble Model Perturbations

a.

The current method to account for model uncertainty in the JME,
developed by NCAR in FY06, includes a multi-model component
(i.e., each ensemble member represents a unique model
configuration or combination of physics schemes) and
perturbations to the surface boundary conditions (SST, albedo,
roughness length, moisture availability). This method will be
further improved by the following additions.
 1)
Incorporation of additional physics schemes.
 2)
Tuning of sea surface temperature (SST)
perturbation.
 3)
Addition of soil condition perturbation. (0.25 FTE)
Improvement of multi-model approach (0.5 FTE)
NCAR Contributions






Development of new approaches
1)
Multiple-parameter (single-model) approach.
NCAR shall examine the representation of model
uncertainty through the use of a single, fixed set of model
physics schemes in which various internal parameters and
"constants" of each scheme are varied among the ensemble
members.
2)
Stochastic-model approach.
NCAR shall adapt to WRF a stochastic modeling approach
(stochastic physics or stochastic kinetic energy
backscatter).
3)
Hybrid approach. As the most straightforward hybrid
method, NCAR shall apply the developed stochastic-model
approach on top of the multi-model approach.
NCAR

Evaluation of approaches (0.4 FTE)
MMM shall evaluate the different approaches for
diversity that properly represent model uncertainty.
 Determination of best approach and assistance with
implementation

UW Contributions 2007

Ensemble Post-processing Calibration

The
University of Washington Atmospheric Sciences
Department (UW) on developing algorithms for post-processing
calibration of mesoscale ensembles. This development effort is
crucial for optimizing the skill of ensemble products and
maximizing JME utility. The UW shall:

a.
Expand model bias correction. The observationbased, grid bias correction developed in FY06 for 2-m
temperature will be extended to additional variables of interest to
include, but not be limited to, 2-m humidity, 10-m winds, and
cumulative precipitation (rain and snow).
 b.
Develop ensemble spread correction. The prototype
Bayesian Model Averaging (BMA) post-processing system
developed in FY06 shall be fully developed for the same
variables as noted for bias correction.
 c.
Evaluate developments. The UW shall evaluate these
calibration techniques to determine the gain in ensemble
forecast skill.
UW JEFS


3.3 Ensemble Products and Applications
For FY07, NCAR/MMM shall continue subcontract work with UW
on developing JME products and applications. The UW, under
direction of NCAR, shall develop the following prototypes.
These deliverables are initial efforts that do not require delivery
of finalized software and documentation.

a.
Extreme forecast index. The UW shall research stateof-art methods for calculating an ensemble-based extreme
forecast index and develop a prototype capability for the JME.
This essentially is the process of comparing the current
ensemble forecast with the ensemble model’s “climatology” to
determine the likelihood of an extreme event, one that might not
even be represented within the ensemble.

b.
General user interface. The UW shall build a webbased, interactive JME interface for the general DoD user
UW Contributions

The UW team will expand in 2007 to include
several members of the UW Statistics Deparment.

Potential for further expansion in FY 2008.
Product Strategy
Tailor products to customers’ needs and weather sensitivities
Forecaster Products/Applications
 Design to help transition from deterministic to stochastic thinking
Warfighter Products/Applications
 Design to aid critical decision making (Operational Risk Management)
UW will aid in developing some of these products
Operational Testing & Evaluation
PACIFIC AIR FORCES Forecasters
20th Operational Weather Squadron
17th Operational Weather Squadron
607 Weather Squadron
Naval Pacific Meteorological and
Oceanographic Center Forecasters
Yokosuka Navy Base
Warfighters
7th Fleet
Warfighters
PACAF
5th Air Force
SEVENTH
Fleet
FIFTH
Air Force
Forecaster Products/Applications
Consensus & Confidence Plot
Maximum
Potential Error
(mb, +/-)
6
5
4
3
2
1
<1
• Consensus (isopleths): shows “best guess” forecast (ensemble mean or median)
• Model Confidence (shaded)
Increase Spread in
the multiple forecasts
Less
Predictability
Decreased confidence
in forecast
Probability Plot
%
• Probability of occurrence of any weather phenomenon/threshold (i.e., sfc wnds > 25 kt )
• Clearly shows where uncertainty can be exploited in decision making
• Can be tailored to critical sensitivities, or interactive (as in IGRADS on JAAWIN)
Multimeteogram
1000/500 Hpa Geopotential Thickness [m] at Yokosuka
Initial DTG 00Z 28 JAN 1999
5520
5460
5400
5340
5280
5220
5160
5100
5040
4980
0
1
2
3
4
5
6
Forecast Day
7
8
9
10
• Show the range of possibilities for all meteogram-type variables
• Box & whisker, or confidence interval plot is more appropriate for large ensembles
• Excellent tool for point forecasting (deterministic or stochastic)
Sample JME Products
P ro b a b ility o f W a rn in g C rite ria a t M c G u ire A F B
B a s e d o n 1 5 /0 6 Z M M 5 E n s e m b l e
100
90
Probability of Warning Criteria at Osan AB
T S t o rm
70
W in d s > 3 5 k t
60
W in d s > 5 0 k t
50
S now > .5"/hr
40
F z g R a in
30
20
10
0
15/06
12
18
16/00
06
12
18
17/00
06
Valid Time (Z)
D a te /T im e
50
45
Wind Speed (kt) .
Probability (%)
80
Surface Wind Speed at Misawa AB
40
Extreme
Max
35
30
Mean
25
90%
CI
20
15
10
Extreme
Min
5
0
11/18
12/00
06
12
18
13/00
06
Valid
Time
Valid Time (Z)
12
18
14/00
06
Warfighter Products/Applications
Bridging the Gap
Stochastic Forecast
Binary Decisions/Actions
?
Integrated Weather Effects Decision Aid (IWEDA)
Deterministic
Stochastic
Forecast
Weapon System
Weather Thresholds*
0.05
0.04
Drop
Zone
Drop Zone
Surface
Winds
Surface Winds
6kt
6kt
0.03
0.02
0.01
0
0
310
20
6
30
9
40
12
50
15
60
18kt
70
> 13kt
10%
10-13kt
20%
0-9kt
70%
*AFI 13-217
Method #2:
Weather Risk Analysis and Portrayal (WRAP)