Monsoon coastal cities often suffer from extreme rain-induced flooding and severe hazard. However, the associated physical mechanisms and detailed storm structures are poorly understood due to the lack of high-resolution data. This study presents an analysis of a thunderstorm that produces extreme hourly rainfall (EXHR) of 219 mm over the Guangzhou megacity on the southern coast of China using integrated multiplatform observations and a four-dimensional variational Doppler radar analysis system. Results indicate that weak environmental flows and convectively generated weak cold pool facilitate the formation of a quasi-stationary storm, while onshore warm and moist flows in the boundary layer (BL) provide the needed moisture supply. The 219-mm EXHR is attendant by a shallow meso-ɣ -scale vortex due to stretching of intense latent heating-induced convergence, which, in turn, helps organize convective updrafts into its core region. Lightning and dual-polarization radar observations reveal active warm-rain (but weak mixed-phase) microphysical processes, with raindrop size distribution (RSD) closer to marine convection. In contrast, another storm develops about 4 h earlier and only 35 km to the northwest, but with more lightning, higher cloud tops, more graupel and supercooled liquid water content, more continental RSD, little evidence of rotation, and much less rainfall; they are attributable to the presence of larger convective available potential energy resulting from the urban heat island effects and less moisture supply in the BL. These results highlight the importance of using multisource remote sensing data sets in understanding the microphysical and kinematic structures of EXHR-producing storms.

To provide guidance for the use of reanalysis data in the Central Taklimakan Desert (CTD), five reanalysis products are evaluated based on the radiosonde data obtained from two field experiments during summer for the first time in the CTD, including the European Center for Medium‐Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), ECMWF Reanalysis‐Interim (ERA‐Interim), Japanese 55‐years Reanalysis (JRA55), Modern‐Era Retrospective Analysis for Research and Applications version 2 (MERRA2), and the National Centers for Environmental Prediction‐Department of Energy Reanalysis version 2 (NCEP2). The results show that reanalysis temperature (T), specific humidity (Q), geopotential height (GPH), and wind field (U and V components) are consistent with the radiosonde observations in terms of the vertical distribution. In general, ERA5 has the best performance in the CTD during the study period, followed closely by ERA‐Interim. However, NCEP2 produces the largest error. The errors of all the reanalysis data show significant diurnal variations, and the diurnal variations differ from each other. Moreover, the results indicate that the reanalysis datasets have the largest deviation at 850 hPa (near the ground), which means that in the desert region complex interactions may exist between the land surface and the atmosphere. Therefore, more attention should be paid to the description of complex interactions between land and atmosphere over the moving‐sand desert region in the numerical models.

Reanalysis data play a vital role in weather and climate study as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis ERA-Interim data at 6 h intervals. Hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA) scheme during the WRF model integration; upper observations are assimilated in three-dimensional variational data assimilation (3D-VAR) mode at the analysis moment. It should be highlighted that many of the assimilated observations have not been used in other reanalysis systems. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12 km horizontal resolution, 74 sigma levels, and 3 h intervals. Finally, an evaluation of EARS has been performed with respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global reanalysis data of ERA-Interim, the regional reanalysis data of EARS are closer to the observations in terms of RMSE in both surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. The study also demonstrates the potential use of the EARS data for applications over East Asia and proposes further plans to provide the latest reanalysis in real-time operation mode. Simple data and updated information are available on Zenodo at https://doi.org/10.5281/zenodo.7404918 (Yin et al., 2022), and the full datasets are publicly accessible on the Data-as-a-Service platform of the China Meteorological Administration (CMA) at http://data.cma.cn (last access: 19 May 2023).