Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

This work proposes and evaluates methods for extending the forecasting horizon of all-sky
imager (ASI)-based solar radiation nowcasts and estimating the uncertainty of these …

The demand for accurate solar irradiance nowcast increases together with the rapidly
growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly …

In recent years, convolutional neural networks (CNNs) have achieved competitive
performance in the field of ground-based cloud image (GCI) classification. Proposed CNN …

This article proposes a two-branch convolutional neural network model (TCIENet) to
estimate the intensity of tropical cyclone (TC) from infrared and water vapor images in the …

Deep learning techniques, especially convolutional neural networks (CNNs), have
dramatically boosted the performance of statistical downscaling. In this study, we propose a …

The study of cloud types is critical for understanding atmospheric motions and climate
predictions; for example, accurately classified cloud products help improve meteorological …

Due to its remote location and extreme weather conditions, atmospheric in situ
measurements are rare in the Southern Ocean. As a result, aerosol-cloud interactions in this …

Clouds are structures formed by ice crystals, water grains, or both that come together in the
atmosphere for various reasons. Clouds have a direct impact on areas such as climate …

High shares of variable energy sources such as photovoltaics (PV) make balancing network
load and generation increasingly challenging. Electricity grids with high PV-penetration …