Abstract
The grid nudging technique is often used in regional climate dynamical downscaling to make the simulated large-scale fields consistent with the driving fields. In this study, we focused on two specific questions about grid nudging: (1) which nudged variable has a larger impact on the downscaling results; and (2) what is the “optimal” grid nudging strategy for each nudged variable to achieve better downscaling result during summer over the mainland China. To solve these queries, 41 three-month-long simulations for the summer of 2009 and 2010 were performed using the Weather Research and Forecasting model (WRF) to downscale National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data to a 30-km horizontal resolution. The results showed that nudging horizontal wind or temperature had significant influence on the simulation of almost all conventional meteorological elements, while nudging water vapor mainly affected the precipitation, humidity, and 500 hPa temperature. As a whole, the optimal nudging time was one hour or three hours for nudging wind, three hours for nudging temperature, and one hour for nudging water vapor. The optimal nudged level was above the planetary boundary layer for almost every nudged variable. Despite these findings, it should be noted that the optimum nudging scheme varied with simulated regions and layers, and dedicated research for different regions, seasons, and model configuration is advisable.
Highlights
Due to low temporal-spatial resolutions, global climate models (GCMs) and reanalysis data often cannot meet the requirements for the analysis of regional scale information [1,2,3,4]
The statistical downscaling needs to have enough observation data to establish a statistical model, and is invalid in regions where large-scale climate elements are not correlated with regional climate elements
The dynamical downscaling process is forced by the large-scale fields, such as GCMs or reanalysis data
Summary
Due to low temporal-spatial resolutions, global climate models (GCMs) and reanalysis data often cannot meet the requirements for the analysis of regional scale information [1,2,3,4]. To obtain regional meteorological and climate datasets with a high temporal-spatial resolution, downscaling is usually conducted. Popular downscaling methods include statistical downscaling and dynamical downscaling [5,6,7,8] The former is based on statistical relationships between large- and fine-scale climate information to obtain regional or local atmospheric structures, while the latter nests a regional fine-grid model to GCMs or reanalysis data. The dynamical downscaling process is forced by the large-scale fields, such as GCMs or reanalysis data. It uses regional climate models (RCMs) to add more detailed descriptions of physical processes, topography, and land coverage of a regional scale
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