A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

04/05/2022
by   Lucy Harris, et al.
8

Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a "ground truth". The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps. Our model compares favourably to the best existing downscaling methods in both pixel-wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall.

READ FULL TEXT

page 17

page 18

page 19

page 30

research
05/20/2020

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network

Generative adversarial networks (GANs) have been recently adopted for su...
research
03/23/2022

Increasing the accuracy and resolution of precipitation forecasts using deep generative models

Accurately forecasting extreme rainfall is notoriously difficult, but is...
research
04/20/2023

Towards replacing precipitation ensemble predictions systems using machine learning

Precipitation forecasts are less accurate compared to other meteorologic...
research
04/09/2020

D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

LIDAR (light detection and ranging) is an optical remote-sensing techniq...
research
08/08/2022

Generating physically-consistent high-resolution climate data with hard-constrained neural networks

The availability of reliable, high-resolution climate and weather data i...
research
01/23/2018

High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks

We present a deep learning approach for high resolution face completion ...

Please sign up or login with your details

Forgot password? Click here to reset