This paper aims to describe and evaluate the proposed calibration model based on a neural
network for post-processing of two essential meteorological parameters, namely near …

The techniques for forecasting meteorological variables are highly studied since prior
knowledge of them allows for the efficient management of renewable energies, and also for …

In this paper major machine learning (ML) tools and the most important applications
developed elsewhere for numerical weather and climate modeling systems (NWCMS) are …

In this article, a machine‐learning‐based tool for calibrating numerical forecasts of near‐
surface air temperature is proposed. The study area covers Poland representing a …

We propose a forecasting tool for precipitation based on analogues of circulation defined
from 5‐day hindcasts and a stochastic weather generator that we call “HC–SWG.” In this …

Accurate temperature forecasts are critical for various industries and sectors. We propose a
probabilistic neural network (PNN), an extension of the distributional regression network …

Accurate daily rainfall predictions are becoming increasingly important, particularly in the
era of changing climate conditions. These predictions are essential for various sectors …

We propose a neural network approach to produce probabilistic weather forecasts from a
deterministic numerical weather prediction. Our approach is applied to operational surface …

Accurate wind data are crucial for successful search and rescue (SAR) operations on the
sea surface in maritime accidents, as survivors or debris tend to drift with the wind. As …

Abstract Surface Air Temperature (SAT) predictions, typically generated by Global Climate
Models (GCMs), carry uncertainties, particularly across different greenhouse gas emission …