Scale-Selective Bias Correction in a Downscaling of Global Analysis Using a Regional Model

Abstract

Systematic large-scale errors are often found within the regional domain in the regional dynamical downscaling procedure. This paper proposes a method to suppress such errors using a combination of spectral tendency damping and area average correction of temperature, humidity, and surface pressure in the Regional Spectral Model. The proposed scale-selective bias-correction method reduces the time tendency of the zonal and meridional wind components for the physical scale greater than a predetermined length. In addition, the area mean perturbations of temperature and humidity are forced to zero. The surface pressure difference between the model field and the global field is adjusted from the hydrostatic equation using the mean elevation difference between the two fields and the area mean temperature. Each of these three components of the technique is necessary for the model to effectively reduce large-scale errors in the regional domain. With this method, the downscaled field becomes less dependent on the domain size. Furthermore, the downscaled precipitation compares better with observations, as do the near-surface temperature and wind fields. The scheme allows much weaker lateral boundary relaxation, although it is still an essential part of the regional model. The use of a similar scheme is recommended for any regional model in the application of dynamical downscaling of analysis for climate studies.