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A Deep Learning Approach to Radar‐Based QPE
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Rainfall Measurement: Gauges
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A machine learning nowcasting method based on real‐time reanalysis data
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Pattern Recognition and Machine Learning
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Impact of Flood Disasters on Taiwan in the Last Quarter Century
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Convolutional neural network: a review of models, methodologies and applications to object detection
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The accuracy of short-term radar rainfall forecasts
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Accuracy of rainfall estimates by radar, part I: Calibration by telemetering raingauges
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Dynamic ANN for precipitation estimation and forecasting from radar observations
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Numerical simulation studies of rain gage data correction due to wind effect
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Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation
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Deep learning and process understanding for data-driven Earth system science
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Sampling Errors of Tipping-Bucket Rain Gauge Measurements
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Gradient-based learning applied to document recognition
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Convolutional Neural Network for Convective Storm Nowcasting Using 3-D Doppler Weather Radar Data
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Accuracy of NWS 8" Standard Nonrecording Precipitation Gauge: Results and Application of WMO Intercomparison
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Local Random Errors in Tipping-Bucket Rain Gauge Measurements
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Assessment of Rainfall Estimates Using a StandardZ-RRelationship and the Probability Matching Method Applied to Composite Radar Data in Central Florida
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THE DISTRIBUTION OF RAINDROPS WITH SIZE
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Relation between Measured Radar Reflectivity and Surface Rainfall
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Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements
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An Exploratory Multisensor Technique for Quantitative Estimation of Stratiform Rainfall
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National Mosaic and Multi-Sensor QPE (NMQ) System: Description, Results, and Future Plans
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Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning
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Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States
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Understanding Winter Precipitation Impacts on Automated Gauge Observations within a Real-Time System
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Temporal and Spatial Characteristics of Typhoon Extreme Rainfall in Taiwan
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The Precipitation Hotspots of Afternoon Thunderstorms over the Taipei Basin: Idealized Numerical Simulations
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A Modeling Study of the Severe Afternoon Thunderstorm Event at Taipei on 14 June 2015: The Roles of Sea Breeze, Microphysics, and Terrain
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Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.52202/079017openalex-snapshot
Advances in Neural Information Processing Systems 37
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Machine Learning Approach to Unraveling the Complex Relationship between Air Pollution and Weather
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Analyzing radar rainfall estimate errors with three vector norms: application to weather radar rainfall data in Muğla, Turkey
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Spatiotemporal deep learning fusion of radar, infrared, and geospatial data for typhoon rainfall estimation in Taiwan
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A multiscale dynamic network-based method for radar precipitation nowcasting: A case study in the Yiluo River Basin
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Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network
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Developing an Explainable Variational Autoencoder (VAE) Framework for Accurate Representation of Local Circulation in Taiwan
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Using the Fourier Neural Operator and Real‐Time GOES‐R Satellite Data for Precipitation Retrievals in the Southern Great Plains
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A three stage ‘Matching-Retrieval-Optimization’ method for radar–rainfall retrieval: a case study in the Yiluo River Basin, China
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Machine Learning-based Model for Enhanced Quantitative Precipitation Estimates (QPE) From Radar Images Extracted Features
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Application of Segmental-Correction Machine Learning Methods in Radar Quantitative Precipitation Estimation Correction by Rain Gauge
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Temporal Super-Resolution, Ground Adjustment, and Advection Correction of Radar Rainfall Using 3-D-Convolutional Neural Networks
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DREE-RF: A Radar-Based Rainfall Energy Estimation Model Using Random Forest
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A Deep Learning-Based Precipitation Nowcasting Model Fusing GNSS-PWV and Radar Echo Observations
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Enhanced Multimodal-Fusion Network for Radar Quantitative Precipitation Estimation Incorporating Relative Humidity Data
inferred← citeshttps://doi.org/10.1109/tgrs.2025.3640078openalex-snapshot
RadarGaugeNet: Radar-Rain-Gauge-Based AI Architecture for Real-Time Quantitative Precipitation Estimation
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Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States
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A Deep Learning–Based Velocity Dealiasing Algorithm Derived from the WSR-88D Open Radar Product Generator
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Traditional and Novel Methods of Rainfall Observation and Measurement: A Review
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Implementation of Rainfall Monitoring through an Information System Based on Radar and Satellite Image Data using the Kalman Filter Method
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Enhancing the Performance of Quantitative Precipitation Estimation Using Ensemble of Machine Learning Models Applied on Weather Radar Data
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Using an Artificial Neural Network to Assess Several Rainfall Estimation Algorithms Based on X-Band Polarimetric Variables in West Africa
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AutoNowP: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction
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Dual-Polarization Radar Quantitative Precipitation Estimation (QPE): Principles, Operations, and Challenges
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Point-Based and Spatial Deep Learning for Radar Quantitative Precipitation Estimation Across Precipitation Intensity Ranges over Hainan Island
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Improving the Gaussianity of radar reflectivity departures between observations and simulations using symmetric rain rates
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