The data set is China's multi scenario and multi-mode monthly precipitation data, with a spatial resolution of 0.0083333 ° (about 1km) from January 2021 to December 2100. The data is in NetCDF format. The data is generated in China through the delta spatial downscaling scheme according to the global > 100 km climate model data set released in the sixth phase of the IPCC coupled model comparison program (cmip6) and the global high-resolution climate data set released by worldclim. The data adopts the latest SSP scenarios (ssp119, ssp245, ssp585) released by IPCC. Each scenario contains the climate data of three GCMS (ec-earth3, gfdl-esm4, mri-esm2-0). The geospatial range contained in the dataset is China's main land, excluding islands and reefs in the South China Sea. The unit is 0.1mm. The file name is GCM_ SSP_ Pre-30s-serial number NC, 30s, i.e. 0.0083333 °, serial number from 1-40, serial number 1 represents 2021.1-2022.12, and represents the year in turn; Based on ec-earth3_ ssp119_ pre-30s-1. NC file, for example, represents the monthly precipitation data of ec-earth3 climate model with 1km resolution from 2021.1 to 2022.12 under ssp119 scenario. For a deeper understanding of the data, please refer to the data cited in the literature and the published papers of the authors.
PENG Shouzhang
Due to the uneven distribution of meteorological stations in the Sanjiang River Basin, most of them are along the traffic trunk lines, and there are many areas without observation data, it is difficult to obtain accurate spatial distribution characteristics by ordinary spatial interpolation methods. Based on worldclim v2 1 rainfall data in the spatial data set, read the rainfall data in the study area of Sanjiang River basin with MATLAB language, calculate and output the data in GIS format, and use ArcGIS software to realize the spatial distribution data set of average annual rainfall in Sanjiang River Basin from 2007 to 2018. The data set effectively solves the problem of uneven distribution of meteorological stations in Sanjiang Basin due to complex terrain and many mountains and valleys, and can better reflect the long-term average distribution of annual rainfall in Sanjiang Basin from 2007 to 2018. It provides a basis for the external dynamic environmental factors of landslide development in the region.
LIU Minghao
Rainfall is one of the important external dynamic environmental factors affecting the stability of landslides in Sanjiang Basin of Qinghai Tibet Plateau. Collect the monthly rainfall data of 10 meteorological observation stations in the typical area of Sanjiang River Basin in the study area, including Wudaoliang, Tuotuo River, qumalai, Naqu, Yushu, Dingqing, Changdu, Batang, Derong and Lijiang. Process the collected data through screening, elimination and classification calculation, and obtain the time series data set of annual rainfall external dynamic environmental factors in key areas of the study area from 2000 to 2020. Through this data set, It can reflect the change law and trend of annual rainfall in key areas of Sanjiang Basin from 2000 to 2020, and understand the change of rainfall, the external dynamic factor affecting the landslide on the Qinghai Tibet Plateau.
LIU Minghao
This data set includes the daily averages of the temperature, pressure, relative humidity, wind speed, precipitation, global radiation, P2.5 concentration and other meteorological elements observed by the Qomolangma Station for Atmospheric and Environmental Observation and Research from 2005 to 2016. The data are aimed to provide service for students and researchers engaged in meteorological research on the Tibetan Plateau. The precipitation data are observed by artificial rainfall barrel, the evaporation data are observed by Φ20 mm evaporating pan, and all the others are daily averages and ten-day means obtained after half hour observational data are processed. All the data are observed and collected in strict accordance with the Equipment Operating Specifications, and some obvious error data are eliminated when processing the generated data.
MA Yaoming
The surface meteorological data of tianmogou in Bomi county are collected from the meteorological monitoring points arranged in the middle reaches of tianmogou in PALONG Zangbu basin. The data collection time is 2020. The main content of the data includes the observation data of rainfall and temperature in tianmogou. The rainfall data is collected by hobo rain gauge. Hobo rain gauge is a tipping bucket rain gauge. Every 0.2mm rainfall is recorded as an event, and the number of recorded events is output. The number of events multiplied by 0.2mm is the rainfall value; The air temperature is measured by a built-in 10 bit resolution temperature sensor in the data recorder. The acquisition method is to collect and store once every hour, and the hourly average value of air temperature can be obtained. The data is reliable in quality and high in accuracy. It can be used to reflect the real-time changes of rainfall and temperature in Tianmo gully, monitor the critical conditions of debris flow start-up, and predict the possibility of future debris flow events in this area.
HOU Weipeng
The data set is a sub data set of the comprehensive observation data set of cloud precipitation process, which is derived from the comprehensive investigation and test carried out in Liupanshan area during 2021. Liupanshan scientific research is carried out in Dawan station, Jingyuan station, Liupanshan station, Longde station, etc. Dawan station is mainly equipped with cfl-06 wind profile radar, ht101 cloud radar, mrr-2 micro rain radar, dsg5 raindrop spectrometer, three-dimensional anemometer, C12 laser cloud altimeter. Jingyuan station is mainly equipped with qfw-6000 microwave radiometer, hmb-kps cloud radar, dsg5 raindrop spectrometer Cl51 laser cloud altimeter. Liupanshan station is mainly equipped with ht101 cloud radar, mrr-2 micro rain radar, Ott laser raindrop spectrometer, cloud condensation nodule (CCN) counter, three-dimensional anemometer, FM120 droplet spectrometer and C12 laser cloud altimeter. Longde station is mainly equipped with rpg-hatpro-g4 microwave radiometer, cfl-06 wind profile radar, ht101 Cloud Radar, mrr-2 micro rain radar Ott laser raindrop spectrometer, C12 laser cloud altimeter. Meanwhile automatic weather station, iron tower (Shangpu), X-band all solid-state dual polarization Doppler Weather Radar (Pengyang County), gradient station and other observations were done. It can be used to study the impact of the eastward movement of the plateau system on the downstream, and to reveal the impact of the atmospheric boundary layer and free atmospheric exchange process on aerosols, clouds Fog and precipitation and their interaction.
FU Danhong
The data set is a sub data set of the comprehensive observation data set of cloud precipitation process, which is derived from the comprehensive investigation and test carried out in Sanjiangyuan area during 2021. The scientific research of Sanjiangyuan mainly focuses on Advanced Air King aircraft observation. The airborne observation system includes aerosol, cloud particle spectrometer and imager observation. The observation elements include precipitation particle concentration and image of IP probe, cloud particle concentration and image of CIP probe, cloud and aerosol particle data of CAS probe and Hotwire_ LWC probe liquid water data, CAPS Summary aerosol, cloud and precipitation comprehensive data, AIMMS probe conventional meteorological elements, PCASP -100 probe aerosol particle data. Ground observation includes raindrop spectrometer, microwave radiometer and X-band radar. Raindrop spectrometer mainly observes equivalent volume diameter and particle falling speed. Microwave radiometer mainly observes temperature, humidity, water vapor and liquid water. And X-band radar mainly observes intensity, velocity and spectral width. It can provide data support for the study of the impact of westerly monsoon synergy on the cloud precipitation process of Sanjiang source.
FU Danhong
The data set records the statistics of precipitation in major areas of Qinghai Province from 2001 to 2020, and the data is divided by month and year. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 20 data tables, all of which have the same structure. For example, the data table for 2020 has 11 fields: Field 1: month Field 2: region Field 3: Xining Field 4: Haidong Field 5: Haibei Field 6: South Yellow Field 7: Hainan Field 8: Golo Field 9: Yushu Field 10: Hersey Field 11: Golmud City
Qinghai Provincial Bureau of Statistics
This meteorological data is the basic meteorological data of air temperature, relative humidity, wind speed, precipitation, air pressure, radiation, soil temperature and humidity observed in the observation site (86.56 ° e, 28.21 ° n, 4276m) of the comprehensive observation and research station of atmosphere and environment of Qomolangma, Chinese Academy of Sciences from 2019 to 2020. Precipitation is the daily cumulative value. All data are observed and collected in strict accordance with the instrument operation specifications, and some obvious error data are eliminated when processing and generating data The data can be used by students and scientific researchers engaged in meteorology, atmospheric environment or ecology (Note: when using, it must be indicated in the article that the data comes from Qomolangma station for atmospheric and environmental observation and research, Chinese Academy of Sciences (QOMS / CAS))
XI Zhenhua
This data set records the meteorological data in the observation field of Ngari Station for Desert Environment Observation and Research (33 ° 23.42 ′ N, 79 ° 42.18 ′ E, 4270 m asl) from 2019 to 2020, with a time resolution of days. It includes the following basic parameters: air temperature (℃), relative humidity (%), wind speed (m/s), wind direction (°), air pressure (hPa), precipitation (mm), water vapor pressure (kPa), downward short wave radiation (W/m^2), Upward short wave radiation (W/m^2), Downward long wave radiation(W/m^2), Upward long wave radiation(W/m^2), Net radiation(W/m^2), Surface albedo (%), soil temperature (℃), soil water content (%). Sensor model of observation instrument: atmospheric temperature and humidity: HMP45C; Precipitation: t200-b; Wind speed and direction: Vaisala 05013; Net radiation: Kipp Zonen NR01; Air pressure: Vaisala PTB210; Soil temperature: 109 temperature probe; Soil moisture content: CS616. Data collector: CR1000. The time resolution of the original data is 30 min. The data can be used by scientific researchers engaged in meteorology, atmospheric environment or ecology.
ZHAO Huabiao
1) Data content (including elements and significance): 19 stations of Alpine network (Southeast Tibet station, Namuco station, Everest station, mustage station, Ali station, Golmud station, Tianshan station, Qilian mountain station, Ruoergai station (2 points in total, Northwest Institute and Chengdu Institute of Biology), Yulong Snow Mountain station and Naqu station (including stations, Qinghai Tibet Institute, Northwest Institute and Geography Institute), Haibei Station, Sanjiangyuan station, Shenza station,, Lhasa station and Qinghai Lake Station) meteorological observation data set of Qinghai Tibet Plateau in 2020 (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and flux) 2) Data source and processing method: Excel format for field observation of 19 stations of Alpine network 3) Data quality description: Daily resolution of the station 4) Data application achievements and prospects: Based on the long-term observation data of field stations of the alpine network and overseas stations in the pan third pole region, a series of data sets of meteorological, hydrological and ecological elements in the pan third pole region are established; Complete the inversion of meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacier and frozen soil change and other data products through intensive observation in key areas and verification of sample plots and sample points; Based on the Internet of things technology, a multi station networked meteorological, hydrological and ecological data management platform is developed to realize real-time acquisition, remote control and sharing of networked data. In addition, the data set is an update of the meteorological data of the surface environment and observation network in China's high and cold regions (2019).
ZHU Liping
This data set includes precipitation data from a total of nine ground-based precipitation observation stations located in the Yadong River Valley in the middle of the Himalayas. The observation data was collected by the Hobo tumbler rain gauge developed by Onset company and exported through supporting data reading software. Accumulated counts, the rain gauge tipped once, indicating that 0.2 mm of precipitation was recorded, and the default value of -999 was used when no precipitation event occurred. We screened the collected data and eliminated abnormal values to ensure its quality. This data set has made some progress in the analysis of precipitation characteristics, satellite data verification and model simulation evaluation in this area and two academic papers have been published, which provides strong support for the analysis of precipitation characteristics in the high-altitude valleys of the Himalayas lacking ground observation data.
YANG Kun
Near surface atmospheric forcing data were produced by using Wether Research and Forecasting (WRF) model over the Heihe River Basin at hourly 0.05 * 0.05 DEG resolution, including the following variables: 2m temperature, surface pressure, water vapor mixing ratio, downward shortwave & upward longwave radiation, 10m wind field and the accumulated precipitation. The forcing data were validated by observational data collected by 15 daily Chinese Meteorological Bureau conventional automatic weather station (CMA), a few of Heihe River eco-hydrological process comprehensive remote sensing observation (WATER and HiWATER) site hourly observations were verified in different time scales, draws the following conclusion: 2m surface temperature, surface pressure and 2m relative humidity are more reliable, especially 2m surface temperature and surface pressure, the average errors are very small and the correlation coefficients are above 0.96; correlation between downward shortwave radiation and WATER site observation data is more than 0.9; The precipitation agreed well with observational data by being verified based on rain and snow precipitation two phases at yearly, monthly, daily time scales . the correlation coefficient between rainfall and the observation data at monthly and yearly time scales were up to 0.94 and 0.84; the correlation between snowfall and observation data at monthly scale reached 0.78, the spatial distribution of snowfall agreed well with the snow fractional coverage rate of MODIS remote sensing product. Verification of liquid and solid precipitation shows that WRF model can be used for downscaling analysis in complex and arid terrain of Heihe River Basin, and the simulated data can meet the requirements of watershed scale hydrological modeling and water resources balance. The data for 2000-2012 was provided in 2013. The data for 2013-2015 was updated in 2016. The data for 2016-2018 was updated in 2019. The data for 2019-2021 was updated in 2021.
PAN Xiaoduo
The observation data set of field meteorological stations in Central Asia and Western Asia (2019-2020) includes the monthly meteorological data of 12 field meteorological stations in Kazakhstan (5 stations), Kyrgyzstan (1 station), Tajikistan (3 stations), Uzbekistan (1 station) and Iran (2 stations), involving 21 observation indicators: Monthly average temperature (TA), monthly average pressure (PA) Monthly average relative humidity (RH), monthly total rainfall (PR), monthly average wind speed (WS), monthly average wind direction (WD), 0cm monthly average soil temperature (TS1), 5cm monthly average soil temperature (TS2), 10cm monthly average soil temperature (Ts3), 15cm monthly average soil temperature (ts4), 20cm monthly average soil temperature (ts5), 40cm monthly average soil temperature (TS6) 60cm monthly average soil temperature (ts7), 100cm monthly average soil temperature (ts8), monthly total solar radiation (SR), monthly total reflected radiation (GR), monthly total ultraviolet radiation (UVR), monthly total net radiation (NR), monthly total photosynthetic effective radiation (PAR), monthly total soil heat flux (HF) and monthly total sunshine duration (SD). The 12 field stations cover farmland, forest, grassland, desert, desert, wetland, plateau, mountain and other different ecosystem types. The data length starts from October 2019 to December 2020. The original meteorological data collected by the ground meteorological observation station is obtained after format conversion after screening and review, and the data quality is good. Central Asia has diverse climate types, fragile ecological environment and frequent meteorological disasters. The establishment of this data set provides data support for long-term research in the fields of ecological environment monitoring, disaster prevention and reduction, climate change and ecological environment in Central Asia. At present, it has been applied in the research of ecological environment monitoring in Central Asia.
LI Yaoming LI Yaoming
This data set is a sub data set of the comprehensive observation data set of cloud precipitation process, which is derived from the comprehensive investigation test carried out on the South and north slopes of Qilian Mountains during 2020. The air observation is mainly conducted by the king aircraft in the air. The ground investigation includes automatic weather station, raindrop spectrometer, microwave radiometer, Cloud Radar, sounding second data, etc. The observation elements of automatic weather station include air temperature, air pressure, humidity Wind direction, wind speed, precipitation. The observation elements of raindrop spectrometer include particle spectrum, precipitation intensity, etc. The observation elements of microwave radiometer are atmospheric temperature and humidity profiles. The observation elements of cloud Radar are mainly fixed-point vertical observation data. Meanwhile aerosol, rain, hail and soil samples are collected. It can provide data support for revealing the influence of westerly monsoon on cloud precipitation process and atmospheric water cycle in Qilian Mountains.
FU Danhong
This data set is a sub data set of the comprehensive observation data set of cloud precipitation process, which is derived from the field observation test of cloud precipitation physical process carried out in Nyingchi area from 2019 to 2021. The observation instruments include Ka band millimeter wave cloud radar, micro rain radar and raindrop spectrometer. The observation elements of Ka band milliwave Cloud Radar include fixed-point vertical observation, RHI scanning observation and volume scanning observation data, The observation elements of micro rain radar include particle spectrum, liquid water content and precipitation intensity. The observation elements of raindrop spectrometer include particle spectrum and precipitation intensity. This data set can provide data support for the study of the formation mechanism and change trend of cloud precipitation physical process in Southeast Tibet and the response mechanism to westerly monsoon change.
FU Danhong
1) This data is the aridity index data calculated based on the latest simulation results of 22 cmip6 coupled global climate models; 2) The calculation formula is p / PET (ratio of precipitation to potential evapotranspiration), and the calculation of pet is based on PM formula; 3) The monthly data of the Great Lakes region of Central Asia from January 1900 to December 2100, including ssp2-4.5 and ssp5-8.5, with a resolution of 1 degree * 1 degree; 4) The data can be used to analyze the distribution and evolution of dry and wet pattern in the Great Lakes region of Central Asia under medium and high emission scenarios in the future. The data has been converted into 3-mongth running means.
HUA Lijuan
The gridded desertification risk data of The Arabian Peninsula in 2021 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in the Arabian Peninsula in 2021.
XU Wenqiang
This data set is the version 2 of "High temporal and spatial resolution precipitation data of Upper Brahmaputra River Basin (1981-2016) ", with additional data from 2017 to 2019. This data set describes the temporal and spatial distribution of precipitation in the Upper Brahmaputra River Basin. We integrate (CMA, GLDAS, ITP-Forcing, MERRA2, TRMM) five sets of reanalysis precipitation products and satellite precipitation products, and combine the observation precipitation of 9 national meteorological stations from China Meteorological Administration (CMA) and 166 rain gauges of the Ministry of Water Resources (MWR) in the basin. The time range is 1981-2019, the time resolution is 3 hours, the spatial resolution is 5 km, and the unit is mm/h. The data will provide better data support for the study of Upper Brahmaputra River Basin, and can be used to study the response of hydrological process to climate change. Please refer to the instruction document uploaded with the data for specific usage information.
WANG Yuanwei, WANG Lei, LI Xiuping, ZHOU Jing
The East Asian summer monsoon (EASM) and its variability involve circulation systems in both the tropics and midlatitudes as well as in both the lower and upper troposphere. Considering this fact, a new EASM index (NEWI) is proposed based on 200-hPa zonal wind, which takes into account wind anomalies in the southern (about 5°N), middle (about 20°N), and northern areas (about 35°N) of East Asia. NEWI = Nor[u(2.5°–10°N, 105°– 140°E) - u(17.5°–22.5°N, 105°– 140E) + u(30°– 37.5°N, 105°– 140°E)] where Nor represents standardization and u is JJA-mean 200-hPa zonal wind. When easterly anomalies appear around 20°N and westerly anomalies appear around 5° and 35°N, the index is positive, and the EASM is stronger. The NEWI can capture the interannual EASM-related climate anomalies and the interdecadal variability well. Compared to previous indices, the NEWI shows a better performance in describing precipitation and air temperature variations over East Asia. It can also show distinct climate anomalous features in early and late summer. The NEWI is tightly associated with the East Asian–Pacific or the Pacific–Japan teleconnection, suggesting a possible role of internal dynamics in the EASM variability. Meanwhile, the NEWI is significantly linked to El Niño–Southern Oscillation and tropical Indian Ocean sea surface temperature anomalies. Furthermore, the NEWI is highly predictable in the ENSEMBLES models, indicating its advantage for operational prediction of the EASM. The physical mechanism of the EASM variability as represented by the NEWI is also explicit. Both warm advection anomalies of temperature by anomalous westerly winds and the advection of anomalous positive relative vorticity by northerly basic winds cause anomalous ascending motion over the mei-yu–changma–baiu rainfall area, and vice versa over the South China Sea area. Hence, this NEWI would be a good choice to study, monitor, and predict the EASM (Zhao et al，2015，J Clim).
HUANG Gang, ZHAO Guijie
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