Impact of bias-corrected reanalysis-derived lateral boundary conditions on WRF simulations.

Lateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydro- logical assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmo- spheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscal- ing and the resultant model simulations after downscaling. Mean and standard deviation bias-corrected WRF simulations are, however, found to be marginally better than mean only bias-corrected WRF simula- tions and raw ERA-I reanalysis-driven WRF simulations. Performances, however, differ when assessing differ- ent attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.

Figure 13. Observational Range Adjusted (ORA) temporal correlation of annual precipitation (mm) for (a) WRF_ERA-Iraw, (b) WRF_ERA-IcorrM, and (c) WRF_ERA-IcorrMSD.