Postprocessing multi-model ensemble temperature forecasts using Distributional Regression Networks

Uncertainty in numerical weather prediction (NWP) models can arise from various sources, such as initial conditions or model parameterizations. Ensemble forecasts, typically generated through perturbed initial conditions or diverse model physics, help address and quantify the uncertainty inherent in raw NWP models. However, these forecasts may still contain biases and dispersion errors, traditionally mitigated using non-homogeneous Gaussian regression (Ensemble Model Output Statistics, EMOS) (Gneiting et al., 2005). Nevertheless, emerging machine learning techniques, like Distributional Regression Networks (DRN) (Rasp and Lerch, 2018), are capable of handling nonlinear relationships between predictors and forecast distributions often yielding similar or superior results. 

At the Meteorological Service of Catalonia (SMC), a Poor Man’s Ensemble (PME) composed by 12 members is constructed using 8 different models: Arome, Arpege, Bolam, ECMWF-HRES, Icon, Moloch, Unified Model, and WRF. These models vary in spatial resolution and are interpolated to a 1 km grid using a lapse-rate correction methodology, accounting for altitude differences between model orography and 1 km digital elevation model (Sheridan et al., 2010). 

The postprocessing of this multi-model ensemble is conducted at point station locations utilizing data from the SMC automatic weather station network as ground truth. A benchmark methodology, EMOS, is applied using an IMPROVER (Roberts et al., 2023) module to calculate a calibration for each station and lead time of the ensemble. The forecast of each model is set as a predictor variable, rather than the commonly used mean and standard deviation of the ensemble. This approach is then compared with a single DRN for each lead time, incorporating all stations via an embedding technique, and using the same predictors. Results indicate a comparable but generally improved performance for DRN compared to EMOS. 

  

References 

Gneiting, T., Raftery, A. E., Westveld, A. H., & Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 133(5), 1098-1118. 

Rasp, S., & Lerch, S. (2018). Neural networks for postprocessing ensemble weather forecasts. Monthly Weather Review, 146(11), 3885-3900. 

Roberts, N., Ayliffe, B., Evans, G., Moseley, S., Rust, F., Sandford, C., ... & Worsfold, M. (2023). IMPROVER: the new probabilistic postprocessing system at the Met Office. Bulletin of the American Meteorological Society, 104(3), E680-E697. 

Sheridan, P., Smith, S., Brown, A., & Vosper, S. (2010). A simple height‐based correction for temperature downscaling in complex terrain. Meteorological Applications, 17(3), 329-339.