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986 MONTHLY WEATHER REVIEW VOLUME II5Development and Testing of Statistical Tropical Cyclone Forecasting Techniques for the Southern Hemisphere THOMAS D. K.EENANBureau of Meteorology Research Centre, Me/bourne, Australia MICHAEL FIORINO*Naval Environmental Prediction Research Facility, Monterey, CA 93943(Manuscript received 3 June 1986, in final form 15
986 MONTHLY WEATHER REVIEW VOLUME II5Development and Testing of Statistical Tropical Cyclone Forecasting Techniques for the Southern Hemisphere THOMAS D. K.EENANBureau of Meteorology Research Centre, Me/bourne, Australia MICHAEL FIORINO*Naval Environmental Prediction Research Facility, Monterey, CA 93943(Manuscript received 3 June 1986, in final form 15
datasets are available during which model formulations have not been changing. The current state of rapid model development cycles makes it difficult to maintain stable regression relationships between model forecasted fields and observations, especially for the diagnosis of infrequently occurring events (e.g., LIFR conditions). Machine learning (e.g., Rasp and Lerch 2018 ) and analog ensemble (e.g., Delle Monache et al. 2011 ) techniques have also been shown to be effective in developing
datasets are available during which model formulations have not been changing. The current state of rapid model development cycles makes it difficult to maintain stable regression relationships between model forecasted fields and observations, especially for the diagnosis of infrequently occurring events (e.g., LIFR conditions). Machine learning (e.g., Rasp and Lerch 2018 ) and analog ensemble (e.g., Delle Monache et al. 2011 ) techniques have also been shown to be effective in developing
must be recomputed to derive a new empirical relationship between the predictands and predictors. Another significant advancement in seasonal climate forecasts over the past few decades was the use of compositing multiple GCM forecast techniques to obtain the multimodel ensemble (MME) forecast ( Krishnamurti et al. 1999 ; Palmer and Shukla 2000 ). The MME technique provides an effective way to handle any uncertainties among the GCMs. Combining the MME and downscaling have proven to have further
must be recomputed to derive a new empirical relationship between the predictands and predictors. Another significant advancement in seasonal climate forecasts over the past few decades was the use of compositing multiple GCM forecast techniques to obtain the multimodel ensemble (MME) forecast ( Krishnamurti et al. 1999 ; Palmer and Shukla 2000 ). The MME technique provides an effective way to handle any uncertainties among the GCMs. Combining the MME and downscaling have proven to have further
: Strategies for assessing skill and significance of screening regression models with emphasis on Monte Carlo techniques. J. Climate Appl. Meteor. , 23 , 1454 – 1458 . Lawley , N. D. , 1956 : Tests of significance for the latent roots of covariance and correlation matrices. Biometrika , 43 , 128 – 136 . Michaelson , J. , 1987 : Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor. , 26 , 1589 – 1600 . Montgomery , R. B. , 1940 : Report on the work of G. T
: Strategies for assessing skill and significance of screening regression models with emphasis on Monte Carlo techniques. J. Climate Appl. Meteor. , 23 , 1454 – 1458 . Lawley , N. D. , 1956 : Tests of significance for the latent roots of covariance and correlation matrices. Biometrika , 43 , 128 – 136 . Michaelson , J. , 1987 : Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor. , 26 , 1589 – 1600 . Montgomery , R. B. , 1940 : Report on the work of G. T
1998 ; Zhu et al. 2002 ). With this additional information, the quality of forecasts can be significantly enhanced. Initially, the use of ensemble techniques focused on random samples (Monte Carlo forecasting) as a description of the probability distribution of initial states ( Epstein 1969 ; Leith 1974 ). However, the atmosphere is an extremely complex system, which has a very high phase-space dimension; that is, the number of random samples must be sufficiently large. Consequently, the cost of
1998 ; Zhu et al. 2002 ). With this additional information, the quality of forecasts can be significantly enhanced. Initially, the use of ensemble techniques focused on random samples (Monte Carlo forecasting) as a description of the probability distribution of initial states ( Epstein 1969 ; Leith 1974 ). However, the atmosphere is an extremely complex system, which has a very high phase-space dimension; that is, the number of random samples must be sufficiently large. Consequently, the cost of
1998 ; Grimit and Mass 2002 ), ensemble forecasts tend to be biased, and typically they are underdispersed ( Hamill and Colucci 1997 ), in that the ensemble spread is too small to be realistic. Furthermore, differing spatial resolutions of the forecast grid and the observation network may need to be reconciled. To address these shortcomings, various techniques for the statistical postprocessing of ensemble model output have been developed ( Wilks and Hamill 2007 ), including ensemble model output
1998 ; Grimit and Mass 2002 ), ensemble forecasts tend to be biased, and typically they are underdispersed ( Hamill and Colucci 1997 ), in that the ensemble spread is too small to be realistic. Furthermore, differing spatial resolutions of the forecast grid and the observation network may need to be reconciled. To address these shortcomings, various techniques for the statistical postprocessing of ensemble model output have been developed ( Wilks and Hamill 2007 ), including ensemble model output
QBOLAM 24-h accumulated precipitation forecasts were compared with equivalent forecasts produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). Since these compared models have significantly different grid-box sizes, it was necessary to verify precipitation forecasts on a common grid. QBOLAM and LAMBO precipitation forecasts were remapped onto a regular 0.5°-spaced ECMWF grid using a remapping technique that conserves, to a desired degree of accuracy, the total forecast
QBOLAM 24-h accumulated precipitation forecasts were compared with equivalent forecasts produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). Since these compared models have significantly different grid-box sizes, it was necessary to verify precipitation forecasts on a common grid. QBOLAM and LAMBO precipitation forecasts were remapped onto a regular 0.5°-spaced ECMWF grid using a remapping technique that conserves, to a desired degree of accuracy, the total forecast
economic losses and casualties ( Hsu and Wei 2007 ). Therefore, a useful scheme for quantitative precipitation forecast (QPF) during typhoon periods is highly desired ( Chang et al. 1993 ; Lee et al. 2006 ; Wei and Hsu 2008a ). In Taiwan, Wang et al. (1986) first developed a technique using the climatology average method (a simple statistical approach developed from the spatial distribution of typhoon center) to forecast typhoon rainfalls over land in Taiwan. This method was adopted to be one of
economic losses and casualties ( Hsu and Wei 2007 ). Therefore, a useful scheme for quantitative precipitation forecast (QPF) during typhoon periods is highly desired ( Chang et al. 1993 ; Lee et al. 2006 ; Wei and Hsu 2008a ). In Taiwan, Wang et al. (1986) first developed a technique using the climatology average method (a simple statistical approach developed from the spatial distribution of typhoon center) to forecast typhoon rainfalls over land in Taiwan. This method was adopted to be one of
). Much of the increase in forecast skill can be attributed to improvements in model physics, increases in model resolution, and the availability of aircraft and satellite observations (e.g., Aberson 2010 ), as well as new data assimilation techniques (e.g., Hamill et al. 2011 ). For the infamous Atlantic-based Hurricane Sandy (2012), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model accurately predicted its landfall location one week ahead of time ( Bassill 2014
). Much of the increase in forecast skill can be attributed to improvements in model physics, increases in model resolution, and the availability of aircraft and satellite observations (e.g., Aberson 2010 ), as well as new data assimilation techniques (e.g., Hamill et al. 2011 ). For the infamous Atlantic-based Hurricane Sandy (2012), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model accurately predicted its landfall location one week ahead of time ( Bassill 2014
. This present paper attempts to put more emphasis on the false alarm aspect by defining a credibility factor. This study is aimed at creating a practical technique for the prediction of significant blowing snow and the associated visibility reductions through available observational data. From an operational forecasting perspective, the definition of “significant” blowing snow varies with the application. For instance, a public weather forecast is mainly concerned with visibility less than 1 km
. This present paper attempts to put more emphasis on the false alarm aspect by defining a credibility factor. This study is aimed at creating a practical technique for the prediction of significant blowing snow and the associated visibility reductions through available observational data. From an operational forecasting perspective, the definition of “significant” blowing snow varies with the application. For instance, a public weather forecast is mainly concerned with visibility less than 1 km
