Precipitation Nowcasting

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Precipitation nowcasting estimates where rain will fall and how intense it will be over the next minutes to hours, supporting warnings and short-term decisions. Methods range from radar-motion extrapolation and recurrent or U-Net models to transformers, state-space models, and diffusion or flow-based generators; many also fuse satellite, station, or numerical weather forecasts and incorporate physical constraints. A central challenge is forecasting rare, high-impact rainfall without blurring storm structure or producing false alarms, especially as forecast horizons lengthen. Current work balances deterministic accuracy with probabilistic calibration, fine-scale detail, computational cost, and reliable performance across regions and weather regimes.

Latest papers

40 papers5 papers in the last four weeks, down 17% on the four weeks before. 0.0% of all new papers.

Wet-Biased Data Pipelines Mislead the Training and Evaluation of Generative Precipitation Nowcasting
Oct 19, 2026Christoph Metzl, Stefan Kapsreiter, Kianusch Vahid Yousefnia +4Precipitation Nowcasting Generative AI Evaluation
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany · Wismar University of Applied Sciences, Wismar, Germany · Arpae Emilia-Romagna, Hydro-Meteo-Climate Service (SIMC), Bologna, Italy +1
Just Weather Scoring: Efficient End-to-end Nowcasting with Distributional Diffusion
Oct 8, 2026Jannik Wiese, Johannes Schusterbauer, Tommaso Martorella +1Precipitation Nowcasting Probabilistic Forecasting
PrecipJEPA: JEPA-Regularized Future-State Prediction with Motion-Source Rendering for Precipitation Nowcasting
Sep 30, 2026Yufeng Zhu, Dan Niu, Qiliang Wu +4Precipitation Forecasting Joint-Embedding Predictive Architecture
Department of Automation, Southeast University, Nanjing 210096, China
NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters
Sep 29, 2026Haoran Xu, Xingzhuo Guo, Yuchen Zhang +3Precipitation Forecasting Diffusion Transformer
School of Software, BNRist, Tsinghua University, China
MW-Nowcast: Six-hour ensemble nowcasting of extreme precipitation
Sep 28, 2026Ning Wang, Zuliang Fang, Weixin Jin +19Precipitation Forecasting Probabilistic Forecasting
Microsoft Corporation. · City University of Hong Kong. · University of Cambridge.
Physics-Guided Flow-Map Matching for Precipitation Nowcasting
Sep 27, 2026Shunya Nagashima, Takumi Bannai, Makoto Misaizu +3Precipitation Nowcasting Conditional Generative Modeling
Neurogica Inc., Tokyo, Japan · LTS, Inc., Tokyo, Japan · ME-Lab Japan, Inc., Tokyo, Japan +1
WeatherDiagFlow: Evidence-Grounded Radar Nowcasting with Diagnostic Flow Refinement
Sep 24, 2026Chunlei Shi, Yufeng Zhu, Yixiao Liang +4Precipitation Nowcasting Weather Forecasting
Department of Automation, Southeast University, Nanjing, China · Beijing Fengyun Meteorological Science and Technology Development Co., Ltd., Beijing, China · State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China +1
IRENE: A Convolutional GRU Ensemble Model for Radar Precipitation Nowcasting over Italy
Sep 15, 2026Alessandro Camilletti, Gabriele Franch, Elena Tomasi +1Convolutional RNNs Precipitation Forecasting
Fondazione Bruno Kessler
MZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting
Sep 4, 2026Yifang Zhang, Shengwu Xiong, Henan Wang +6Precipitation Nowcasting Physics-Informed ML
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya, 572000, China · School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China · School of Earth and Space Science and Technology, Wuhan University, Wuhan 430072, China +2
GenONet: A Generative operator Network for High-Resolution Precipitation Nowcasting
Sep 1, 2026Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohammad KhanjaniPrecipitation Forecasting Spatiotemporal Forecasting
University of Illinois Urbana-Champaign · Sharif University of Technology
Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting
Aug 31, 2026Dohyun Park, Changhoon Song, Tengyuan Chang +2Precipitation Forecasting Probabilistic Forecasting
Department of Mathematical Sciences, Seoul National University, Seoul, Republic of Korea. · Research Institute of Mathematics, Seoul National University, Seoul,2026 Republic of Korea. · Department of Environmental Management, Seoul National University, Seoul, Republic of Korea.
MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations
Aug 18, 2026Xiang Lin, Yunying Li, Chengzhi Ye +2Precipitation Nowcasting Spatiotemporal Forecasting
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China · Key Laboratory of High Impact Weather (Special), China Meteorological Administration, Changsha 410073, China · Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Changsha 410118, China +1
FreCast: Refining Radar Echo Intensity via Phase-Preserving Amplitude Residual Diffusion for Precipitation Nowcasting
Aug 9, 2026Heping Fang, Zihuai Yin, Kaicheng Mao +2Precipitation Forecasting Spatiotemporal Forecasting
Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China · Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering and the Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China
Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting
Aug 6, 2026Gourav Jyoti Kalita, Hidam Kumarjit SinghPrecipitation Forecasting Deep Ensembles
Department of Electronics and Communication Technology, Gauhati University, Guwahati-781014, Assam, India
QWRF-Net: A Quantum-Wavelet Framework with Rectified Flow for Short-Term Precipitation Nowcasting
Aug 3, 2026Zhuo Wang, Chaorong Li, Wenjie Luo +1Precipitation Forecasting Spatiotemporal Forecasting
School of Computer Science and Technology (School of Artificial Intelligence), Yibin University, Yibin, 644000, Sichuan, China · College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China
Physics-Based Deep Spatiotemporal Hyperlocal Radar Nowcasting with a Multi-Variable U-Net for High-Resolution Precipitation Forecasting
Jul 17, 2026Akshay Sunil, Muhammed Rashid, Raja Sekhar Sivaraju +2Precipitation Forecasting U-Net
Centre for Climate Studies, Indian Institute of Technology Bombay, Mumbai, India · Regional Meteorological Centre (RMC) in Colaba, Mumbai, India
RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs
Jun 29, 2026Yeeun Seong, Doyi Kim, Minseok Seo +1Precipitation Forecasting Time Series Forecasting
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks
Jun 16, 2026Ophélia Miralles, Máté Mile, Christoffer Artturi +2Precipitation Forecasting Probabilistic Forecasting
Norwegian Meteorological Institute, Oslo, Norway
When the Past Matters: FlashBack Memory for Precipitation Nowcasting
Jun 15, 2026Yuhao Du, Boxiao Huang, Chengrong Wu +1Memory-Augmented Neural Networks Precipitation Forecasting
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China · Fuqua School of Business, Duke University, North Carolina 27705, USA · Department of Computer Science, University of Manchester, Manchester M139PL, UK
Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall
Jun 8, 2026Gijs van Nieuwkoop, Siamak MehrkanoonPrecipitation Forecasting U-Net
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting
Jun 1, 2026Yunlong Zhou, Chen Zhao, Danyang Peng +2Precipitation Forecasting Spatiotemporal Forecasting
School of Intelligence Science and Technology, Nanjing University, Suzhou, 215163, China. · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China.
Probabilistic Precipitation Nowcasting with Rectified Flow Transformers
May 29, 2026Johannes Schusterbauer, Jannik Wiese, Nick Stracke +2Precipitation Forecasting Rectified Flow
Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
May 28, 2026Gijs van Nieuwkoop, Siamak MehrkanoonPrecipitation Forecasting Quantile Regression
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Seeing Inside the Storm: Improving Nowcasting by Integrating Meteorological Drivers
May 22, 2026Minghui Qiu, Jun Chen, Lin Chen +5Precipitation Forecasting Remote Sensing
HKUST(GZ) · Guangzhou Meteorological Observatory
MambaRain: Multi-Scale Mamba-Attention Framework for 0-3 Hour Precipitation Nowcasting
May 14, 2026Chunlei Shi, Cui Wu, Xiang Xu +10Mamba Precipitation Forecasting
School of Automation, Southeast University · Nanjing XinDa Institute of Meteorological Science and Technology · Beijing Leninainfo Technology Co., Ltd. +5
VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting
May 14, 2026Chunlei Shi, Hao Li, Yufeng Zhu +6Precipitation Forecasting Probabilistic Forecasting
Department of Automation, Southeast University, Nanjing 210096, China
McCast: Memory-Guided Latent Drift Correction for Long-Horizon Precipitation Nowcasting
May 13, 2026Penghui Wen, Yu Luo, Lintao Wang +4Memory-Augmented Neural Networks Precipitation Forecasting
School of Computer Science, The University of Sydney, Australia · School of Life and Environmental Science, The University of Sydney, Australia
Stable Attention Response for Reliable Precipitation Nowcasting
May 13, 2026Penghui Wen, Zexin Hu, Sen Zhang +6Precipitation Forecasting Attention Head Analysis
School of Computer Science, The University of Sydney Australia · Independent Researcher Australia · School of Life and Environmental Science, The University of Sydney Australia +3
PixelFlowCast: Latent-Free Precipitation Nowcasting via Pixel Mean Flows
May 11, 2026Yufeng Zhu, Chunlei Shi, Yongchao Feng +1Precipitation Forecasting Efficient Inference
Department of Automation, Southeast University, Nanjing 210096, China · State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
IMPA-Net: Meteorology-Aware Multi-Scale Attention and Dynamic Loss for Extreme Convective Radar Nowcasting
Apr 27, 2026Haofei Cui, Guangxin He, Juanzhen Sun +5Precipitation Forecasting Multi-Scale Feature Fusion
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME) / International Joint Research Laboratory on Climate and Environment Change (ILCEC) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China. · Nanchang Meteorological Bureau, Nanchang 330000, China. · National Center for Atmospheric Research, Boulder, CO 80301, USA. +3

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Wet-Biased Data Pipelines Mislead the Training and Evaluation of Generative Precipitation Nowcasting

Christoph Metzl, Stefan Kapsreiter, Kianusch Vahid Yousefnia, Virginia Poli+3

Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany · Wismar University of Applied Sciences, Wismar, Germany · Arpae Emilia-Romagna, Hydro-Meteo-Climate Service (SIMC), Bologna, Italy+1

Precipitation Nowcasting Generative AI Evaluation Selection Bias Model Calibration Probabilistic Forecasting

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Generative models for precipitation nowcasting are trained and evaluated predominantly on wet-biased datasets, which over-represent precipitation events to emphasize rare extremes. Selecting test cases based on target observations is known to favor forecasts that match the selected distribution rather than the intended deployment distribution. We show analytically how this target-dependent sampling also shifts the conditional distribution learned by generative models. When training and test sampling match, even an ideal model learns precisely the distribution favored by evaluation, allowing the evaluation bias to conceal the training bias. Using a fixed satellite-to-radar diffusion model, we demonstrate that target-dependent test sampling can reverse the calibration assessment. The model appears under-predictive on a wet-biased test set but over-predictive on a deployment-representative test set. This result is replicated outside the model's training domain after a seven-year gap. Utilizing classifier-free guidance as an example, we demonstrate how the contradictory calibration assessment can misdirect downstream model development.