Search Results

You are looking at 1 - 2 of 2 items for

  • Author or Editor: Deqin Li x
  • Refine by Access: All Content x
Clear All Modify Search
Xue Yi
,
Deqin Li
,
Chunyu Zhao
,
Lidu Shen
, and
Xiaoyu Zhou

Abstract

High-density surface networks have become available in recent years in a number of regions throughout the world, but their utility in high-resolution dynamic downscaling has not been examined. As an attempt to fill such a gap, a suite of high-resolution (4 km) dynamical downscaling simulations is developed in this study with the Weather Research and Forecasting (WRF) Model and observation nudging over Liaoning in northeastern China. Three experiments, including no nudging (CTL), analysis nudging (AN), and combined analysis nudging and observation nudging with surface observations (AON), are conducted to downscale the CFSv2 reanalysis with the WRF Model for the year 2015. The three 1-yr regional climate simulations were compared with the independent surface observations. The results show that observational nudging can improve the simulation of surface variables, including temperature, wind speed, humidity, and pressure, more than nudging large-scale driving data with AN alone. The two nudging simulations can improve the cold bias for the temperature of the WRF Model. For precipitation, both the simulations with AN and observation nudging can capture the pattern of precipitation; however, with the introduction of small-scale information at the surface, AON cannot further improve the simulation of precipitation.

Free access
Ying Gong
,
Sen Yang
,
Jinfang Yin
,
Shu Wang
,
Xiao Pan
,
Deqin Li
, and
Xue Yi

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−1) by approximately 1.2 mm and underestimate heavy rain (≥20 mm day−1) by over 6.7 mm.

Free access