High‐Resolution Regional Reanalysis in China: Evaluation of 1 Year Period Experiments

Abstract

Globally, reanalysis data sets are widely used in assessing climate change, validating numerical models, and understanding the interactions between the components of a climate system. However, due to the relatively coarse resolution, most global reanalysis data sets are not suitable to apply at the local and regional scales directly with the inadequate descriptions of mesoscale systems and climatic extreme incidents such as mesoscale convective systems, squall lines, tropical cyclones, regional droughts, and heat waves. In this study, by using a data assimilation system of Gridpoint Statistical Interpolation, and a mesoscale atmospheric model of Weather Research and Forecast model, we build a regional reanalysis system. This is preliminary and the first experimental attempt to construct a high‐resolution reanalysis for China main land. Four regional test bed data sets are generated for year 2013 via three widely used methods (classical dynamical downscaling, spectral nudging, and data assimilation) and a hybrid method with data assimilation coupled with spectral nudging. Temperature at 2 m, precipitation, and upper level atmospheric variables are evaluated by comparing against observations for one‐year‐long tests. It can be concluded that the regional reanalysis with assimilation and nudging methods can better produce the atmospheric variables from surface to upper levels, and regional extreme events such as heat waves, than the classical dynamical downscaling. Compared to the ERA‐Interim global reanalysis, the hybrid nudging method performs slightly better in reproducing upper level temperature and low‐level moisture over China, which improves regional reanalysis data quality.

Impact of Large-Scale Afforestation on Surface Temperature: A Case Study in the Kubuqi Desert, Inner Mongolia Based on the WRF Model

“…Model evaluation was carried out as shown in Figure 4 (monthly mean) and Figure 5 (hourly scale). A general agreement is found between the monthly mean air temperatures (2 m level result) in the EXP_2008 simulation and the observations at the nine meteorological stations within the model domain (Figure 4), with the R 2 value of around 0.99 and a mean bias of −0.74 • C. This result is consistent with the WRF simulation by Zhang, et al [52] which showed a cold bias between 1.0 • C and 1.5 • C for the whole of China. The root mean square error (RMSE) is 1.76 • C, slightly better than the result (1.92 • C) obtained by Cao, et al [20].…”

“…The root mean square error (RMSE) is 1.76 • C, slightly better than the result (1.92 • C) obtained by Cao, et al [20]. In the summer, the RMSEs, ranging from 0.94 (June) to 1.33 • C (July), are smaller than the results of Zhang, et al [52] (ranging from 1 to 2.5 • C). In the winter, the RMSEs range from 1.64 (January) to 2.39 • C (November).…”

“…In the winter, the RMSEs range from 1.64 (January) to 2.39 • C (November). According to Zhang, et al [52], the RMSEs range from 2-3 • C in the winter. The simulated results from 14 February to 24 February and 4 August to 14 August 2008 were extracted from the nearest grid to the eddy covariance tower and compared with the observations.…”

Evaluation of Precipitation in the Chinese Regional Reanalysis Using Satellite Estimates, Gauge-Based Observations and Global Reanalysis

“…Over TP, WRF-driven regional reanalysis shows advantage in simulating precipitation compared to ERA5 (He et al, 2019). The ability of such global reanalysis datasets in depicting regional-scale, complex terrain climate and diurnal-scale variability requires further evaluation and improvement (von Storch, 1999;Sotillo et al, 2005;Bromwich et al, 2016;Zhang et al, 2017). Thus, long-term reanalysis datasets with higher spatial and temporal resolutions are still in need for regional climate researches and investigating phenomena such as extreme rainfall events and mesoscale weather systems.…”

Development and Evaluation of an Ensemble‐Based Data Assimilation System for Regional Reanalysis Over the Tibetan Plateau and Surrounding Regions

“…The East Asia Regional Reanalysis (Yang & Kim, , ) was developed using a four‐dimensional variational (4DVAR) data assimilation technique (Courtier et al, ; Rabier et al, ) by the Korea Meteorological Administration. Zhang et al () also produced a competitive regional reanalysis over mainland China by using the Gridpoint Statistical Interpolation data assimilation system and the Advanced Research WRF (ARW‐WRF; Skamarock et al, ) model. Recently, the regional Indian Monsoon Data Assimilation and Analysis (Mahmood et al, , ), with an emphasis of support in the study of the Asian monsoon characteristics over the Indian subcontinent, has been developed using the 4DVAR method by the Met Office, the National Centre for Medium Range Weather Forecasting, and the India Meteorological Department.…”