A deep learning approach for spatial error correction of numerical seasonal weather prediction simulation data

Abstract

ABSTRACT Numerical Weather Prediction (NWP) simulations produce meteorological data in various spatial and temporal scales, depending on the application requirements. In the current study, a deep learning approach, based on convolutional autoencoders, is explored to effectively correct the error of the NWP simulation. An undercomplete convolutional autoencoder (CAE) is applied as part of the dynamic error correction of NWP data. This work is an attempt to improve the seasonal forecast (3–6 months ahead) data accuracy for Greece using a global reanalysis dataset (that incorporates observations, satellite imaging, etc.) of higher spatial resolution. More specifically, the publically available Meteo France Seasonal (Copernicus platform) and the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) (NOAA) datasets are utilized. In addition, external information is used as evidence transfer, concerning the time conditions (month, day, and season) and the simulation characteristics (initialization of simulation). It is found that convolutional autoencoders help to improve the resolution of the seasonal data and successfully reduce the error of the NWP data for 6-months ahead forecasting. Interestingly, the month evidence yields the best agreement indicating a seasonal dependence of the performance.