Postprocessing of gridded precipitation forecasts using conditional generative adversarial networks and quantile regression

Probabilistic predictions of precipitation call for rather sophisticated postprocessing approaches due to its low predictability, high spatio-temporal variability and highly positive skewness. Moreover, the large number of zeros makes the generation of physically realistic postprocessed forecast scenarios using standard approaches like ensemble copula coupling (ECC) rather difficult. In addition to classical statistical approaches, recently, machine learning based methods gained increasing popularity in the field of postprocessing of probabilistic weather forecasts.

In this study, we compare conditional generative adversarial network (cGAN) based postprocessing of daily precipitation with a quantile regression based approach. Recent publications on applying cGAN to precipitation forecasts have shown its potential to generate forecast scenarios that improve forecast skill and cannot be distinguished from observed data in terms of spatial structure (Harris et al., 2022; Price and Rasp, 2022). While we use ECC to generate physically realistic forecast scenarios from quantile regression, cGAN does not need any additional ECC steps. For training and verification, we use COSMO-E ensemble forecasts with a grid resolution of about 2 km over Switzerland and the corresponding CombiPrecip observations, which are a gridded blend of radar and gauge observations. Preliminary results obtained by using loss functions that are tailored to precipitation postprocessing as proposed by Harris et al. 2022 confirm the potential of cGAN for precipitation also for our study domain. Using cGAN, we aim to generate realistic looking forecast scenarios while also increasing forecast skill compared to COSMO-E. Furthermore, we provide a comparison of multivariate verification measures between COSMO-E, cGAN and quantile regression, which does increase forecast skill at the expense of relying on an additional ECC step to generate forecast scenarios. 

 

References: 

• Harris, L., McRae, A. T., Chantry, M., Dueben, P. D., & Palmer, T. N. (2022). A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts. arXiv preprint arXiv:2204.02028.

• Price, I., & Rasp, S. (2022). Increasing the accuracy and resolution of precipitation forecasts using deep generative models. arXiv preprint arXiv:2203.12297.