Horizontal resolution in a nested-domain WRF simulation: a Bayesian analysis approach

TL;DR

This paper uses a Bayesian approach to evaluate how increasing horizontal resolution in WRF simulations affects surface temperature estimates in a Norwegian fjord, providing a new method for sensitivity analysis.

Contribution

It introduces a Bayesian framework for comparing WRF model sensitivity to horizontal resolution, offering a novel statistical perspective on model assessment.

Findings

Higher resolution improves temperature estimation accuracy.

Bayesian analysis quantifies uncertainty in model outputs.

Resolution impacts are statistically significant.

Abstract

The fast-paced development of state-of-the-art limited area models and faster computational resources have made it possible to create simulations at increasing horizontal resolution. This has led to a ubiquitous demand for even higher resolutions from users of various disciplines. This study revisits one of the simulations used in marine ecosystem projects at the Bjerknes Centre. We present a fresh perspective on the assessment of these data, related more specifically to: a) the value added by increased horizontal resolution; and b) a new method for comparing sensitivity studies. The assessment is made using a Bayesian framework for the distribution of mean surface temperature in the Hardanger fjord region in Norway. Population estimates are calculated based on samples from the joint posterior distribution generated using a Monte Carlo procedure. The Bayesian statistical model is applied to output data from the Weather Research and Forecasting (WRF) model at three horizontal resolutions (9, 3 and 1 km) and the ERA Interim Reanalysis. The period considered in this study is from 2007 to 2009, for the months of April, May and June.