Automated Author Profile

Zhengyan Zhang

East China Normal University
/0009-0004-7757-7948

Current S-Index

0.6

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.3

Average Dataset Index per dataset

Total Datasets

2

Total datasets for this author

Average FAIR Score

69.2%

Average FAIR Score per dataset

Total Citations

0

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

A 1 km Hourly High-Resolution Thermodynamic Dataset over the Yangtze River Delta during June-August 2021-2023 (Version: V1)

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
0 Citations0 Mentions69% FAIR0.4 Dataset Index
10.57760/sciencedb.279402026

A 1 km Hourly High-Resolution 3D Wind Field Dataset over the Yangtze River Delta during June-August 2021-2023 (Version: V2)

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
0 Citations0 Mentions69% FAIR0.6 Dataset Index
10.57760/sciencedb.237522025