China meteorological forcing dataset (1979-2018)

The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.

0 2020-04-01

China regional atmospheric driving dataset based on geostationary satellites and reanalysis data (2005-2010)

Based on the geostationary satellites and reanalysis data, the China Regional Atmospheric Driving Dataset is a set of atmospheric driving data sets with high spatiotemporal resolution prepared by the China Meteorological Administration, with a spatial resolution of 0.1 ° × 0.1 ° and a temporal resolution of 1 Hours, covering a range of 75 ° -135 ° east longitude and 15 ° -55 ° north latitude, include 6 elements of near-surface temperature, relative humidity, ground pressure, near-surface wind speed, incident solar radiation on the ground, and ground precipitation rate. The preparation process of precipitation products is as follows: The 6-hour cumulative precipitation estimated from the multi-channel data of the China Fengyun-2 geostationary satellite is integrated with the 6-hour cumulative precipitation from conventional ground observations to obtain 6-hour cumulative precipitation spatial distribution data, and then use the high-resolution cloud classification information retrieved from the multi-channel inversion of the geostationary satellites determines the interpolation time weight of the cumulative precipitation and obtains an estimated one-hour cumulative precipitation. The preparation process of the radiation data is as follows: The surface incident solar radiation based on FY-2C, uses the radiation transmission model DISORT (Discrete Ordinates Radiative Transfer Program for a Multi-Layered Plane-parallel Medium) to calculate the radiation transmission and obtains the data of surface incident solar radiation in China. Preparation process of other elements: The space and time interpolation method is used for the NCEP reanalysis data of 1.0 ° × 1.0 ° to obtain driving factors such as near-surface air temperature, relative humidity, ground pressure, and near-surface wind speed of 0.1 ° × 0.1 ° per hour. Physical meaning of each variable: Meteorological Elements || Variable Name || Unit || Physical Meaning | Surface temperature || TBOT || K || Surface temperature (2m) | Surface pressure || PSRF || Pa || Surface pressure | Relative humidity on the ground || RH || kg / kg || Relative humidity near the ground (2m) | Wind speed on the ground || WIND || m / s || Wind speed near the ground (anemometer height) | Surface incident solar radiation || FSDS || W / m2 || Surface incident solar radiation | Precipitation Rate || PRECTmms || mm / hr || Precipitation Rate For more information, see the data documentation published with the data.

0 2020-03-31

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, 2018)

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 August 31 to December 24, 2018. 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) (°), 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_1 and IRT_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), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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 2020-03-30

Reanalysis data for surface meteorological elements for western China (2002)

The research project on land surface data assimilation system in western China belongs to the major research plan of "environment and ecological science in western China" of the national natural science foundation. the person in charge is Li Xin, researcher of the institute of environment and engineering in cold and arid regions of the Chinese academy of sciences. the project runs from January 2003 to December 2005. One of the data collected in this project is the reanalysis data of surface climate factors in western China in 2002. This data set is generated based on the daily 1 × 1 provided by the National Environmental Prediction Center (NCEP). However, the re-analysis of the data has the following problems: (1) the temporal and spatial resolution is not high enough (the horizontal resolution is 1 degree and the time is 6 hours); (2) The low-level errors in plateau areas are large; (3) The data are standard isosurface data and need interpolation. The 2002 reanalysis data set of surface climate elements in western China was generated by combining NCEP reanalysis data and MM5 model by Dr. Longxiao and Professor Qiu Chongjian of Lanzhou University using Newton relaxation data assimilation method (Nudging), including 10m horizontal and vertical wind speed (m/s), 2m air temperature (k), 2m mixing ratio, surface pressure (Pa), upstream and downstream short wave and long wave radiation (w/m2), convective precipitation and large scale precipitation (mm/s) at 0.25 degree per hour throughout 2002. I. preparation background The quality of the driving data seriously affects the ability of the land surface model to simulate the land surface state, so a very important component of the land surface modeling research is the driving data used to drive the land surface model. No matter how realistic these models are in describing the surface process, no matter how accurate the boundary and initial conditions they input, if the driving data are not accurate, they cannot get the results close to reality. Land surface models are so dependent on the quality of externally provided data that any error in these externally provided data will seriously affect the ability of land surface models to simulate soil moisture, runoff, snow cover and latent heat flux. These externally provided data include: precipitation, radiation, temperature, wind field, humidity and pressure. The 2002 reanalysis data set of surface climate elements in western China uses Newton relaxation data assimilation method (Nudging) to combine NCEP reanalysis data and MM5 model to generate driving data with higher spatial and temporal resolution suitable for complex terrain in western China. Second, the basic parameters of the operation mode 1. Using the US PSU/NCAR mesoscale model MM5 as a simulation model; The selection of simulation grid domain: center (32°N, 90°E), grid distance of 36km, number of horizontal grid points of 131*151, vertical resolution of 25 layers, and mode top of 100hPa;; 2. The data used for initialization are 1 * 1 GRIB grid data of NCEP in the United States. 3. The time step is 120s. Third, the physical process 1. physical process treatment of cloud and precipitation: Grell cumulus cloud parameterization scheme is adopted for sub-grid scale precipitation, and Reisner mixed phase microphysical explicit scheme is adopted for distinguishable scale precipitation; 2. MRF parameterization scheme is adopted for planetary boundary layer process. 3. the radiation process adopts CCM2 radiation scheme. IV. File Format and Naming It is stored in a monthly folder and contains 24 hours of data every day. The naming rules are as follows: 2002***&.forc, where * * * is Julian day and 2002***& is time (in hours), where. forc is the file extension. V. data format Stored in binary floating point type, each data takes up 4 bytes.

0 2020-03-29

Standardized dataset on surface temperature for performance assessment of climate models in the great lakes region of central Asia (1850 --2014)

1) Data content: including the central Asian region, the regional scope: 30°N ~ 60°N, 40°E ~ 90°E; 2) Data source: process the CMIP data set and use bilinear interpolation to interpolate the data of different resolution modes to 0.5°× 0.5°,CRU observation data from 1901 to 2014;; 3) Data quality: the time length is long, the data quality is good, and the missing values are marked by 999; 3) Prospect of data application achievement set: the data has been used to evaluate the simulation capability of temperature in central Asia, and the capability of climate system model to simulate historical climate change in central Asia has been evaluated through calculation and analysis of regional mean, relative error, root-mean-square error, Taylor diagram, EOF. 4) data reliability: by comparing and analyzing the annual changes of the observed and simulated data, the data results show a significant warming trend. By carrying out correlation test on the data results, they all pass the 99% reliability test.At the same time, CMIP plan data and CRU data are also common data sets, which are often used in many studies on climate change.

0 2020-03-29

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 2020-03-29

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 2020-03-29

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 2020-03-29

Nonstandard weather station diurnal data of Inner Mongolia Reach of the Yellow River’s Upstream (1956-2006)

I. Overview This data set contains daily meteorological data from the Inner Mongolia section of the Yellow River from Wuhai to Dalat Banner from 1952 to 2006. Non-standard station data includes two elements, namely: temperature and precipitation. 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, temperature and precipitation are stored separately, which are temperature file and precipitation file. 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 changes in the basin, and is also a necessary input condition for remote sensing inversion.

0 2020-03-29

Temperature and precipitation dataset of WRF model in Northwest China (1979-2013)

This data is the NCEP/DOE reanalysis data of 6h interval nested downscaling by WRF model in northwest China to a horizontal resolution of 12km, 364 grid points in the east-west direction, 251 grid points in the north-south direction and 31 layers in the vertical direction. The simulation time starts from 1979-01-01,06:00:00 and ends at 2013-12-31,23:00:00. The parameterization schemes of the model are as follows: Kain Frisch cumulus convection scheme, WSM3 cloud microphysics scheme, RRTM long wave scheme, Dudhia short wave scheme, Noah land surface model, YSU planetary boundary layer scheme. The file naming rules in the data set are: wrf_t2_YYYY.nc and wrf_rain_YYYY.nc, where YYYY is the annual abbreviation, t2 is the 2m temperature (unit ℃), and rain is the total surface precipitation (unit mm).

0 2020-03-28