This study presents a hybrid approach that integrates seasonal-trend decomposition and
machine learning (termed STL-ML) for predicting the rainfall time series one step ahead …

Kilometer-scale modeling of global atmosphere dynamics enables fine-grained weather
forecasting and decreases the risk of disastrous weather and climate activity. Therefore …

The growth of air transport demand expected over the next decades, along with the
increasing frequency and intensity of extreme weather events, such as heavy rainfalls and …

Soil moisture (SM) datasets at high spatial resolutions are beneficial for a wide range of
applications, such as monitoring and prediction of hydrological extremes, numerical weather …

High-Performance Big Data Analytics (HPDA) applications are characterized by huge
volumes of distributed and heterogeneous data that require efficient computation for …

The current study attempted to evaluate the impact of Soil Moisture (SM) initial conditions in
a regional NWP system on the simulation of land falling cyclones over North Indian Ocean …

The impact of assimilating GNSS-ZTD (global navigation satellite system–zenith total delay)
on the precipitable water vapor and precipitation forecast over Italy is studied for the month …

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of
convective events and has been widely applied with this purpose in the past two decades …

Two and half years of Interferometric Synthetic Aperture Radar (InSAR) images obtained by
Sentinel‐1 near Santa Cruz de la Sierra, Bolivia, before August 2019 are here analyzed and …

A synthetic aperture radar (SAR) has the capability to observe ocean surface winds with a
high spatial resolution, even under extreme conditions. The purpose of this work was to …