Automated Author ProfileZhengyan Zhang
East China Normal University/0009-0004-7757-7948
Zhengyan Zhang
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
High-resolution atmospheric thermodynamic fields are essential for elucidating the evolution mechanisms of localized weather systems over complex terrain regions such as the Yangtze River Delta. The thermodynamic dataset was generated using the same dynamical downscaling strategy and numerical simulation framework as the corresponding high-resolution three-dimensional wind field dataset. Specifically, the dataset was produced through dynamic downscaling with the Weather Research and Forecasting (WRF) model, driven by ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The simulations incorporate multi-source observational nudging, optimized physical parameterization schemes, and updated high-resolution land use data to better represent local atmospheric processes.This thermodynamic dataset includes five variables: 2-meter air temperature (T2m), 2-meter relative humidity (RH2m), surface pressure (PSFC), and vertical profiles of air temperature (T) and relative humidity (RH). The three-dimensional thermodynamic profiles are provided on 32 ERA5-standard pressure levels ranging from 1000 hPa to 10 hPa. All variables are stored in NetCDF format with lossless compression enabled. Key variables are encoded using the scale_factor and add_offset attributes following the relation: real_value = scale_factor × packed_value + add_offset.The dataset fully complies with CF conventions and can be automatically decoded by commonly used tools such as xarray, NCL, and CDO, without requiring any manual post-processing.
Authors
- Zhengyan Zhang ;
- Li, Jun ;
- Liu, Yan-An
The 3D wind field dataset was generated through dynamic downscaling using the Weather Research and Forecasting (WRF) model, driven by ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The simulation incorporates multi-source observational nudging, optimized physical parameterization schemes, and high-resolution land use updates to enhance the accuracy of local atmospheric processes. This dataset includes four variables: 10-meter zonal wind (U10m), 10-meter meridional wind (V10m), zonal wind profile (U), and meridional wind profile (V). The vertical wind profiles are provided on 32 ERA5-standard pressure levels from 1000 hPa to 10 hPa. All data are stored in NetCDF format with lossless compression enabled. Key variables are encoded using scale_factor and add_offset attributes, following the relation: real_value = scale_factor × packed_value + add_offset. The dataset complies with CF conventions and can be automatically decoded by standard tools such as xarray, NCL, and CDO, without manual intervention.
Authors
- Zhengyan Zhang ;
- Liu, Yan-An