Current Browsing: Precipitation


Meteorological data of the integrated observation and research station of Ngari for desert environment (2009-2017)

The data set includes meteorological data from the Ngari Desert Observation and Research Station from 2009 to 2017. It includes the following basic meteorological parameters: temperature (1.5 m from the ground, once every half hour, unit: Celsius), relative humidity (1.5 m from the ground, once every half hour, unit: %), wind speed (1.5 m from the ground, once every half hour, unit: m/s), wind direction (1.5 m from the ground, once every half hour, unit: degrees), atmospheric pressure (1.5 m from the ground, once every half hour, unit: hPa), precipitation (once every 24 hours, unit: mm), water vapour pressure (unit: kPa), evaporation (unit: mm), downward shortwave radiation (unit: W/m2), upward shortwave radiation (unit: W/m2), downward longwave radiation (unit: W/m2), upward longwave radiation (unit: W/m2), net radiation (unit: W/m2), surface albedo (unit: %). The temporal resolution of the data is one day. The data were directly downloaded from the Ngari automatic weather station. The precipitation data represent daily precipitation measured by the automatic rain and snow gauge and corrected based on manual observations. The other observation data are the daily mean value of the measurements taken every half hour. Instrument models of different observations: temperature and humidity: HMP45C air temperature and humidity probe; precipitation: T200-B rain and snow gauge sensor; wind speed and direction: Vaisala 05013 wind speed and direction sensor; net radiation: Kipp Zonen NR01 net radiation sensor; atmospheric pressure: Vaisala PTB210 atmospheric pressure sensor; collector model: CR 1000; acquisition interval: 30 minutes. The data table is processed and quality controlled by a particular person based on observation records. Observations and data acquisition are carried out in strict accordance with the instrument operating specifications, and some data with obvious errors are removed when processing the data table.

2020-06-24

Monthly standard weather station dataset in Sanjiangyuan (1957-2015)

Monthly meteorological data of Sanjiangyuan includes 32 national standard meteorological stations. There are 26 variables: average local pressure, extreme maximum local pressure, date of extreme maximum local pressure, extreme minimum local pressure, date of extreme minimum local pressure, average temperature, extreme maximum temperature, date of extreme maximum temperature, extreme minimum temperature and date of extreme minimum temperature, average temperature anomaly, average maximum temperature, average minimum temperature, sunshine hours, percentage of sunshine, average relative humidity, minimum relative humidity, date of occurrence of minimum relative humidity, precipitation, days of daily precipitation >=0.1mm, maximum daily precipitation, date of maximum daily precipitation, percentage of precipitation anomaly, average wind speed, maximum wind speed, date of maximum wind speed, maximum wind speed, wind direction of maximum wind speed, wind direction of maximum wind speed and occurrence date of maximum wind speed. The data format is txt, named by the site ID, and each file has 26 columns. The names and units of each column are explained in the SURF_CLI_CHN_MUL_MON_readme.txt file. site_id lat lon elv name_cn 52754 37.33 100.13 8301.50 Gangcha 52833 36.92 98.48 7950.00 Wulan 52836 36.30 98.10 3191.10 Dulan 52856 36.27 100.62 2835.00 Qiapuqia 52866 36.72 101.75 2295.20 Xining 52868 36.03 101.43 2237.10 Guizhou 52908 35.22 93.08 4612.20 Wudaoliang 52943 35.58 99.98 3323.20 Xinghai 52955 35.58 100.75 8120.00 Guinan 52974 35.52 102.02 2491.40 Tongren 56004 34.22 92.43 4533.10 Togton He 56018 32.90 95.30 4066.40 Zaduo 56021 34.13 95.78 4175.00 Qumalai 56029 33.02 97.02 3681.20 Yushu 56033 34.92 98.22 4272.30 Maduo 56034 33.80 97.13 4415.40 Qingshui River 56038 32.98 98.10 9200.00 Shiqu 56043 34.47 100.25 3719.00 Guoluo 56046 33.75 99.65 3967.50 Dari 56065 34.73 101.60 8500.00 Henan 56067 33.43 101.48 3628.50 Jiuzhi 56074 34.00 102.08 3471.40 Maqu 56080 35.00 102.90 2910.00 Hezuo 56106 31.88 93.78 4022.80 Suo County 56116 31.42 95.60 3873.10 Dingqing 56125 32.20 96.48 3643.70 Nangqian 56128 31.22 96.60 3810.00 Leiwuqi 56137 31.15 97.17 3306.00 Changdu 56151 32.93 100.75 8530.00 Banma 56152 32.28 100.33 8893.90 Seda

2020-06-24

Daily standard weather station dataset in Sanjiangyuan region (1981-2015)

The files in this data set are named as: 1. Pressure of the station: SURF_CLI_CHN_MUL_DAY-PRS-10004-SITEID.TXT 2. Temperature: SURF_CLI_CHN_MUL_DAY-TEM-12001-SITEID.TXT 3. Relative humidity: SURF_CLI_CHN_MUL_DAY-RHU-13003-SITEID.TXT 4. Precipitation: SURF_CLI_CHN_MUL_DAY-PRE-13011-SITEID.TXT 5. Evaporation: SURF_CLI_CHN_MUL_DAY-EVP-13240-SITEID.TXT 6. Wind direction and wind speed: SURF_CLI_CHN_MUL_DAY-WIN-11002-SITEID.TXT 7. Sunshine: SURF_CLI_CHN_MUL_DAY-SSD-14032-SITEID.TXT 8.0cm Ground Temperature: SURF_CLI_CHN_MUL_DAY-GST-12030-0cm-SITEID.TXT Detailed format descriptions for each data file are given in the SURF_CLI_CHN_MUL_DAY_FORMAT.doc file. The meteorological site information contained in this data set is as follows: Site_id lat lon ELV name_En 52754 37.33 100.13 8301.50 Gangcha 52833 36.92 98.48 7950.00 Uran 52836 36.30 98.10 3191.10 Dulan 52856 36.27 100.62 2835.00 Chabcha 52866 36.72 101.75 2295.20 Xining 52868 36.03 101.43 2237.10 Guizhou 52908 35.22 93.08 4612.20 Wu Daoliang 52943 35.58 99.98 3323.20 Xinghai 52955 35.58 100.75 8120.00 Guinan 52974 35.52 102.02 2491.40 Tongren 56004 34.22 92.43 4533.10 Toto River 56018 32.90 95.30 4066.40 Zaduo 56021 34.13 95.78 4175.00 Qumalai 56029 33.02 97.02 3681.20 Yushu 56033 34.92 98.22 4272.30 Maddo 56034 33.80 97.13 4415.40 Qingshui River 56038 32.98 98 98.10 9200.00 Shiqu 56 043 34.47 100.25 3719.00 Golo 56 046 33.75 99.65 3967.50 Dari 56065 34.73 101.60 8500.00 Henan 56 067 33.43 101.48 3628.50 Jiuzhi 56074 34.00 102.08 3471.40 Marqu 56080 35.00 102.90 2910.00 Hezuo 56106 31.88 93.78 4022.80 Suoxian 56116 31.42 95.60 3873.10 Ding Qing 56125 32.20 96.48 3643.70 Xiangqian 56128. 31.22. 96.60. 3810.00 Leiwuqi 56 137 31.15 97.17 3306.00 Changdu 56151 32.93 100.75 8530.00 Banma 56152 32.28 100.33 8893.90 Saida

2020-06-23

China meteorological assimilation driving datasets for the SWAT model Version 1.1 (2008-2016)

CMADS V1.1(The China Meteorological Assimilation Driving Datasets for the SWAT model Version 1.1) Version of the data set introduced the STMAS assimilation algorithm. It was constructed using multiple technologies and scientific methods, including loop nesting of data, projection of resampling models, and bilinear interpolation. The CMADS series of datasets can be used to drive various hydrological models, such as SWAT, the Variable Infiltration Capacity (VIC) model, and the Storm Water Management model (SWMM). It also allows users to conveniently extract a wide range of meteorological elements for detailed climatic analyses. Data sources for the CMADS series include nearly 40,000 regional automatic stations under China’s 2,421 national automatic and business assessment centres. This ensures that the CMADS datasets have wide applicability within the country, and that data accuracy was vastly improved. The CMADS series of datasets has undergone finishing and correction to match the specific format of input and driving data of SWAT models. This reduces the volume of complex work that model builders have to deal with. An index table of the various elements encompassing all of East Asia was also established for SWAT models. This allows the models to utilize the datasets directly, thus eliminating the need for any format conversion or calculations using weather generators. Consequently, significant improvements to the modelling speed and output accuracy of SWAT models were achieved. Most of the source data in the CMADS datasets are derived from CLDAS in China and other reanalysis data in the world. The integration of air temperature (2m), air pressure, humidity, and wind speed data (10m) was mainly achieved through the LAPS/STMAS system. Precipitation data were stitched using CMORPH’s global precipitation products and the National Meteorological Information Center’s data of China (which is based on CMORPH’s integrated precipitation products). The latter contains daily precipitation records observed at 2,400 national meteorological stations and the CMORPH satellite’s inversion precipitation products.The inversion algorithm for incoming solar radiation at the ground surface makes use of the discrete longitudinal method by Stamnes et al.(1988)to calculate radiation transmission. The resolutions for CMADS V1.0, V1.1, V1.2, and V1.3 were 1/3°, 1/4°, 1/8°, and 1/16°, respectively. In CMADS V1.0 (at a spatial resolution of 1/3°), East Asia was spatially divided into 195 × 300 grid points containing 58,500 stations. Despite being at the same spatial resolution as CMADS V1.0, CMADS V1.1 contains more data, with 260 × 400 grid points containing 104,000 stations. For both versions, the stations’ daily data include average solar radiation, average temperature (2m), average pressure, maximum and minimum temperature (2m), specific humidity, cumulative precipitation, and average wind speed (10m). The CMADS comprises other variables for any hydrological model(under 'For-other-model' folder): Daily Average Temperature (2m), Daily Maximum Temperature (2m), Daily Minimum Temperature (2m), Daily cumulative precipitation (20-20h), Daily average Relative Humidity, Daily average Specific Humidity, Daily average Solar Radiation, Daily average Wind (10m), and Daily average Atmospheric Pressure. Introduction to metadata of CMADS CMADS storage path description:(CMADS was divided into two datesets) 1.CMADS-V1.0\For-swat\ --specifically driving the SWAT model 2.CMADS-V1.0\For-other-model\ --specifically driving the other hydrological model(VIC,SWMM,etc.) CMADS--\For-swat-2009\ folder contain:(Station\ and Fork\) 1).Station\ Relative-Humidity-58500\ Daily average relative humidity(fraction) Precipitation-58500\ Daily accumulated 24-hour precipitation(mm) Solar radiation-58500\ Daily average solar radiation(MJ/m2) Tmperature-58500\ Daily maximum and minimum 2m temperature(℃) Wind-58500\ Daily average 10m wind speed(m/s) Where R, P, S, T, W+ dimensional grid number - the number of longitude grid is the station in the above five folders respectively.(Where R,P,S,T,W respective Daily average relative humidity,Daily cumulative precipitation(24h),Daily mean solar radiation(MJ/m2),Daily maximum and minimum temperature(℃) and Daily mean wind speed (m/s)) respectively.Data format is (.dbf) 2).Fork\ (Station index table over East Asia) PCPFORK.txt (Precipitation index table) RHFORK.txt (Relative humidity index table) SORFORK.txt (Solar radiation index table) TMPFORK.txt (Temperature index table) WINDFORK.txt (Wind speed index) CMADS--\For-swat-2012\ folder contain:(Station\ and Fork\) Storage structure is consistency with \For-swat- 2009\.However, all the data in this directory are only available in TXT format and can be readed by SWAT2012. 3)\For-other-model\ (Includes all weather input data required by the any hydrologic model (daily).) Atmospheric-Pressure-txt\ Daily average atmospheric pressure(hPa) Average-Temperature-txt\ Daily average 2m temperature(℃) Maximum-Temperature-txt\ Daily maximum 2m temperature(℃) Minimum-Temperature-txt\ Daily minimum 2m temperature(℃) Precipitation-txt\ Daily accumulated 24-hour precipitation (mm) Relative-Humidity-txt\ Daily average relative humidity(fraction) Solar-Radiation-txt\ Daily average solar radiation(MJ/m2) Specific-Humidity-txt\ Daily average Specific Humidity(g/kg) Wind-txt\ Daily average 10m wind speed(m/s) Data storage information: data set storage format is .dbf and .txt Other data information: Total data:45GB Occupied space: 50GB Time: From year 2008 to year 2014 Time resolution: Daily Geographical scope description: East Asia Longitude: 60° E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 58500 stations Spatial resolution: 1/3 * 1/3 * grid points Vertical range: None

2020-06-23

China meteorological assimilation driving datasets for the SWAT model Version 1.0 (2008-2016)

CMADS V1.0(The China Meteorological Assimilation Driving Datasets for the SWAT model Version 1.0)Version of the data set introduces the technology of STMAS assimilation algorithm . It was constructed using multiple technologies and scientific methods, including loop nesting of data, projection of resampling models, and bilinear interpolation. The CMADS series of datasets can be used to drive various hydrological models, such as SWAT, the Variable Infiltration Capacity (VIC) model, and the Storm Water Management model (SWMM). It also allows users to conveniently extract a wide range of meteorological elements for detailed climatic analyses. Data sources for the CMADS series include nearly 40,000 regional automatic stations under China’s 2,421 national automatic and business assessment centres. This ensures that the CMADS datasets have wide applicability within the country, and that data accuracy was vastly improved. The CMADS series of datasets has undergone finishing and correction to match the specific format of input and driving data of SWAT models. This reduces the volume of complex work that model builders have to deal with. An index table of the various elements encompassing all of East Asia was also established for SWAT models. This allows the models to utilize the datasets directly, thus eliminating the need for any format conversion or calculations using weather generators. Consequently, significant improvements to the modelling speed and output accuracy of SWAT models were achieved. Most of the source data in the CMADS datasets are derived from CLDAS in China and other reanalysis data in the world. The integration of air temperature, air pressure, humidity, and wind velocity data was mainly achieved through the LAPS/STMAS system. Precipitation data were stitched using CMORPH’s global precipitation products and the National Meteorological Information Center’s data of China (which is based on CMORPH’s integrated precipitation products). The latter contains daily precipitation records observed at 2,400 national meteorological stations and the CMORPH satellite’s inversion precipitation products.The inversion algorithm for incoming solar radiation at the ground surface makes use of the discrete longitudinal method by Stamnes et al.(1988)to calculate radiation transmission. The resolutions for CMADS V1.0, V1.1, V1.2, and V1.3 were 1/3°, 1/4°, 1/8°, and 1/16°, respectively. In CMADS V1.0 (at a spatial resolution of 1/3°), East Asia was spatially divided into 195 × 300 grid points containing 58,500 stations. Despite being at the same spatial resolution as CMADS V1.0, CMADS V1.1 contains more data, with 260 × 400 grid points containing 104,000 stations. For both versions, the stations’ daily data include average solar radiation, average temperature, average pressure, maximum and minimum temperature, specific humidity, cumulative precipitation, and average wind velocity. The CMADS comprises other variables for any hydrological model(under 'For-other-model' folder ): Daily Average Temperature, Daily Maximum Temperature, Daily Minimum Temperature, Daily cumulative precipitation (20-20h), Daily average Relative Humidity, Daily average Specific Humidity, Daily average Solar Radiation, Daily average Wind, and Daily average Atmospheric Pressure. Introduction to metadata of CMADS CMADS storage path description:(CMADS was divided into two datesets) 1.CMADS-V1.0\For-swat\ --specifically driving the SWAT model 2.CMADS-V1.0\For-other-model\ --specifically driving the other hydrological model(VIC,SWMM,etc.) CMADS--\For-swat-2009\ folder contain:(Station\ and Fork\) 1).Station\ Relative-Humidity-58500\ Daily average relative humidity(fraction) Precipitation-58500\ Daily accumulated 24-hour precipitation(mm) Solar radiation-58500\ Daily average solar radiation(MJ/m2) Tmperature-58500\ Daily maximum and minimum temperature(℃) Wind-58500\ Daily average wind speed(m/s) Where R, P, S, T, W+ dimensional grid number - the number of longitude grid is the station in the above five folders respectively.(Where R,P,S,T,W respective Daily average relative humidity,Daily cumulative precipitation(24h),Daily mean solar radiation(MJ/m2),Daily maximum and minimum temperature(℃) and Daily mean wind speed (m/s)) respectively.Data format is (.dbf) 2).Fork\ (Station index table over East Asia) PCPFORK.txt (Precipitation index table) RHFORK.txt (Relative humidity index table) SORFORK.txt (Solar radiation index table) TMPFORK.txt (Temperature index table) WINDFORK.txt (Wind speed index) CMADS--\For-swat-2012\ folder contain:(Station\ and Fork\) Storage structure is consistency with \For-swat- 2009\.However, all the data in this directory are only available in TXT format and can be readed by SWAT2012. 3)\For-other-model\ (Includes all weather input data required by the any hydrologic model (daily).) Atmospheric-Pressure-txt\ Daily average atmospheric pressure(hPa) Average-Temperature-txt\ Daily average temperature(℃) Maximum-Temperature-txt\ Daily maximum temperature(℃) Minimum-Temperature-txt\ Daily minimum temperature(℃) Precipitation-txt\ Daily accumulated 24-hour precipitation (mm) Relative-Humidity-txt\ Daily average relative humidity(fraction) Solar-Radiation-txt\ Daily average solar radiation(MJ/m2) Specific-Humidity-txt\ Daily average Specific Humidity(g/kg) Wind-txt\ Daily average wind speed(m/s) Data storage information: data set storage format is .dbf and .txt Other data information: Total data: 33.6GB Occupied space: 35.2GB Time: From year 2008 to year 2016 Time resolution: Daily Geographical scope description: East Asia Longitude: 60°E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 58500 stations Spatial resolution: 1/3 * 1/3 * grid points Vertical range: None

2020-06-23

Integrated hydrometeorological – snow – frozen ground observations in the alpine region of the Heihe River Basin, China

Alpine region is an important contributor in riverine and watershed ecosystems, which supplies freshwater and stimulates specific habitats of biodiversity. In parallel, extreme events (such as flood, wildfire, early snowmelt, drought and etc.) and other perturbations may reformat the hydrological processes and eco-functions in the area. It is then critical to advance a predictive understanding of the alpine hydrological processes through data-model integration. However, several formidable challenges, including the cold and harsh climate, high altitude and complex topography, inhibit complete and consistent data collection where/when needed, which hinders the associated development of interdisciplinary research in the alpine region. The current study presents a suite of datasets consisted of long-term hydrometeorological, snow cover and frozen ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin (HRB) in China. Gap-free meteorological and hydrological data were monitored from the observation network connecting a group of automatic meteorological stations (AMSs), wireless sensors network (WSN) and runoff measurement spots. In addition, to capture snow accumulation and ablation processes, with the state-of-the-art techniques and instruments, snow cover properties were collected from a snow observation superstation. High-resolution soil physics datasets were also obtained to capture the freeze-thaw processes from a frozen ground observation superstation. The up-to-date datasets have been released to scientists with multidisciplinary backgrounds (i.e. cryosphere, hydrology, and meteorology) and expected to serve as a testing platform to provide accurate forcing data, validate and evaluate remote sensing data and distributed models to a broader community.

2020-06-23

Basin scale hydrological and ecological processes and their impacts on global climate change data in the Loess Region

The hydrological ecological process at the loess basin scale and its response to global climate change is a project of the Major Research plan of the National Natural Science Foundation of China - Environmental and Ecological Science in Western China. The project is led by liu wenzhao, a researcher from the institute of water and soil conservation, ministry of water resources, Chinese academy of sciences. The project runs from January 2003 to December 2005. The project submitted data: The CLIGEN parameter and output dataset of the Loess Plateau: It was generated during the evaluation and improvement of the practicality of the weather generator CLIGEN in the Loess Plateau. The dataset includes parameter data files for driving CLIGEN and 100-year daily weather data files generated by running CLIGEN from 71 meteorological stations on the Loess Plateau. The 71 sites are distributed in 7 provinces (Shanxi, Shanxi, Gansu, Inner Mongolia, Ningxia, Henan, and Qinghai). Each file is individually saved in ASCII format and can be opened for viewing with text programs. This data set is generated based on long-term serial daily meteorological data measured by 71 meteorological stations on the Loess Plateau. Daily meteorological parameters include: precipitation, maximum, minimum, and average temperature, solar radiation, relative humidity, wind speed and direction. The data comes from the China Meteorological Science Data Sharing Service Network and the Loess Plateau Soil and Water Conservation Database. Among them, solar radiation data is available at only 12 sites on the Loess Plateau. The solar radiation parameters at other sites are generated by kriging space interpolation. The dew point temperature is calculated using the average temperature and relative humidity.

2020-06-09

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.

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.

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.

2020-06-05