Bayesian Inference aided analog downscaling for near-surface winds in complex terrain

Assessing atmospheric boundary layer flows in complex terrain for short-range real-time applications demands fast and reliable downscaling from coarser-resolution meteorological data to the relevant scale. An ideal statistical downscaling numerical experiment was performed for surface winds above complex terrain in Israel's northern Negev desert region. Dynamical downscaling have been performed by the WRF model to create a historical database by the following two sets. The first set used 5 nested domains from 40.5 km to 0.5 km. The second set used 3 nested domains ranging from 40.5 km to 4.5 km. The 4.5 km data (stage 2) was defined as predictors while data on 0.5 km (stage 1) served as predictands for statistical downscaling. Two statistical downscaling algorithms: minimal distance analog and a Bayesian inference aided analog (hereafter Bayesian algorithm) were tested by the above data. Unlike most analog algorithms, the Bayesian algorithm refers to the probability to get the best analog instead of the minimal differences between predictands. The comparison of the two algorithms shows that the Bayesian approach yields improved results. The Bayesian algorithm reproduces the 0.5 km resolution dynamically downscaled surface winds with an average absolute direction difference of 43 and 20 for calm winds and moderate/strong winds respectively. Its average wind speed error is ~ 1.1 ms− 1. ~ 40 days are sufficient to create a representative database. Given the database, the procedure is extremely fast (a few seconds) and easy to operate, which makes it suitable for real-time non-expert fast-response applications.