Daily meteorological observation datasets of the Heihe River Basin (1951-2012)

Based on the "China Meteorological science data sharing service network", the daily data sets of five meteorological observation stations since 1951-2012 have been sorted out. It is mainly the daily data set of five base ground meteorological observation stations and automatic stations since 1951, including daily average pressure, maximum pressure, minimum pressure, average temperature, maximum temperature, minimum temperature, average relative humidity, minimum relative humidity, average wind speed, maximum wind speed and direction, maximum wind speed and direction Sunshine hours and precipitation.

0 2020-06-05

Standard weather station annual data of the Yellow River’s Upstream (1952-2011)

Ⅰ. Overview This dataset contains annual meteorological data from the upper Yellow River and its surroundings from 1952 to 2011. The standard station data includes 38 elements: average station pressure, extreme maximum station pressure, date of extreme maximum station pressure, month of extreme maximum station pressure, month of extreme minimum station pressure, date of extreme minimum station pressure, day, extreme Lowest station pressure month, average temperature, extreme maximum temperature, extreme maximum temperature day, extreme maximum temperature month, extreme minimum temperature, extreme minimum temperature day, extreme minimum temperature month, average temperature anomaly, average maximum temperature , Average minimum temperature, average relative humidity, minimum relative humidity, minimum relative humidity occurrence day, minimum relative humidity occurrence month, precipitation, daily precipitation ≥0.1mm days, percentage of precipitation anomaly, maximum daily precipitation, maximum daily precipitation Appearance day, month of maximum daily precipitation, average wind speed, maximum wind speed, maximum wind speed, wind direction of maximum wind speed, day of maximum wind speed, month of maximum wind speed, direction of maximum wind speed, day of maximum wind speed The month of maximum wind speed, the number of hours of sunshine, and the percentage of sunshine. Ⅱ. Data processing description The data is stored as integers, the temperature unit is (0.1 ° C) value, the precipitation unit is (0.1 mm), and it is stored as an ASCII text file. Ⅲ. Data content description Standard station data, all meteorological elements are stored in one text, respectively: average own station pressure (V10004), extreme highest station pressure (V10201), extreme highest station pressure (V10201_001), extreme highest station pressure appears Month (V10201_002), Extreme Lowest Station Pressure (V10202), Extreme Lowest Station Pressure (V10202_001), Extreme Lowest Station Pressure (V10202_002), Average Temperature (V12001), Extreme Maximum Temperature (V12011), Extreme Maximum Temperature appearance day (V12011_101), extreme maximum temperature appearance month (V12011_102), extreme minimum temperature (V12012), extreme minimum temperature appearance day (V12012_101), extreme minimum temperature appearance month (V12012_102), average temperature anomaly (V12201), average Highest temperature (V12211), average minimum temperature (V12212), average relative humidity (V13003), minimum relative humidity (V13007), minimum relative humidity occurrence day (V13007_001), minimum relative humidity occurrence month (V13007_002), precipitation (V13011) , Daily precipitation ≥ 0.1mm days (V13011_000), percentage of precipitation anomaly (V13012), maximum daily precipitation (V13052), day of maximum daily precipitation (V13052_00 1), the month when the maximum daily precipitation occurs (V13052_002), the average wind speed (V11002), the maximum wind speed (V11041), the maximum wind speed (V11042), the direction of the maximum wind speed (V11043), and the day when the maximum wind speed appears (V11043_001) , The month of maximum wind speed (V11043_002), the direction of maximum wind speed (V11212), the day of maximum wind speed (V11212_001), the month of maximum wind speed (V11212_002), the number of hours of sunshine (V14032), and the percentage of sunshine (V14033). Ⅳ. Data usage description In terms of resources and environment, meteorological data is used to simulate the regional climate change and runoff, sediment, water and soil loss and vegetation change in the basin, and it is also a necessary input condition for remote sensing inversion.

0 2020-06-05

Standard weather station monthly data of the Yellow River’s Upstream (1952-2011)

I. Overview This dataset contains monthly meteorological data for the upper Yellow River and its surroundings from 1952 to 2011. The standard station data includes 30 elements: average station pressure, extreme maximum station pressure, date of extreme maximum station pressure, extreme minimum station pressure, date of extreme minimum station pressure, average temperature, and extreme maximum temperature. , Extreme high temperature appearance day, extreme minimum temperature, extreme minimum temperature appearance day, average temperature anomaly, average maximum temperature, average minimum temperature, average relative humidity, minimum relative humidity, minimum relative humidity occurrence date, precipitation, daily precipitation > = 0.1mm days, maximum daily precipitation, maximum daily precipitation occurrence day, percentage of precipitation anomaly, average wind speed, maximum wind speed, day of maximum wind speed, maximum wind speed, wind direction of maximum wind speed, wind direction of maximum wind speed , The day of maximum wind speed, the hours of sunshine, and the percentage of sunshine. Ⅱ. Data processing description The data is stored as integers, the temperature unit is (0.1 ° C) value, the precipitation unit is (0.1 mm), and it is stored as an ASCII text file. Ⅲ. Data content description Standard station data, all meteorological elements are stored in one text, each element is: average own station pressure (V10004), extreme highest station pressure (V10201), extreme highest station pressure (V10201_001), extreme lowest station Barometric pressure (V10202), the day when the extreme minimum atmospheric pressure appeared (V10202_002), the average temperature (V12001), the extreme maximum temperature (V12011), the extreme maximum temperature (V12011_001), the extreme minimum temperature (V12012), the extreme minimum temperature (V12012_002), average temperature anomaly (V12201), average maximum temperature (V12211), average minimum temperature (V12212), average relative humidity (V13003), minimum relative humidity (V13007), minimum relative humidity occurrence date (V13007_001), precipitation Amount (V13011), daily precipitation> = 0.1mm days (V13011_000), maximum daily precipitation (V13052), maximum daily precipitation (V13052_001), percentage of precipitation anomaly (V13212), average wind speed (V11002), polar High wind speed (V11041), the day when the maximum wind speed appears (V11041_001), the maximum wind speed (V11042), the wind direction of the maximum wind speed (V11043), the wind direction of the maximum wind speed (V11212), the maximum wind speed Today (V11212_001), hours of sunshine (V14032), percentage of sunshine (V14033). Ⅳ. Data usage description In terms of resources and environment, meteorological data is used to simulate the regional climate change and runoff, sediment, water and soil loss and vegetation change in the basin, and it is also a necessary input condition for remote sensing inversion.

0 2020-06-05

Standard weather station diurnal data of the Yellow River’s Upstream (1952-2011)

Ⅰ. Overview This dataset contains daily meteorological data for the upper Yellow River and its surroundings from 1952 to 2011. Standard station data includes 15 elements: average pressure, maximum pressure, minimum pressure, average temperature, maximum temperature, minimum temperature, average relative humidity, minimum relative humidity, precipitation, average wind speed, maximum wind speed, maximum wind speed and direction, Maximum wind speed, maximum wind speed and direction and sunshine hours. Ⅱ. Data processing description The data is stored as integers, the temperature unit is (0.1 ° C) value, the precipitation unit is (0.1 mm), and it is stored as an ASCII text file. Ⅲ. Data content description Standard station data. All meteorological elements are stored in one text. V0100 indicates the station number, v04001 indicates the year, v04002 indicates the month, v04003 indicates the day, v10004 indicates the average pressure, v10201 indicates the maximum pressure, v10202 indicates the minimum pressure, and v12001 indicates the average temperature. v12052 indicates the highest temperature, v12053 indicates the lowest temperature, v13003 indicates the average relative humidity, v13007 indicates the minimum relative humidity, v13201 indicates the precipitation, v11002 indicates the average wind speed, v11042 maximum wind speed, v11212 indicates the maximum wind speed and direction, v11041 indicates the maximum wind speed, and v11043 indicates Extreme wind speed and direction, v14032 represents sunshine hours. Ⅳ. Data usage description In terms of resources and environment, meteorological data is used to simulate the regional climate change and runoff, sediment, water and soil loss and vegetation change in the basin, and it is also a necessary input condition for remote sensing inversion.

0 2020-06-05

Automatic weather station dataset from Guoluo station (2017)

The data set contains meteorological observations from Guoluo Station from January 1, 2017, to December 31, 2017, and includes temperature (Ta_1_AVG), relative humidity (RH_1_AVG), vapour pressure (Pvapor_1_AVG), average wind speed (WS_AVG), atmospheric pressure (P_1), average downward longwave radiation (DLR_5_AVG), average upward longwave radiation (ULR_5_AVG), average net radiation (Rn_5_AVG), average soil temperature (Ts_TCAV_AVG), soil water content (Smoist_AVG), total precipitation (Rain_7_TOT), downward longwave radiation (CG3_down_Avg), upward longwave radiation (CGR3_up_Avg), average photosynthetically active radiation (Par_Avg), etc. The temporal resolution is 1 hour. Missing observations have been assigned a value of -99999.

0 2020-06-03

Yulong snow mountain glacier No.1, 4 300 m altitude, 2014-2018, the daily average meteorological observation dataset

1.The data content: air temperature, relative humidity, precipitation, air pressure, wind speed and vapor pressure. 2. Data sources and processing methods: campel mountain type automatic meteorological station observation by the United States, including air temperature and humidity sensor model HMP155A;Wind speed and direction finder models: 05103-45;The atmospheric pressure sensor: CS106;The measuring cylinder: TE525MM.Automatic meteorological station every ten minutes automatic acquisition data, after complete automatic acquisition daily meteorological data then daily mean value were calculated statistics. 3.Data quality description: automatic continuous access to data. 4.Data application results and prospects: the weather stations set in the upper of the glacier terminal, meteorological data can be used to simulate for predict the future climate change under the background of type Marine glacial changes in response to global climate change research provides data.

0 2020-06-02

Yulong snow mountain glacier No.1, 4 506 m altitude the daily average meteorological observation dataset (2014-2018)

1. Data content: air temperature, relative humidity, precipitation, air pressure, wind speed, average total radiation, total net radiation value and daily average water vapor pressure data. 2. Data source and processing method: Observed by American campel high-altitude automatic weather station, air temperature and humidity sensor model HMP155A; wind speed and wind direction model: 05103-45; net radiometer: CNR 4 Net Radiometer four component; atmospheric pressure sensor: CS106; Rain gauge: TE525MM. The automatic weather station automatically collects data every 10 minutes, and collects daily statistical data to obtain daily average weather data. 3. Data quality description: Data is automatically acquired continuously. 4. Data application results and prospects: The weather station is located in the middle of the glacier, and the meteorological data can provide data guarantee for simulating the response of oceanic glacier changes to global climate change in the context of future climate change.

0 2020-06-01

Monthly dataset of ERA-Interim based on pressure levels from 1979 to 2018 released from ECMWF

This dataset is derived from the global atmospheric reanalysis dataset, ERA-Interim, based on the 4-dimensional variational analysis (4D-Var) released by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA-Interim represents a major undertaking by ECMWF (European Centre for Medium-Range Weather Forecasts) to produce a reanalysis with an improved atmospheric model and assimilation system which replaces those used in ERA-40, particularly for the data-rich 1990s and 2000s, and to be continued as an ECMWF Climate Data Assimilation System (ECDAS) until superseded by a new reanalysis. Through systematic increases in computing power, 4-dimensional variational assimilation (4D-Var) became feasible and part of ECMWF operations since 1997. Enhanced computing power also allowed horizontal resolution to be increased from T159 to T255, and the latest Integrated Forecasting System(IFS CY31r1 and CY31r2) to be used, taking advantage of improved model physics. ERA-interim retains the same 60 model levels used for ERA-40 with the highest level being 0.1 hPa. Besides, data assimilation of ERA-Interim also benefits from quality control that draws on experience from ERA-40 and JRA-25, variational bias correction of satellite radiance data, and more extensive use of radiances with an improved fast radiative transfer model. In addition, ERA-Interim uses the new ERS (European Remote Sensing Satellite) altimeter wave heights, EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) reprocessed winds and clear-sky radiances, GOME (Global Ozone Monitoring Experiment) ozone data from the Rutherford Appleton Laboratory, and CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and Climate Experiment), and COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) GPS radio occultation measurements processed and archived by UCAR (University Corporation for Atmospheric Research).

0 2020-05-30

Observation of water and heat flux in alpine meadow ecosystem —automatic weather station of Jingyangling station (2015-2017)

The data set contains the meteorological element observation data of jingyangling station in the upper reaches of heihe hydrometeorological observation network on January 1, 2015 and December 31, 2017.The site is located in pass, jingyangling mountain, qilian county, qinghai province.The longitude and latitude of the observation point are 101.1160E, 37.8384N and 3750m above sea level.The air temperature and relative humidity sensor is set up at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tipping bucket rain gauge is installed at 10m;The wind speed and direction sensor is mounted at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing south, with the probe facing vertically downward;The soil temperature probe is buried at the surface of 0cm and underground of 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil moisture probe is buried underground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil heat flow plates (3 pieces) are successively buried 6cm underground, 2m south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: percent). Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).

0 2020-05-29

Regular meteorological element datasets for 22 observing sites in Sri Lanka (2008-2018)

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

0 2020-05-14