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;
1) Data content: species list and distribution data of Phrynocephalus and Eremais in Tarim Basin, including class, order, family, genus, species, and detailed distribution information including country, province, city and county; 2) Data source and processing method: Based on the field survey of amphibians and reptiles in Tarim Basin from 2008 to 2020, and recording the species composition and distribution range of Phrynocephalus and Eremias in this area; 3) Data quality description: the investigation, collection and identification of samples are all conducted by professionals, and the collection of samples information are checked to ensure the quality of distribution data; 4) Data application results and prospects: Through comprehensive analysis of the dataset, the list of species diversity and distribution can provide important data for biodiversity cataloguing in arid central Asia, and provide scientific basis for assessing biodiversity pattern and formulating conservation strategies.
In April 2014 and may 2016, 21 Lakes (7 non thermal lakes and 14 thermal lakes) were collected in the source area of the Yellow River (along the Yellow River) respectively. The abundance of hydrogen and oxygen allogens was measured by Delta V advantage dual inlet / hdevice system in inno tech Alberta laboratory in Victoria, Canada. The isotope abundance was expressed in the form of δ (‰) (relative to the average seawater abundance in Vienna) ）Test error: δ 18O: 0.1 ‰, δ D: 1 ‰. The data also includes Lake area and lake basin area extracted from Landsat 2017 image data in Google Earth engine.
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.
The land-sea thermal contrast is an important driver for monsoon interannual and interdecadal variability and the monsoon onset. The importance of the thermal contrast between the Tibetan Plateau (TP) and the Indian Ocean (IO) in driving the establishment of Indian Summer Monsoon (ISM) has been recognized. The South Asian Summer Monsoon (SASM) is primarily a tropical summer monsoon. As a direct dynamic response to the diabatic heating, the difference between upper and lower-layer winds can be closely linked to the strength of the heat source. The upper-layer thermal contrast is more important for the SASM (Sun et al., 2010; Sun and Ding，2011; Dai et al., 2013). Thermal contrast between the TP and the IO at the mid-upper troposphere is closely related to the onset and the variability of ISM. Considering that the temperature above the TP and IO are the two centers which are most sensitive to the change of ISM, a thermal contrast index (TCI) is proposed based on 500-200hPa air temperature: TCI = Nor[T(25°N-38°N, 65°E-95°E) - T(5°S-8°N, 65°E-95°E)] Where Nor represents standardization and T is 500-200hPa air temperature. The TCI is larger, and the ISM is stronger. The TCI can capture the interannual and interdecadal variability of ISM well. The cooperative thermal effect between TP and IO may contributes more to the ISM than the separately temperature of TP or IO. In addition, from the view of climate mean state, the pentad-by-pentad increment of TCI has a 15-pentad lead when the correlation coefficient between it and the ISM index reaches the maximum. And the correlation coefficient between the pentad-by-pentad increment of TCI and the ISM index is significant when the pentad-by-pentad increment of TCI has a 3-pentad lead. The result indicates the advantage of the TCI for prediction of the ISM. Meanwhile, the averaged pentad-by-pentad increment of TCI for the first 25 (TCI25) pentads may be a predictor of the early or late onset of the ISM. The ISM onset will be earlier when the TCI25 is larger.
LI Zhangqun, XIAO Ziniu, ZHAO Liang
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, Jozsef Szilagyi, ZHANG Yinsheng, LIU Wenbin
This dataset contains the flux measurements from the Guazhou station eddy covariance system (EC) in the middle reaches of the Heihe integrated observatory network from September 24 to December 31 in 2018. The site (95.673E, 41.405N) was located in a desert in Liuyuan Guazhou, which is near Jiuquan city in Gansu Province. The elevation is 2016 m. The EC was installed at a height of 4.0 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 3% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2011) for data processing) in the Citation section.
ZHAO Changming, ZHANG Renyi
Surface evapotranspiration (ET) is an important variable that connects the land energy balance, water cycle and carbon cycle. The accurate acquisition of ET is helpful to the research of global climate change, crop yield estimation, drought monitoring, and it is of great significance to regional and global water resource planning and management. The methods of obtaining evapotranspiration mainly include ground observation, remote sensing estimation, model simulation and assimilation. The high-precision surface evapotranspiration data can be obtained by ground observation, but the spatial representation of observation stations is very limited; remote sensing estimation, model simulation and assimilation methods can obtain the spatial continuous surface evapotranspiration, but there are problems in the verification of accuracy and the rationality of spatial-temporal distribution pattern. Therefore, this study makes full use of a large number of high-precision station observation data, combined with multi-source remote sensing information, to expand the observation scale of ground stations to the region, to obtain high-precision, spatiotemporal distribution of continuous surface evapotranspiration. Based on the "Heihe River Integrated Remote Sensing joint experiment" (water), "Heihe River Basin Ecological hydrological process integrated remote sensing observation joint experiment" (hiwater), the accumulated station observation data (automatic meteorological station, eddy correlator, large aperture scintillation instrument, etc.), 36 stations (65 station years, distribution map is shown in Figure 1) are selected in combination with multi-source remote sensing data (land cover) Five machine learning methods (regression tree, random forest, artificial neural network, support vector machine, depth belief network) were used to construct different scale expansion models of surface evapotranspiration, and the results showed that: compared with The other four methods, random forest method, are more suitable for the study of the scale expansion of surface evapotranspiration from station to region in Heihe River Basin. Based on the selected random forest scale expansion model, taking remote sensing and air driven data as input, the surface evapotranspiration time-space distribution map (etmap) of Heihe River Basin during the growth season (May to September) from 2012 to 2016 was produced. The results show that the overall accuracy of etmap is good. The RMSE (MAPE) of upstream (las1), midstream (las2-las5) and downstream (las6-las8) are 0.65 mm / day (18.86%), 0.99 mm / day (19.13%) and 0.91 mm / day (22.82%), respectively. In a word, etmap is a high-precision evapotranspiration product in Heihe River Basin, which is based on the observation data of stations and the scale expansion of random forest algorithm. Please refer to Xu et al. (2018) for all station information and scale expansion methods, and Liu et al. (2018) for observation data processing.
LIU Shaomin, XU Tongren
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.
This data set includes the daily values of temperature, pressure, relative humidity, wind speed, wind direction, precipitation, radiation, and water vapor pressure observed from 22 international exchange stations in Sri Lanka from January 1, 2008 to October 1, 2018. The data was downloaded from the NCDC of NOAA. The data set processing method is that the original data is quality-controlled to form a continuous time series. It satisfies the accuracy of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO), and eliminates the systematic error caused by the failure of the tracking data and the sensor. The meteorological site information contained in this dataset is as follows: LATITUDE LONGITUDE ELEVATION  COUNTRY  STATION NAME +09.800  +080.067   +0015.0   SRI LANKA  KANKASANTURAI +09.650  +080.017   +0003.0   SRI LANKA  JAFFNA +09.267  +080.817   +0002.0   SRI LANKA  MULLAITTIVU +08.983  +079.917   +0003.0   SRI LANKA  MANNAR +08.750  +080.500   +0098.0   SRI LANKA  VAVUNIYA +08.539  +081.182   +0001.8   SRI LANKA  CHINA BAY +08.301  +080.428   +0098.8   SRI LANKA  ANURADHAPURA +08.117  +080.467   +0117.0   SRI LANKA  MAHA ILLUPPALLAMA +08.033  +079.833   +0002.0   SRI LANKA  PUTTALAM +07.706  +081.679   +0006.1   SRI LANKA  BATTICALOA +07.467  +080.367   +0116.0   SRI LANKA  KURUNEGALA +07.333  +080.633   +0477.0   SRI LANKA  KANDY +07.181  +079.866   +0008.8   SRI LANKA  BANDARANAIKE INTL COLOMBO +06.900  +079.867   +0007.0   SRI LANKA  COLOMBO +06.822  +079.886   +0006.7   SRI LANKA  COLOMBO RATMALANA +06.967  +080.767   +1880.0   SRI LANKA  NUWARA ELIYA +06.883  +081.833   +0008.0   SRI LANKA  POTTUVIL +06.817  +080.967   +1250.0   SRI LANKA  DIYATALAWA +06.983  +081.050   +0667.0   SRI LANKA  BADULLA +06.683  +080.400   +0088.0   SRI LANKA  RATNAPURA +06.033  +080.217   +0013.0   SRI LANKA  GALLE +06.117  +081.133   +0020.0   SRI LANKA  HAMBANTOTA
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
This data set includes daily average data of atmospheric temperature, relative humidity, precipitation, wind speed, wind direction, net radiance, and atmospheric pressure from 1 January 2007 to 31 December 2016 derived from the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data set has been used by students and researchers in the fields of meteorology, atmospheric environment and ecological research. The units of the various meteorological elements are as follows: temperature °C; precipitation mm; relative humidity %; wind speed m/s; wind direction °; net radiance W/m2; pressure hPa; and particulate matter with aerodynamic diameter less than 2.5 μm μg/m3. All the data are the daily averages calculated from the raw observations. Observations and data collection were carried out in strict accordance with the instrument operating specifications and the guidelines published in relevant academic journals; data with obvious errors were eliminated during processing, and null values were used to represent the missing data. In 2015, due to issues related to the age of the observation probe at the station, only the wind speed data for the last 8 months were retained.
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
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 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.
The object of this dataset is to support the atmospheric correction data for the satellite and airborne remote-sensing. It provides the atmospheric aerosol and the column content of water vapor. The dataset is sectioned into two parts: the conventional observations data and the observations data synchronized with the airborne experiments. The instrument was on the roof of the 7# in the Wuxing Jiayuan community from 1 to 24 in June. After 25 June, it was moved to the ditch in the south of the Supperstaiton 15. The dataset provide the raw observations data and the retrieval data which contains the atmosphere aerosol optical depth (AOD) of the wavebands at the center of 1640 nm, 1020 nm, 936 nm, 870 nm, 670 nm, 500 nm, 440 nm, 380 nm and 340 nm, respectively, and the water vapor content is retrieved from the band data with a centroid wavelength of 936 nm. The continuous data was obtained from the 1 June to 20 September in 2012 with a one minute temporal resolution. The time used in this dataset is in UTC+8 Time. Instrument: The sun photometer is employed to measure the character of atmosphere. In HiWATER, the CE318-NE was used.
YU Wenping, WANG Zengyan, MA Mingguo
Data source: China l Meteorological Administration Network; Data Content: Daily Rainfall Data Series of Heihe River Basin from 1990 to 2004; Evaporation Data of Heihe River Basin from 2000 to 2012. Data Spatial Range: Rainfall Data (Yingluoxia, Shandan, Gaoya, Pingchuan, Ganzhou Pingshan Lake, Zhengyixia Gorge, Liyuan River); Evaporation Data (Zhangye, Gaotai, Dingxin, Jiuquan, Jinta, Shandan, Ejina, Hequ)
WANG Zhongjing, ZHENG Hang
The routine meteorological observation data set of four times a day provided by the data management center of Heihe plan is adopted, including 13 stations. The daily evaporation was statistically sorted out, and the monthly evaporation data of 2000-2009 years was calculated. The spatial stability analysis is carried out to calculate the coefficient of variation. If the coefficient of variation is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly evaporation distribution trend is obtained; if the coefficient of variation is less than or equal to 100%, the common least square regression is used to calculate the relationship between the station evaporation value and the geographical terrain factors (latitude, longitude, elevation, slope, aspect, etc.) After the trend is removed, the residuals are fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average evaporation distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average evaporation in 2000-2009. Spatial resolution: 500M.
YUE Tianxiang, ZHAO Na
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 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