Automated Organization ProfileCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences (UCAS), Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences (UCAS), Beijing, China
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization'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
The datasets contain four shallow convection cases (RICO, BOMEX, ATEX, and ARM-SGP) simulated by three large eddy models (SAM, WRF, UCLA-LES). For each convection case, there are nineteen two-dimensional variables, including winds, temperature, and humidity. Three types of plumes are provided, i.e., convective core refers to rising plumes that have positive buoyancy and contain condensed water, and convective updraft defined as plumes containing liquid water and upward vertical velocity, and cloud defined as grid points containing liquid water. Conditionally sampled variables such as in-cloud temperature, moisture and vertical velocity are also provided, which are indispensable for calculating entrainment rate. These datasets will be used as a reference to help users verify and improve parameterization schemes of shallow convection. Table 2 List of the LES output variables Variable Long name Unit U Zonal wind m/s V Meridional wind m/s W Vertical velocity m/s W2 Variance of vertical velocity m2/s2 QT Total water g/kg QV Water vapor g/kg QC Cloud condensate g/kg P Pressure hPa Z Height m A_cor Core fraction 100% A_upd Updraft fraction 100% A_cld Cloud fraction 100% QT_cor Mean qt in core g/kg QT_upd Mean qt in updraft g/kg QT_cld Mean qt in cloud g/kg W_cor Mean w in core m/s W_upd Mean w in updraft m/s W_cld Mean w in cloud m/s QTFLUX Total water flux W/m2
Authors
- Yaxin, Zhao ;
- Xiaocong, Wang ;
- Yimin, Liu ;
- Guoxiong, Wu ;
- Yanjie, Liu
The datasets contain four shallow convection cases (RICO, BOMEX, ATEX, and ARM-SGP) simulated by three large eddy models (SAM, WRF, UCLA-LES). For each convection case, there are nineteen two-dimensional variables, including winds, temperature, and humidity. Three types of plumes are provided, i.e., convective core refers to rising plumes that have positive buoyancy and contain condensed water, and convective updraft defined as plumes containing liquid water and upward vertical velocity, and cloud defined as grid points containing liquid water. Conditionally sampled variables such as in-cloud temperature, moisture and vertical velocity are also provided, which are indispensable for calculating entrainment rate. These datasets will be used as a reference to help users verify and improve parameterization schemes of shallow convection. Table 2 List of the LES output variables Variable Long name Unit U Zonal wind m/s V Meridional wind m/s W Vertical velocity m/s W2 Variance of vertical velocity m2/s2 QT Total water g/kg QV Water vapor g/kg QC Cloud condensate g/kg P Pressure hPa Z Height m A_cor Core fraction 100% A_upd Updraft fraction 100% A_cld Cloud fraction 100% QT_cor Mean qt in core g/kg QT_upd Mean qt in updraft g/kg QT_cld Mean qt in cloud g/kg W_cor Mean w in core m/s W_upd Mean w in updraft m/s W_cld Mean w in cloud m/s QTFLUX Total water flux W/m2
Authors
- Yaxin, Zhao ;
- Xiaocong, Wang ;
- Yimin, Liu ;
- Guoxiong, Wu ;
- Yanjie, Liu