Numerical experiments: The climate model used is the fast air sea coupling model (FAMOUS) jointly developed by the British Meteorological Office and British universities The horizontal resolution of the atmospheric model in the FAMOUS model is 5 ° × 7.5 °, 11 layers in vertical direction; The horizontal resolution of the ocean model is 2.5 ° × 3.75 °, 20 layers in vertical direction The atmosphere and ocean are coupled once a day without flux adjustment The tests included the Middle Paleocene (MP,~60Ma BP, test name flat_60ma_1xCO2_sea_3d_ * * 100yr_mean. nc) and the Late Oligocene (LO,~25Ma BP, test name orog_25ma_1xCO2_sea_3d_ * * 100yr_mean. nc) The sea land distribution data is mainly taken from the global coastline basic data set (abbreviated as Gplates, website: http://www.gplates.org/ ）Considering that the initial uplift of Cenozoic terrains such as the Qinghai Tibet Plateau started at about 50~55 Ma (Searle et al., 1987), the global terrain height was set to 0 in the MP test to omit the role of plateau terrain. At 25 Ma, Greenland (Zachos et al., 2001) and the Qinghai Tibet Plateau (for example, Wang et al., 2014; Ding et al., 2014; Rowley and Currie, 2006; DeCells et al., 2007; Polisar et al., 2009) were revised The change of ancient latitude is also considered when reconstructing the ancient topography of the Qinghai Tibet Plateau (Besse et al., 1984; Chatterjee et al., 2013; Wei et al., 2013) At the same time, referring to the change of Cenozoic atmospheric CO2 (Beerling and Royer, 2011), the atmospheric CO2 concentration in the two periods of experiments was 280 ppmv (1 ppmv=1 mg L – 1) before the industrial revolution For simplicity, all land vegetation and soil properties are set to globally uniform values, that is, various land surface properties on each land grid point except Antarctica are assigned to the global average value of non glacial land surface before the industrial revolution, which is also convenient for highlighting the impact of land sea distribution and topographic changes In addition, since we mainly discuss the average climate state and its change in the characteristic geological period on the scale of millions of years, we can omit the influence of orbital forcing, that is, the Earth's orbital parameters are set to their modern values in all experiments Output time: All tests were integrated for 1000 years, using the average results of the last 100 years of each test. This data is helpful to explore the formation and evolution mechanism of the Cenozoic monsoon and drought.
LIU Xiaodong
Atmospheric water vapor is an important parameter for studying the water cycle. In the context of global warming, in order to better study the impact of atmospheric water vapor on the water cycle, a global daily scale AMSR-E/AMSR2 all-weather atmospheric precipitable water (TPW) dataset with a spatial resolution of 0.25 ° was constructed. In the data set, the TPW over land is mainly obtained by our newly developed 18.7 and 23.8 GHz brightness temperature data inversion algorithm based on AMSR-E and AMSR2; The ocean sky TPW data integrates AMSR-E/AMSR2 official TPW products. As a post-processing, in order to eliminate the systematic deviation between AMSR-E TPW and AMSR2 TPW, using AIRX2RET TPW as the benchmark, the histogram matching method was used to correct the systematic deviation of AMSR-E and AMSR2 TPW data on a global scale, to ensure the continuity of the data, and finally the global daily scale AMSR-E and AMSR2 TPW all-weather data sets were obtained. Among them, the time range of AMSR-E data is from July 8, 2002 to September 27, 2011, and the time range of AMSR-2 data is from January 1, 2013 to August 31, 2017. Each date contains two files: orbit raising and orbit lowering. The data format is Geotiff. The number of data layers is 2. The first layer is TPW data, with the unit of mm. The second layer is time information, which represents the number of seconds elapsed between the pixel observation time with UTC as the time base and 0:00:00 of the current day. The data set has reliable quality. Through verification and analysis with the global SuomiNET GPS TPW, the root mean square error of the data set is 3.5-5.2mm. As atmospheric precipitable water is an important geophysical parameter affecting surface remote sensing and also has an important impact on the earth's climate change, this data can be used for research on the impact of atmospheric water vapor on the water cycle, the assessment of atmospheric water resources and atmospheric correction in the context of climate warming.
JI Dabin, SHI Jiancheng, HUSI Letu, LI Wei , ZHANG Hongxing , SHANG Huazhe
The reconstruction of sunshine hours can better reflect the long-term change trend of surface solar radiation, but only the station data. Therefore, in order to obtain high-resolution grid point data and ensure its accuracy in long-term changes, it is necessary to fuse a variety of surface solar radiation related data. Using the geographic weighted regression (GWR) method, the MODIS 0.1 ° resolution cloud and aerosol retrieval and the surface sunshine hours are combined to reconstruct the surface solar radiation station data. By adding the combination judgment of adjacent point schemes, the accuracy of downscaling results of geographical weighted regression is effectively improved, and the multi-year average value and long-term trend of China are basically consistent with the observation and satellite remote sensing inversion results. Using geographic weighted regression and other methods, the surface wind speed and relative humidity data of 0.1 degree grid are generated; The improved Penman formula is used to calculate the land surface evapotranspiration data.
WANG Kaicun
This data set includes daily values of temperature, pressure, relative humidity, wind speed, wind direction, precipitation, radiation, water vapour pressure and other elements obtained from the Integrated Observation and Research Station of the Westerly Environment in Muztagh Ata from 18 May 2003 to 31 December 2016. The data are obtained by an automatic meteorological station (Vaisala) that recorded one measurement every 30 minutes. The data set was processed as a continuous time series after the original data were quality controlled. This data set satisfies the accuracy requirements of the meteorological observations of the National Weather Service and the World Meteorological Organization (WMO), and the systematic errors caused by the tracking data and sensor failure have been eliminated. The data set has mainly been applied in the fields of glaciology, climatology, environmental change research, cold zone hydrological process research and frozen soil science. Furthermore, this data set is mainly used by professionals engaged in scientific research and training in atmospheric physics, atmospheric environment, climate, glaciers, frozen soil and other disciplines.
WANG Yuanwei, XU Baiqing
The evapotranspiration (ET) is an important variable connecting land energy balance, water cycle and carbon cycle. Accurate monitoring and estimations of ET are essential not only for water resources management but also for simulating regional, global climate, and hydrological cycles. Remote sensing technology is an effective method to monitor ET. At present, a variety of ET remote sensing products have been produced and released. However, in the process of validation, there is a problem of spatial scale mismatch between ET remote sensing estimation value and station observation value, especially on heterogeneous surface. Therefore, it is very important to obtain the ground truth ET values at the satellite pixel scale by upscaling method on heterogeneous surface. In this study, using the station observation data and multi-source remote sensing information, the ET observed at a single ground station is upscaled to the satellite pixel scale, and the ground truth ET values at the satellite pixel scale in Heihe River Basin is obtained. Based on the ET data observed by the eddy covariance (EC) at 15 stations (3 superstations and 12 ordinary stations) in the Heihe integrated observatory network, combined with the fused high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) and atmospheric reanalysis data, the upscaling is carried out to obtain the ground truth ET at the satellite pixel scale. The distribution diagram is shown in Figure 1. Specifically, firstly, the spatial heterogeneity of the spatial heterogeneity of the land surface hydrothermal conditions was evaluated; Secondly, nine upscaling methods (the integrated Priestley-Taylor equation method, the Penman-Monteith equation combined with EnKF method, the Penman-Monteith equation combined with SCE_UA method, EC observation value, artificial neural network, Bayesian linear regression, deep belief network, Gaussian process regression, and random fores and directly taking the EC observation value as the ground truth ET) were compared and analyzed through direct validation and cross-validation; Finally, a comprehensive method (directly using the EC observation value on the homogeneous underlying surface; using the Gaussian process regression method for upscaling on the moderately heterogeneous underlying surface and highly heterogeneous underlying surface) was optimized to obtain the groud truth ET at the satellite pixel scale at 15 typical underlying surfaces in Heihe River Basin (2010-2016, spatial resolution of 1km). The results showed that the ground truth ET at the satellite pixel scale is relatively reliable. Compared with the pixel scale reference value (LAS observation value), the MAPE of the ground turth ET at the satellite pixel scale at the three superstations are 1.57%, 3.23% and 4.59% respectively, which can meet the needs of the validation of ET remote sensing products. For all site information and data processing, please refer to Liu et al. (2018), and for upscaling methods, please refer to Li et al. (2021).
LIU Shaomin, LI Xiang , XU Ziwei
As the “water tower of Asia”, the Tibetan Plateau has a profound impact on the global natural environment and climate change. Therefore, analyzing the distribution characteristics of troposphere-stratospheric water vapor over the Qinghai-Tibet Plateau is an important part of understanding the water vapor source and change characterize. In situ observations are limit in this region, and the water vapor sounding data set is needed. Therefore, we carried out balloon-borne measurements at Lhasa and Kunming over the Qinghai-Tibet Plateau, and then obtained the vertical distribution of water vapor in the troposphere and lower stratosphere over the Qinghai-Tibet Plateau. The dataset is named “Pan-Third Pole Water Vapor Sounding”, which is mainly the water vapor profile data obtained by balloon sounding conducted at Lhasa and Kunming in August from July 2009 to 2019. Altitude (Altitude), Water vapor (H2O), temperature (Temp), potential temperature (K), and air pressure (Press) from near the surface to 20 km are obtained by conventional balloons soundings payloaded with the Cryogenic Frost Point Hygrometer (CFH) and radiosonde (iMet). Data is transmitted in real time to the ground receiving station via a radiosonde.
BIAN Jianchun
1) Data content : total column water / precipitable water; 2) Data sources and processing methods: ECMWF-interm monthly mean analysis; 3) Data quality description: time resolution: monthly, spatial resolution: 0.7°*0.7°; 4) Data application results and prospects: this data can be used for analysis of water resources in the air.
YAN Hongru
To describing the quantity of atmospheric water resource gaining over the TP, we provide two indexs based on ERA5 monthly reanalysis. One is called column water income (CWI), defined as the sum of vertical integrated divergence of water vapor flux and surface evaporation. It is 0.25 ×0.25 gridded with unit of kg/m2 or millimeter. Another one is Atmospheric water tower index (AWTI), total of net income of atmospheric water resource for the entire TP area, i.e., and unit is Gt.
YAN Hongru
This dataset (version 1.5) is derived from the complementary-relationship method, with inputs of CMFD downward short- and long-wave radiation, air temperature, air pressure, GLASS albedo and broadband longwave emissivity, ERA5-land land surface temperature and humidity, and NCEP diffuse skylight ratio, etc. This dataset covers the period of 1982-2017, and the spatial coverage is Chinese land area. This dataset would be helpful for long-term hydrological cycle and climate change research. Land surface actual evapotranspiration (Ea)，unit: mm month-1. The spatial resolution is 0.1-degree; The temporal resolution is monthly; The data type is NetCDF; This evapotranspiration dataset is only for land surface.
MA Ning, MA Ning, Jozsef Szilagyi, ZHANG Yinsheng, LIU Wenbin
This data set is a sub data set of the comprehensive observation data set of cloud precipitation process, which is derived from the comprehensive investigation test carried out on the South and north slopes of Qilian Mountains during 2020. The air observation is mainly conducted by the king aircraft in the air. The ground investigation includes automatic weather station, raindrop spectrometer, microwave radiometer, Cloud Radar, sounding second data, etc. The observation elements of automatic weather station include air temperature, air pressure, humidity Wind direction, wind speed, precipitation. The observation elements of raindrop spectrometer include particle spectrum, precipitation intensity, etc. The observation elements of microwave radiometer are atmospheric temperature and humidity profiles. The observation elements of cloud Radar are mainly fixed-point vertical observation data. Meanwhile aerosol, rain, hail and soil samples are collected. It can provide data support for revealing the influence of westerly monsoon on cloud precipitation process and atmospheric water cycle in Qilian Mountains.
FU Danhong
1. The data content includes: year, month, day, hour, longitude, latitude, altitude, meridional (UQ) and latitudinal (VQ) components of water vapor flux; 2. Data source and processing method: GPS meteorological sounding data of voyages in the eastern Indian Ocean, and calculate water vapor flux through relative humidity, wind field, air pressure and altitude; 3. Data quality description: vertical continuous observation with 1 second vertical resolution; 4. Data application achievements and prospects: Study on the changes of water vapor transport in the tropical Indian Ocean;
WANG Dongxiao
The dataset of sun photometer observations was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas. 24 times observations were carried out by CE318 from BNU (at 1020nm, 936nm, 870nm, 670nm and 440nm, and column water vapor by 936 nm data) and from Institute of Remote Sensing Applications, CAS (at 1640nm, 1020nm, 936nm, 870nm, 670nm, 550nm, 440nm, 380nm and 340nm, and column water vapor by 936 nm data) on May 20, 23, 25 and 27, Jun. 4, 6, 16, 20, 22, 23, 27 and 29, Jul. 1, 7 and 11, 2008. Those atmospheric measurements synchronized with airborne (i.e. WiDAS, OMIS) and spaceborne sensors (i.e. TM, ASTER,CHRIS and Hyperion) Accuracy of CE318 could be influenced by local air pressure, instrument calibration parameters, and convertion factors. (1) Most air pressure was derived from elevation-related empiricism, which was not reliable. For more accurate result, simultaneous data from the weather station are needed. (2) Errors from instrument calibration parameters. Field calibration based on Langly or interior instrument calibrationcin the standard light is required. (3) Convertion factors for retrieval of aerosol optical depth and the water vapor of the water vapor channel were also from empiricism, and need further checking. Raw data were archived in k7 format and can be opened by ASTPWin. ReadMe.txt is attached for details. Preprocessed data (after retrieval of the raw data) in Excel format are on optical depth, Rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. Langley was used for the instrument calibration. Two parts are included in CE318 result data (see Geometric Positions and the Total Optical Depth of Each Channel and Rayleigh Scattering and Aerosol Optical Depth of Each Channel).
REN Huazhong, YAN Guangkuo, GUANG Jie, SU Gaoli, WANG Ying, ZHOU Chunyan
This dataset includes 5 sub-datasets obtained from measurements in the flux observing matrix at observing site No.15 (the Daman superstation) and 13. Specifically, the sub-datasets include the following: (1) a dataset that contains atmospheric water vapor D/H and 18O/16O isotopic and flux ratio measurements from site No.15 from 27 May to 21 September in 2012, (2) a dataset that contains D/H and 18O/16O isotopic ratios of water in soil and in corn xylem at site No.15 from 27 May to 21 September 2012, (3) a dataset that contains atmospheric water vapor D/H and 18O/16O isotopic ratios at site No.13 when airborne surveys occurred, and (4) a dataset that contains D/H and 18O/16O isotopic ratios of water in soil and in corn xylem at sites No.13 and 15 when airborne surveys occurred, (5) a dataset that contains the ratios of evaporation and transpiration to evapotranpiration at site No.15. The experiment area was located in a corn cropland in the Daman irrigation district of Zhangye, Gansu Province, China. The positions of observing sites No.15 and 13 were 100.3722° E, 38.8555° N and 100.3785° E, 38.8607° N, respectively, with an elevation of 1552.75 m above sea level. The atmospheric water vapor D/H and 18O/16O isotopic and flux ratios at site No.15 were continuously measured using an in situ observation system. The system consisted of an H218O, HDO and H2O analyzer (Model L1102-i, Picarro Inc.), a CTC HTC-Pal liquid auto sampler (LEAP Technologies) and a multichannel solenoid valve (Model EMT2SD8 MWE, Valco Instruments CO. Inc.). The heights of the two intakes were 0.5 and 1.5 m above the corn canopy. The water vapor D/H and 18O/16O isotopic ratio analyzer recorded signals at 0.2 Hz; data were recorded for 2 minutes per intake. The data were block-averaged to hourly intervals. The sampling frequency of soil and xylem at site No. 15 was 1-3 days. The atmospheric water vapor D/H and 18O/16O isotopic and flux ratios at site No.13 were measured using a cold traps/mass spectrometer. The sampling frequency of atmospheric water vapor, soil water and xylem water at site No.13 was the same as that of the airborne surveys. Briefly, the Picarro analyzer measurements were calibrated during every 3 h switching cycle using a two-point concentration interpolation procedure in which the water vapor mixing ratio was dynamically controlled to track the ambient water vapor mixing ratio. Possible delta stretching effects were not considered. A schematic diagram of the Picarro analyzer and its operation principles and calibration procedure are described elsewhere in the literature (Huang et al., 2014; Wen et al. 2008, 2012). The dataset of atmospheric water vapor D/H and 18O/16O isotopic and flux ratios at site No.15 includes the following variables: Timestamp (time, timestamp without time zone), Number (available record number), δD for r1 (δD for the lower intake, ‰), δD for r2 (δD for the higher intake, ‰), δ18O for r1 (δ18O for the lower intake, ‰), δ18O for r2 (δ18O for the higher intake, ‰), vapor mixing ratio for r1 (vapor mixing ratio for the lower intake, mmol/mol), vapor mixing ratio for r2 (vapor mixing ratio for the higher intake, mmol/mol), δET_D (δD of evapotranspiration, ‰), and δET_18O (δ18O of evapotranspiration, ‰). The dataset of D/H and 18O/16O isotopic ratios of water in soil and in corn xylem at site No.15 includes the following variables: Timestamp (time, timestamp without time zone), Remark (treatment: soil without mulch (Ld)=1; soil with mulch (Fm)=2; soil with male corns (F)=3; Xylem=4), δD (‰), and δ18O (‰). The dataset for the ratio of soil evaporation and transpiration to the evapotranspiration at site 15 includes the following variables: Timestamp (time, timestamp without time zone), E/ET (ratio of soil evaporation to the evapotranspiration, %), and T/ET (ratio of transpiration to the evapotranspiration, %). The mean (±one standard deviation) ratio of transpiration to evapotranspiration was 86.7±5.2% (the range was 71.3 to 96.0%). The mean (±one standard deviation) ratio of soil evaporation to the evapotranspiration was 13.3 ±5.2% (the range was 4.0 to 28.7%). The dataset of atmospheric water vapor D/H and 18O/16O isotopic ratio at site No. 13 when airborne surveys occurred includes the following variables: Timestamp1 (start time, timestamp without time zone), Timetamp2 (end time, timestamp without time zone), Height (observation height, cm), δD (‰), and δ18O (‰). The dataset of D/H and 18O/16O isotopic ratios of water in soil and in corn xylem at sites No. 13 and 15 when airborne surveys occurred include the following variables, Timestamp (time, timestamp without time zone), Remark (treatment: soil without mulch (Ld)=1; soil with mulch (Fm)=2; Xylem=4), δD (‰), δ18O (‰), and Location (observing site 13 or 15) . The missing measurements were replaced with -6999. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Wen et al. (2016) (for data processing) in the Citation section.
WEN Xuefa, LIU Shaomin, LI Xin
The assessment of changes in the atmospheric water cycle and the associated impacts in a key area of the Tibetan Plateau under the background of the global warming was a major component of the research project “The Environmental and Ecological Science of West China” run by the National Natural Science Foundation of China. The leading executive of the project was Xiangde Xu from the Chinese Academy of Meteorological Sciences. The project ran from January 2006 to December 2008. The following data were collected by the project of the Sino-Japan Joint Research Center of Meteorological Disaster (JICA Project): 1. Observation category, time period and number of stations 1) JICA AWS data: From January to July of 2008, 73 automatic stations (including 5 automatic stations of the Chinese Academy of Sciences) collected data in Tibet, Yunnan, Sichuan and other provinces or autonomous regions. 2) JICA GPS water vapour data: From January to October of 2008, 24 observation stations collected data in Tibet, Yunnan, Sichuan and other provinces or autonomous regions. 3) JICA encrypted observation GPS sonde data: From March to July of 2008, observations were made in Tibet, Yunnan, Sichuan and other provinces or autonomous regions (detailed observation time and location data can be found in the data catalogue). 2. Observation categories, data content 1) GPS water vapour Data content: serial number, station name (Chinese), station number, longitude, latitude, altitude, year, month, day, time, surface pressure, surface air temperature, relative humidity, total delay (m), precipitation (cm) (Measurement interval: 1 hour). 2) GPS encrypted sonde Data content: air pressure P, temperature T, relative humidity RH, V component, U component, vertical height H, dew point temperature Td, water vapour content Mr, wind direction Wd, wind speed Ws, longitude Lon, latitude Lat, radar height RdH. A value of "-999.90" means no observation data. 3) AWS Data content: station number, longitude, latitude, elevation, site level, total cloud volume, wind direction, wind speed, sea level pressure, 3-hour pressure variable, past weather 1, past weather 2, 6-hour precipitation, low cloud form, low cloud volume, low cloud height, dew point, visibility, current weather, temperature, medium cloud form, high cloud form, 24-hour temperature variable, 24-hour pressure variable. Project Science Advisers: Guoguang Zheng, Xiaofeng Xu, Xiuji Zhou, Zechun Li, Jifan Niu, Jianmin Xu, Lianshou Chen, Dahe Qin, Yihui Ding Project Superintendent: Jixin Yu Project Executives: Renhe Zhang, Xiangde Xu Data set hosting organizations: Chinese Academy of Meteorological Sciences, JICA Project Implementation Expert Group, State Key Laboratory of Severe Weather, JICA Project Implementation Office. Collaborative organizations involved in the production of the data set: Chinese Academy of Meteorological Sciences, State Key Laboratory of Severe Weather, National Satellite Meteorological Center, The Research Center for Atmospheric Sounding Techniques, National Meteorological Center, National Meteorological Information Center, National Climate Center, Sichuan Meteorological Department, Yunnan Meteorological Department, Tibet Autonomous Region Meteorological Department, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Tianjin Meteorological Department. Data set implementation organizations: Beijing Headquarters of JICA Project; JICA Project Sub-center in Sichuan Province, Yunnan Province, Tibet Autonomous Region and Institute of Tibetan Plateau Research, Chinese Academy of Sciences.
XU Xiangde
The measurement data of the sun spectrophotometer can be directly used to perform inversion on the optical thickness of the non-water vapor channel, Rayleigh scattering, aerosol optical thickness, and moisture content of the atmospheric air column (using the measurement data at 936 nm of the water vapor channel). The aerosol optical property data set of the Tibetan Plateau by ground-based observations was obtained by adopting the Cimel 318 sun photometer, and both the Mt. Qomolangma and Namco stations were involved. The temporal coverage of the data is from 2009 to 2016, and the temporal resolution is one day. The sun photometer has eight observation channels from visible light to near infrared. The center wavelengths are 340, 380, 440, 500, 670, 870, 940 and 1120 nm. The field angle of the instrument is 1.2°, and the sun tracking accuracy is 0.1°. According to the direct solar radiation, the aerosol optical thickness of 6 bands can be obtained, and the estimated accuracy is 0.01 to 0.02. Finally, the AERONET unified inversion algorithm was used to obtain aerosol optical thickness, Angstrom index, particle size spectrum, single scattering albedo, phase function, birefringence index, asymmetry factor, etc.
CONG Zhiyuan
This dataset contains monthly 0.05°×0.05° (1982, 1985, 1990, 1995, and 2000) and 0.01°×0.01° (2005, 2010, 2015 and 2017) LST products in Qilian Mountain Area. The dataset was produced based on SW algorithm by AVHRR BT from thermal infrared channels (CH4: 10.5µm to 11.3µm; CH5: 11.5µm to 12.5µm) at a resolution of 0.05°, MYD21A1 LST products at a resolution of 0.01° along with some auxiliary datasets. The auxiliary datasets include IGBP land cover type, AVHRR NDVI products, Modern Era Retrospective-Analysis for Research and Applications-2 (MERRA-2) reanalysis data, ASTER GED, Lat/Lon and the Julian Day information.
WANG Junbo, SHAO Xuemei
The dataset of ground truth measurement synchronizing with Landsat TM was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on May 20, 2008. Observation items included: (1) LAI in Yingke oasis maize field. The maximum leaf length and width of each alfalfa and barley were measured. Data were archived in Excel format. (2) Reflectance spectra in Yingke oasis maize field by ASD FieldSpec (350-2500nm, the vertical canopy observation and the transect observation) from Institute of Remote Sensing Applications (CAS), and in Huazhaizi desert No. 2 plot by ASD FieldSpec (350-1603nm, the vertical observation and the transect observation for reaumuria soongorica and the bare land) from Beijing Academy of Agriculture and Forestry Sciences. The grey board and the black and white cloth were also used for calibration spectrum. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (3) the radiative temperature by 3 handheld radiometers in Yingke oasis maize field (Institute of Remote Sensing Applications, BNU and Institute of Geographic Sciences and Natural Resources respectively, the vertical canopy observation and the transect observation), and by 3 handheld infrared thermometers in Huazhaizi desert No. 2 plot (the vertical vegetation and bare land observation). The data included raw data (in Word format), recorded data and the blackbody calibrated data (in Excel format). (4) the radiative temperature of maize, wheat and the bare land of Yingke oasis maize field by ThermaCAM SC2000 (1.2m above the ground, FOV = 24°×18°). The data included raw data (read by ThermaCAM Researcher 2001), recorded data and the blackbody calibrated data (archived in Excel format). (5) Photosynthesis of maize, wheat and the bare land of Yingke oasis maize field by LI6400, carried out according to WATER specifications. Raw data were archived in the user-defined format (by notepat.exe) and processed data were in Excel format. (6) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (7) Atmospheric parameters in Huazhaizi desert No. 2 plot by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (8) Coverage fraction of Reaumuria soongorica by the self-made coverage instrument and the camera (2.5m-3.5m above the ground) in Huazhaizi desert No. 2 plot. Based on the length of the measuring tape and the bamboo pole, the size of the photo can be decided. GPS data was used for the location and the technology LAB was used to retieve the coverage fractionof the green vegetation. Besides, such related information as the surrounding environment was also recorded. Data included the vegetation iamge and coverage (by .exe). (9) The radiative temperature of Reaumuria soongorica canopy and the bare land by 2 fixed automatic thermometers (FOV: 10°; emissivity: 0.95) in Huazhaizi desert No. 2 plot, observing straight downwards at intervals of 1s. Raw data, blackbody calibrated data and processed data were all archived in Excel format.
CHAI Yuan, CHEN Ling, KANG Guoting, LI Jing, QIAN Yonggang, REN Huazhong, WANG Haoxing, WANG Jindi, XIAO Zhiqiang, YAN Guangkuo, SHU Lele, GUANG Jie, LI Li, Liu Qiang, LIU Sihan, XIN Xiaozhou, ZHANG Hao, ZHOU Chunyan, TAO Xin, YAN Binyan, YAO Yanjuan, TIAN Jing, LI Xiaoyu
The dataset of ground truth measurement synchronizing with PROBA CHRIS was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 22, 2008. Observation items included: (1) Albedo by the shortwave radiometer in Huazhaizi desert No. 2 plot. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (2) BRDF of maize in Yingke oasis maize field by ASD (350-2 500 nm) from Beijing University and the observation platform of BNU make. The maximum height of the platform was 5m above the ground with the azimuth 0~360° and the zenith angle -60°~60°; BRDF in Huazhaizi desert No. 2 plot by ASD from Institute of Remote Sensing Applications (CAS) and the observation platform of its own make, whose maximum height was 2m above the ground with the zenith angle -70°~70°. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (3) Atmospheric parameters in Huazhaizi desert No. 2 plot by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in .k7 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number.
CHEN Ling, GUO Xinping, REN Huazhong, ZOU Jie, LIU Sihan, ZHOU Chunyan, FAN Wenjie, TAO Xin
1) The data set is composed of global atmospheric reanalysis data jointly produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). These grid data are generated by reanalysing the global meteorological data from 1948 to present by applying observation data, forecasting models and assimilation systems. The data variables include surface, near-surface (.995 sigma layer) and multiple meteorological variables in different barospheres, such as precipitation, temperature, relative humidity, sea level pressure, geopotential height, wind field, heat flux, etc. 2) The coverage time is from 1948 to 2018, and the data from 1948 to 1957 are non-Gaussian grid data. The data cover the whole world. The spatial resolution is a 2.5° latitude by 2.5° longitude grid. The vertical resolution is a 17-layer standard pressure barosphere, with layer boundaries at 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa, and 28 sigma levels. Some variables are calculated for 8 layers (omega) or 12 layers (humidity), with temporal resolutions of 6 hours, daily, monthly or a long-term monthly average (from 1981 to 2010). The daily data are obtained by averaging the daily values of 0Z, 6Z, 12Z and 18Z. 3) Missing values are assigned a value of -9.99691e+36f. The data are stored in the .nc format with the file name var.time.stat.nc, and each file includes data on latitude, longitude, time, and atmospheric variables. For detailed data specifications, please visit http://www.esrl.noaa.gov/pad/data.
National Oceanic and Atmospheric Administration, National Center for Atmospheric Research
The dataset of GPS radiosonde observations was obtained at an interval of 2 seconds in the cold region hydrology experimental area in March, 2008 and the arid region hydrology experimental area from May to July, 2008. The items were the air temperature, relative humidity, air pressure, the dew temperature, the water vapor mixing ratio, latitudinal and longitudinal wind speeds, the wind speed and direction. Simultaneous with the satellite/airplane overpass, GPS radiosonde observations were carried out: Binggou watershed on Mar. 14, A'rou on Mar. 15, Binggou watershed on Mar. 15, Biandukou on Mar. 17, Binggou watershed on Mar. 22, Binggou watershed on Mar. 29, and A'rou on Apr. 1 for the upper stream experiments; Linze grassland station on May 30, Yingke oasis on Jun.1, Huazhaizi desert station on Jun. 4, Linze grassland station on Jun. 5, Linze grassland station on Jun. 6, Huazhaizi desert station on Jun. 16, Yingke oasis on Jun. 29, Binggou watershed on Jul. 5, Yingke oasis on Jul. 7, Linze grassland station on Jul. 11, and Yingke oasis at 0, 4:10, 8:09, and 12:09 on Jul. 14 for middle stream experiments.
GU Lianglei, HU Zeyong, LI Maoshan, MA Weiqiang, SUN Fanglei
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