Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Alpine meadow and grassland ecosystem Superstation, 2021)

This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from January 1 to October 9 in 2021. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content). 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 missing data were denoted by -6999. (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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.

0 2022-06-29

Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Subalpine shrub, 2021)

This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from January 1 to October 13, 2021. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, volumetric water content). 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 missing data were denoted by -6999. (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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.

0 2022-06-29

Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of the temperate steppe, 2021

This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient from Janurary 1 to October 13 in 2021. The site (100°14'8.99"E, 37°14'49.00"N) was located in Sanjiaocheng sheep breeding farm, Gangcha County, Qinghai Province. The elevation is 3210m.The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; towards north), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m; RH_3 m, RH_5 m, RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content). 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 missing data were denoted by -6999. (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: 2018/8/31 10:30.

0 2022-06-29

Pan-Third Pole Water Vapor Sounding (2009-2019)

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.

0 2022-06-06

Driving data of surface meteorological elements in the eastern Qinghai Tibet Plateau with a horizontal resolution of 3km * 3km and an hour (2010)

Based on the regional environment integrated system model developed by the Key Laboratory of regional climate and environment, Chinese Academy of Sciences, a regional climate model for convective analysis of the Qinghai Tibet Plateau is established. The grid center of the model simulation area is located at (34n, 100e), the horizontal resolution is 3km, and the number of simulation grid points of the model is 465 (longitude) x 375 (latitude). The vertical direction is 27 floors. The air pressure at the top of the model layer is 50 HPA. The buffer zone consists of 15 grids, the integration time is one year in 2010, and the horizontal resolution of the European medium range weather forecast center is 0.25x0 25. The reanalysis data of era5 with a time interval of 6 hours is used as the driving field to generate the driving data of surface meteorological elements on the Qinghai Tibet Plateau in 2010 with a horizontal resolution of 3 km * 3 km and a time interval of 1 hour After dynamic downscaling by using the convection analysis regional climate model of the Qinghai Tibet Plateau, the bottleneck problem of the lack of meteorological data sets with long-time series and high spatial-temporal resolution in the Qinghai Tibet Plateau and other regions is solved, so as to provide a solid and reliable scientific data foundation for the future change of climate and environment and the construction of ecological security barrier in the Qinghai Tibet Plateau.

0 2022-05-31

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Large aperture scintillometer of Arou Superstation, 2021)

This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Arou Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2021. There were two types of LASs at Arou Superstation: BLS900 and RR-RSS460, produced by Germany and China, respectively. The north tower was set up with the RR-RSS460 receiver and the BLS900 transmitter, and the south tower was equipped with the RR-RSS460 transmitter and the BLS900 receiver. The site (north: 100.471° E, 38.057° N; south: 100.457° E, 38.038° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 13.0 m, and the path length was 2390 m. The data were sampled 1 minute at both BLS900 and RR-RSS460. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900: Cn2>7.25E-14, RR-RSS460: Cn2>7.84E-14). (2) The data were rejected when the demodulation signal was small (BLS900: Average X Intensity<1000; RR-RSS460: Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl (1992) and Andreas (1988) were selected for BLS900 and RR-RSS460, respectively. Detailed can refer to Liu et al. (2011, 2013). Several instructions were included with the released data. (1) The data were primarily obtained from BLS900 measurements, and missing flux measurements from the BLS900 instrument were substituted with measurements from the RR-RSS460 instrument. The missing data were denoted by -6999. Due to the problems of storing and wireless transmission. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.

0 2022-05-27

Glacier meteorological data from Wetern Pamir in Tajikstan (2021)

Tajikistan West Pamir Glacier Meteorological Station (38°3′15″N, 72°16′52″E, 3730m), the station is the Urumqi Desert Meteorological Institute of the China Meteorological Administration and the Tajikistan National Academy of Sciences for Water Issues, Water Energy and Ecology The Institute and the Tajikistan Hydrological and Meteorological Service are jointly constructed. Observation data includes hourly meteorological elements (average wind direction (°), average wind speed (m/s), wind direction at maximum wind speed (°), maximum wind speed (m/s), average temperature (°C), maximum Air temperature (°C), minimum air temperature (°C), average relative humidity (%), minimum relative humidity (%), average atmospheric pressure (hPa), maximum atmospheric pressure (hPa), minimum atmospheric pressure (hPa)). The data period is from December 10, 2020 to October 13, 2021 Meteorological observation data can provide important basic data for studying the relationship between climate change, glaciers and water resources in the West Pamir Mountains, and provide important data for the economic construction of the lower reaches of the Amu Darya River Basin in Tajikistan.

0 2022-05-23

Monthly meteorological data set with 1km resolution on the Qinghai Tibet Plateau (2018-2019)

The monthly meteorological data set with 1km resolution on the Qinghai Tibet Plateau from 2018 to 2019 is from January 2018 to December 2019. The original data comes from chelsa (climatology at high resolution for the earth's land surface areas). After spatial correction, accuracy verification and cutting, 1km resolution precipitation, wind speed, air temperature and humidity data are obtained. The data can be opened and used by ArcGIS, envi or other geographic information systems and remote sensing software.

0 2022-05-19

Hourly meteorological forcing & land surface state dataset of Tibet Plateau with 10 km spatial resolution (2000-2010)

The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.

0 2022-05-17

Cold and Arid Research Network of Lanzhou university (an observation system of Meteorological elements gradient of Guazhou Station, 2020)

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 Guazhou Station from January 1 to December 31, 2021. The site (95.673E, 41.405N) was located on a desert in the Liuyuan Guazhou, which is near Jiuquan city, Gansu Province. The elevation is 2014 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, 8, 16, 32, and 48 m, towards north), wind speed and direction profile (windsonic; 2, 4, 8, 16, 32, and 48 m, towards north), air pressure (1.5 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil temperature/ moisture/ electrical conductivity profile -0.05, -0.1m, -0.2m, -0.4m, -0.6m and -0.8m in south of tower), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_1_2_1, Ta_1_4_1, Ta_1_8_1, Ta_1_16_1, Ta_1_32_1 and Ta_1_48_1; RH_2 m, RH_1_2_1, RH_1_4_1, RH_1_8_1, RH_1_16_1, RH_1_32_1, and RH_1_48_1) (℃ and %, respectively), wind speed (WS_1_2_1, WS_1_4_1, WS_1_8_1, WS_1_16_1, WS_1_32_1 and WS_1_48_1) (m/s), wind direction (WD_1_2_1, WD_1_4_1, WD_1_8_1, WD_1_16_1, WD_1_32_1 and WD_1_48_1) (°), air pressure (PA_1_1_1) (hpa), precipitation (P_1_4_1) (mm), four-component radiation (SWIN_1_4_1, incoming shortwave radiation; SWOUT_1_4_1, outgoing shortwave radiation; LWIN_1_4_1, incoming longwave radiation; LWOUT_1_4_1, outgoing longwave radiation; RN_1_4_1, net radiation) (W/m^2), infrared temperature (TC_1_4_1) (℃), photosynthetically active radiation (PPFD_1_4_1) (μmol/ (s m^2)), soil heat flux (SHF_1_5_1, SHF_1_10_1) (W/m^2), soil temperature (TS_1_5_1, TS_1_10_1, TS_1_20_1, TS_1_40_1, TS_1_60_1 and TS_1_80_1) (℃), soil moisture (SWC_1_5_1, SWC_1_10_1, SWC_1_20_1, SWC_1_40_1, SWC_1_60_1 and SWC_1_80_1) (%, volumetric water content),soil water potential (SWP_1_5_1, SWP_1_10_1, SWP_1_20_1, SWP_1_40_1, SWP_1_60_1 and SWP_1_80_1)(kpa), soil conductivity (EC_1_5_1, EC_1_10_1, EC_1_20_1, EC_1_40_1, EC_1_60_1 and EC_1_80_1)(μs/cm), Sun_time_1_4_1 (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. Missing or abnormal data is replaced by – 6999. (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: 2021-6-10 10:30.

0 2022-05-17