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.
And its Implications for the UW MURI Effort
University of Washington
Prove the value, utility, and operational feasibility of ensemble forecasting to DoD operations.
1Toth, Zoltan, and Eugenia Kalnay, 1997: Ensemble Forecasting at NCEP and the Breeding Method. Monthly Weather Review: Vol. 125, No. 12, pp. 3297–3319.
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 team making major contributions to the JEFS mesoscale system including:
Observation-based bias correction on a grid
Work on a variety of output productsUW MURI Contributions
Ensemble Model Perturbations system including:
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 system including:
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
Evaluation of approaches (0.4 FTE) system including:
MMM shall evaluate the different approaches for diversity that properly represent model uncertainty.
Determination of best approach and assistance with implementationNCAR
Ensemble Post-processing Calibration system including:
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
3.3 Ensemble Products and Applications system including:
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
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
Product Strategy of the UW Statistics Deparment.
Tailor products to customers’ needs and weather sensitivities
Designto help transition from deterministic to stochastic thinking
Design to aid critical decision making (Operational Risk Management)
UW will aid in developing some of these products
Operational Testing & Evaluation of the UW Statistics Deparment.
PACIFIC AIR FORCES Forecasters
20th Operational Weather Squadron
17th Operational Weather Squadron
607 Weather Squadron
5th Air Force
Naval Pacific Meteorological andOceanographic Center Forecasters
Yokosuka Navy Base
Forecaster Products/Applications of the UW Statistics Deparment.
Consensus & Confidence Plot of the UW Statistics Deparment.
Probability Plot of the UW Statistics Deparment.
Multimeteogram of the UW Statistics Deparment.
Sample JME Products of the UW Statistics Deparment.
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
Requires paradigm shift into “stochastic thinking”
11/18 12/00 06 12 18 13/00 06 12 18 14/00 06
Valid Time (Z)
Warfighter Products/Applications of the UW Statistics Deparment.
Bridging the Gap of the UW Statistics Deparment.
Integrated Weather Effects Decision Aid (IWEDA)
-- for Operational
3 6 9 12 15 18kt
Clear & 7
Go / No Go
IFR / VFR
In / Out
Method #2: of the UW Statistics Deparment.Weather Risk Analysis and Portrayal (WRAP)