Precipitation Measurement and Analysis

Latest papers

• WeatherDiagFlow: Evidence-Grounded Radar Nowcasting with Diagnostic Flow Refinement

  Chunlei Shi, Yufeng Zhu, Yixiao Liang, Dan Niu, Yongchao Feng, Qiliang Wu, Jiong Wang · 25 September 2026

  Radar nowcasting is essential for short-term warning and emergency response, yet conventional systems mainly return future radar fields and provide limited support for operational communication and post-event verification. We formulate radar nowcasting as an evidence-grounded forecast--bulletin--aud…

• FluidRain: Incompressible Rain Flow as an Attention Bias for Loop-in-Loop Video Deraining

  Pu Wang, Yongcong Wang, Wenhao Li, Xiang Chen, Guangwei Gao, Jinshan Pan, Siyuan Yao, Shujun Fu, Zhuoran Zheng · 25 September 2026

  Existing video deraining methods typically exploit neighboring frames through either explicit alignment or implicit spatiotemporal aggregation. Explicit alignment relies on accurate motion estimation, which can become unreliable under dense rain, while implicit aggregation avoids alignment but lacks…

• IRENE: A Convolutional GRU Ensemble Model for Radar Precipitation Nowcasting over Italy

  Alessandro Camilletti, Gabriele Franch, Elena Tomasi, Marco Cristoforetti · 16 September 2026

  We present IRENE (Italian Radar Ensemble Nowcasting Experiment), a deep learning model for probabilistic short-range precipitation nowcasting over the Italian domain at \SI{1}{km} spatial and 5 min temporal resolution. IRENE adopts an encoder--forecaster architecture built on multi-scale Convolution…

• Hurdle-RMIL: Addressing Zero Inflation and Long-Tailed Imbalance in Infrared Rainfall Retrieval

  Fangjian Zhang, Xiaoyong Zhuge, Wenlan Wang, Haixia Xiao, Yuying Zhu, Siyang Cheng, Ali Mamtimin · 14 September 2026

  Imbalanced labels can cause frequent samples to dominate AI-based quantitative remote sensing, degrading rare-event retrieval. In rain-rate retrieval based on satellite infrared brightness temperatures, this imbalance leads to systematic underestimation of rare high-intensity rainfall. In this study…

• PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast

  Yuze Sun, Shiyi Wang, Jiancheng Pan, Die Wang, Andreas F. Prein, Wentao Luo, Linhan Jiang, Jie Wu, Quan Zhang, Xiaomeng Huang · 9 September 2026

  Medium-range precipitation forecasts are impaired by persistent systematic biases, lead-time-dependent error accumulation, and coarse spatial resolution, restricting their reliability for flood-drought risk assessment. Existing AI correction techniques lack dedicated modeling for multi-day dynamic b…

• MZ-Rain: Moisture-Budget-Guided Zero-Inflated Model for Station-Level Precipitation Nowcasting

  Yifang Zhang, Shengwu Xiong, Henan Wang, Wenjie Yin, Yuqiang Zhang, Chen Zhou, Hua Chen, Qile Zhao, Pengfei Duan · 7 September 2026

  Accurate station-level precipitation nowcasting is critical for agriculture, water resource management, and disaster prevention, which typically is formulated as a time series forecasting problem. However, conventional time-series modeling techniques face two major challenges in addressing station-l…

• GenONet: A Generative operator Network for High-Resolution Precipitation Nowcasting

  Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohammad Khanjani · 2 September 2026

  High-resolution precipitation nowcasting is critical for reducing the impacts of severe weather but remains difficult because of rapid storm evolution. Deep learning models have shown great promise for this task, but their predictive skill often deteriorates over longer forecast horizons. This leads…

• A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data

  Andres F. Monsalve, Hernan A. Moreno, Christian D. Kummerow · 2 September 2026

  Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of cheaper, more compact satellites (SmallSats) capable of detecting rainfall proxies has led to a significant increase in private-sector initiatives for sat…

• MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations

  Xiang Lin, Yunying Li, Chengzhi Ye, Zitong Chen, Jing Sun · 19 August 2026

  Short-duration heavy-rainfall warning determines whether 1 h rainfall will exceed a threshold within a target-station neighborhood over the next few hours. Multitemporal infrared and water-vapor observations from the Fengyun-4A Advanced Geostationary Radiation Imager (FY-4A AGRI) capture cloud-top c…

• FreCast: Refining Radar Echo Intensity via Phase-Preserving Amplitude Residual Diffusion for Precipitation Nowcasting

  Heping Fang, Zihuai Yin, Kaicheng Mao, Peiguang Zhang, Peng Yang · 11 August 2026

  Precipitation nowcasting predicts the spatiotemporal evolution of future radar echoes from historical radar echo sequences, thereby estimating the occurrence, development, and movement of precipitation over the near term. In recent years, deep learning has become an important approach to precipitati…

• Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting

  Gourav Jyoti Kalita, Hidam Kumarjit Singh · 7 August 2026

  Proper short-term forecasting of precipitation is crucial in disaster management and preparedness. Nonetheless, the variability and nonlinearity of precipitation make short-term forecasting challenging for meteorologists. Moreover, capturing temporal dependencies in spatiotemporal data is a challeng…

• QWRF-Net: A Quantum-Wavelet Framework with Rectified Flow for Short-Term Precipitation Nowcasting

  Zhuo Wang, Chaorong Li, Wenjie Luo, Chuanhu Deng · 4 August 2026

  Short-term precipitation nowcasting is important for hydrometeorological early warning, especially when intense convective rainfall may trigger urban flooding, flash floods, and other high-impact hazards. A key challenge in warning-oriented nowcasting is that radar precipitation fields contain stron…

• Benchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities

  Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar, Nithin Nagaraj · 30 July 2026

  Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences. This study tests whether convolutional recurrence improves one-day-ahead rainfall-field prediction on sma…

• Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities

  Jeremy Ovadia · 28 July 2026

  Nonstationary Gaussian process (GP) models are powerful tools for capturing input-dependent variability by adapting to observed data. However, with limited sampling and highly parameterized covariance structure, they are often prone to overfitting and overconfident uncertainty estimates, potentially…

• Physics-Informed Feature Engineering 1D-CNN for Multilayer Cloud Detection from Geostationary Satellites

  Fu Wang, Chi Yang, Qi-Feng Lu, Rui-Xia Liu, Xiao-Fei Yang, Xiao-Fang Liu, Bo Li, Lin Chen · 21 July 2026

  Multilayer cloud detection from active--passive observation is vital for numerical weather prediction. In this study, channel selections derived from threshold-based algorithms are embedded as feature-engineering priors into a 1D-CNN, and machine learning (ML) is used to learn latent physical relati…

• Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence

  Shohini Sarkar, Smithi Mahendran, Rishi Chudasama, Varun Mannam, Arav Luthra, Yuvraj Rekhi, Vivek Nadig, Arsh Goenka · 17 July 2026

  Representative clutter height (RCH) is a key parameter in radio propagation and interference analysis because it captures the dominant height of local obstructions that drive terminal clutter loss. Current practice often relies on fixed clutter heights assigned to land use classes in Recommendation …

• CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting

  Tianyi Xiong, Haonan Chen, Kelly Mahoney, Jingyin Tang, Tim Smith, Janice Bytheway · 26 June 2026

  Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerical weather prediction (NWP) systems and data-driven models. We present CSU-PCAST, a deep learning-based ensemble forecasting framework for global precipitation…

• Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks

  Oph\'elia Miralles, M\'at\'e Mile, Christoffer Artturi, Thomas Nipen, Ivar Seierstad · 18 June 2026

  Sparse point observations are increasingly available for precipitation nowcasting, but it is unclear how much they improve dense radar-field forecasts. We partially address this question with a multimodal graph neural network nowcasting system over the Nordic radar domain. The model predicts rain ra…

• When the Past Matters: FlashBack Memory for Precipitation Nowcasting

  Yuhao Du, Boxiao Huang, Chengrong Wu, Jiankai Zhang · 16 June 2026

  Accurate precipitation nowcasting is crucial for disaster mitigation and socio-economic planning, yet existing methods often struggle with false alarms, missed events, and long range dependency modeling at high spatiotemporal resolution. To address these challenges, we propose FlashBack Memory (FB),…

• From Nominal Intensity to Equivalent Rainfall: A Path-Based Credibility Evaluation Framework for Simulated Rainfall in Autonomous-Driving Perception Tests

  Tian Xia, Xin Zhao, Shaolingfeng Ye, Junyi Chen · 11 June 2026

  Credible simulated-rainfall conditions are essential for identifying perception-system boundaries and supporting SOTIF-oriented risk assessment in automated driving. However, closed-field tests are often described only by nominal rainfall intensity or single-point measurements, making it difficult t…

• Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall

  Gijs van Nieuwkoop, Siamak Mehrkanoon · 10 June 2026

  Precipitation nowcasting is increasingly being approached with deep learning models that learn directly from recent radar observations. Although such models can efficiently capture short-term precipitation motion, they often lack broader contextual information about the meteorological conditions und…

• VMU-Diff: A Coarse-to-fine Multi-source Data Fusion Framework for Precipitation Nowcasting

  Chunlei Shi, Hao Li, Yufeng Zhu, Boyu Liu, Yongchao Feng, Zengliang Zang, Hongbin Wang, Yanlan Yang, Dan Niu · 15 May 2026

  Precipitation nowcasting is a vital spatio-temporal prediction task for meteorological applications but faces challenges due to the chaotic property of precipitation systems. Existing methods predominantly rely on single-source radar data to build either deterministic or probabilistic models for ext…

• From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation

  Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk, Ira Assent · 8 May 2026

  High-resolution rainfall observations are crucial for weather forecasting, water management, and hazard mitigation. Traditional operational measurements are often biased and low-resolution, limiting their ability to capture local rainfall. Accurate high-resolution rainfall maps require integrating s…

• Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors

  Badr Moufad, Albina Ilina, Hai Victor Habi, Salem Lahlou, Yazid Janati, Hagit Messer, Eric Moulines · 8 May 2026

  Commercial Microwave Links (CMLs) offer dense spatial coverage for rainfall sensing but produce path-integrated measurements that make accurate ground-level reconstruction challenging. Existing methods typically oversimplify CMLs as point sensors and neglect line integration relating rainfall to sig…

• Federated Weather Modeling on Sensor Data

  Shengchao Chen, Guodong Long · 4 May 2026

  Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including ground weather stations, satellites and IoT devices, to collaboratively train deep learning models without sharing raw data. This method safeguards da…