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Long-term series of daily snow depth dataset over the Northern Hemisphere based on machine learning (1980-2019)
Long-term series of daily snow depth dataset over the Northern Hemisphere based on machine learning (1980-2019)

With the support of the first topic "sharing and integration of three pole big data" (xda19070100) of the special space-time three pole environment project of the earth big data science project, Che Tao research group of Northwest Institute of ecological environment and resources, Chinese Academy of Sciences uses machine learning methods combined with multi-source snow depth product data The daily snow depth data set of long-time Series in the northern hemisphere is prepared. Firstly, the applicability of artificial neural network, support vector machine and random forest method in snow depth fusion is compared. It is found that random forest method has strong advantages in snow depth data fusion. Secondly, using the random forest method, combined with remote sensing snow depth products such as AMSR-E, amsr2, NHsd and globsnow and reanalysis data such as era interim and merra2, the grid snow depth products and environmental factor variables are used as the input independent variables of the model, and the data of China Meteorological Station (945), Russia meteorological station (620) and Russia snow survey data (514) The snow depth data of 43340 ground observation stations such as the daily data of the global historical meteorological network (41261) are used as the reference truth to train and verify the model, and the daily grid snow depth data set of the snow hydrological year from 1980 to 2019 (September 1 of the previous year to May 31 of the current year) is prepared on the cloud platform provided by the special "earth big data science project". Using the global snow model comparison program and independent ground observation data for verification, the quality of the fusion data set has been improved as a whole. According to the comparison between the ground observation data and the snow depth products before fusion, the determination coefficient (R2) of the fusion data is increased from 0.15 (globsnow snow depth products) to 0.91, and the corresponding root mean square error (RMSE) and mean absolute error (MAE) are also reduced to 5.5 cm and 2.2 cm. The following is the header file content of each file. Adding it to the front of each file can display the data in ArcMap. Ncols 1440 / / 1440 columns in the data matrix Nrows 360 / / the data matrix has 360 rows in total Xllcenter - 180 / / the corner coordinates of the grid at the lower left corner of the matrix in the X direction Yllcenter 0 / / the corner coordinates of the grid at the lower left corner of the y-direction axis of the matrix Cellsize 0.25 / / size of each grid NODATA_ Value - 9999 / / default value

Created Time:2021-09-13
Surface elevation time series over the Greenland Ice Sheet (1991-2020)
Surface elevation time series over the Greenland Ice Sheet (1991-2020)

The elevation change of ice sheet is the comprehensive result of ice dynamic process and ice sheet surface process, and is sensitive to climate change. The long-term time series of ice sheet surface elevation is of great scientific value to study the stability of ice sheet and its response to climate change. Satellite altimetry observation missions have provided a large number of surface elevation observations over ice sheet. However, the life of a single satellite altimetry mission is limited. To obtain a long-term ice sheet surface elevation time series, different satellite altimetry missions need to be linked. We use an updated strategy of Plane-fit method to achieve cross-calibration the missions. After correcting the ascending-descending bias more fully, a larger amount of observations is used to correct the intermission bias. Meanwhile, an interpolation method based on the EOF reconstruction is used to suppress the influence of interpolation error. Finally, by combining the observations of ERS-1, ERS-2, Envisat and CryoSat-2, we successfully constructed the monthly surface elevation time series with 5-km grid resolution of the Greenland ice sheet for 30 years from 1991 to 2020. Subsequently, we used the airborne laser altimeter data from Operation IceBridge and the Greenland ice sheet surface elevation change product provided by ESA Climate Change Initiative (CCI) to validate the time series. It is found that our time series are reliable. The accuracy of ice sheet surface elevation changes obtained from our time series is 19.3% higher than that of ESA CCI products. Benefiting from our more accurate correction of intermission bias, the accuracy across the over the overlapping observation period of Envisat and CryoSat-2 missions are improved more, up to 30.9%. Based on this time series, we find that the volume of Greenland ice sheet has accelerated at an initial rate of -53.8 ± 4.5 km3/yr and an acceleration of -2.2 ± 0.3 km3/yr2 in recent 30 years. We also find that the transformation of the North Atlantic Oscillation has significant impacts on the surface elevation changes of the Greenland ice sheet. In addition, the dataset can be used as fundamental data for assessing the mass balance of Greenland ice sheet and its contribution to global sea level rise and studying the response process and mechanism of Greenland ice sheet to climate change.

Created Time:2021-08-25
The mean annual ground temperature (MAGT) and permafrost thermal stability dataset over Tibetan Plateau for 2005-2015
The mean annual ground temperature (MAGT) and permafrost thermal stability dataset over Tibetan Plateau for 2005-2015

Mean annual ground temperature (MAGT) at a depth of zero annual amplitude and permafrost thermal stability type are fundamental importance for engineering planning and design, ecosystem management in permafrost region. This dataset is produced by integrating remotely sensed freezing degree-days and thawing degree-days, snow cover days, leaf area index, soil bulk density, high-accuracy soil moisture data, and in situ MAGT measurements from 237 boreholes for the 2010s (2005-2015) on the Tibetan Plateau (TP) by using an ensemble learning method that employs a support vector regression (SVR) model based on distance-blocked resampling training data with 200 repetitions. Validation of the new permafrost map indicates that it is probably the most accurate of all available maps at present. The RMSE of MAGT is approximately 0.75 °C and the bias is approximately 0.01 °C. This map shows that the total area of permafrost on the TP is approximately 115.02 (105.47-129.59) *104 km2. The areas corresponding to the very stable, stable, semi-stable, transitional, and unstable types are 0.86*104 km2, 9.62*104 km2, 38.45*104 km2, 42.29*104 km2, and 23.80*104 km2, respectively. This new dataset is available for evaluate the permafrost change in the future on the TP as a baseline. More details can be found in Ran et al., (2020) that published at Science China Earth Sciences.

Created Time:2019-11-17
China meteorological assimilation driving datasets for the SWAT model Version 1.1 (2008-2016)
China meteorological assimilation driving datasets for the SWAT model Version 1.1 (2008-2016)

CMADS V1.1(The China Meteorological Assimilation Driving Datasets for the SWAT model Version 1.1) Version of the data set introduced the STMAS assimilation algorithm. It was constructed using multiple technologies and scientific methods, including loop nesting of data, projection of resampling models, and bilinear interpolation. The CMADS series of datasets can be used to drive various hydrological models, such as SWAT, the Variable Infiltration Capacity (VIC) model, and the Storm Water Management model (SWMM). It also allows users to conveniently extract a wide range of meteorological elements for detailed climatic analyses. Data sources for the CMADS series include nearly 40,000 regional automatic stations under China’s 2,421 national automatic and business assessment centres. This ensures that the CMADS datasets have wide applicability within the country, and that data accuracy was vastly improved. The CMADS series of datasets has undergone finishing and correction to match the specific format of input and driving data of SWAT models. This reduces the volume of complex work that model builders have to deal with. An index table of the various elements encompassing all of East Asia was also established for SWAT models. This allows the models to utilize the datasets directly, thus eliminating the need for any format conversion or calculations using weather generators. Consequently, significant improvements to the modelling speed and output accuracy of SWAT models were achieved. Most of the source data in the CMADS datasets are derived from CLDAS in China and other reanalysis data in the world. The integration of air temperature (2m), air pressure, humidity, and wind speed data (10m) was mainly achieved through the LAPS/STMAS system. Precipitation data were stitched using CMORPH’s global precipitation products and the National Meteorological Information Center’s data of China (which is based on CMORPH’s integrated precipitation products). The latter contains daily precipitation records observed at 2,400 national meteorological stations and the CMORPH satellite’s inversion precipitation products.The inversion algorithm for incoming solar radiation at the ground surface makes use of the discrete longitudinal method by Stamnes et al.(1988)to calculate radiation transmission. The resolutions for CMADS V1.0, V1.1, V1.2, and V1.3 were 1/3°, 1/4°, 1/8°, and 1/16°, respectively. In CMADS V1.0 (at a spatial resolution of 1/3°), East Asia was spatially divided into 195 × 300 grid points containing 58,500 stations. Despite being at the same spatial resolution as CMADS V1.0, CMADS V1.1 contains more data, with 260 × 400 grid points containing 104,000 stations. For both versions, the stations’ daily data include average solar radiation, average temperature (2m), average pressure, maximum and minimum temperature (2m), specific humidity, cumulative precipitation, and average wind speed (10m). The CMADS comprises other variables for any hydrological model(under 'For-other-model' folder): Daily Average Temperature (2m), Daily Maximum Temperature (2m), Daily Minimum Temperature (2m), Daily cumulative precipitation (20-20h), Daily average Relative Humidity, Daily average Specific Humidity, Daily average Solar Radiation, Daily average Wind (10m), and Daily average Atmospheric Pressure. Introduction to metadata of CMADS CMADS storage path description:(CMADS was divided into two datesets) 1.CMADS-V1.0 For-swat --specifically driving the SWAT model 2.CMADS-V1.0 For-other-model --specifically driving the other hydrological model(VIC,SWMM,etc.) CMADS-- For-swat-2009 folder contain:(Station and Fork ) 1).Station Relative-Humidity-58500 Daily average relative humidity(fraction) Precipitation-58500 Daily accumulated 24-hour precipitation(mm) Solar radiation-58500 Daily average solar radiation(MJ/m2) Tmperature-58500 Daily maximum and minimum 2m temperature(℃) Wind-58500 Daily average 10m wind speed(m/s) Where R, P, S, T, W+ dimensional grid number - the number of longitude grid is the station in the above five folders respectively.(Where R,P,S,T,W respective Daily average relative humidity,Daily cumulative precipitation(24h),Daily mean solar radiation(MJ/m2),Daily maximum and minimum temperature(℃) and Daily mean wind speed (m/s)) respectively.Data format is (.dbf) 2).Fork (Station index table over East Asia) PCPFORK.txt (Precipitation index table) RHFORK.txt (Relative humidity index table) SORFORK.txt (Solar radiation index table) TMPFORK.txt (Temperature index table) WINDFORK.txt (Wind speed index) CMADS-- For-swat-2012 folder contain:(Station and Fork ) Storage structure is consistency with For-swat- 2009 .However, all the data in this directory are only available in TXT format and can be readed by SWAT2012. 3) For-other-model (Includes all weather input data required by the any hydrologic model (daily).) Atmospheric-Pressure-txt Daily average atmospheric pressure(hPa) Average-Temperature-txt Daily average 2m temperature(℃) Maximum-Temperature-txt Daily maximum 2m temperature(℃) Minimum-Temperature-txt Daily minimum 2m temperature(℃) Precipitation-txt Daily accumulated 24-hour precipitation (mm) Relative-Humidity-txt Daily average relative humidity(fraction) Solar-Radiation-txt Daily average solar radiation(MJ/m2) Specific-Humidity-txt Daily average Specific Humidity(g/kg) Wind-txt Daily average 10m wind speed(m/s) Data storage information: data set storage format is .dbf and .txt Other data information: Total data:45GB Occupied space: 50GB Time: From year 2008 to year 2014 Time resolution: Daily Geographical scope description: East Asia Longitude: 60° E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 58500 stations Spatial resolution: 1/3 * 1/3 * grid points Vertical range: None

Created Time:2018-08-06
Glacier coverage data  on the Tibetan Plateau  in 1970s (TPG1976, Version 1.0)
Glacier coverage data on the Tibetan Plateau in 1970s (TPG1976, Version 1.0)

The Tibetan Plateau Glacial Data -TPG1976 is a glacial coverage data on the Tibetan Plateau in the 1970s. It was generated by manual interpretation from Landsat MSS multispectral image data. The temporal coverage was mainly from 1972 to 1979 by 60 m spatial resolution. It involved 205 scenes of Landsat MSS/TM. There were 189 scenes(92% coverage on TP)in 1972-79,including 116 scenes in 1976/77 (61% of all the collected satellite data).As high quality of MSS data is not accessible due to cloud and snow effects in the South-east Tibetan Plateau, earlier Landsat TM data was collected for usage, including 14 scenes of 1980s(1981,1986-89,which covers 6.5% of TP) and 2 scenes in 1994(by 1.5% coverage on TP).Among all satellite data,77% was collected in winter with the minimum effects of cloud and seasonal snow. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 1976. Glacier outlines were digitized on-screen manually from the 1976 image mosaic, relying on false-colour image composites (MSS: red, green and blue (RGB) represented by bands 321; TM: RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG1976. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG1976 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 6.4% due to the 60 m spatial resolution images.

Created Time:2018-02-24

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