1 / 16

Andrea Schumacher, CIRA/CSU Mark DeMaria, NOAA/NESDIS/StAR Dan Brown and Ed Rappaport, NHC

Andrea Schumacher, CIRA/CSU Mark DeMaria, NOAA/NESDIS/StAR Dan Brown and Ed Rappaport, NHC. Using the NHC Tropical Cyclone Wind Speed Probability Product to Quantify Potential Socioeconomic Impacts of Hurricane Forecast Improvements. Motivation.

Download Presentation

Andrea Schumacher, CIRA/CSU Mark DeMaria, NOAA/NESDIS/StAR Dan Brown and Ed Rappaport, NHC

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Andrea Schumacher, CIRA/CSU Mark DeMaria, NOAA/NESDIS/StAR Dan Brown and Ed Rappaport, NHC Using the NHC Tropical Cyclone Wind Speed Probability Product to Quantify Potential Socioeconomic Impacts of Hurricane Forecast Improvements

  2. Motivation • Generally accepted that improvements to hurricane forecasts will benefit society • Longer lead times  more time to prepare • Better track forecasts  reduce areas warned and/or evacuated unnecessarily • However, quantifying these benefits a difficult task • How much money will a better forecast save? • How many lives could be saved? 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  3. Project Outline • Use wind speed probability model to… • Develop an objective warning scheme that reasonably simulates official NHC warnings (building off previous work by M. Mainelli and M. DeMaria) • Artificially “improve” input forecasts, use warning scheme to diagnose changes in warning properties • Warning propertiesconsidered here • Coastal distance • Duration --> time until warning is dropped 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  4. Monte Carlo Wind Speed Probability Model • Operational at NHC since 2006 (replaced Strike Probability Program) • Methodology • Samples errors from NHC track and intensity forecasts over last 5 years to generate 1,000 forecast realizations • Wind radii of realizations from radii CLIPER model • Calculates probabilities over domain from realizations • Versions for Atlantic, NE and NW Pacific • Current products • Cumulative and incremental probabilities • 34, 50 and 64 kt winds • 0, 12, …, 120 hr • Text and graphical products • Distributed via NHC web page, NDFD, AWIPS 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  5. Step 1: Develop an objective hurricane warning scheme that simulates NHC warnings 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  6. Methodology –Objective Hurricane Warning Scheme • Rerun MC probability model • Used 64-kt (hurricane force) wind probabilties • Used 36-h cumulative probabilities (best match for NHC hurricane warning criteria) • U.S. mainland hurricane warnings from 2004-2008 (20 tropical cyclones) • 343 breakpoints • Choose wind speed probability thresholds • p > pup –> put warning up • p < pdown –> take warning down 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  7. Scheme Validation - Statistics First Guess (Prelim w/ Ivan) : pup = 10.0%, pdown = 2.0% Best fit: pup = 8.0%, pdown = 0.0% 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  8. Warning Scheme PerformanceExample: Hurricane Gustav 2008 NHC Hurricane Warnings Objective Scheme Hurr Warnings 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  9. Scheme Validation – TC by TC 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  10. Step 2: Artificially “improve” forecasts and apply warning scheme to examine changes in hurricane warning properties 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  11. Artificially “Improving” forecasts • Two steps needed • Use best tracks from ATCF to adjust tracks and intensities closer to observed values • Scale the sampled track (intensity) errors in the Monte Carlo scheme • For this study, 20% and 50% error reductions were used • Apply objective hurricane warning scheme to MC wind speed probabilities based on “improved” forecasts 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  12. Hurricane Warning Property Changes with Prescribed Forecast Improvements Average = 33.6 hr Average = 378.6 mi We’re closer.. Developed relationship between forecast improvements and warning length & duration… but what are these worth to society? 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  13. Future Work First Steps– Case by case analysis 50% Track and Intensity Forecast Improvement 20% Track and Intensity Forecast Improvement Warning Reductions Length (blue) ~ 100mi Duration ~ 6 h Warning Reductions Length (blue) ~50mi Duration ~ 7 h 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  14. Summary & Conclusions • An objective hurricane warning scheme was developed • Scheme issues hurricane warnings when p>8% and lowers warnings when p=0% • Scheme simulates official NHC hurricane warnings from 2004-2008 relatively well • 20% (50%) forecast improvement in both track & intensity yields • 29 mi or 5% (91 mile or 13%) reduction in coastal length of warning • 2 hr or 8% (5 hr or 24%) reduction in warning duration (i.e., dropped earlier) 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  15. Future Work • Integrate social science research • Previous social science work focused on evacuation behavior, but there are connections (e.g., links between warnings, risk perception, and evacuation behavior) • $600,000 - $1 million per mile estimate for cost of evacuation • Too generic, doesn’t account for population density differences • Whitehead 2003 suggests might actually be less • Some emergency management guidance products estimate costs of evacuation decisions • Emergency Management Decision Support System (EMDSS, Lindell and Prater 2007) 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

  16. References • DeMaria, M., J. A. Knaff, R. Knabb, C. Lauer, C. R. Sampson, R. T. DeMaria, 2009: A New Method for Estimating Tropical Cyclone Wind Speed Probabilities. Wea. Forecasting, Submitted. • Jarell, J.D. and M. DeMaria, 1999. An Examination of Strategies to Reduce the Size of Hurricane Warning Areas. 23rd Conference on Hurricanes and Tropical Meteorology, Dallas, TX, 10-15 Janurary 1999. • Lindell, M.K. and C.S. Prater, 2007: A hurricane evacuation management decision support system (EMDSS). Natural Hazards, 40, 627-634. • Whitehead, J.C., 2003: One million dollars per mile? The opportunity costs of Hurricane evacuation. Ocean and Coastal Management, 46, 1069-1083. 63rd Interdepartmental Hurricane Conference, 2-5 Mar 2009

More Related