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Evaluation of 12‐Years Chinese Regional Reanalysis (1998–2009): Comparison of Dynamical Downscaling Methods With/Without Local Data
Author(s) -
Lu Yutong,
Fang Juan,
Pan Yig,
Wang Shuyu,
Zhou Peifeng,
Yang Yi,
Shao Min,
Tang Jianping
Publication year - 2021
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2020jd034259
Subject(s) - downscaling , environmental science , climatology , data assimilation , precipitation , meteorology , geography , geology
High‐resolution regional reanalysis is one of the most powerful tool to study local and regional high‐impact weather events, climate extremes and climate change impact. In this study, two high‐resolution Chinese Regional Reanalysis (CNRR) datasets with a resolution of 18 km over 1998–2009, which are produced using the Gridpoint Statistical Interpolation (GSI) data assimilation system and spectral nudging (SN) technique, were assessed. The reliability of the surface and upper air variables of the CNRR datasets was evaluated by comparing with in situ observations and the European Centre for Medium‐Range Forecasts (ECMWF) ERA‐Interim (ERAIN) and ERA5 global reanalysis dataset. The evaluation of climatological and seasonal spatial distribution of near‐surface variables shows that the CNRR can provide more accurate near‐surface variables than the driving ERAIN global reanalysis, and CNRR‐GSI shows advantages against ERA5 in representing near‐surface atmosphere during cold season especially for daily maximum temperature. However, care should be taken when using the CNRR precipitation dataset, especially for heavy rainfall cases. CNRR datasets are also able to generate high‐quality upper atmospheric products especially in CNRR‐GSI experiment, which assimilates long time series of local observations. CNRR‐GSI can better represent the lower‐level horizontal wind and temperature than ERA5. By using the three‐dimensional variational data assimilation (3D‐Var) method, CNRR‐GSI outperforms the CNRR‐SN and the driving global reanalyzes. With the increase of the computing resources, the potential opportunities for improving CNRR can be expected by applying more advanced methods.