Exploring Geometric Deep Learning for Precipitation Nowcasting
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023 submitted and accepted in ...
Shan Zhao 0007 +4 more
openaire +3 more sources
STVMamba: precipitation nowcasting with spatiotemporal prediction model. [PDF]
Sci RepA lightweight rainfall nowcasting model is required by Sichuan provincial meteorological bureaus. Deep learning methods such as recurrent, convolutional, and Transformer models have been applied to precipitation prediction. However, recurrent models struggle with suboptimal parallel computational efficiency and error accumulation, convolutional models ...
Zou M, Wen L, Huang Y, He Y, Xiao J.
europepmc +3 more sources
RainAI -- Precipitation Nowcasting from Satellite Data
CoRR, 2023 This paper presents a solution to the Weather4Cast 2023 competition, where the goal is to forecast high-resolution precipitation with an 8-hour lead time using lower-resolution satellite radiance images. We propose a simple, yet effective method for spatiotemporal feature learning using a 2D U-Net model, that outperforms the official 3D U-Net baseline ...
Pablos Sarabia, Rafael +3 more
openaire +2 more sources
Advances of precipitation nowcasting and its application in hydrological forecasting
Shui kexue jinzhan, 2020 With the global climate change and the imbalance of the ecological environment, extreme weather frequently occurs and presents multi-scale temporal and spatial variability characteristics.
Jia LIU +5 more
doaj +1 more source
MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather
CoRR, 2021 We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours.
Sylwester Klocek +9 more
openaire +2 more sources
Nowcasting of Extreme Precipitation Using Deep Generative Models [PDF]
, 2023 Nowcasting is an observation-based method that uses the current state of the atmosphere to forecast future weather conditions over several hours. Recent studies have shown the promising potential of using deep learning models for precipitation nowcasting.
Kyryliuk, Maksym (author) +7 more
core +1 more source
Combination of XGBoost and PPLK method for improving the precipitation nowcasting [PDF]
MATEC Web of Conferences, 2022 The Precipitation nowcasting can provide high-resolution forecasts of rainfall and hydrometeors in 2 hours and play an important role in risk management for flash flood and debris flow events, but it is a very challenge work.
Mai Xiongfa, Zhong Haiyan, Li Ling
doaj +1 more source
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting [PDF]
Geoscientific Model Development, 2023 The prediction of precipitation patterns up to 2 h ahead, also known as precipitation nowcasting, at high spatiotemporal resolutions is of great relevance in weather-dependent decision-making and early warning systems.
Y. Ji +5 more
doaj +1 more source
Convcast architecture for precipitation nowcasting using the IMERG dataset. [PDF]
, 2020 Convcast architecture for precipitation nowcasting using the IMERG dataset.
Yoshihide Sekimoto (499186) +4 more
core +1 more source
Application of Multiple Wind Retrieval Algorithms in Nowcasting
Atmosphere, 2015 Multiple wind retrieval algorithms are performed to retrieve wind fields, based on which radar reflectivity is extrapolated to implement nowcasting. The frequently used nowcasting algorithm COTREC (continuity of tracking radar echo by correlation), based
Nan Li +5 more
doaj +1 more source