Bita Rabbani, Hossein Chiti, Faranak Sharifi, Saeedeh Mazloomzadeh
Effect of lifestyle modification for two years on obesity and metabolic syndrome components in elementary students: A community- based trial
Caspian journal of internal medicine
predicting rain largescale environmental variables remains challenging problem climate models unclear well numerical methods predict true characteristics rainfall without smaller storm scale information study explores ability three statistical machine learning methods predict 3hourly rain occurrence intensity 05 resolution tropical pacific ocean using rain observations global precipitation measurement gpm satellite radar largescale environmental profiles temperature moisture merra2 reanalysis also separated rain different types deep convective stratiform shallow convective varying kinematic thermodynamic structures might respond largescale environment different ways expectation popular machine learning methods ie neural network random forest would outperform standard statistical method generalized linear model flexible structures especially predicting highly skewed distribution rain rates rain type however none methods obviously distinguish one another method still issues predicting rain often fully capturing high end rain rate distributions common problems climate models one implication study machine learning tools must carefully assessed necessarily applicable solving big data problems another implication traditional climate model approaches sufficient predict extreme rain events avenues need pursued