Objective Estimation of the 24-h Probability of Tropical Cyclone Formation

Abstract

A new product for estimating the 24-h probability of TC formation in individual 58 3 58 subregions of the North Atlantic, eastern North Pacific, and western North Pacific tropical basins is developed. This product uses environmental and convective parameters computed from best-track tropical cyclone (TC) positions, National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) analysis fields, and water vapor (;6.7 mm wavelength) imagery from multiple geostationary satellite platforms. The parameters are used in a two-step algorithm applied to the developmental dataset. First, a screening step removes all data points with environmental conditions highly unfavorable to TC formation. Then, a linear discriminant analysis (LDA) is applied to the screened dataset. A probabilistic prediction scheme for TC formation is developed from the results of the LDA.Coefficients computed by the LDA show that the largest contributors to TC formation probability are climatology, 850-hPa circulation, and distance to an existing TC. The product was evaluated by its Brier and relative operating characteristic skill scores and reliability diagrams. These measures show that the algorithmgenerated probabilistic forecasts are skillful with respect to climatology, and that there is relatively good agreement between forecast probabilities and observed frequencies. As such, this prediction scheme has been implemented as an operational product called the National Environmental Satellite, Data, and Information Services (NESDIS) Tropical Cyclone Formation Probability (TCFP) product. The TCFP product updates every 6 h and displays plots of TC formation probability and input parameter values on its Web site. At present, the TCFP provides real-time, objective TC formation guidance used by tropical cyclone forecast offices in the Atlantic, eastern Pacific, and western Pacific basins.

An Objective Satellite-Based Tropical Cyclone Size Climatology

“…Indirect evidence that similar analysis-derived diagnostics do a good job in estimating the TC environment come from the success of a number of statistical TC applications that have been trained using such diagnostics. A few examples of such applications include the statistical hurricane intensity prediction scheme (DeMaria et al 2005), the logistic growth equation model (DeMaria 2009), the rapid intensification index (Kaplan et al 2010), the tropical cyclone formation product (Schumacher et al 2009), and tropical cyclone wind-pressure relationships . For our study we use the NCEP reanalyses and operational GFS analyses and accept that these have known shortcomings 1 and that the type and quality of data used in data assimilation has changed over time.…”

Tropical cyclogenesis in a tropical wave critical layer: easterly waves

“…Although some theoreticians have expressed acceptance of the "moisture supply" theory, the newer results of Bender and collaborators are consistent with our reasoning. Two reasons underlying this "common sense" viewpoint are (i) it is likely for physical and statistical reasons that if more storms are generated, the number of intense storms will increase -"more eggs" once fertilized "imply more chicks, implying more chickens" given enough food -and (ii) regions of genesis and highest intensity are (for the most part) well separated spatially, thanks to the zonal motion of storms, restricting the ability of intense storms to inhibit new storms in their wake to distances of less than 1000 km (Schumacher et al, 2009). In this respect, storms of the real world differ from those of a horizontally periodic geometry, in which the inhibition can be significant.…”

Tropical Cyclone–like Vortices Detection in the NCEP 16-Day Ensemble System over the Western North Pacific in 2008: Application and Forecast Evaluation

“…Nevertheless, some indication is given that the NCEP GEFS may be simulating the general state of the atmospheric environment at long lead times that is favorable for the stronger typhoon developments. Therefore, statistical approaches that use environmental parameters to predict the TC formation or estimate the formation probability (e.g., DeMaria et al 2001;Hennon and Hobgood 2003;Schumacher et al 2009) may be extended by inserting the long-lead NWP model outputs to provide alternate objective guidance for typhoon formation and activity forecasts.…”