Long-term series of daily snow depth dataset in China (1979-2019)


This data set is an upgraded version of the “Long-term series of daily snow depth dataset in China". This dataset provides daily data of snow depth distribution in China from January 1, 1979, to December 31, 2019, with a spatial resolution of 0.25 degrees. The original data used to derive the snow depth dataset are the daily passive microwave brightness temperature data (EASE-Grid) from SMMR (1979-1987), SSM/I (1987-2007) and SSMI/S (2008-2019) which were archived in the National Snow and Ice Data Center (NSIDC). Because the brightness temperatures come from different sensors, there is a certain system inconsistency among them. Therefore, before the derivation of snow depth, the inter-sensor calibration were performed to improve the temporal consistency of the brightness temperature data. Based on the calibrated brightness temperatures, the modified Chang algorithm developed by Dr. Tao Che, was used to retrieve daily snow depth. The algorithm details were introduced in the data specification document- “Long-term Sequence Data Set of China Snow Depth (1979-2019) Introduction. doc". The projection of the data set is latitude and longitude. The data of each day was stored in a file, and the naming convention of which is year + day; for example, 1990001 represents the first day of 1990, and 1990207 represents the 207th day of 1990. For a detailed data description, please refer to the data specification document.


Data file naming and use method

Naming convention of which is year + day; for example, 1990001 represents the first day of 1990, and 1990207 represents the 207th day of 1990. For a detailed data description, please refer to the data specification document.


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Citations

1. Che, T., Li, X., Jin, R., Armstrong, R., &Zhang, T.J. (2008). Snow depth derived from passive microwave remote-sensing data in China. Annals of Glaciology, 49, 145-154.( View Details | Bibtex)

2. Dai, L.Y., Che, T., &Ding, Y.J. (2015). Inter-calibrating SMMR, SSM/I and SSMI/S data to improve the consistency of snow-depth products in China. Remote Sensing, 7(6), 7212-7230.( View Details | Download | Bibtex)

3. Dai, L.Y., Che, T., Ding, Y.J., &Hao, X.H. (2017). Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing. The Cryosphere, 11(4), 1933-1948.( View Details | Bibtex)

Cite the data

CHE Tao. Long-term series of daily snow depth dataset in China (1979-2019). National Tibetan Plateau Data Center, 2015. doi: 10.11888/Geogra.tpdc.270194. (Download the reference: RIS | Bibtex )

Using this data, you must reference article references listed in the Required Data Citation and reference data


Support Program

CASEarth:Big Earth Data for Three Poles(grant No. XDA19070000) (No:XDA19000000)

User Limit

To respect the intellectual property rights, protect the rights of data authors,expand servglacials 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.

Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).


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


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Keywords
Geographic coverage
Spatial coverage

East: 140.00

South: 15.00

West: 60.00

North: 55.00

Detail
  • Temporal resolution: Daily
  • Spatial resolution: km
  • File size: 5,493 MB
  • Browse count: 17,696 Times
  • Apply count: 544 Times
  • Share mode: online
  • Temporal coverage: 1979-01-01 To 2019-12-31
  • Updated time: 2019-08-09
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Contact Information
: CHE Tao   DAI Liyun  

Distributor: National Tibetan Plateau Data Center

Email: data@itpcas.ac.cn

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