Clouds cover 70% of the earth's surface and are one of the important factors affecting the balance of atmospheric radiation and climate change. They are also an important part of the global water cycle. Considering the lack of reliable cloud parameter data with high temporal and spatial resolutions in the East Asia-Pacific (EAP) region, the 2016 data were developed using the next-generation geostationary satellite Himawari-8 with a temporal resolution of 1h and spatial resolutions of 0.1° and 0.25°. , 1° cloud parameters datasets. The cloud products include macro- and micro parameters. The macro parameters include: cloud cover (CF), cloud detection (CM), cloud phase detection (CP), cloud top pressure (CTP), cloud top height (CTH) ), cloud top temperature (CTT), cloud type (CT), supercooled water detection (SWC); micro parameters include cloud optical depth (COT), cloud particle effective radius (CER). These cloud parameters produced have reached the international advanced level in terms of precision.
HUSI Letu
This data set is based on the lightning location data calculation of TBB products, cloud classification (CLC) products and world wide lightning location network (wwlln) in the full disk area detected by fy-2e satellite (fy-2e) from 2010 to 2018 to establish the lightning storm cloud feature data set. The algorithm used for wwlln lightning clustering is DBSCAN algorithm. According to Hutchins et al. (2014), it is required that the number of lightning in each lightning cluster in the thunderstorm cloud is greater than 2 and all fall within the radius of 12 km. The data set includes thunderstorm cloud time and location information, thunderstorm cloud shape (long, short axis, rotation angle, etc.) information represented by fitting ellipse, cloud area representing thunderstorm cloud structure, statistical value of black body temperature (TBB), included flash information, and included strong convection core, lightning cluster information and other data information.
MA Ruiyang , ZHENG Dong
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 of cloud observations at a site in Arctic Alaska is based on the fusion of five cloud inversion products that are well known worldwide. The temporal coverage of the data is from 1999 to 2009, the temporal resolution is one hour, and there are 512 layers vertically with a vertical resolution of 45 m. The spatial coverage is one site in Arctic Alaska, with latitude and longitude coordinates of 71°19′22.8′′N, 156°36′32.4′′ W. The remote sensing cloud inversion data products include the following official products: the all-phase cloud characteristic products produced by the Atmospheric Radiation Measurement Program of the US Department of Energy adopting a parametric method for remote sensing inversion, the ice cloud and hybrid cloud feature products obtained from the US NOAA researchers Matt Shupe and Dave Turner based on cooperative remote sensing inversion (optimization method + parametric method), the hybrid cloud feature (optimization method) products produced by Zhien Wang of the University of Wyoming, USA, the ice cloud feature (parametric method) products produced by Min Deng of the University of Wyoming, USA, and the cloud optical thickness products produced by Qilong Min of the State University of New York at Albany adopting remote sensing inversion (optimization method). The variables of the remote sensing products include cloud water effective radius, cloud water content, cloud ice effective radius, cloud ice content, cloud optical thickness, and cloud water column content; the corresponding observed inversion error ranges are approximately 10-30%, 30-60%, 10-30%, 30-60%, 10-30% and 10-20%. The data files are in the NC format, and an NC file is stored every month.
ZHAO Chuanfeng
This dataset contains the flux measurements from the Subalpine shrub eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from April 28 to December 31 in 2019. The site (100°6'3.62"E, 37°31'15.67" N ) was located near Dasi, Shaliuhe Town, Gangcha County, Qinghai Province. The elevation is 3495m. The EC was installed at a height of 2.5m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (Gill&Li7500A) was about 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 3% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. The released data contained the following variables: DATE/TIME, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). The quality marks of sensible heat flux, latent heat flux and carbon flux are divided into three levels (quality marks 0 have good data quality, 1 have good data quality and 2 have poor data quality). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references.
ZHAO Chuanfeng
The dataset of the truck-mounted dual polarized doppler radar observations (time-continuous 10-minute on the 250m×250m horizontal grid) was obtained in the arid region hydrology experiment area from May 20 to Jul. 5, 2008. The observation site (38.73°N, 100.45°E, 1668m) was typical of complex underlying surface and transit zone landscapes. The aim was to explore and retrieve precipitation type and intensity by radar in cold regions, with the precipitation particle drop size analyzer and ground intensive measurements occurring simultaneously, thus making it possible to produce a high resolution precipitation dataset. The 714XDP X-band dual-linear polarization Doppler weather radar was with a horizontal resolution of 150 m, an azimuth resolution of 1, VCP from 10-22 layers and the scanning cycle 10 minutes. ZH, ZDR and KDP could be acquired together. For more details, please refer to Readme file.
CHU Rongzhong, ZHAO Guo, HU Zeyong, ZHANG Tong, JIA Wei
The dataset of the truck-mounted dual polarized doppler radar observations (time-continuous 10-minute on the 250m×250m horizontal grid) was obtained in A'rou (39.06°N, 100.44°E, 3002m, typical of complex terrain in high altitude), Qilian county in the upper stream of Heihe river from Mar. 14 to Apr. 14, 2008. The aim was to explore and retrieve precipitation type and intensity by radar in cold regions, with the precipitation particle drop size analyzer and ground intensive measurements occurring simultaneously, thus making it possible to produce a high resolution precipitation dataset. The 714XDP X-band dual-linear polarization Doppler weather radar was with a horizontal resolution of 150 m, an azimuth resolution of 1, VCP from 10-22 layers and the scanning cycle 10 minutes. ZH, ZDR and KDP could be acquired together. For more details, please refer to Readme file.
CHU Rongzhong, ZHAO Guo, HU Zeyong, ZHANG Tong, JIA Wei
The assessment of changes in the atmospheric water cycle and the associated impacts in a key area of the Tibetan Plateau under the background of the global warming was a major component of the research project “The Environmental and Ecological Science of West China” run by the National Natural Science Foundation of China. The leading executive of the project was Xiangde Xu from the Chinese Academy of Meteorological Sciences. The project ran from January 2006 to December 2008. The following data were collected by the project of the Sino-Japan Joint Research Center of Meteorological Disaster (JICA Project): 1. Observation category, time period and number of stations 1) JICA AWS data: From January to July of 2008, 73 automatic stations (including 5 automatic stations of the Chinese Academy of Sciences) collected data in Tibet, Yunnan, Sichuan and other provinces or autonomous regions. 2) JICA GPS water vapour data: From January to October of 2008, 24 observation stations collected data in Tibet, Yunnan, Sichuan and other provinces or autonomous regions. 3) JICA encrypted observation GPS sonde data: From March to July of 2008, observations were made in Tibet, Yunnan, Sichuan and other provinces or autonomous regions (detailed observation time and location data can be found in the data catalogue). 2. Observation categories, data content 1) GPS water vapour Data content: serial number, station name (Chinese), station number, longitude, latitude, altitude, year, month, day, time, surface pressure, surface air temperature, relative humidity, total delay (m), precipitation (cm) (Measurement interval: 1 hour). 2) GPS encrypted sonde Data content: air pressure P, temperature T, relative humidity RH, V component, U component, vertical height H, dew point temperature Td, water vapour content Mr, wind direction Wd, wind speed Ws, longitude Lon, latitude Lat, radar height RdH. A value of "-999.90" means no observation data. 3) AWS Data content: station number, longitude, latitude, elevation, site level, total cloud volume, wind direction, wind speed, sea level pressure, 3-hour pressure variable, past weather 1, past weather 2, 6-hour precipitation, low cloud form, low cloud volume, low cloud height, dew point, visibility, current weather, temperature, medium cloud form, high cloud form, 24-hour temperature variable, 24-hour pressure variable. Project Science Advisers: Guoguang Zheng, Xiaofeng Xu, Xiuji Zhou, Zechun Li, Jifan Niu, Jianmin Xu, Lianshou Chen, Dahe Qin, Yihui Ding Project Superintendent: Jixin Yu Project Executives: Renhe Zhang, Xiangde Xu Data set hosting organizations: Chinese Academy of Meteorological Sciences, JICA Project Implementation Expert Group, State Key Laboratory of Severe Weather, JICA Project Implementation Office. Collaborative organizations involved in the production of the data set: Chinese Academy of Meteorological Sciences, State Key Laboratory of Severe Weather, National Satellite Meteorological Center, The Research Center for Atmospheric Sounding Techniques, National Meteorological Center, National Meteorological Information Center, National Climate Center, Sichuan Meteorological Department, Yunnan Meteorological Department, Tibet Autonomous Region Meteorological Department, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Tianjin Meteorological Department. Data set implementation organizations: Beijing Headquarters of JICA Project; JICA Project Sub-center in Sichuan Province, Yunnan Province, Tibet Autonomous Region and Institute of Tibetan Plateau Research, Chinese Academy of Sciences.
XU Xiangde
This dataset contains Doppler Weather Radar data from the Zhangye National Climate Observatory during the Watershed Allied Telemetry Experimental Research from 2008-03-08 to 2008-06-30. The latitude and longitude of the observation point are 100°16.8'E, 39°05.094'N; the altitude is 1378m. The main observation items are: rainfall, cloud physics, weather radar, etc.
Zhangye National Climate Observatory
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
