| dc.description.abstract | Reanalysis datasets have been widely used in meteorological research, including studies of Northeast China cold vortices (NCCVs), where these datasets act as effective substitutes for observations. However, to date, no studies have focused on their performance in reproducing NCCVs. To address this knowledge gap, we adopted an automatic three-step identification algorithm (TIA) and used it to detect NCCVs from ERA5 and MERRA-2 reanalysis datasets spanning 39 warm seasons (May–September) during the period from 1980 to 2018. A comparative method was employed for a rough verification of the characteristics of the reproduced NCCVs. Moreover, a dataset derived from 1370 Chinese ground-based observational stations was used to verify the performance of the reanalysis models in reproducing the precipitation and air temperature associated with NCCVs. The results show that the TIA identified the majority of NCCVs, with an accuracy of approximately 90% from ERA5 or MERRA-2. Both reanalysis models can reproduce the characteristics of NCCVs (including location, strength, and duration), and both replicate air temperature better than precipitation. ERA5 and MERRA-2 showed strong consistency in reproducing the central longitude, central latitude, central height, and range of NCCVs, with correlation coefficients of 0.974, 0.972, 0.996, and 0.919, respectively, at the 99.9% significance level. The daily average 2-m temperatures in both reanalysis datasets were in good agreement with observations; however, overestimations of approximately 7°–8°C arose in steep high-altitude regions. In addition, both models tended to overestimate light rain (≤5 mm day | |
