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

Abstract

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 hydrological 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) atmospheric 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 downscaling 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 simulations and raw ERA‐I reanalysis‐driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.

Using Megha‐Tropiques satellite data to constrain humidity in regional convective simulations: A northern Australian test case

“…To investigate the implications of this error, these biases should be removed if possible. It is widely known that the lateral boundary conditions can be important for climate simulations (Moalafhi et al ., 2017). Meanwhile, initial conditions determine the current state of regional patterns and are crucial in weather prediction, especially through probabilistic forecasting with several perturbations in initial conditions (Rabier et al ., 1996).…”

Performance of the RegCM4.6 for High-Resolution Climate and Extreme Simulations over Tibetan Plateau

“…Bias correction of the ERA-Interim reanalysis dataset would lead to better skill and consistent downscaled results [82]. However, it is still difficult to correct the bias of ERA-Interim reanalysis over the TP for the following reasons: (1) the ground-based observation is the only available meteorological data over the TP, and the data such as wind speed, temperature, geopotential height, and specific humidity at different heights above ground are absent; (2) part of the ground-based observation over the TP has been merged in the ERA-Interim reanalysis dataset; and (3) satellite data are considered as a promising choice to correct the ERA-Interim reanalysis datasets [83]. However, these data usually begin at the end of the 20th century or the beginning of the 21st century, which cannot cover the time span applied in this study.…”

“…(2) part of the ground-based observation over the TP has been merged in the ERA-Interim reanalysis dataset; and (3) satellite data are considered as a promising choice to correct the ERA-Interim reanalysis datasets [83]. However, these data usually begin at the end of the 20th century or the beginning of the 21st century, which cannot cover the time span applied in this study.…”

An Investigation into the Future Changes in Onset and Cessation of Rain and Their Variability over the Aswa Catchment, Uganda

“…Systematic biases for the current climate are also unavoidable in GCMs [22]. The biases can be propagated through the downscaling process with far reaching implications on the simulations and any subsequent applications [23,24]. The first step towards reducing the bias and improving the simulations is usually to avoid using unrealistic or inaccurate datasets for calibrating the downscaling.…”

“…The first step towards reducing the bias and improving the simulations is usually to avoid using unrealistic or inaccurate datasets for calibrating the downscaling. In this regard input data used for downscaling can either be bias corrected before downscaling or the simulations are bias corrected themselves after downscaling [17,24]. Caution is also needed during post processing of the simulations to keep minimal chances that the original climate change signal could be affected as this alteration can substantially affect impact model results [25].…”