Zoom in

Near surface atmospheric forcing data were produced by using Wether Research and Forecasting (WRF) model over the Heihe River Basin at hourly 0.05 * 0.05 DEG resolution, including the following variables: 2m temperature, surface pressure, water vapor mixing ratio, downward shortwave & upward longwave radiation, 10m wind field and the accumulated precipitation. The forcing data were validated by observational data collected by 15 daily Chinese Meteorological Bureau conventional automatic weather station (CMA), a few of Heihe River eco-hydrological process comprehensive remote sensing observation (WATER and HiWATER) site hourly observations were verified in different time scales, draws the following conclusion: 2m surface temperature, surface pressure and 2m relative humidity are more reliable, especially 2m surface temperature and surface pressure, the average errors are very small and the correlation coefficients are above 0.96; correlation between downward shortwave radiation and WATER site observation data is more than 0.9; The precipitation agreed well with observational data by being verified based on rain and snow precipitation two phases at yearly, monthly, daily time scales . the correlation coefficient between rainfall and the observation data at monthly and yearly time scales were up to 0.94 and 0.84; the correlation between snowfall and observation data at monthly scale reached 0.78, the spatial distribution of snowfall agreed well with the snow fractional coverage rate of MODIS remote sensing product. Verification of liquid and solid precipitation shows that WRF model can be used for downscaling analysis in complex and arid terrain of Heihe River Basin, and the simulated data can meet the requirements of watershed scale hydrological modeling and water resources balance.

The data for 2000-2012 was provided in 2013.

The data for 2013-2015 was updated in 2016.

The data for 2016-2018 was updated in 2019.

The data for 2019-2021 was updated in 2021.

File naming and required software

Each file contains hourly simulation for a whole month, with UTC time system in netcdf data format. Each file is named as: wrfout_heihe_yyyy_mm_01, where "wrfout_heihe_" is a fixed format, representing the result of nesting in the second layer for Heihe region, yyyy is the year, mm is the month, and "01" is the representative of every month starting from 1st day.

Data Citations Data citation guideline What's data citation?

Pan, X. (2020). The atmospheric forcing data in the Heihe River Basin (2000-2021). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Meteoro.tpdc.271245. CSTR: 18406.11.Meteoro.tpdc.271245. (Download the reference： RIS | Bibtex )

1. Pan, X.D., Li, X., Shi, X.K., Han, X.J., Luo, L.H., Wang, L.X. Dynamic downscaling of near-surface air temperature at the basin scale using WRF–a case study in the Heihe River Basin, China. Frontiers of Earth Science, 2012, 6(3): 314-323, doi: 10.1007/s11707-012-0306-2.( View Details | Download | Bibtex)

2. Pan, X.D., Ma, H.Q. (2019). Near-Surface Hourly Atmospheric Driving Data at 0.05°×0.05°based on WRF Model Simulation over 2000-2016 Years for the Heihe River Basin. Plateau Meteorology, 38(1): 209-219.( View Details | Download | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

References literature

1.Pan, X.D., Li, X., Cheng, G.D., Li, H.Y., He, X.B.. (2015). Development and evaluation of a river-basin-scale high spatio-temporal precipitation data set using the WRF model: a case study of the Heihe River Basin. Remote Sensing, 7(7): 9230-9252. (View Details | Download )

2.Pan, Xiaoduo, Li, Xin, Yang, Kun, He, Jie, Zhang, Yanlin, Han, Xujun. Comparison of Downscaled Precipitation Data over a Mountainous Watershed: A Case Study in the Heihe River Basin. Journal of Hydrometeorology, 2014, 15(4):1560-1574. doi:10.1175/JHM-D-13-0202.1 (View Details )

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)

Related Resources

Comments

Current page automatically show English comments Show comments in all languages