A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

• URL: http://arxiv.org/abs/2204.02028v1
• Date: Tue, 5 Apr 2022 07:19:42 GMT
• Title: A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts
• Authors: Lucy Harris, Andrew T. T. McRae, Matthew Chantry, Peter D. Dueben, Tim N. Palmer
• Abstract summary: Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems. 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.
• Score: 0.5906031288935515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, 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.

Related papers

• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Generative Adversarial Models for Extreme Geospatial Downscaling [0.0]
  This paper describes a conditional GAN-based geospatial downscaling method that can accommodate very high scaling factors. The method explicitly considers the uncertainty inherent to the downscaling process that tends to be ignored in existing methods. It produces a multitude of plausible high-resolution samples instead of one single deterministic result.
  arXiv  Detail & Related papers  (2024-02-21T18:25:04Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Precipitation Downscaling with Spatiotemporal Video Diffusion [19.004369237435437]
  This work extends recent video diffusion models to precipitation super-resolution. We use a deterministic downscaler followed by a temporally-conditioned diffusion model to capture noise characteristics and high-frequency patterns. Our analysis, capturing CRPS, MSE, precipitation distributions, and qualitative aspects using California and the Himalayas, establishes our method as a new standard for data-driven precipitation downscaling.
  arXiv  Detail & Related papers  (2023-12-11T02:38:07Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Towards replacing precipitation ensemble predictions systems using machine learning [0.0]
  We propose a new approach to generating ensemble weather predictions for high-resolution precipitation. The method uses generative adversarial networks to learn the complex patterns of precipitation. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions.
  arXiv  Detail & Related papers  (2023-04-20T12:20:35Z)
• Increasing the accuracy and resolution of precipitation forecasts using deep generative models [3.8073142980733]
  We train a conditional Generative Adversarial Network -- coined CorrectorGAN -- to produce ensembles of high-resolution, bias-corrected forecasts. CorrectorGAN, once trained, produces predictions in seconds on a single machine. Results raise exciting questions about the necessity of regional models, and whether data-driven downscaling and correction methods can be transferred to data-poor regions.
  arXiv  Detail & Related papers  (2022-03-23T09:45:12Z)
• Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization [67.99082021804145]
  We propose an explicit solution to the COO problem, called Detail Enhanced Contrastive Loss (DECLoss) DECLoss utilizes the clustering property of contrastive learning to directly reduce the variance of the potential high-resolution distribution. We evaluate DECLoss on multiple super-resolution benchmarks and demonstrate that it improves the perceptual quality of PSNR-oriented models.
  arXiv  Detail & Related papers  (2022-01-04T08:30:09Z)
• TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall [21.399707529966474]
  We present TRU-NET, an encoder-decoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers. We use a conditional-continuous loss function to capture the zero-skewed %extreme event patterns of rainfall. Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction.
  arXiv  Detail & Related papers  (2020-08-20T17:27:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.