Results 71 to 80 of about 1,500 (207)
Precipitation nowcasting with generative diffusion models
Applied Intelligence21 pages, 6 ...
Andrea Asperti +5 more
openaire +3 more sources
Data for paper "Quality Aware Conditional Generative Adversarial Networks for Precipitation Nowcasting" submitted to AAI. [PDF]
, 2023 Dataset for paper "Quality Aware Conditional Generative Adversarial Networks for Precipitation Nowcasting" submitted to Applied Artificial ...
Jonnalagadda, Jahnavi
core +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Precipitation nowcasting is an important tool for nowcasting weather. In recent years, progress has been achieved in some models based on deep learning for precipitation nowcasting.
Taisong Xiong +5 more
doaj +1 more source
The Synoptic, Microphysical, and Radar Characteristics of Upper Great Lakes Snow Events
Journal of Geophysical Research: Atmospheres, Volume 131, Issue 14, 28 July 2026.Abstract The upper Great Lakes region is well‐known for significant snowfall during the winter months. The microphysical characteristics of the snow depend to a great degree on snow particle formation and growth processes that are linked to regime‐dependent environmental conditions.
Timothy J. Wagner +4 more
wiley +1 more source
Convolutional LSTM network: A machine learning approach for precipitation nowcasting [PDF]
, 2015 The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine ...
Yeung, Dit Yan +5 more
core
International Journal of Climatology, Volume 46, Issue 9, July 2026.Future advances in the fields of meteorology and climate science will require scientists to increasingly strive towards the provision of new high‐resolution services. In this context, the development of new products and services along the weather chain may greatly benefit from the adoption of second‐and‐third‐party data (23PD) as a source of high ...
Irene Garcia‐Marti +5 more
wiley +1 more source
Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches
Frontiers in Earth ScienceAccurate and timely prediction of short-term rainfall is crucial for reducing the damages caused by heavy rainfall events. Therefore, various precipitation nowcasting models have been proposed.
Ji-Hoon Ha, Junsang Park
doaj +1 more source
Prediction of Radar Echo Space-Time Sequence Based on Improving TrajGRU Deep-Learning Model
Remote Sensing, 2022 Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack the
Qiangyu Zeng +8 more
doaj +1 more source
Meteorological Applications, Volume 33, Issue 4, July/August 2026.A radar‐based tracking algorithm analyzed Precipitation Systems (PS) over burned areas. At estimated debris flow timing, warmer‐season systems were smaller, slower, and more circular with localized high reflectivity (> 45 dBZ), while cooler‐season systems were larger, faster, and elongated with widespread moderate reflectivity (25–45 dBZ).
Silvana Castillo‐Guerra +2 more
wiley +1 more source
Investigation of Severe Turbulence Over China During 2018–2025 From In Situ EDR Data
Meteorological Applications, Volume 33, Issue 4, July/August 2026.Based on in situ EDR data from Xiamen Airlines, this study systematically quantifies aviation turbulence characteristics over China, identifies high‐risk turbulence regions and their correlations with weather systems, and proposes targeted forecasting strategies through persistent turbulence events analysis.
Cai Xuewei +5 more
wiley +1 more source