Abstract Rainfall nowcasting, the short‐term prediction of precipitation, is a vital component of early warning systems aimed at mitigating the effects of extreme weather events. In this study, we develop a deep learning approach based on convolutional neural networks (CNNs) for rainfall nowcasting and train it exclusively on data from rain gauge stations. Specifically, we adapt the original U‐Net architecture and tailor it for regression tasks to forecast short‐term precipitation at the Forio rain gauge station on the Island of Ischia, Italy. Two model input configurations are examined to assess the potential value of incorporating data from multiple sources: (a) using data solely from the Forio station, and (b) integrating data from multiple rain gauge stations across the island. The CNN‐based nowcasting performance is evaluated across various lead times, ranging from 10 min to 3 hr. The results show that both models achieve high predictive accuracy ( across most lead times), with the single‐station model outperforming the multi‐station configuration, indicating that adding data from other stations does not necessarily improve forecasting performance. These findings contribute to the advancement of rainfall nowcasting to inform real‐time early warning systems.
Abstract India often faces challenges in monitoring atmospheric carbon dioxide (CO 2 ) through satellite observations due to persistent cloud cover, especially during the monsoon season. This limitation affects the continuous tracking of carbon and hinders accurate assessments of…
Abstract Accurate forecasting of PM2.5 (particulate matter with diameter ≤2.5 μm) and AOD550 (aerosol optical depth at 550 nm) is crucial for air quality management, public health, and environmental policy. Traditional physics‐based forecasting systems, though robust, require com…
Abstract Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high‐dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed‐form, mult…
Abstract Glacier mass balance (MB) is a key indicator of climate change and a central driver of glacier evolution, yet most glaciers worldwide lack long‐term in situ measurements. For estimating glacier MB, data‐driven models provide a complementary alternative to traditional num…
Abstract Currently, satellite imagery serves as the primary means of observing terrestrial planets such as the Mars, the Moon, and Mercury. Enhancing the resolution and quality of these images can provide more detailed insights into planetary surfaces. However, improvements in im…
Abstract Rainfall is often highly localized and its location is difficult to predict exactly with a numerical weather prediction (NWP) model. Previous research has shown that this problem can be mitigated by spatially aware calibration methods which incorporate forecast informati…