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Probabilistic Forecasts of Extreme Events and Weather Hazards

This project aims to develop probabilistic forecasts of extreme events and weather hazards in the United States, focusing on severe weather, floods, heavy snows, cold outbreaks, heat waves, wind storms, coastal flooding, and beach erosion.

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Probabilistic Forecasts of Extreme Events and Weather Hazards

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  1. Probabilistic Forecasts of Extreme Events and Weather Hazards in the United States Charles Jones1, Leila Carvalho1 Jon Gottschalck2 1 Institute for Computational Earth System Science (ICESS) University of California, Santa Barbara 2 Climate Prediction Center NOAA/NCEP

  2. Motivation • Extreme weather events lead to severe and hazardous conditions: • Severe weather (lightning, hail, tornadoes) • Floods • Crippling Heavy Snows • Cold outbreaks (freezes) • Heat waves • Widespread wind storms • Coastal flooding and beach erosion Average numbers of weather related fatalities over the U.S. Source: National Weather Service (NOAA) www.weather.gov/os

  3. Motivation • CPC hazard assessment: • Subjective in nature but a set of guidelines are defined • Days 3-14 days but often focuses on Week 1 Exchange of information, ideas and methodology between projects CPC probabilistic hazards project == Current CTB Project Focus on events, GFS, Week 1-2Integrated over period, CFS, Weeks 2-4

  4. Project Relevance • Contribute to these CTB Science Priority Areas: • Intraseasonal forecasting capability • Capability to predict extremes for Weeks 2 to 4 • Predictive understanding of the impacts of climate on the statistics of extreme events

  5. Datasets 1. CFS Retrospective Forecasts 1981-2006 2. NCEP Reanalysis 2 1981-2006 3. CPC gridded precipitation 1948-present 4. North American Regional Reanalysis (NARR) 1979-present 5. NCDC/NWS Storm Data 1960s-present • Reports of significant weather phenomena resulting in loss of life, injuries, significant property or crop damage, and/or disruption to commerce • Includes date of occurrence, State, County or NWS forecast zone, event type, geographical extent and duration, fatalities and injuries, estimated property or crop damage and remarks • Digital format with quality control by Hazards & Vulnerability Research Institute, University of South Carolina

  6. Storm Data Example Total number of winter weather related hazards in California (1979-04)

  7. Project Phases 1. Evaluate the skill of CFS probabilistic forecasts of extreme events during the winter season • Extremes inprecipitation, surface temperature and surface wind speed (i.e., primary variables related to weather hazards in NWS Storm Data) • Fraction of CFS members of total ensemble forecasting an extreme event • Calibration and verification using CDAS2, NARR, etc. • Spatial and temporal sensitivity tests: Spatial aggregation; moving window in time • Stratify by leading climate modes (i.e., MJO, ENSO, etc.)

  8. Project Phases • Planned utilization of probabilistic forecasts of extreme events

  9. Project Phases 2. Develop probabilistic forecasts of winter hazards • Linking extreme event forecasts with historical hazard records • Evaluate optimal thresholds to define extreme events • Develop hazard indices based on: (1) CFS and empirical forecast data (2) Storm Data occurrences --Joint probabilities, conditional probabilities • --Regional forecasts within the CONUS, Alaska and Hawaii • --Aggregate storm reports across counties

  10. Project Phases 3. Adjust probabilistic forecast models based on relevant climate modes Adjust hazard indices based on current and forecast MJO phase and amplitude

  11. Project Phases 4. Implementation of deliverables to CPC • Develop website for CFS probabilistic forecasts of extremes --Evaluation of CFS historical forecast skill --Realtime display of CFS probabilistic forecasts at CPC Dec 2008 • Implement probabilistic forecasts of extremes events / hazards at UCSB Dec 2009 • Develop products in GIS format at UCSB Mar 2010 • Migrate all models and product data to CPC Jun 2010 • Implement experimental forecasts of hazards in realtime at CPC --Link to operational CFS forecast data --Link to operational CPC MJO forecasts --Inclusion of forecast data into arcGIS web server at CPC Sep 2010

  12. Comments and Suggestions? cjones@icess.ucsb.edu Jon.Gottschalck@noaa.gov

  13. Key Science Issues • Degree of skill of the CFS in forecasting extreme events? • How to best relate a probabilistic extreme event forecast (e.g. precipitation) with a probability of hazard (e.g. flood)? • Storm Data reports: --Variable coverage among counties --Greater frequency in densely populated counties

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