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GIVE ME ODDS

ENSEMBLE. FORECASTING. GIVE ME ODDS. KEY POINTS. THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION WEATHER FORECASTING, THEREFORE, IS INHERENTLY STOCHASTIC, NOT DETERMINISTIC IN NATURE

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GIVE ME ODDS

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  1. ENSEMBLE FORECASTING GIVE ME ODDS

  2. KEY POINTS THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION WEATHER FORECASTING, THEREFORE, IS INHERENTLY STOCHASTIC, NOT DETERMINISTIC IN NATURE ENSEMBLE PREDICTION - REVOLUTIONARY CHANGE IN THE THRUST OF OPERATIONAL NWP (“WAVE OF THE FUTURE”) - CONSISTS OF MULTIPLE PREDICTIONS FROM SLIGHTLY DIFFERENT INITIAL CONDITIONS AND/OR WITH VARIOUS VERSIONS OF MODELS, THE OBJECTIVES BEING TO: IMPROVE SKILL THROUGH ENSEMBLE AVERAGING, WHICH ELIMINATES NON-PREDICTABLE COMPONENTS PROVIDE RELIABLE INFORMATION ON FORECAST UNCERTAINTIES (E.G., PROBABILITIES) FROM THE SPREAD (DIVERSITY) AMONGST ENSEMBLE MEMBERS REALITY - POSITIVE RESULTS ON BOTH COUNTS WITH OPERATIONAL GLOBAL MODEL ENSEMBLE SYSTEM; EXPERIMENTAL REGIONAL MODEL ENSEMBLES ENCOURAGING (OPERATIONAL EARLY 2000?) NET RESULT - ENHANCE UTILITY OF NWP FOR VIRTUALLY ALL APPLICATIONS REALIZING THE PRACTICAL UTILITY OF ENSEMBLES ACCOMPLISHED VIA A VARIETY OF NEW PRODUCTS DESIGNED TO CONDENSE AND MAXIMIZE INFORMATION CONTENT FOR USERS; USER FEEDBACK ESSENTIAL AND ENCOURAGED!!!

  3. KEY CONSIDERATIONS STRATEGIES FOR CREATING ENSEMBLES PROCDEDURES FOR GENERATING INITIAL STATE PERTURBATIONS RANDOM TIME LAGGING ANALYSES FROM OTHER CENTERS “BREEDING” SINGULAR VECTORS PERTURBING MODEL (E.G., CONVECTIVE PARAMETERIZATION) AND/OR MULTI-MODEL ENSEMBLES MODEL CONFIGURATION? RESOLUTION PHYSICAL SOPHISTICATION DOMAIN ENSEMBLE SIZE NOTE: OPTIMUM STRATEGY UNKNOWN (NO CONCENSUS)!! IDEAL: EFFECTIVE/EFFICIENT SAMPLING OF ALTERNATIVE SCENARIOS, I.E., PROBABILITY DISTRIBUTIONS. LIMITED COMPUTER RESOURCES GENERALLY REQUIRE COMPROMISES RELATIVE TO PERCEIVED OPTIMUM, E.G., MODEL RESOLUTION VERSUS ENSEMBLE SIZE)

  4. KEY CONSIDERATIONS(CONT.) PRODUCT DEVELOPMENT OBJECTIVE: CONDENSE LARGE AMOUNTS OF OUTPUT INTO A “USER FRIENDLY” FORM THAT PROVIDES RELIABLE ESTIMATES OF THE RANGE AND LIKLIHOOD OF ALTERNATIVE SCENARIOS PRODUCTS CAN RANGE FROM DISPLAY OF ALL FORECASTS THROUGH MEANS/SPREAD AND CLUSTERS TO FULL PROBABILITIY DISTRIBUTIONS DISPLAYED IN VARIOUS FORMATS STATISTICAL POSTPROCESSING (E.G., BIAS CORRECTIONS, CALIBRATION OF PROBABILITIES ENSEMBLE OUTPUT STATISTICS ADDITIONAL/ALTERNATIVE PRODUCTS CONTINUAL INTERACTION AMONGST DEVELOPERS AND USERS VALIDATION STANDARD SKILL SCORES MEASURES OF SPREAD MEASURES OF RELIABILITY EDUCATION AND TRAINING COMET SYMPOSIUM TRAINING MODULES ON SITE VISITS WEB BASED ??

  5. N-AWIPS GRAPHICAL PRODUCTS(GEMPAK META FILES) • SPAGHETTI CHARTS • 500 Z • 1000Z • 1000/500 TCK • MSLP • 850 T • 700 RH • SPREAD • 1000 Z • 500 Z • CLUSTERS • 1000 Z • 500 Z • PROBABILITIES • 500 Z > THRESHOLDS • 700 RH > 70% • TCK <540 • 250 V > THRESHOLDS • 850 T > 0C • MSLP CENTERS

  6. PRODUCT DEVELOPMENT INCLUDES • PROBABILITIES • VIRTUALLY ALL RELEVANT AND MODEL DERIVED PARAMETERS, E.G., • SEVERE WEATHER INDICES • AVIATION WINDS > THRESHOLD • SENSIBLE WEATHER ELEMENTS (MODEL DERIVED/INFERRED • CIRCULATION INDICES (E.G., BLOCKING) • EXPANDED CLUSTERED PARAMETERS AND FOR SPECIALIZED REGIONS • VERTICAL PROFILES • METEOGRAMS • ENSEMBLE DERIVED MOS • TROPICAL STORM TRACKS • DIRECT FROM ENSEMBLES • BACKGROUND FOR GFDL MODEL ENSEMBLES

  7. SOME APPLICATIONS FORECASTS OF ENSEMBLE MEAN, SPREAD, PROBABILITY DISTRIBUTIONS, ETC. OF ANY MODEL FIELD/PARAMETER OR QUANTITIES DERIVED THEREFROM ENHANCE THE UTILITY OF FORECASTS APPLICABLE TO MODELS FROM VERY SHORT RANGE CLOUD SCALE THROUGH REGIONAL MESOSCALE SHORT RANGE AND GLOBAL MEDIUM RANGE TO COUPLED OCEAN/ATMOSPHERE CLIMATE PREDICTION SYSTEMS IMPROVE DATA ASSIMILATION SYSTEMS ADAPTIVE/TARGETED OBSERVATIONS DATA SETS FOR FUNDAMENTAL RESEARCH ON PREDICTABILITY ISSUES NOTE: LARGE CURRENT USER COMMUNITY (OPERATIONAL GLOBAL SYSTEM) INCLUDES NCEP SERVICE CENTERS, WFO’S, USAF, OH, PRIVATES/BROADCASTERS

  8. CLUSTER ANALYSIS • OBJECTIVELY GROUP TOGETHER FORECASTS WHICH ARE SIMILAR ACCORDING TO SOME CRITERIA • GOAL: IDENTIFY EXTREMES, GROUPINGS (CLUSTERS) WITHIN ENVELOPE OF POSSIBILITIES (“ATTRACTORS”) • ISSUES: • QUANTITY • MSLP • 500 Z • ETC. • MEASURE • ANOMALY CORRELATION • CIRCULATION PARAMETERS • PATTERN RECOGNITION • PHASE-SPACE MEASUREMENTS • REGION

  9. EVALUATION/VERIFICATION • SITUATIONAL AND PHENOMENOLOGICAL CASE STUDIES (E.G., CYCLOGENESIS, FLOOD POTENTIAL) • STATISTICAL • STANDARD AC, RMS, SCORES (E.G., APPLIED TO ENSEMBLE MEAN VS. CONTROL, RELATIVE CLOSENESS OF MEMBERS TO ANALYSIS) • “TALAGRAND” (VERIFICATION RANK) DIAGRAMS - MEASURES OF BIASES IN DISTRIBUTION OF ENSEMBLE MEMBERS INCLUDING FREQUENCY OF OUTLIERS) • BRIER, RANKED PROBABILITY SCORES (PROBABILITY SKILL SCORES) • RELIABILITY DIAGRAMS (OBSERVED VERSUS FORECAST FREQUENCIES; ENABLES CALIBRATION OF PROBABILITIES) • MOS VERSUS ENSEMBLE POPS • RELATIVE OPERATING CHARACTERISTICS (ROC); (EXPLICIT COMPARISON OF THE RELATIVE UTILITY OF DETERMINISTIC AND ENSEMBLE PREDICTIONS)

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