Weather Simulation Uncertainty Estimation Using Bayesian Hierarchical Models

This publication has been published in American Meteorological Society, Journal of applied meteorology and climatology on March 8 2019. Link to publication: https://journals.ametsoc.org/doi/10.1175/JAMC-D-18-0018.1

Abstract
Estimates of the uncertainty of model output fields (e.g., 2-m temperature, surface radiation fluxes, or wind speed) are of great value to the weather and climate communities. The traditional approach for the uncertainty estimation is to conduct an ensemble of simulations where the model configuration is perturbed and/or different models are considered. This procedure is very computationally expensive and may not be feasible, in particular for higher-resolution experiments. In this paper, a new method based on Bayesian hierarchical models (BHMs) that requires just one model run is proposed. It is applied to the Weather Research and Forecasting (WRF) Model’s 2-m temperature in the Botnia–Atlantica region in Scandinavia for a 10-day period in the winter and summer seasons. For both seasons, the estimated uncertainty using the BHM is found to be comparable to that obtained from an ensemble of experiments in which different planetary boundary layer (PBL) schemes are employed. While WRF-BHM is not capable of generating the full set of products obtained from an ensemble of simulations, it can be used to extract commonly used diagnostics including the uncertainty estimation that is the focus of this work. The methodology proposed here is fully general and can easily be extended to any other output variable and numerical model.