FIFTH INTERNATIONAL WORKSHOP ON TROPICAL CYCLONES
Topic 5.3 : Effective Warnings
Rapporteur : T.C. Lee
Hong Kong Observatory
134A Nathan Road, Kowloon,
E-mail : email@example.com
Fax : (852) 2377 3472
Working Group Members : Jim Davidson, S. R. Kalsi, Kriengkrai Khovadhana,
H. K. Lam,
Glendell De Souza, Max Mayfield, Rajendra Prasad, Alan Radford,
Jose Rubiera, Michel Rosengaus, Steve Ready, Shourong Wang.
An effective tropical cyclone (TC) warning system requires the warnings and information prepared by the National Meteorological Service (NMS) and Tropical Cyclone Warning Centre (TCWC) to be accurate, disseminated to the designated users in a timely and comprehensive manner, and responded to properly by the emergency organizations and communities at risk. Factors affecting the effectiveness of a TC warning system can be categorized under four main areas:
This report reviews the latest development in these four areas. Potential roadblocks and future opportunities in relation to effective warnings include:(a) Track forecasting of landfalling TCs; (b) TC intensity prediction; and (c) Impact of the internet are discussed in Section 5.3.6.
In addition to the valuable inputs of working group members, this report draws on published and unpublished sources of information, including the Draft Guidelines on Improving Public Understanding of and Response to Warnings compiled by Davidson et al. (WMO 2002).
Forecast Accuracy and Reliability
A modern TC warning service should provide accurate and quantitative predictions of high winds, heavy rain, and storm surge. Over- or under-warnings of these hazards will result in socio-economic loss and, to a certain extent, affect the credibility of the NMS or TCWC. An accurate TC forecast track and a good knowledge of the wind and rain distribution of the TC are essential.
Forecasters have for a long time pinned their hope on numerical modeling (Bell 1979). Over the last decade, the skill of numerical models in TC track forecasting has improved significantly (JMA 1998; Heming 2000; Lam 2001; Aberson 2001) and forecasters are relying increasingly on the guidance from numerical models that are available via the WMO Global Telecommunication System (GTS)(WMO 1999). As an example, Figure 1 shows the skill scores of the TC track forecast of the UK Meteorological Office (UKMO) global model from 1988-2001. An improving trend can be observed, especially for the 24- and 48-hour forecasts. The better Numerical Weather Prediction (NWP) guidance has resulted in better subjective forecasts issued by NMSs. For example, Figure 2 shows the U.S.A. National Hurricane Center (NHC) yearly-average Atlantic track forecast errors. Trend lines show the largest improvements are at the longer time periods.
Ensemble forecasting techniques are increasingly used in TC track prediction in recent years. The single model ensemble approach defines perturbed initial conditions that represent the uncertainty in the analysis and then integrates the ensemble members with a single model (Aberson et al. 1995; Cheung 2001; Zhang and Krishnamurti 1997). The multiple model ensemble approach makes simple averages of the forecast outputs of several models from different centres. Verifications show that the multiple model ensemble approach results in noticeable improvement in TC forecast track errors (Goerss 2000; Lee and Wong 2002). Carr and Elsberry (2000) also conducted research that relates the large forecast position errors to the models excessive prediction of certain categories of synoptic patterns bearing on TC movement. This suggests that a TC track forecasting system can be developed to assist forecasters in selecting the best performing NWP models to construct the ensemble forecast on each occasion (Carr et al. 2001).
Figure 1 Skill relative to climatology-persistence (CLIPER) of the UK Met Office Global Model TC track forecast from 1988-2001 in western North Pacific. (http://www.metoffice.com/sec2/sec2cyclone/tcver.html)
Figure 2 Yearly-average Atlantic track forecast errors with trend lines at 24-, 48- and 72-hour time periods.
Prediction of Severe Weather and Storm Surges
To predict the time of onset of severe weather and storm surges at specific locations, detailed knowledge of the wind and precipitation structure of the TC (e.g. TC size, gale wind radius) have to be known besides the forecast track. Observations in recent years from advanced sensors (e.g. Tropical Rainfall Measuring Mission (TRMM), Special Sensor Microwave/Imager (SSM/I), Advanced Microwave Sounding Unit (AMSU), and QuikSCAT) on the new generation of meteorological satellites (Kidder et al 2000; Imaoka and Roy 2000; Yeh et al. 2002; Bankert et al. 2002) have increased knowledge on the initial conditions of wind and precipitation fields. Valuable information on wind fields can also be obtained from GPS dropsondes and Stepped Frequency Microwave Radiometers from aircraft reconnaissance. Radar is another important tool in very short-range forecasting of landfalling TCs (Tuttle and Gall 1999; Blackwell 2000). Correlation and extrapolation techniques have been usefully employed to depict the wind structure of TCs and to make very short-range rainfall forecasts (Li et al. 1999; Lai 1999).
For storm surge prediction, the Sea, Lake, and Overland Surge from Hurricane (SLOSH) model (Jelesnianski 1992) is still one of the most popular models used by TCWCs and NMSs (Houston et al. 1999; Tam 1996). A meteorological hazard model, The Arbiter of Storm (TAOS), has also been developed and incorporated into the Caribbean Disaster Mitigation Project (Watson et al. 1999). India Meteorological Department (IMD) has been using locally developed nomograms (Ghosh 1977) and a PC-based storm surge model developed by the Indian Institute of Technology Delhi for storm surge prediction (Dube et al. 1985; Kalsi et al. 2002).
TC Intensity Prediction
As far as TC intensity prediction is concerned, Dvorak analysis (Dvorak 1984) and NWP guidance are commonly used by NMSs and TCWCs in assessing and forecasting TC intensity. The U.S.A. NHC also uses the Statistical Hurricane Intensity Forecast (SHIFOR) model and Statistical Hurricane Intensity Prediction Scheme (SHIPS) model in predicting TC intensity in the Atlantic and eastern North Pacific (Jarvinen and Neumann 1979; DeMaria and Kaplan 1994). Over the western North Pacific, the Joint Typhoon Warning Centre at Pearl Harbour employs the Statistical Typhoon Intensity Forecast (STIFOR) model and the Typhoon Intensity Prediction Scheme (TIPS) for TC intensity forecasts (Chu 1994; Fitzpatrick 1997).
TC warnings are only useful if they are received by the targeted user in a timely and comprehensive manner. This requires efficient communication channels, effective warning presentation, and user-specific warnings.
In order to promote public awareness, a Hurricane Awareness Week (HAW) has been held in the U.S.A. in 2001 and 2002. The HAW is scheduled prior to the start of the Atlantic TC season. A comprehensive web page, which contains detailed information on TC basics, climatology, hazards and forecast limitations, has been developed to support the HAW (http://www.nhc.noaa.gov/HAW2/index.htm).
As regards disaster preparedness, it is becoming a common practice for NMSs to conduct pre-TC season preparation meetings and post-TC season review meetings with all government or non-government partners participating in the TC disaster contingency plan. These meetings serve to develop and maintain strong partnerships between NMSs and emergency organizations, to help all participating parties understand their respective roles and responsibilities in the contingency plan, and to solve possible problems in the warning-response system. Moreover, NMSs strive to maintain close liaison with the media partners, especially radio and TV, to ensure that the most up-to-date TC warning information can be disseminated to the public accurately, regularly, and with high priority.
Tropical cyclones do not recognize political borders. NMSs need to work together in a spirit of mutual assistance and cooperation to meet increasing demands on the tropical warning services. NMSs are cooperating in the dissemination of TC warnings, exchange of data/information, and the promotion of public awareness on TC hazards. Some recent activities are:
(c) Information exchange
Besides traditional synoptic observations and TC warning advisories, NMSs, TCWCs, and major research institutes have put much effort in sharing NWP products, best track databases, radar images and satellite images as well as scatterometer data over the internet in recent years. To facilitate timely preparation of TC warnings by NMSs/TCWCs, the Japan Meteorological Agency (JMA) and UKMO also disseminate the TC track and intensity forecasts made by their global and typhoon models in standard text format to NMSs/TCWCs via the WMO GTS.
Roadblocks and Future Opportunities
Accurate determination of TC landfall location and timing is the key to the effectiveness of TC warnings/forecasts for a specific region.
The critical period for some countries/places is the 6-12 hours before TC landfall. In such a short time frame, NWP guidance with an average 12-hour forecast error of about 100 km cannot offer much assistance to forecasters. Very often, forecasters have to subjectively nowcast the TC movement in the next 6 hours based on radar images and limited synoptic/AWS data. Significant error in landfall location and timing could result if the TC moves erratically near landfall. Figures 5(a) to (d) show respectively the tracks of Typhoon Kim (1980), Typhoon Hal (1985), Typhoon Maggie (1999), and Typhoon Utor (2001) which affected the coast of Guangdong, China. The abrupt change in direction of movement and/or looping motion of these TCs near Hong Kong posed much difficulty in forecasting the landfall location and timing. In situations of close approaches, a timing error of a few hours and/or forecasting a TC landing to the west of a city when it in fact lands to its east could reduce the warning to ridicule. Therefore, movement of the TC prior to landfall has an important bearing in the formulation of an effective warning strategy. Research initiatives to improve the capability of forecasters in short-range forecasting of landfalling TCs are imperative.
Figure 5 (a)
Figure 5 (b)
Figure 5 (c)
Figure 5 (d)
(ii) Longer range forecasting
Longer-range forecasting of landfalling TCs is also important. Some coastal areas have developed to such an extent that much longer than 12 hours are needed for TC preparations. For example, Southeast Louisiana (including New Orleans), Southeast Florida (including Miami and Ft. Lauderdale), and the Delmarva Peninsula (including Ocean City, Maryland) all need 36 hours or more to respond to the more powerful TCs. Although track forecasting has improved dramatically at the longer time periods (section 5.3.2(a)), additional track improvements are required to minimize the areas needing to respond. This becomes imperative as population increases rapidly in many coastal communities.
Aberson, S. D., Lord, S. J., DeMaria, M., and Tracton, M. S., (1995). Short-range ensemble forecasting of hurricane tracks. In Preprints, 21st Conf. Hurr. Trop. Meteorol., Miami, Am. Meteorol. Soc., 290-292.
Aberson, S. D., (2001). The ensemble of tropical cyclone track forecasting models in the North Atlantic Basin (1976-2000), Bull. Amer. Meteor. Soc., 82 , 1895-1904.
Bankert, R. L., and Tag, P. M., (2002). An automated method to estimate tropical cyclone intensity using SSM/I Imagery. J. Appl. Meteorol., 41, 461472.
Bell, G. J., (1979). Operational forecasting of tropical cyclones: past, present and future. Aust. Meteorol. Mag., 27: 249-258.
Blackwell, K. G., (2000). The evolution of hurricane Danny (1997) at landfall: Doppler-observed eyewall replacement, vortex contraction/intensification, and low-level wind maxima. Mon. Wea. Rev., 128, 4002-4016.
Carr, L. E., III, and Elsberry, R. L., (2000). Dynamical tropical cyclone track forecasting errors. Part I: Tropical region error sources. Wea. Forecasting, 15, 641-661.
Carr, L. E., III, and Elsberry, R. L., (2001). Notes and Correspondence: Beta test of the systematic approach expert system prototype as a tropical cyclone track forecasting aid. Wea. Forecasting, 16, 355-368.
Chan, J. C. L., and Kwok, R. H. F., (1997). A diagnostic study on the improvement in tropical cyclone motion prediction by the UK Met. Office Global Model, Meteorol. Appl. 4. 1-9.
Cheng, C. M., Tsui, K. C., and Lee, B. Y., (2000). Landfalling Tropical Cyclones near Hong Kong in 1999 and 2000, WMO Regional Technical Conference on Tropical Cyclones and Storm Surges, Chiang Mai, Thailand, 13-17 November 2000
Cheung, K. K. W., (2001). A review of ensemble forecasting techniques with a focus on tropical cyclone forecasting. Meteorol. Appl. 8, 315-332.
Chu, J. H., (1994). A Regression Model for the Western North Pacific Tropical Cyclone Intensity Forecast. Naval Res. Lab., Monterey, CA. NRL/MR/7541--94-7215. 33 pp.
DeMaria, M., and Kaplan, J., (1994). A statistical hurricane intensity prediction system (SHIPS) for the Atlantic basin. Weather and Forecasting, 9, 209-220.
Dube, S. K., Sinha, P. C., Rao, A. D. and Rao, G. S., (1985). Numerical modeling of storm surges in the Arabian Sea. Appl. Math. Modeling, 9, 289-294.
Dvorak, V. F., (1975). Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420-430.
Fitzpatrick, P., (1997). Understanding and forecasting tropical cyclone intensity change with typhoon Intensity Prediction Scheme (TIPS). Weather and Forecasting, 12, 826-846.
Ghosh, S. K., (1977). Prediction of storm surges on the East Coast of India, Ind. Jourl. Met. Hyd. And Geophy, 29(2), 157-168.
Goerss, J. S., (2000). Tropical cyclone track forecasts using an ensemble of dynamical models. Mon. Wea. Rev., 128, 1187-1193.
Heming, J., (2000). Tropical cyclone forecasting the last decade. NWP Gazette, June, 4-5.
Houston, S. H., Shaffer, W. A., Powell, M. D., and Chen, J., (1999). Comparisons of HRD and SLOSH surface wind fields in hurricanes: Implications for storm surge forecasting. Wea. Forecasting, 14, 671-686.
Imaoka, K, and Spencer, R. W., (2000). Diurnal variation of precipitation over the tropical oceans observed by TRMM/TMI combined with SSM/I. J. Climat., 13, 41494158.
Jarvinen, B. R, and Neumann, C. J., (1979). Statistical forecasts of tropical cyclone intensity for the North Atlantic Basin. NOAA Technical Memorandum NWS NHC-10. 22 pp.
Jelesnianski, C. P., Chen J., and Shafer, W. A., (1992). SLOSH : Sea, lake, and overland surges from hurricanes. NOAA Technical report NWS 48.
JMA, (1998). Annual Report on Activities of the RSMC Tokyo Typhoon Center. Japan Meteorological Agency, 98 pp.
Kalsi, S. R., Roy Bhowmik, S. K., Gupta, D. C. and Nath, S. M., (2002). Development of super cyclone of Orissa and its impact, Proceedings of the national symposium TROPMET-2001, Indian Meteorological Society, Mumbai, India, February, 2001, 60-67.
Kidder, S. Q., Goldberg, M. D., Zehr, R. M., Demaria, M., Purdom, J. F. W., Velden, C. S., Grody, N. C. and Kusselson, S. J., (2000). Satellite analysis of tropical cyclones using the Advanced Microwave Sounding Unit (AMSU). Bull. Amer. Met. Soc., 81, 1241-1259.
Lai, E. S. T., (1999). TREC application in tropical cyclone observation. In ESCAP/WMO Typhoon Annual Review 1998, 135-139, Typhoon Committee Secretariat, Manila.
Lam, C.C., (2001). Performance of the ECMWF model in forecasting the tracks of tropical cyclones in the South China Sea and parts of the western North Pacific. Meteorological Applications, Vol. 8, 339-344.
Lam, C. Y., (2000). Tropical cyclone warning system in Hong Kong. Regional Technical Conference on Tropical Cyclones and Storm Surges, Chiang Mai, Thailand, 13-17 November, 2000.
Lam, C. Y., (2001). Tropical cyclone forecasting : Linkage between theory and practice, WMO Workshop on Typhoon Forecasting Research, Jeju, Korea 25-28 September 2001
Lam, H. K., (2001). The World Meteorological Organization Pilot Websites: Severe Weather Information Centre and World Weather Information Service, 34th Session of the ESCAP/WMO Typhoon Committee.
Lee, T. C., and Wong, M. S., (2002). The use of multiple-model ensemble techniques for tropical cyclone track forecast at the Hong Kong Observatory. To be presented at the WMO technical conference on data processing and forecasting systems, Cairns, Australia, 2-3 Dec. 2002.
Li, P. W., Wong, W. K., and Lai, E. S. T., (1999). Short-range forecast of heavy rain based on radar echo analysis. Presented at 3rd Int. Conf. on the Global Energy and water Cycle, June 1999, Beijing.
Mileti, D. S., and Sorenson, J. H., (1990). Communication of emergency public warnings A social science perspective and state-of-the-art assessment, Oak Ridge National Laboratory, ORNL-6609, Oak Ridge, Tennessee, USA.
Nagata, M., Leslie, L., Kurihara, Y., Elsberry, R. L., Yamasaki, M., Kamahori, H., Abbey Jr, R.., Bessho, K., Calvo, J., Chan, J. C. L., Clark, P., Desgagne, M., Hong, S. Y., Majewski, D., Malguzzi, P., McGregor, J. ,
Mino, H., Murata, A., Nachamkin, J., Roch, M., and Wilson, C., (2001). Third COMPARE Workshop: A model intercomparison experiment of tropical cyclone intensity and track prediction. Bulletin of the American Meteorological Society: Vol. 82, No. 9, 20072020.
Sakai, R., Mino, H., and Nagata, M., (2002). Verifications of tropical cyclone predictions of the new numerical models at JMA, Technical Review No.5, Japan Meteorological Agency, March 2002.
Tam, K. H., (1996). Tropical cyclone and storm surge risk assessment, Seminar on Meteorological and Hydrological Hazard Assessment, New Delhi, India, 16-19 March 1996.
Tuttle, J. D., and Gall, R. L., (1999). A single-radar technique for estimating the winds in tropical cyclones. Bull. Amer. Meteor. Soc., 80 , 653-668.
Watson, C., and Johnson, M. E. (1999), Proceedings of the 23rd Conference on Hurricanes and Tropical Meteorology, 10-15 Jan. 1999, Dallas, TX by the AMS, Boston MA.
World Meteorological Organization, (1989). Human response to tropical cyclone warnings and their content, Report No. TCP-11.
World Meteorological Organization, (1990). Tropical cyclone warning systems. WMO/TD-No. 394, Report No. TCP-26.
World Meteorological Organization, (1998), Tropical Meteorology research Programme report Series, Report No, 59, Topic chairman and rapporteur reports of the fourth WMO International Workshop on Tropical Cyclones (IWTC-IV), Haikou, China, 21-30 April, 1998.
World Meteorological Organization, (1999). Tropical cyclone-related NWP products and their guidance. WMO/TD-No. 966, Report No. TCP-41.
World Meteorological Organization, (2002). Draft Guidelines on improving public understanding of and response to warnings, compiled by Christine Alex, Linda Anderson-Berry and Jim Davidson (to be published).
Yeh, Hsi-Chyi., Chen, G. T. J., Liu, W. T. (2002). Kinematic characteristics of a Mei-yu front detected by the QuikSCAT Oceanic Winds. Mon. Wea. Rev., 130, 700711.
Zhang, Z., and Krishnamurti, T. N., (1997). Ensemble forecasting of hurricane tracks. Bull. Am. Meteorol. Soc., 78: 2785-2795.