DeepAI AI Chat
Log In Sign Up

Generative Modeling of High-resolution Global Precipitation Forecasts

by   James Duncan, et al.
berkeley college
Berkeley Lab

Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change. Making accurate high-resolution precipitation forecasts using traditional physical models remains a major challenge in operational weather forecasting as they incur substantial computational costs and struggle to achieve sufficient forecast skill. Recently, deep-learning-based models have shown great promise in closing the gap with numerical weather prediction (NWP) models in terms of precipitation forecast skill, opening up exciting new avenues for precipitation modeling. However, it is challenging for these deep learning models to fully resolve the fine-scale structures of precipitation phenomena and adequately characterize the extremes of the long-tailed precipitation distribution. In this work, we present several improvements to the architecture and training process of a current state-of-the art deep learning precipitation model (FourCastNet) using a novel generative adversarial network (GAN) to better capture fine scales and extremes. Our improvements achieve superior performance in capturing the extreme percentiles of global precipitation, while comparable to state-of-the-art NWP models in terms of forecast skill at 1–2 day lead times. Together, these improvements set a new state-of-the-art in global precipitation forecasting.


page 3

page 8

page 9


FuXi: A cascade machine learning forecasting system for 15-day global weather forecast

Over the past few years, due to the rapid development of machine learnin...

Deep Learning Hydrodynamic Forecasting for Flooded Region Assessment in Near-Real-Time (DL Hydro-FRAN)

Hydrodynamic flood modeling improves hydrologic and hydraulic prediction...

AI Increases Global Access to Reliable Flood Forecasts

Floods are one of the most common and impactful natural disasters, with ...

Precipitaion Nowcasting using Deep Neural Network

Precipitation nowcasting is of great importance for weather forecast use...

Graph Neural Networks for Improved El Niño Forecasting

Deep learning-based models have recently outperformed state-of-the-art s...

Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention

Rising penetration levels of (residential) photovoltaic (PV) power as di...

High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning

Climate change impacts could cause progressive decrease of crop quality ...