Advanced machine learning models have recently achieved high predictive accuracy for
weather and climate prediction. However, these complex models often lack inherent …

Atmospheric extreme events cause severe damage to human societies and ecosystems.
The frequency and intensity of extremes and other associated events are continuously …

With the advent of networked rail operations and increasing risks caused by extreme
weather conditions, there is an urgent need to enhance the safety early warning and …

The utilization of 3D-Convolutional Long Short-Term Memory (3D-Conv-LSTM) networks for
precipitation nowcasting through satellite data integration has emerged as a significant …

Climate change and land use are major drivers of environmental and socioeconomic
transformations in landscapes and in coastal areas. The objective of this study was to …

This article introduces an innovative ensemble troposphere conditions forecasting method
using wet refractivity within the context of Global Navigation Satellite System (GNSS) …

Every farmer requires access to rainfall prediction (RP) to continue their exploration of
harvest yield. The proper use of water assets, the successful collection of water, and the …

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation
operations and safety. In this context, the TELMo (Time-series Embeddings from Language …

This article explores the enhancement of Global Navigation Satellite Systems (GNSS)
tropospheric tomography by integrating remote sensing data and employing various vertical …

Convolution-based recurrent neural networks and convolutional neural networks have been
used extensively in spatiotemporal prediction. However, these methods tend to concentrate …