Statistical forecast of the marine surge     

Quintana  Gonzalo Iñaki  Tandeo  Pierre  Drumetz  Lucas  Leballeur  Laurent  Pavec  Marc 《Natural Hazards》2021,108(3):2905-2917

Natural Hazards - This paper studies different machine learning methods for solving the regression problem of estimating the marine surge value given meteorological data. The marine surge is...  相似文献   

Linear Gaussian state-space model with irregular sampling: application to sea surface temperature   总被引：1，自引：1，他引：0  

Pierre Tandeo  Pierre Ailliot  Emmanuelle Autret 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(6):793-804

Satellites provide important information on many meteorological and oceanographic variables. State-space models are commonly used to analyse such data sets with measurement errors. In this work, we propose to extend the usual linear and Gaussian state-space to analyse time series with irregular time sampling, such as the one obtained when keeping all the satellite observations available at some specific location. We discuss the parameter estimation using a method of moment and the method of maximum likelihood. Simulation results indicate that the method of moment leads to a computationally efficient and numerically robust estimation procedure suitable for initializing the Expectation–Maximisation algorithm, which is combined with a standard numerical optimization procedure to maximize the likelihood function. The model is validated on sea surface temperature (SST) data from a particular satellite. The results indicate that the proposed methodology can be used to reconstruct realistic SST time series at a specific location and also give useful information on the quality of satellite measurement and the dynamics of the SST.  相似文献