JEFS Project UpdateAnd its Implications for the UW

MURI Effort

Cliff MassAtmospheric Sciences

University of Washington

ENSEMBLESAHEAD

Joint Ensemble Forecast System(JEFS)

NCAR

Prove the value, utility, and operational feasibility of ensemble forecasting to DoD operations.

Deterministic Forecasting

?• Ignores forecast uncertainty• Potentially very misleading• Oversells forecast capability

• Reveals forecast uncertainty• Yields probabilistic information• Enables optimal decision making

EnsembleForecasting

…etc

JEFS’ Goal

JEFS

TEAM

Organization Contribution Players AFWA - JEFS integration

- FY05-FY07 Funding Maj Tony Eckel Dr. Jerry Wegiel Mr. Norm Mandy

FNMOC - JEFS integration - NOGAPS members for JGE

Dr. Mike Sestack

HPCMP - Primary Hardware Funding - Programming Environment and Training (PET) onsite at AFWA

Mr. John Boisseau Dr. Steve Klotz (at AFWA)

NRL - JGE and JME initial conditions - COAMPS model perturbations

Dr. Craig Bishop Dr. Jim Doyle Dr. Carolyn Reynolds Ms. Sue Chen Mr. Justin McLay

ARL

- Uncertainty visualization tool: Weather Risk Analysis and Portrayal (WRAP)

Mr. Dave Knapp Ms. Barb Sauter Mr. Hyam Singer (Next Century) Mr. Allen Hill (Next Century)

DTRA - FY05-FY09 Funding CDR Stephanie Hamilton Mr. Pat Hayes

NCAR - WRF model perturbations Dr. Jordan Powers Dr. Chris Snyder

UW - Calibration (bias correction and BMA) - Product Design/Development

Dr. Cliff Mass Dr. Eric Grimit

20 OWS - JEFS operational testing and evaluation Lt Col Mike Farrar Maj David Andrus

17 OWS - JEFS operational testing and evaluation Maj Christopher Finta 1Lt Perry Sweat

Yokosuka NPMOC

- JEFS operational testing and evaluation ?

NPS - Research project(s) Dr. Russ Elsberry Maj Bob Stenger

ONR - Consultation Dr. Steve Tracton

& AFIT

• 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 Global Ensemble (JGE)

5 km

15 km

• 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 ~7 h production

/cycle

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.

Joint Mesoscale Ensemble (JME)

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. Improvement of multi-model approach (0.5 FTE) 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)

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 observation-based, 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 state-of-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 web-based, interactive JME interface for the general DoD user designed to provide basic stochastic weather forecast information. This will be similar in nature to the current Probcast interface (http://www.probcast.com/) except geared to address the specific interests of military operations (e.g., probability of low ceiling and visibility).

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.

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)

Product Strategy

UW will aid in developing some of these products

PACIFIC AIR FORCES Forecasters20th Operational Weather Squadron17th Operational Weather Squadron607 Weather Squadron

WarfightersPACAF5th Air Force

Naval Pacific Meteorological and Oceanographic Center ForecastersYokosuka Navy Base

Warfighters7th Fleet

FIFTHAir Force

SEVENTHFleet

Operational Testing & Evaluation

Forecaster Products/Applications

• Consensus (isopleths): shows “best guess” forecast (ensemble mean or median)

• Model Confidence (shaded)

Increase Spread in Less Decreased confidence the multiple forecasts Predictability in forecast

MaximumPotential Error

(mb, +/-)

6

5

4

3

2

1

<1

Consensus & Confidence 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)

%Probability Plot

Current

Deterministic

Meteogram

• 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)

1000/500 Hpa Geopotential Thickness [m] at YokosukaInitial DTG 00Z 28 JAN 1999

0 1 2 3 4 5 6 7 8 9 10Forecast Day

5520

5460

5400

5340

5280

5220

5160

5100

5040

4980

Multimeteogram

Probability of Warning Criteria at McGuire AFB Based on 15/06Z MM5 Ensemble

010

20304050

607080

90100

Date/Time

T Storm

Winds>35kt

Winds>50kt

Snow>.5"/hr

Fzg Rain

15/06 12 18 16/00 06 12 18 17/00 06

Probability of Warning Criteria at Osan AB

What is the potential

risk to the mission?When is a warning required?

0

5

10

15

20

25

30

35

40

45

50

Valid Time

Wind Speed (kt) .

0

5

10

15

20

25

30

35

40

45

50

11/18 12/00 06 12 18 13/00 06 12 18 14/00 06 Valid Time (Z)

90%CI

ExtremeMin

ExtremeMax

Surface Wind Speed at Misawa AB

Mean

Valid Time (Z)

Requires paradigm shift into

“stochastic thinking”

Sample JME Products

Warfighter Products/Applications

Integrated Weather Effects Decision Aid (IWEDA)Deterministic

Forecast

> 13kt

10-13kt

0-9kt

Weapon SystemWeather Thresholds*

Drop ZoneSurface Winds

6kt

*AFI 13-217

?

Stochastic Forecast Binary Decisions/Actions

Bombs

on

Target

Go / No Go AR RouteClear & 7

CrosswindsIn / Outof Limits

T-StormWithin 5

Flight Hazards

IFR / VFR

GPSScintillation

Bridging the Gap

10%

20%

70%

Stochastic Forecast

Drop ZoneSurface Winds

6kt3 6 9 12 15 18kt0 10 20 30 40 50 60 70

0

0.01

0.02

0.03

0.04

0.05

Probabilistic IWEDA

-- for Operational

Risk Management

(ORM)

Method #2:Weather Risk Analysis and Portrayal (WRAP)