WATER: Dataset of automatic meteorological observations at the Huazhaizi desert station (2008-2011)

The dataset of automatic meteorological observations was obtained from Jun. 1, 2008 to Dec. 31, 2009 at the Huazhaizi desert station which is located in Anyangtan (E100°19'06.9″/N38°45'54.7″), south of Zhangye city, Gansu province,. The experimental area, situated in the middle stream of Heihe river, with a flat and open terrain and sparse vegetation cover is an ideal desert observing field. Observation items included the multi-layer (2m and 10m) wind speed and direction, the air temperature, precipitation, the four components of radiation, the surface infrared temperature, the multi-layer soil temperature (5cm, 10cm, 20cm, 40cm, 80cm and 160cm), soil moisture (5cm, 10cm, 20cm, 40cm, 80cm and 160cm) and soil heat flux (5cm & 10cm). The raw data were level0 and the data after basic processes were level1; the data after strict quality control were defined as Level2. The data files were named as follows: station+datalevel+AMS+datadate.. As for detailed information, please refer to “Meteorological and Hydrological Flux Data Guide".

0 2019-09-15

WATER: Dataset of automatic meteorological observations at the Dadongshu mountain pass snow observation station (2007-2009)

The dataset of automatic meteorological observations was obtained at the Dadongshu mountain snow observation station (E100°14′/N38°01′, 4101m) from Oct. 29, 2007 to Oct. 1, 2009. The experimental area with a flat and open terrain was slightly sloping from southeast to northwest. With alpine meadow and stones, and snow in autumn, winter and spring, the landscape was ideal. Observation items were multilayer (2m and 10m) of the wind speed, the air temperature and air humidity, the air pressure, rain and snow gauges, snow depth, four components of radiation, the multilayer soil temperature (5cm, 10cm, 20cm, 40cm, 80cm, and 120cm), soil moisture (5cm, 10cm, 20cm, 40cm, 80cm, and 120cm), and soil heat flux (5cm & 15cm). The raw data were level0 and the data after basic processes were level1, in which ambiguous ones were marked; the data after strict quality control were defined as Level2. The data files were named as follows: station+datalevel+AMS+datadate. Level2 or above were strongly recommended to domestic users. As for detailed information, please refer to Meteorological and Hydrological Flux Data Guide.

0 2019-09-15

Qilian Mountains integrated observatory network: cold and arid research network of Lanzhou university (an observation system of meteorological elements gradient of Dunhuang Station, 2018)

This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2018. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The data were missing during Jan. 23 to Jan. 24 because of collector failure; the data during Mar. 17 and May 24 were wrong because of the tower body tilt; The air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.

0 2019-09-15

WATER: Dataset of sun photometer observations in the Linze grassland foci experimental area (2008)

The dataset of sun photometer observations was obtained in Linze grassland station, the reed plot A, the saline plot B, the barley plot E, the observation stationof the Linze grassland foci experimental areaand Jingdu hotel of Zhangye city. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318 from May 30 to Jun. 11, 2008. And from Jun. 15 to Jul.11, the data of 1640nm, 1020nm, 936nm, 870nm, 670nm, 550nm, 440nm, 380nm and 340nm were acquired. Both measurements were carried out at intervals of 1 minute. Optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, air temperature and pressure near land surface, the solar azimuth and zenith could all be further retrieved. Readme file was attached for detail.

0 2019-09-15

WATER: Dataset of automatic meteorological observations at the Binggou cold region hydrometerological station (2007-2009)

The dataset of automatic meteorological observations was obtained at the Binggou cold region hydrometerological station (N38°04′/E100°13′), south of Qilian county, Qinghai province, from Sep. 25, 2007 to Dec. 31, 2009. The experimental area with paramo and riverbed gravel, situated in the upper stream valley of Heihe river, is ideal for the flat and open terrain and hills and mountains stretching outwards. The items were multilayer (2m and 10m) of the air temperature and air humidity, the wind speed, the air pressure, precipitation, four components of radiation, the multilayer soil temperature (5cm, 10cm, 20cm, 40cm, 80cm and 120cm), soil moisture (5cm, 10cm, 20cm, 40cm, 80cm and 120cm), and soil heat flux (5cm and 15cm). The raw data were level0 and the data after basic processes were level1, in which ambiguous ones were marked; the data after strict quality control were defined as Level2. The data files were named as follows: station+datalevel+AMS+datadate. Level2 or above were strongly recommended to domestic users. The period from Sep. 25, 2007 to Mar. 12, 2008 was the pre-observing duration, during which hourly precipitation data (fragmented) and the soil temperature and soil moisture data were to be obtained. Stylized observations began from Mar. 12, 2008. As for detailed information, please refer to Meteorological and Hydrological Flux Data Guide.

0 2019-09-15

Central Asian meteorological station observation dataset (2017-2018)

Central Asian meteorological station observation data set includes field observation data of temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation, soil heat flux, sunshine time and soil temperature at 10 field weather stations in central Asia. The 10 field stations cover different ecosystem types such as farmland, forest, grassland, desert, desert, wetland, plateau and mountain. The original meteorological data collected by the ground meteorological observation stations in this data set are obtained after format conversion after screening and auditing. The data quality is good. Various types of climate in the Middle East, fragile ecological environment, the frequent meteorological disasters, the establishment of the data set for long-term ecological environment monitoring, disaster prevention and mitigation in central Asia, central Asia, climate change and ecological environment in the areas of study provides data support, ecological environment monitoring in central Asia has been obtained in the study of the application.

0 2019-09-14

WATER: Dataset of automatic meteorological observations at the A'rou freeze/thaw observation station (2007-2015)

The dataset of automatic meteorological observations was obtained at the A'rou freeze/thaw observation station from Jul. 25, 2008 to Dec. 31, 2009, in Wawangtan pasture (E100°28′/N38°03′, 3032.8), Daban, A'rou. The experimental area, situated in the valley highland of south Babaohe river, an upper stream branch of Heihe river, with a flat and open terrain slightly sloping from southeast to southeast and hills and mountains stretching for 3km is ideal for a horizontal homogeneous underlying surface. Observation items included multilayer (2m and 10m) of the wind speed, the air temperature and air humidity, the air pressure, precipitation, four components of radiation, the multilayer soil temperature (10cm, 20cm, 40cm, 80cm, 120cm and 160cm), soil moisture (10cm, 20cm, 40cm, 80cm, 120cm and 160cm), and soil heat flux (5cm & 15cm). The raw data were level0 and the data after basic processes were level1, in which ambiguous ones were marked; the data after strict quality control were defined as Level2. The data files were named as follows: station+datalevel+AMS+datadate. Level2 or above were strongly recommended to domestic users. As for detailed information, please refer to Meteorological and Hydrological Flux Data Guide.

0 2019-09-14

Meteorological observation data from Qomolangma station for atmospheric and environmental observation and research (2005-2016)

This data set includes the daily averages of the temperature, pressure, relative humidity, wind speed, precipitation, global radiation, P2.5 concentration and other meteorological elements observed by the Qomolangma Station for Atmospheric and Environmental Observation and Research from 2005 to 2016. The data are aimed to provide service for students and researchers engaged in meteorological research on the Tibetan Plateau. The precipitation data are observed by artificial rainfall barrel, the evaporation data are observed by Φ20 mm evaporating pan, and all the others are daily averages and ten-day means obtained after half hour observational data are processed. All the data are observed and collected in strict accordance with the Equipment Operating Specifications, and some obvious error data are eliminated when processing the generated data.

0 2019-09-14

The meteorological data of Mt. Qomolangma, Namco, and Linzhi Stations on the Tibetan Plateau (2006-2008)

The data set collects the long-term monitoring data on atmosphere, hydrology and soil from the Integrated Observation and Research Station of Multisphere in Namco, the Integrated Observation and Research Station of Atmosphere and Environment in Mt. Qomolangma, and the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data have three resolutions, which include 0.1 seconds, 10 minutes, 30 minutes, and 24 hours. The temperature, humidity and pressure sensors used in the field atmospheric boundary layer tower (PBL) were provided by Vaisala of Finland. The wind speed and direction sensor was provided by MetOne of the United States. The radiation sensor was provided by APPLEY of the United States and EKO of Japan. Gas analysis instrument was provided by Licor of the United States, and the soil moisture content, ultrasonic anemometer and data collector were provided by CAMPBELL of the United States. The observing system is maintained by professionals on a regular basis (2-3 times a year), the sensors are calibrated and replaced, and the collected data are downloaded and reorganized to meet the meteorological observation specifications of the National Weather Service and the World Meteorological Organization (WMO). The data set was processed by forming a time continuous sequence after the raw data were quality-controlled, and the quality control included eliminating the systematic error caused by missing data and sensor failure.

0 2019-09-14

0.25 degree climate dataset in the northeastern part of the Tibetan Plateau (1957-2009)

The 0.25 Degree climate data set in the northeastern part of the Tibetan Plateau from 1957 to 2009 contains four meteorological elements, which are precipitation, maximum and minimum temperatures, and wind speed. The time resolution is daily. The data set contains 2400 text files, each with precipitation (the 1st column), highest (the 2nd column) and lowest (the 3rd column) temperatures and wind speed (the 4th column). Each file name contains latitude and longitude. Each file represents the four meteorological element values for the corresponding grid point (0.25*0.25 degrees). These data are formed by gridding the observation data of 81 meteorological stations in the northeast of the plateau, considering the change of meteorological conditions with the elevation. The gridding methods and steps are as follows. Download the original daily maximum and minimum temperatures, precipitation, and wind speed from the China Meteorological Data Network (http://data.cma.cn). Then, perform quality control on the data. The principle used is 1) to remove daily precipitations below 0 and greater than 150 mm, daily temperatures below -50 °C and greater than 50 °C and wind speeds below 0 m / s, 2) draw annual sequence precipitation, temperature and wind speed, check for abnormal year-to-year changes, and conduct quality control through station migration records. For data with abnormal changes but with station migration records, the data are segmented by modifying the station name. For example, at Xining Station (52866), abnormal temperature changes occurred in 1996, which was found through records that Xining Station migrated after 1996. Therefore, the records before 1996 are recorded as virtual station 52867 data, and after 1996, the data are still recorded as 52866 stations. If the data change abnormally but there is no station migration record, the abnormally changed data are eliminated, for example, the data from Delingha Station before 1975. Some stations have migration records, but the data do not change abnormally; then, it is assumed that the stations before and after the migration are still in the same climate environment, so there is no change in station name and data record. Interpolation begins after quality control. The method begins with (1) calculating the changes in daily average temperature, precipitation and wind speed as the altitude changes. It is concluded that the temperature decreases with altitude by 4.3 °C/km, and the coefficient of determination R2 is 0.65. In the warm and humid season (from May to September), the average daily precipitation has an insignificant increase with altitude (0.5 mm/km, R2 is 0.1). The average daily precipitation in the cold dry season (from October to April) does not change with altitude. The wind speed also has an insignificant increase with altitude, with an increase rate of 0.4 m/s/km and R2 of 0.1. (2) The spatial interpolation is performed using the Synographic Mapping System (SYMAP, Shepard, 1984) method. In this method, the distance between stations and the angle between surrounding stations are taken into account in interpolation to indicate the density of stations. The distance and angle are integrated into a weight. In addition, the stations that are close and have a large angle between each other are given a large weight. (3) The latitude and longitude of the station, the meteorological element value, the altitude, the rate of change with the altitude, and the weight are considered simultaneously, and the value of the target grid is interpolated. The maximum search range for interpolation is 55 stations around, and the smallest search range is 4 stations around. (4) Integrate the precipitation in the warm and dry seasons to form a precipitation sequence throughout the period. (5) During the method test period, some stations are set aside to check the gridded data. (6) After the verification is passed, all 81 stations are used in the final gridding process and form this set of data sets. Shepard, D. S., 1984: Computer Mapping: The SYMAP interpolation algorithm. Spatial Statistics and Models, G.Gaile and C. Willmot, Eds., Reidel 133-145.

0 2019-09-14