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An Objective Aid for DoD Base Preparations in Advance of Tropical Cyclones

An Objective Aid for DoD Base Preparations in Advance of Tropical Cyclones. Charles R. Sampson Naval Research Laboratory Monterey, California Andrea B. Schumacher CIRA Colorado State University Fort Collins, Colorado John A. Knaff and Mark DeMaria

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An Objective Aid for DoD Base Preparations in Advance of Tropical Cyclones

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  1. An Objective Aid for DoD Base Preparations in Advance of Tropical Cyclones Charles R. Sampson Naval Research Laboratory Monterey, California Andrea B. Schumacher CIRA Colorado State University Fort Collins, Colorado John A. Knaff and Mark DeMaria NOAA/NESDIS – Regional and Mesoscale Meteorology Branch Fort Collins, Colorado Edward M. Fukada Joint Typhoon Warning Center Pearl Harbor, Hawaii Chris A. Sisko and David P. Roberts National Hurricane Center Miami, Florida Katherine A. Winters Patrick AFB Melbourne, Florida Harold M. Wilson Fleet Weather Center Norfolk, Virginia

  2. Need interesting picture/pictures of typhoon or hurricane damage at military base

  3. Introduction The Department of Defense uses a Tropical Cyclone Conditions of Readiness (TC-CORs) system to prepare bases and evacuate assets and personnel in advance of adverse weather associated with tropical cyclones (TCs). TC-CORS are recommended by weather facilities either on base or at central sites and generally are related to the timing and potential for destructive (50-knot) sustained winds. Recommendations are then considered by base or area commanders along with other factors for setting the TC-CORs. Ideally, the TC-CORs are set sequentially, from TC-COR IV (destructive winds within 72 hours) through TC-COR III (destructive winds within 48 hours) and TC-COR II (destructive winds within 24 hours), and finally to TC-COR I (destructive winds within 12 hours) if needed. Each TC-COR, once set, initiates a series of preparations (Table 1). As illustrated, preparations for TC-COR IV can be as unobtrusive as obtaining emergency supplies while preparations leading up to TC-COR I are generally far more costly, intrusive and labor intensive activities. Note that east of the Dateline the rules are generally called Hurricane Conditions of Readiness (HURCONs) while the same rules are called Tropical Cyclone Conditions of Readiness (TC-CORs) at bases west of the Dateline.

  4. Datasets The forecast and best track data used for this study are taken from the Automated Tropical Cyclone Forecasting System (ATCF; Sampson and Schrader 2000) operational archives at the NHC and JTWC. There are 113 operational TC-COR cases (where a tropical cyclone approached close enough to a base to investigate TC-CORs) from tropical cyclone passages in the TC-COR database, and it includes 79 cases from 8 bases in the western North Pacific, and 34 cases from four bases along the Eastern Seaboard and in the Gulf of Mexico (Table 2).

  5. Methods The same sample of Atlantic tropical cyclones making landfall along the U.S. mainland from 2004 to 2010 used in Schumacher et al. (2010) was used here. Following their methodology, 64-kt wind speed probabilities were computed at 12, 24, 48 and 72 hours prior to the arrival of the 64-kt winds to correspond to the time criteria for TC-COR settings I, II, III, and IV, respectively. Wind speed probabilities were generated using the 2010 developmental version of the Monte Carlo Wind Speed Probability (MCWSP) product. Locations along the U.S. mainland coast where near-surface winds met or exceeded 64 knots were determined from tropical cyclone positions and radius of 64-kt winds (R64) recorded in the ATCF.

  6. Working under the assumptions that 1) missed TC-CORs can have extreme negative consequences (e.g., loss of life) and 2) false alarms are also costly, a selection scheme for our first-guess TC-COR probability thresholds was specified so that 1) POD ≥ 0.99 and 2) FAR is minimized; implying the cost-to-loss ratio is very small (Wilks 2006; pp 321-324). The thresholds that we derived are shown below. These thresholds appear at first glance to be quite low, but they are defined to minimize the missed cases (POD ≥ 0.99). If it is acceptable to miss 50-kt wind events (i.e., the cost-to-loss ratio was close to one), these thresholds could be raised to minimize the FAR and improve statistical skill scores (e.g., Peirce’s Skill Score or Threat Score) discussed in Wilks (2006), but would increase the possibility of very costly misses.

  7. EXPERIMENTAL TC-COR SETTINGS • Uses cumulative 50-kt wind probabilities • Thresholds based Atlantic Hurricane Watches/Warnings • Strategic guidance for onset of 50-kt winds • TC-CORThreshold ProbabilitiesForecast Lead • 4 5% 72 H • 3 6% 48 H • 2 8% 24 H • 1 12% 12 H

  8. Results and Findings

  9. Figure 1. Objective aid and Operational TC-COR settings for all events with elevated winds. Quartiles shown as thick bars, extremes shown as thin bars. Numbers of cases for COR 1, 2, 3, 4 are 32, 46, 52, 59, respectively. Some of the largest differences are due to base considerations (e.g., keeping a COR 3 up in preparation for the next TC).

  10. TC-COR Aid Timing The TC-COR aid IV, III, II, and I times. Box indicates 1st and 3rd quartiles, whiskers indicates entire range of cases. Number of cases is 33, 43, 53, 54 for IV, III, II and I, respectively, from twelve military bases in Pacific and Atlantic.

  11. Sensitivity Analysis Doubling thresholds improves TC-COR III and IV Peirce’s Scores (1=perfect, 0=no skill), but reduces forecast lead times (~7 hours for TC-COR IV) and misses a few cases. CNMOC DOO suggests adding “Low”, “Medium”, “High” confidence. Current thresholds are “Low” confidence by design.

  12. Fig. 6. Percentage of lead times reduced (top) and the average number of hours the lead time was delayed for all the cases (bottom) when the wind probability thresholds associated with the TC-CORs were doubled.

  13. References: • DeMaria, M. , J .A. Knaff, R. Knabb, C. Lauer, C. R. Sampson, and R. T. DeMaria, 2009:A new method for estimating tropical cyclone wind speed probabilities, Wea. Forecasting, 24, 1573-1591. • Sampson, C. R., and A. J. Schrader, 2000: The Automated Tropical Cyclone Forecasting System (Version 3.2). Bull. Amer. Meteor. Soc., 81, 1131-1240. • Schumacher, A.B., M. DeMaria, J. A. Knaff, C. R. Sampson and D. Brown, 2010: Objective Tropical Cyclone Warning Guidance Using Monte Carlo Wind Speed Probabilities, 29th Conference on Hurricanes and Tropical Meteorology, 10-14 May 2010, Tucson, AZ. • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 467 pp.

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