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

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 Ensemble Forecasting …etc JEFS’ Goal

JEFSTEAMJEFSTEAM & 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 ( 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 Forecasters 20 th Operational Weather Squadron 17 th Operational Weather Squadron 607 Weather Squadron Warfighters PACAF 5 th Air Force Naval Pacific Meteorological and Oceanographic Center Forecasters Yokosuka Navy Base Warfighters 7 th Fleet FIFTH Air Force SEVENTH Fleet 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 Maximum Potential Error (mb, +/-) <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) Multimeteogram

Probability of Warning Criteria at Osan AB What is the potential risk to the mission? When is a warning required? 11/18 12/ / /00 06 Valid Time (Z) 90% CI Extreme Min Extreme Max 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 System Weather Thresholds* Drop Zone Surface Winds 6kt *AFI ? Stochastic Forecast Binary Decisions/Actions Bombs on Target Go / No Go AR Route Clear & 7 Crosswinds In / Out of Limits T-Storm Within 5 Flight Hazards IFR / VFR GPS Scintillation Bridging the Gap 10% 20% 70% Stochastic Forecast Drop Zone Surface Winds 6kt kt Probabilistic IWEDA -- for Operational Risk Management (ORM)

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