scholarly journals The use of the Conway-Maxwell-Poisson in the seasonal forecasting of tropical cyclones

2021 ◽  
Author(s):  
Timothy D. Mitchell ◽  
Joanne Camp
Keyword(s):  
Tropical Cyclone ◽  
Poisson Process ◽  
Tropical Cyclones ◽  
Dispersion Characteristic ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Mean And Variance ◽  
The Mean ◽  
Over Dispersion ◽  
Tropical Cyclone Landfall

AbstractThe Conway-Maxwell-Poisson distribution improves the precision with which seasonal counts of tropical cyclones may be modelled. Conventionally the Poisson is used, which assumes that the formation and transit of tropical cyclones is the result of a Poisson process, such that their frequency distribution has equal mean and variance (‘equi-dispersion’). However, earlier studies of observed records have sometimes found over-dispersion, where the variance exceeds the mean, indicating that tropical cyclones are clustered in particular years. The evidence presented here demonstrates that at least some of this over-dispersion arises from observational inhomogeneities. Once this is removed, and particularly near the coasts, there is evidence for equi-dispersion or under-dispersion. In order to more accurately model numbers of tropical cyclones, we investigate the use of the Conway-Maxwell-Poisson as an alternative to the Poisson that represents any dispersion characteristic. An example is given for east China where using it improves the skill of a prototype seasonal forecast of tropical cyclone landfall.

scholarly journals Seasonal Forecasting of Tropical Cyclone Landfall Using ECMWF’s System 4

2019 ◽  
Vol 34 (5) ◽  
pp. 1239-1255 ◽  
Author(s):  
Dan L. Bergman ◽  
Linus Magnusson ◽  
Johan Nilsson ◽  
Frederic Vitart
Keyword(s):  
Tropical Cyclone ◽  
North American ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Forecast System ◽  
Forecast Period ◽  
The North ◽  
Hurricane Season ◽  
Tropical Cyclone Landfall

Abstract A method has been developed to forecast seasonal landfall risk using ensembles of cyclone tracks generated by ECMWF’s seasonal forecast system 4. The method has been applied to analyze and retrospectively forecast the landfall risk along the North American coast. The main result is that the method can be used to forecast landfall for some parts of the coast, but the skill is lower than for basinwide forecasts of activity. The rank correlations between forecasts issued on 1 May and observations are 0.6 for basinwide tropical cyclone number and 0.5 for landfall anywhere along the coast. When the forecast period is limited to the peak of the hurricane season, the landfall correlation increases to 0.6. Moreover, when the forecast issue date is pushed forward to 1 August, basinwide tropical cyclone and hurricane correlations increase to 0.7 and 0.8, respectively, whereas landfall correlations improve less. The quality of the forecasts is in line with that obtained by others.

Seasonal Forecasting of Tropical Cyclone Landfall over Mozambique

2003 ◽  
Vol 16 (23) ◽  
pp. 3932-3945 ◽  
Author(s):  
Frédéric Vitart ◽  
David Anderson ◽  
Tim Stockdale
Keyword(s):  
Tropical Cyclone ◽  
Seasonal Forecasting ◽  
Tropical Cyclone Landfall

scholarly journals Probabilistic Prediction of Tropical Cyclones. Part I: Position

2005 ◽  
Vol 133 (7) ◽  
pp. 1840-1852 ◽  
Author(s):  
Harry C. Weber
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Numerical Models ◽  
Probabilistic Prediction ◽  
Automatic Production ◽  
Position Errors ◽  
Prediction Time ◽  
Forecasting System ◽  
The Mean ◽  
Geographical Maps

Abstract A new objective aid for operational prediction of the positions of tropical cyclones is presented. Its method is based on a simple analysis of the performance of all operationally available numerical models during a training period. In a subsequent forecast period, the results of this analysis are used to produce both high-quality deterministic (as by-product) and probabilistic storm position forecasts in the form of geographical maps of strike probability for all prediction times out to 120 h from a given base date and time. The model was developed using operationally available position predictions of all global tropical-cyclone events of the years 2000, 2001, and 2002 as provided in the U.S. Navy’s Automated Tropical Cyclone Forecasting System (ATCF). Forecasts have been carried out for the years 2001 and 2002, with corresponding training periods 2000 and 2001. The global annual mean deterministic position errors for the years 2001 (2002) were 148 (136), 266 (235), 393 (329), 541 (435), and 733 (554) km at 24-, 48-, 72-, 96-, and 120-h prediction times, respectively. The deterministic mean errors and their corresponding standard deviations were found to be lower than those of most statistical and dynamical operational models and approximately equal to those of all consensus approaches currently in operational use. The main feature of the new method is the automatic production of geographical strike probability maps. For all tropical-cyclone events during the year 2001 (2002), the mean annual diameters of, for example, the 66% strike probability regions (i.e., the regions inside which future storm positions can be expected with a probability of 66%) at 24-, 48-, 72-, 96-, and 120-h prediction time were found to be 290 (280), 490 (480), 710 (660), 1020 (780), and 1310 (1020) km, respectively. At all prediction times, the predicted sizes of areas of given strike probability represent conservative estimates in that the observed percentages of storm positions inside these areas are larger than the corresponding expected percentages.

The Science of William M. Gray: His Contributions to the Knowledge of Tropical Meteorology and Tropical Cyclones

2017 ◽  
Vol 98 (11) ◽  
pp. 2311-2336 ◽  
Author(s):  
Philip J. Klotzbach ◽  
Johnny C. L. Chan ◽  
Patrick J. Fitzpatrick ◽  
William M. Frank ◽  
Christopher W. Landsea ◽  
...  
Keyword(s):  
Climate Change ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Conceptual Development ◽  
Tropical Meteorology ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Theoretical Concepts ◽  
Flight Data ◽  
Scientific Legacy

Abstract Advances in knowledge in tropical meteorological research are discussed in the context of contributions made by Professor William M. Gray. Gray pioneered the compositing approach to observational tropical meteorology through assembling of global radiosonde datasets and tropical cyclone research flight data. In the 1970s, he made fundamental contributions to knowledge of convective–larger-scale interactions. Throughout his career, he wrote seminal papers on tropical cyclone structure, cyclogenesis, motion, and seasonal forecasts. His conceptual development of a seasonal genesis parameter also laid an important framework for both seasonal forecasting as well as climate change studies on tropical cyclones. His work was a blend of both observationally based studies and the development of theoretical concepts. This paper reviews the progress in knowledge in the areas where Dr. Gray provided his largest contributions and describes the scientific legacy of Gray’s contributions to tropical meteorology.

Comparison of Local and Basinwide Methods for Risk Assessment of Tropical Cyclone Landfall

2008 ◽  
Vol 47 (2) ◽  
pp. 361-367 ◽  
Author(s):  
Timothy M. Hall ◽  
Stephen Jewson
Keyword(s):  
Risk Assessment ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Sampling Error ◽  
Local Model ◽  
Coastal Regions ◽  
The North ◽  
Out Of Sample ◽  
Scoring Schemes ◽  
Tropical Cyclone Landfall

Abstract Two statistical methods for predicting the number of tropical cyclones (TCs) making landfall on sections of the North American coastline are compared. The first method—the “local model”—is derived exclusively from historical landfalls on the particular coastline section. The second method—the “track model”—involves statistical modeling of TC tracks from genesis to lysis, and is based on historical observations of such tracks. Identical scoring schemes are used for each model, derived from the out-of-sample likelihood of a Bayesian analysis of the Poisson landfall number distribution. The track model makes better landfall rate predictions on most coastal regions, when coastline sections at a scale of several hundred kilometers or smaller are considered. The reduction in sampling error due to the use of the much larger dataset more than offsets any bias in the track model. When larger coast sections are considered, there are more historical landfalls, and the local model scores better. This is the first clear justification for the use of track models for the assessment of TC landfall risk on regional and smaller scales.

scholarly journals Pacific subsurface ocean temperature as a long-range predictor of South China tropical cyclone landfall

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Nathan Sparks ◽  
Ralf Toumi
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
South China ◽  
Southern Oscillation ◽  
Warm Pool ◽  
Ocean Temperature ◽  
Seasonal Forecasts ◽  
Subsurface Ocean ◽  
Landfalling Tropical Cyclones ◽  
Tropical Cyclone Landfall

Abstract Seasonal forecasts of the tropical cyclones which frequently make landfall along the densely populated South China coast are highly desirable. Here, we analyse observations of landfalling tropical cyclones in South China and of subsurface ocean temperatures in the Pacific warm pool region, and identify the possibility of forecasts of South China tropical cyclone landfall a year ahead. Specifically, we define a subsurface temperature index, subNiño4, and build a predictive model based on subNiño4 anomalies with a robust double cross-validated forecast skill against climatology of 23%, similar in skill to existing forecasts issued much later in the spring. We suggest that subNiño4 ocean temperatures precede the surface El Niño/Southern Oscillation state by about 12 months, and that the zonal shifts in atmospheric heating then change mid-level winds to steer tropical cyclones towards landfall in South China. We note that regional subsurface ocean temperature anomalies may permit atmospheric predictions in other locations at a longer range than is currently thought possible.

scholarly journals Seasonal Tropical Cyclone Rain Volumes over Australia

2013 ◽  
Vol 26 (16) ◽  
pp. 5958-5964 ◽  
Author(s):  
Richard A. Dare
Keyword(s):  
Tropical Cyclone ◽  
Interannual Variability ◽  
Tropical Cyclones ◽  
Correlation Coefficients ◽  
Rainfall Data ◽  
Valuable Resource ◽  
Land Area ◽  
The Mean ◽  
Two Parameters

Abstract For a continent as dry as Australia, where water is a valuable resource, it is important to understand the sources of rainfall. The volume of water contributed by tropical cyclones (TCs) during the November–April season is investigated using 42 seasons of TC and rainfall data. The seasonal total TC rain volume (SRV) ranges from a minimum of 34.2 km3 in 1987/88 to a maximum of 564.4 km3 in 2000/01, with a long-term mean of 198.6 ± 107.4 km3. In terms of mean percentage, TCs contribute 7.6% to the seasonal total rain volume over Australia. The number of landfalling TCs and the number of TCs that individually produce more than the mean individual TC rain volume (25.7 km3) during a season are significant influences on the SRV. The TCs passing near the coast without landfalling have little impact on the SRV. The two parameters that correlate best with SRV are the total time spent over land by TCs during a season and the total land area covered by TCs during a season (correlation coefficients of 0.79 and 0.84, respectively). Although the highest SRVs occur almost exclusively during La Niña and neutral seasons, there is a mixture of ENSO seasons corresponding to the lowest SRVs. In general, the large interannual variability, even within a particular ENSO class, indicates that caution should be used when attempting to relate SRV to the phase of ENSO.

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