JEFS Project Update
This presentation is the property of its rightful owner.
Sponsored Links
1 / 24

JEFS Project Update And its Implications for the UW MURI Effort PowerPoint PPT Presentation


  • 96 Views
  • Uploaded on
  • Presentation posted in: General

JEFS Project Update And its Implications for the UW MURI Effort. Cliff Mass Atmospheric Sciences University of Washington. ENSEMBLES AHEAD. JEFS. Joint Ensemble Forecast System (JEFS). NCAR. JEFS’ Goal. Deterministic Forecasting . Ensemble Forecasting. ?. …etc.

Download Presentation

JEFS Project Update And its Implications for the UW MURI Effort

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Jefs project update and its implications for the uw muri effort

JEFS Project Update

And its Implications for the UW MURI Effort

Cliff Mass

Atmospheric Sciences

University of Washington


Jefs project update and its implications for the uw muri effort

ENSEMBLES

AHEAD

JEFS


Jefs project update and its implications for the uw muri effort

Joint Ensemble Forecast System(JEFS)

NCAR


Jefs project update and its implications for the uw muri effort

JEFS’ Goal

Deterministic

Forecasting

Ensemble

Forecasting

?

…etc

  • Ignores forecast uncertainty

  • Potentially very misleading

  • Oversells forecast capability

  • Reveals forecast uncertainty

  • Yields probabilistic information

  • Enables optimal decision making

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


Jefs project update and its implications for the uw muri effort

J

E

F

S

T

E

A

M

& AFIT


Jefs project update and its implications for the uw muri effort

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

1Toth, Zoltan, and Eugenia Kalnay, 1997: Ensemble Forecasting at NCEP and the Breeding Method. Monthly Weather Review: Vol. 125, No. 12, pp. 3297–3319.


Jefs project update and its implications for the uw muri effort

Joint Mesoscale Ensemble (JME)

5km

15km

  • Description: Multiple high resolution, mesoscale model runs generated at FNMOC

  • and AFWA

  • Initial Conditions: Ensemble Transform Filter2run 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

~7h production

/cycle

2Wang, 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

UW MURI Contributions


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


Ncar contributions1

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 Contributions


Jefs project update and its implications for the uw muri effort

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

NCAR


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 Contributions 2007


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 JEFS


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.

UW Contributions


Jefs project update and its implications for the uw muri effort

Product Strategy

Tailor products to customers’ needs and weather sensitivities

Forecaster Products/Applications

 Designto 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


Jefs project update and its implications for the uw muri effort

Operational Testing & Evaluation

PACIFIC AIR FORCES Forecasters

20th Operational Weather Squadron

17th Operational Weather Squadron

607 Weather Squadron

Warfighters

PACAF

5th Air Force

Naval Pacific Meteorological andOceanographic Center Forecasters

Yokosuka Navy Base

Warfighters

7th Fleet

SEVENTH

Fleet

FIFTH

Air Force


Jefs project update and its implications for the uw muri effort

Forecaster Products/Applications


Jefs project update and its implications for the uw muri effort

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 Less Decreased confidence

  • the multiple forecasts Predictability in forecast


Jefs project update and its implications for the uw muri effort

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)


Jefs project update and its implications for the uw muri effort

Multimeteogram

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)


Jefs project update and its implications for the uw muri effort

Sample JME Products

Probability of Warning Criteria at Osan AB

When is a warning required?

What is the potential

risk to the mission?

Valid Time (Z)

Surface Wind Speed at Misawa AB

Extreme

Max

Requires paradigm shift into “stochastic thinking”

Mean

90%

CI

Extreme

Min

11/18 12/00 06 12 18 13/00 06 12 18 14/00 06

Valid Time (Z)


Jefs project update and its implications for the uw muri effort

Warfighter Products/Applications


Jefs project update and its implications for the uw muri effort

Bridging the Gap

Integrated Weather Effects Decision Aid (IWEDA)

Deterministic

Forecast

Weapon System

Weather Thresholds*

Stochastic

Forecast

Probabilistic IWEDA

-- for Operational

Risk Management

(ORM)

> 13kt

10-13kt

0-9kt

10%

Drop Zone

Surface Winds

6kt

Drop Zone

Surface Winds

6kt

20%

70%

3 6 9 12 15 18kt

*AFI 13-217

Stochastic Forecast

Binary Decisions/Actions

AR Route

Clear & 7

Go / No Go

T-Storm

Within 5

?

IFR / VFR

GPS

Scintillation

Bombs

on

Target

Crosswinds

In / Out

of Limits

Flight Hazards


Jefs project update and its implications for the uw muri effort

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


  • Login