Hurricanes: Their Nature and Impacts on Society - Climate Science ...

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HURRICANE FORECASTS 105 uses information from the National Weather Service (NWS) global prediction model (NBC 90; McAdie 1991), <strong>and</strong> several which solve mathematical equations for atmospheric flow including VICBAR (Aberson <strong>and</strong> DeMaria 1994), BAM (Marks 1992), the Geophysical Fluid Dynamics Lab (GFDL) model (Bender et al. 1993), <strong>and</strong> the National Meteorological Center (NMC) Aviation Medium Range Forecast (MRF) global forecast models (Lord 1993). During the 1992 <strong>and</strong> 1993 seasons, the GFDL model track forecasts were superior to the other track models (Aberson <strong>and</strong> DeMaria 1994); the improved forecasts of hurricane track using this model are also summarized by Sawyer (1995). Track forecasts are very sensitive to how the actual hurricane is initially defined in the model (Leslie <strong>and</strong> Holl<strong>and</strong> 1995). For instance, track predictions depend on the choice of hurricane radius <strong>and</strong> strength, <strong>and</strong> its initial location. Improved measurements of temperature, wind, <strong>and</strong> humidity in <strong>and</strong> around tropical cyclones have contributed to improved operational track forecasts (Burpee et al. 1996). An example of a 72-hour track forecast for Hurricane Hugo for different models is given in Figure 4.8. This figure illustrates that despite significant progress in hurricane forecasting, exact track prediction remains fraught with difficulties. Hugo actually made l<strong>and</strong>fall near Charleston, South Carolina. Hurricane track models are also summarized in Puri <strong>and</strong> Holl<strong>and</strong> (1993).* Figure 4.9 illustrates the trend <strong>and</strong> accuracy of 24-hour, 48-hour, <strong>and</strong> 72-hour forecasts of storm position between 1970 <strong>and</strong> 1992. As of 1997, average forecast errors are on the order of 115 miles (185 kIn) for 24-hour forecasts, 230 miles (368 kIn) for 48-hour forecasts, <strong>and</strong> 345 miles (552 kIn) for 72-hour forecasts (C. L<strong>and</strong>sea, 1997, personal communication). Note that an improvement of only about 20 miles (42 kIn) has been achieved in 24-hour position forecasts over 23 years, despite the great advances both in monitoring these storms (e.g. radar, satellite, reconnaissance aircraft) <strong>and</strong> in computer power to process <strong>and</strong> analyze the data. .Four criteria have been proposed for accurate track forecasts using models such as VICBAR, BAM, the GFDL model, <strong>and</strong> the Aviation global model (Elsberry 1995). These are: adequate initial specifications of the environmental wind field, the symmetric <strong>and</strong> asymmetric cyclone vortex structure, <strong>and</strong> the adequacy of the prediction models to forecast the time evolution of the vertical <strong>and</strong> horizontal wind field. An accurate representation of the diabatic heating of the atmosphere by the hurricane, <strong>and</strong> the prediction of winds <strong>and</strong> temperature in the upper troposphere are also essential to accurately characterize hurricane-environmental interactions (WU <strong>and</strong> Kurihara 1996). When the large-scale weather pattern is changing with time, these reqnirements are difficult to achieve with sufficient accuracy. The MRF model is used for general weather forecasting in addition to its 'application for tropical cyclone track prediction. VICBAR, BAM, NHC90 <strong>and</strong> the GFDL model use forecast fields from the MRF model for input. One version of NHC90 (referred to as UK90) uses output from the United Kingdom Meteorological Office global forecast model. The GFDL model includes the most physical realism in its formulation, including moving nested grids which translate with the cyclone <strong>and</strong> a sophisticated vortex initialization scheme with the finest horizontal grid interval of 20 km (DeMaria 1995; Bender et aI. 1993).
106 HURRICANES: THEIR NATURE AND IMPACT ON SOCffiTY Figure 4.8 National Hurricane Center 72-hour forecast tracks for Hurricane Hugo 1989, starting from its position at 0 GMT on 21 September 1989. The forecast 12-hour positions for each of six forecast techniques are shown as black dots Since 1983, probabilities of a tropical cyclone passing within 75 miles (121 kIn) of specific geographic locations have also been publicly distributed. An example of the format used in these probability forecasts is shown in Figure 4.10, in this case for Hurricane Erin in 1995. 4.5.2 Tropical cyclone intensity change predictions Forecasting of changes in tropical cyclone intensity is a much more difficult task than forecasting tropical cyclone tracks. Several methods are used to predict changes in intensity. The simple climatology <strong>and</strong> persistence intensity technique (SHIFOR) <strong>and</strong> the statistical hurricane intensity prediction scheme
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Hurricanes
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Copyright @ 1997 by John Wiley & So
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Dedication Foreword. Preface. Conte
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CONTENTS 5.3 5.2.3 WindY. . 5.2.4 R
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Foreword Before the mid-l 960s, stu
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X11 FOREWORD The lessons of Camille
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XVI PREFACE One of the fundamental
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XVlll PREFACE about the need for so
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2 HURRICANES: THEIR NATURE AND IMPA
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4 HURRICANES: THEIR NATURE AND IMPA
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INTRODUCfION: SCffiNCE, POLICY, AND
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INTRODUcnON: SCIENCE, POLICY, AND H
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INTRODUCTION: SCIENCE, POLICY, AND
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INTRODUcrION: SCIENCE, POLICY, AND
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INTRODUcrION: SCIENCE, POLICY, AND
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I I I I A -Nr"1v ~ r/) ,:,.," 0- 0-
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INTRODUCfION: SCIENCE, POLICY, AND
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21 INTRODUCTION: SCIENCE, POLICY, A
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INTRODUCfION: SCffiNCE, POLICY, AND
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INTRODUCfION: SCIENCE, POLICY, AND
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CHAPTER 2 The US Hurricane Problem
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THE US HURRICANE PROBLEM human acti
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THE US HURRICANE PROBLEM of their p
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THE US HURRICANE PROBLEM 37 2.1.3 S
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THE US HURRICANE PROBLEM 39 activit
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THE US HURRICANE PROBLEM Figure 2.3
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THE US HURRICANE PROBLEM on an intr
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THE US HURRICANE PROBLEM Table 2.1
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TIm us HURRICANE PROBLEM ~ I (a) -.
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THE US HURRICANE PROBLEM emissions)
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THE US HURRICANE PROBLEM 51 Figure
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THE US HURRICANE PROBLEM (ODDs) 193
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Page 83 and 84: THE US HURRICANE PROBLEM 67 Appendi
Page 85 and 86: TRC!>PICAL CYCLONES ON PLANET EARTH
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Page 105 and 106: 35 ,~ 20 1201 55 TROPICAL CYCLONES
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Page 109 and 110: HURRICANE FORECASTS Figure 4.1 The
Page 111 and 112: * t " , " ' " i : , , j !' \ HURRIC
Page 113 and 114: HURRICANE FORECASTS (e) 7AM ~ ..i~~
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Page 119 and 120: 102 HURRICANES: THEIR NATURE AND IM
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Page 135 and 136: CHAPTERS When a hurricane forms, it
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CHAPTER? 7.1 INTRODUCflON As recoun
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158 HURRICANES: THEIR NATURE AND IM
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APPENDIX A CHAPTER! Byerly, R., 199
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40 000 30 000 20'000 10 000 APPENDI
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APPENDIX C Selected Data on Tropica
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Figure C.4 Cont: Tracks of intense
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'-'2 ..~ ,-..~ \OQ. ~~ -I" -(') 3.
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APPENDIX C 209 '";ij' 40 Q) ~ C) Q)
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211 APPENDIX D Table D.2 Eastern No
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APPENDIX D Table D.6 Western Austra
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APPENDIX D Table D.8 Fiji Region Na
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APPENDIX D Table 0.10 Southeast Ind
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: 8. ~A ". ~ 00 tU : V ~ ~ ~ V z 8,
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on "'-va , £.c 0 I::.cp. V'O cn ,'
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APPENDIX E 233
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APPENDIX E In sum, TDS provides an
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APPENDIX 0 ~ION E 237 A period of t
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APPENDIX E IF SITUATION W~S P (alp)
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29. APPENDIX E p (0) 5 (a/p) T (alp
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APPENDIX E p (0) p p S T (a/o) (a/p
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APPENDIX E p p 5 p (alp) (r) S (r)
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APPENDIX E 247 P (al p p P (alp) p
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D APPENDIX E s s s s s s S T T T T
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APPENDIX E 251 IMMEDIATE EMERGENCY
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APPENDIX E 253 p p T (alp) (a/o) (0
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APPENDIX E P (a/s) P falsI 5 (alp)
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~ APPENDIX F 257 lOP = 5/9°C 1°C
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REFERENCES 259 Nemec, J.M. Nigg <st
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REFERENCES 261 Cochran, L.
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REFERENCES 263 Etzioni, A., 1985: M
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REFERENCES 265 Graham, B., 1992: An
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REFERENCES 267 Katz, R.W., 1993: To
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REFERENCES 269 Mesthene, E.G., 1967
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REFERENCES 271 south Florida. Part
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REFERENCES 273 Smith, K., 1992: Env
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REFERENCES 275 Wolensky, R.P. <stro
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INDEX 277 eye wall concentric eye w
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INDEX silver iodide 112 Simpson, Ro
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