Machine Learning for Precipitation Nowcasting from Radar Images

Abstract

High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dramatic promise in many domains, including the geosciences, we present an application of DL to the problem of precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1 hour) predictions of precipitation. We treat forecasting as an image-to-image translation problem and leverage the power of the ubiquitous UNET convolutional neural network. We find this performs favorably when compared to three commonly used models: optical flow, persistence and NOAA's numerical one-hour HRRR nowcasting prediction.

References in corpus (1)

• The Importance of Skip Connections in Biomedical Image Segmentation

Cited by in corpus (23)

• Skillful Precipitation Nowcasting using Deep Generative Models of Radar
• WeatherBench: A benchmark dataset for data-driven weather forecasting
• Deep Learning for Post-Processing Ensemble Weather Forecasts
• A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts
• Broad-UNet: Multi-scale feature learning for nowcasting tasks
• Short-term precipitation prediction using deep learning
• A review of radar-based nowcasting of precipitation and applicable machine learning techniques
• Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation
• Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
• TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression
• Cloud Cover Nowcasting with Deep Learning
• Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge
• Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
• PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network
• Black Hole Weather Forecasting with Deep Learning: A Pilot Study
• Compute, Time and Energy Characterization of Encoder-Decoder Networks with Automatic Mixed Precision Training
• Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
• Deep Probability Estimation
• A Vision for Numerical Weather Prediction in 2030
• Hybrid Scheme of Kinematic Analysis and Lagrangian Koopman Operator Analysis for Short-term Precipitation Forecasting
• Deep Orthogonal Decompositions for Convective Nowcasting
• Post-processing Multi-Model Medium-Term Precipitation Forecasts Using Convolutional Neural Networks
• PlumeCityNet: Multi-Resolution Air Quality Forecasting