This data set is the daily snow depth data of the Northern Hemisphere from 2001 to 2010, covering the range of 0-86 ° n, 180 ° w-180 ° e. the data set combines the snow process model and snow microwave radiation transmission model, absorbs the ground observation station data to extract the snow depth, introduces the forest coverage factor to reduce the impact of forest on snow depth retrieval, and uses Ensemble Kalman filter method to synthesize The results show that the parameters of snow cover are optimized by microwave brightness temperature and simulated brightness temperature; the influence of forest is reduced by forest coverage in forest area, and the snow depth data set in northern hemisphere is obtained with spatial resolution of 0.250 * 0.250.
The original data used in this data set is AMSR-E ease grid, which is downloaded from the snow and Ice Data Center of the United States.
(1) Snow process model and snow microwave radiation transmission model, real-time update of snow state variables from snow observation data of northern hemisphere stations, optimization of snow parameters by integrating microwave brightness temperature observed by satellite and simulated brightness temperature by Kalman filter method, and reducing forest impact through forest coverage rate in forest areas.
Compared with the observed snow depth (the snow depth is more than 5cm), the relative deviation of the month with deeper snow depth is smaller, while the relative deviation of the month with shallow snow depth is larger. For the deep snow months from January to April, the average snow depth is 30cm, the relative deviation is - 21%; for the shallow snow depth from May to June and October to December, the relative deviation is - 63.2%.
| # | number | name | type |
| 1 | 2013CBA01800 | Study on the changes of cryosphere and its influence | National Program on Key Basic Research Project (973 Program) |
| 2 | 2013CBA01802 | National Program on Key Basic Research Project (973 Program) |
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CSTR:11738.11.ncdc.CCI.2020.90
10.12072/ncdc.CCI.db0005.2020