A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts. (12th October 2022)

• Record Type: Journal Article
• Authors: Harris, Lucy; McRae, Andrew T. T.; Chantry, Matthew; Dueben, Peter D.; Palmer, Tim N.
• Is Part Of: Journal of advances in modeling earth systems. Volume 14:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-12
• Subjects: deep learning -- machine learning -- postprocessing -- downscaling -- neural networks -- precipitation forecasting; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2022MS003120
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: 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, that is, learning to add fine‐scale structure to coarse images. Leinonen et al. (2020, https://doi.org/10.1109/TGRS.2020.3032790 ) 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 favorably 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. Plain Language Summary: The processes that lead to precipitation (rainfall) happen on a very small scale. Weather forecast computer models work on much larger scales, so rainfall is often poorly predicted. In this paper, we develop a method that enhances the resolution of rainfall forecasts by a factor of 10, and makes the forecasts more accurate. We generate many samples of what the rainfall pattern could be, which gives an idea of the uncertainty in the forecast. Our method is based on machine learning and neural networks, which means that we use many past examples of weather forecasts, together with the rainfall that actually happened, and our method "automatically" learns how the forecasts can be improved. We use an existing idea called "Generative Adversarial Networks, " which has been used very successfully in image‐related tasks, such as producing realistic higher‐resolution images from low‐resolution ones. Our task is similar to producing a high‐resolution image from a low‐resolution one, hence this approach is promising. Our method outperforms a variety of existing approaches, and even produces good predictions for the most extreme rainfall situations in our data set. These are the scenarios that cause the most real‐world disruption, the most useful events to produce good forecasts for. Key Points: We use generative adversarial neural networks to post‐process global weather forecast model output over the UK We produce more realistic precipitation forecasts than the input forecast data, at 10X resolution, with excellent statistical properties We match or outperform a state‐of‐the‐art pointwise downscaling scheme, while also producing spatially coherent images