S2s Forecasting The novelty of this study lies in developing an ensemble dl framework that combines six distinct spatiotemporal architectures via bma to bridge the s2s scale fd forecasting gap, followed by the application of interpretability techniques to elucidate underlying drought inducing mechanisms. We comprehensively examined the prediction skill of nine global s2s prediction models for precipitation and dry and wet extremes over india during the summer monsoon season (june to september).
S2s Forecasting We assess the relative contributions of land, atmosphere, and oceanic initializations to the forecast skill of root zone soil moisture (sm) utilizing the community earth system model version 2. Flash droughts are becoming increasingly frequent, significantly impacting food and water security. here, we investigate postprocessing of ensembles from s2s forecast with several deep learning architectures to enhance the prediction of soil moisture, a key factor for drought identification. Given the large uncertainty in s2s atmospheric forecasts, and the potential for errors in meteorological inputs to have nonlinear impacts on hydrological simulations, skillful downscaling of a global forecast system could be very important when forecasting drought indices. Recognize functionality of nasa’s sub seasonal to seasonal (s2s) forecast system and data. assess evolving drought conditions using given s2s temperature and precipitation prediction data for a region of interest.
S2s Forecasting Given the large uncertainty in s2s atmospheric forecasts, and the potential for errors in meteorological inputs to have nonlinear impacts on hydrological simulations, skillful downscaling of a global forecast system could be very important when forecasting drought indices. Recognize functionality of nasa’s sub seasonal to seasonal (s2s) forecast system and data. assess evolving drought conditions using given s2s temperature and precipitation prediction data for a region of interest. Here, we present various data‐driven deep learning (dl) frameworks designed to bridge this s2s fd forecasting gap and uncover underlying drought‐inducing mechanisms via interpretability. To address this gap, here we present the first global community effort at summarizing relevant applications of s2s forecasts to guide further decision making and support the continued development of s2s forecasts and related services. Thus, here, we innovatively integrate the model based soil moisture predictions from a sub seasonal to seasonal (s2s) model into a data driven stacked deep learning model to construct a hybrid ssm and rzsm forecasting framework. Here, we present various data driven deep learning (dl) frameworks designed to bridge this s2s fd forecasting gap and uncover underlying drought inducing mechanisms via interpretability.
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