Over the past few years, the rapid development of machine learning (ML) models for
weather forecasting has led to state-of-the-art ML models that have superior performance …

In numerical weather prediction, data assimilation of atmospheric observations traditionally
relies on variational and Kalman filter methods. Here, we propose an alternative full neural …

Numerical weather prediction is an established weather forecasting technique in which
equations describing wind, temperature, pressure and humidity are solved using the current …

The unprecedented progress in ensemble hydro‐meteorological modelling and forecasting
on a range of temporal and spatial scales, raises a variety of new challenges which formed …

Abstract Numerical Weather Prediction (NWP) models are extensively utilized worldwide
and have played a pivotal role in weather forecasting. Precipitation is subject to various …

Abstract Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF;
EC for short) can provide a basis for the establishment of maritime-disaster warning systems …

Over the past 10 years, a paradigm shift has happened in the world of science and
information technology. Open science is becoming the de facto standard, as underlined by …

Data assimilation of atmospheric observations traditionally relies on variational and Kalman
filter methods. Here, an alternative neural network data assimilation (NNDA) with variational …

The article presents a study on the impact of the domain nesting method on the results of
simulated solar conditions using the mesoscale Weather Research and Forecasting model …

Statistical post-processing of systematic errors is required for numerical weather predictions
to obtain accurate and credible forecasts. Traditionally, this is accomplished separately with …