Abstract: Methods for finding (i) the parameter var(?2), representing the variance of a prior historical distribution ?C (?2) of hidden error variances ?2; (ii) the parameter “a” defining the rate of change of the mean ensemble variance response to changes in true error variance; (iii) the parameter ?min2 representing a prior historical minimum of true error variance; (iv) the parameter k?1, representing the relative variance of the stochastic component of variance prediction error; and (v) the parameter M, representing the effective ensemble size.

Abstract: A method for predicting the risk of a pirate attack in a geographical area and for generating a distribution of probabilities of a pirate attack based on intelligence (INTEL) information regarding the pirates and pirate behavior combined with information regarding environmental and meteorological (METOC) conditions and information regarding shipping activity and shipping vulnerabilities. The method can also be used to predict the probability of other activities that are subject to METOC conditions, such as anti-narcotics efforts, undersea warfare operations, mine warfare operations, and human trafficking interdiction.

Abstract: A computer-implemented method to provide stabilized and spatially smooth regression coefficients for weather forecast error correction from small training data sets. In accordance with the present invention, an MOS estimate of the regression coefficient calculated from a small data set can be optimally combined with a smooth prior estimate of the regression coefficient, an estimate of the spatial error covariance of that prior estimate, and an estimate of the spatial error covariance of the MOS estimate. The result is a filtered MOS (FMOS) regression coefficient which can be used to more accurately estimate and correct errors in weather forecasts even using only small data sets.

Abstract: Methods for finding (i) the parameter var(?2), representing the variance of a prior historical distribution ?C (?2) of hidden error variances ?2; (ii) the parameter “a” defining the rate of change of the mean ensemble variance response to changes in true error variance; (iii) the parameter ?min2 representing a prior historical minimum of true error variance; (iv) the parameter k?1, representing the relative variance of the stochastic component of variance prediction error; and (v) the parameter M, representing the effective ensemble size.

Abstract: Methods for finding (i) the parameter var(?2), representing the variance of a prior historical distribution ?C(?2) of hidden error variances ?2; (ii) the parameter “a” defining the rate of change of the mean ensemble variance response to changes in true error variance; (iii) the parameter ?2min representing a prior historical minimum of true error variance; (iv) the parameter k?1, representing the relative variance of the stochastic component of variance prediction error; and (v) the parameter M, representing the effective ensemble size.

Abstract: A method for predicting the risk of a pirate attack in a geographical area and for generating a distribution of probabilities of a pirate attack based on intelligence (INTEL) information regarding the pirates and pirate behavior combined with information regarding environmental and meteorological (METOC) conditions and information regarding shipping activity and shipping vulnerabilities. The method can also be used to predict the probability of other activities that are subject to METOC conditions, such as anti-narcotics efforts, undersea warfare operations, mine warfare operations, and human trafficking interdiction.

Abstract: A computer-implemented method to provide stabilized and spatially smooth regression coefficients for weather forecast error correction from small training data sets. In accordance with the present invention, an MOS estimate of the regression coefficient calculated from a small data set can be optimally combined with a smooth prior estimate of the regression coefficient, an estimate of the spatial error covariance of that prior estimate, and an estimate of the spatial error covariance of the MOS estimate. The result is a filtered MOS (FMOS) regression coefficient which can be used to more accurately estimate and correct errors in weather forecasts even using only small data sets.