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Seasonal Tropical Cyclone Forecasts by Suzana J Seasonal tropical cyclone forecasts by Suzana J. Camargo¹, Anthony G. Barnston1, Philip J. Klotzbach2 and Christopher W. Landsea3 Introduction Statistical and dynamical seasonal tropical cyclone Seasonal forecasts of tropical activity forecasts are proposed to be made available cyclone activity in various regions have been developed since the fi rst on a public Website for forecasters and other users. attempts in the early 1980s by Neville Nicholls (1979) for the Australian region and William Gray (1984(a), (b)) for the North Atlantic region. Over et al., 2006). These quadrennial seasonal tropical cyclone forecasts time, forecasts for different regions, workshops, co-sponsored by the has increased tremendously since using differing methodologies, have WMO Commission for Atmospheric they were fi rst produced, especially been developed. Tourism in various Science Tropical Meteorology after 2004, when 10 tropical cyclones regions, such as the US Gulf and Research Programme and the struck Japan and four hurricanes East Coasts and the Caribbean, World Weather Watch Tropical impacted Florida, USA. is impacted by these seasonal Cyclone Programme, bring together forecasts. Insurance and re-insurance tropical cyclone forecasters and Although landfall forecasts are companies also make use of seasonal researchers to review progress and particularly important to users, forecasts in their policy decisions. plan for future activities in topics landfall forecast skill is still limited. It is fundamental to provide these such as seasonal forecasts. During As seasonal tropical cyclone forecasts users with information about the IWTC-VI, forecasters from various improve, more attention will be given accuracy of seasonal forecasts. countries shared information about to particular details such as regional Seasonal forecasts have limited use seasonal tropical cyclone forecasts landfall probabilities. The use of for emergency managers, because of currently being issued by their such specifi c forecasts will become the lack of skill in predicting impacts respective countries—which was more widespread and signifi cant to at the city or county level. often information not well known decision-makers and residents in by other scientists present. coastal areas. As has been the case in some of the previous WMO International Forecasters in National Meteorological With the popularization of these Workshops on Tropical Cyclones and Hydrological Services are forecasts, it is fundamental that (ITWC), a review of the progress interested in seasonal forecasts their documentation and verifi cation on seasonal forecasts of tropical because they are frequently asked become widely available. It is cyclone activity was presented at questions by the media and various recommended that WMO develop the IWTC-VI in San José, Costa decision-makers. Interest from the guidelines for the development Rica, in November 2006 (Camargo media and the general public in and validation of these forecasts, similar to the protocol that has been developed for global seasonal climate 1 International Research Institute for Climate and Society, The Earth Institute at Columbia (temperature and precipitation) University, Palisades, New York, USA forecasts (WMO, 2001). A summary 2 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA of grops that issue tropical cyclone 3 NOAA National Hurricane Center, Miami, Florida, USA seasonal forecasts is given in Table I. WMO Bulletin 56 (4) - October 2007 | 297 Table I — Seasonal tropical cyclone forecasts: groups that issue the forecasts, regions in which the forecasts are issued, forecast type, Website where the forecast is available. Group Basins Type Website City University of Hong Kong, Western North Pacifi c Statistical http://aposf02.cityu.edu.hk China (CityU) Colorado State University, Atlantic Statistical http://hurricane.atmos.colostate.edu USA (CSU) Cuban Meteorological Atlantic Statistical http://www.met.inf.cu Institute (INSMET) European Centre for Medium- Atlantic Dynamical http://www.ecmwf.int Range Weather Forecasts Australian (collaborating agencies only) (ECMWF) Eastern North Pacifi c North Indian South Indian South Pacifi c Western North Pacifi c International Research Atlantic Dynamical http://iri.columbia.edu/forecast/tc_fcst/ Institute for Climate and Australia Society (IRI) Eastern North Pacifi c South Pacifi c Western North Pacifi c Macquarie University, Australia / southwest Statistical http://www.iges.org/ellfb/past.html Australia Pacifi c Meteorological Offi ce, United North Atlantic Dynamical http://www.metoffi ce.gov.uk/weather/ Kingdom (MetOffi ce) tropicalcyclone/northatlantic National Meteorological Eastern North Pacifi c Statistical http://smn.cna.gob.mx Service, Mexico (NSM) National Climate Centre, Western North Pacifi c Statistical http://bcc.cma.gov.cn China NOAA hurricane outlooks Atlantic Statistical http://www.cpc.noaa.gov Eastern North Pacifi c http://www.cpc.noaa.gov Central North Pacifi c http:// www.prh.noaa.gov/hnl/cphc Tropical Storm Risk (TSR) Atlantic Statistical http://tsr.mssl.ucl.ac.uk Western North Pacifi c Australian region Statistical seasonal of ENSO, when the QBO was in its Atlantic seasonal tropical cyclone hurricane forecasts west phase and Caribbean basin sea- forecasts relative to climatology and level pressures were below normal. persistence. Their analysis indicated Colorado State University Statistical forecast techniques for that for the analysed period (1984– North Atlantic tropical cyclones have 2001), both the basic statistical Initial seasonal predictions for the evolved since these early forecasts. forecasts and an adjusted version North Atlantic basin (Gray, 1984(a), Additional predictors were added to demonstrated skill over climatology (b)) were issued by Colorado State the original forecast scheme, the QBO and persistence, with the adjusted University in early June and early is not used as a predictor anymore forecasts being more skilful than the August, beginning in 1984, using and the seasonal forecasts started basic forecasts. statistical relationships between being issued in early December of tropical cyclone activity and El Niño/ the previous year. Klotzbach and Gray Figure 1 shows the skill of the CSU Southern Oscillation (ENSO), the (2004) and Klotzbach (2007) explain forecasts for various leads, using linear Quasi-Biennial Oscillation (QBO) and the current forecast scheme. correlation as a skill measure. The skill Caribbean basin sea-level pressures. improves tremendously in June and Comparatively, more tropical cyclones Owens and Landsea (2003) examined August, probably because the ENSO were predicted in the cool phase the skill of Gray’s operational spring barrier is over. Since the ENSO 298 | WMO Bulletin 56 (4) - October 2007 the number of tropical cyclones in the 1 Central North Pacifi c region based on NS NSD the ENSO state and the Pacifi c decadal 0.8 H oscillation. HD IH 0.6 IHD Tropical Storm Risk (TSR) NTC Tropical Storm Risk issues statistical 0.4 forecasts for tropical cyclone activity Correlation in the Atlantic, western North Pacifi c 0.2 and Australian regions. The seasonal prediction model uses ENSO forecasts 0 (Lloyd-Hughes et al., 2004) to predict the western North Pacifi c ACE index and is skilful in hindcast mode in that -0.2 region (Lea and Saunders, 2006). -0.4 In a recent paper (Saunders and Lea, December April June August 2005), TSR describes its new forecast Month model, issued in early August, for Figure 1 — Correlations of the CSU seasonal forecasts for different leads: December seasonal predictions of hurricane (1992– 2006), April (1995-2006), June (1984-2006 or 1990-2006) and August (1984-2006 landfall activity for the US coastline. or 1990-2006). The correlations are given for: number of named storms (NS), number The model uses July wind patterns of named storm days (NSD), number of hurricanes (H), number of hurricane days (HD), to predict the seasonal US ACE index number of intense hurricanes (IH), number of intense hurricane days (IHD) and net (effectively, the cumulative wind tropical cyclone activity (NTC). Signifi cant correlations at the 95% signifi cance level are: energy from all tropical cyclones June – NS, NSD, H, HD, IHD, NTC, August – NS, NSD, H, HD, IH and NTC. None of the which strike the USA). The July height- correlations is signifi cant for the December and April leads. averaged winds in these regions are indicative of atmospheric circulation patterns that either favour or hinder state is usually defi ned by June, the as deterministic and probabilistic, hurricanes from reaching US shores. hurricane forecasts made in June or using terciles. They are based on The model correctly anticipates later become more skilful. Another the state of ENSO (Gray, 1984(a)) whether US hurricane losses are reason for a higher skill in June and and the tropical multi-decadal mode above- or below-median in 74 per cent August is that the season is about to (e.g. Chelliah and Bell, 2004), which of the hindcasts for the 1950–2003 start or has already started. incorporates the leading modes of period. The model also performed tropical convective rainfall variability well in “real-time” operation in 2004 CSU started issuing forecasts of occurring on multi-decadal time and 2005, while over-predicting in landfall probabilities in August 1998. scales. Important aspects of this signal 2006. The landfall probabilities are based that are related to an active Atlantic upon a forecast of net tropical cyclone hurricane season include a strong City
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