Abstract: A method for automated location dependent probabilistic tropical cyclone forecast. A plurality of new data records representative of alternative tracks are generated based on historical tracks by a first MonteCarlo-module. Points of the new data records are generated from points along the historical track by a dependent sampling process, whereas an intensity climatology is generated, based upon intensity data associated with at least some of the plurality of points along the historical tracks located within a certain grid cell. New intensity data are generated by a second MonteCarlo-module, from the intensity data associated with at least some of the plurality of points along the historical tracks by a MonteCarlo sampling process.

Abstract: A computer method of generating a probabilistic dataset relating to a weather event includes inputting data representative of an historical weather event, and generating data representative of a plurality of alternative events. The data for the alternative events are generated, in certain embodiments, by a dependent sampling process. In particular embodiments, the dependent sampling process is a directed random walk process. Data for the alternative events may include data relating to the geographical positions or “tracks” of the event, data relating to atmospheric pressures, or both.

Abstract: A method for automated location dependent probabilistic tropical cyclone forecast. A plurality of new data records representative of alternative tracks are generated based on historical tracks by a first MonteCarlo-module. Points of the new data records are generated from points along the historical track by a dependent sampling process, whereas an intensity climatology is generated, based upon intensity data associated with at least some of the plurality of points along the historical tracks located within a certain grid cell. New intensity data are generated by a second MonteCarlo-module, from the intensity data associated with at least some of the plurality of points along the historical tracks by a MonteCarlo sampling process.

Abstract: A computer method of generating a probabilistic dataset relating to a weather event includes inputting data representative of an historical weather event, and generating data representative of a plurality of alternative events. The data for the alternative events are generated, in certain embodiments, by a dependent sampling process. In particular embodiments, the dependent sampling process is a directed random walk process. Data for the alternative events may include data relating to the geographical positions or “tracks” of the event, data relating to atmospheric pressures, or both.

Abstract: A computer method of generating a probabilistic dataset relating to a weather event includes inputting data representative of an historical weather event, and generating data representative of a plurality of alternative events. The data for the alternative events are generated, in certain embodiments, by a dependent sampling process. In particular embodiments, the dependent sampling process is a directed random walk process. Data for the alternative events may include data relating to the geographical positions or “tracks” of the event, data relating to atmospheric pressures, or both.

Abstract: A computer method of generating a probabilistic dataset relating to a weather event includes inputting data representative of an historical weather event, and generating data representative of a plurality of alternative events. The data for the alternative events are generated, in certain embodiments, by a dependent sampling process. In particular embodiments, the dependent sampling process is a directed random walk process. Data for the alternative events may include data relating to the geographical positions or “tracks” of the event, data relating to atmospheric pressures, or both.