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
Observation data of atmospheric average electric field and ground meteorological elements of Thunderstorm Activities in Lhasa from 2019 to 2021, including continuously monitored atmospheric average electric field intensity and polarity per second / minute, precipitation per minute, wind speed, air temperature, relative humidity, atmospheric pressure and other meteorological elements, as well as equipment status parameters. The data comes from the field observation experiments of severe convection and lightning disasters carried out in Lhasa urban area from 2019 to 2021. The data can be applied to the study of severe convection and lightning disasters in the Qinghai Tibet Plateau. The observation and data acquisition shall be carried out in strict accordance with the instrument operation specifications. Measurement accuracy of atmospheric average electric field: ± 5% × Measured value + 8V / M offset; Measurement accuracy of air temperature, Rh, wind speed and wind direction: ± 0.1 ℃, ± 0.8%, ± 0.3m/s and ± 3 º; Precipitation measurement accuracy: ± 1% (≤ 10mm / HR), ± 3% (10 ~ 20mm / HR), ± 5% (20 ~ 30mm / HR) Atmospheric pressure measurement accuracy: ± 0.3hpa @ 20 ℃, ± 0.6hpa @ 0 ~ 40 ℃, ± 1.0hpa @ - 20 ~ 45 ℃, ± 1.5hpa @ - 40 ~ 60 ℃.
FU Danhong
The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.
WU Pute, TIAN Lei, ZHANG Baoqing
The accuracy of tropical cyclone (tropical storm) track forecasting improved by nearly 50% for lead times of 24–72 h since 1990s. Over the same period forecasting of tropical cyclone intensity showed only limited improvement. Given the limited prediction skill of models of tropical cyclone intensity based on environmental properties, there have been a wealth of studies of the role of internal dynamical processes of tropical cyclones, which are largely linked to precipitation properties and convective processes. The release of latent heat by convection in the inner core of a tropical cyclone is considered crucial to tropical cyclone intensification. 16-year satellite-based precipitation, and clouds top infrared brightness temperature were used to explore the relationship between precipitation, convective cloud, and tropical cyclone intensity change. The 6-hourly TC centers were linearly interpolated to give the hourly and half hourly tropical cyclone center positions, to match the temporal resolution of the precipitation and clouds top infrared brightness temperature. More precipitation is found as storms intensify, while tropical cyclone 24 h future intensity change is closely connected with very deep convective clouds with IR BT < 208 K. Intensifying tropical cyclones follow the occurrence of colder clouds with IR BT < 208 K with greater areal extents. As an indicator of very deep convective clouds, IR BT < 208 K is suggested to be a good predictor of tropical cyclone intensity change（Ruan&Wu，2018，GRL）. The properties of the satellite-based precipitation, and clouds top infrared brightness temperature are therefore suggested to be important measurements to study tropical cyclone intensity, intensity change and their underlying mechanisms. The high resolution of the satellite-based precipitation (3h), and cloud top infrared brightness temperature (half hour) datasets also makes them possible to be used to study tropical cyclone variability associated with diurnal cycle.
WU Qiaoyan
Precipitation stable isotopes (2H and 18O) are adequately understood on their climate controls in the Tibetan Plateau, especially the north of Himalayas via about 30 years’ studies. However, knowledge of controls on precipitation stable isotopes in Nepal (the south of Himalayas), is still far from sufficient. This study described the intra-seasonal and annual variations of precipitation stable isotopes at Kathmandu, Nepal from 10 May 2016 to 21 September 2018 and analysed the possible controls on precipitation stable isotopes. All samples are located in Kathmandu, the capital of Nepal (27 degrees north latitude, 85 degrees east longitude), with an average altitude of about 1400 m. Combined with the meteorological data from January 1, 2001 to September 21, 2018, the values of precipitation (P), temperature (T) and relative humidity (RH) are given.
GAO Jing
This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff，soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.
WANG Lei
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
According to the monthly temperature observation data of each conventional meteorological station in Heihe River basin set up by China Meteorological Administration, the annual air freeze-thaw index of each meteorological station is calculated, and then the annual average value of 1960-2004 is obtained. Finally, based on the regression relationship between the multi-year mean value of air freeze-thaw index and altitude of each meteorological station, and with the aid of 1 km DEM data, the spatial distribution map of air freeze-thaw index in Heihe River Basin is constructed.
ZHANG Tingjun
The experimental project of vegetation degradation mechanism and reconstruction in Yuanjiang dry-hot valley in Yunnan belongs to the major research program of "Environmental and Ecological Science in Western China" of the National Natural Science Foundation. The principal is researcher Cao Kunfang of Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences. The project runs from January 2004 to December 2007. Data collected for this project include: 1. Excel table of multi-year average temperature and rainfall in Yuanjiang dry-hot valley (1961-2004), with attribute fields including monthly average temperature and monthly average rainfall. 2. excel table of annual average temperature (1750-2006) in the middle of Hengduan Mountain in China based on tree ring, with attribute fields including year and reconstructed average temperature. 3. excel table of summer temperatures (1750-2006) in the central Hengduan Mountains in southern China based on tree rings. The attribute fields include the year and the reconstructed average temperature in summer (April-September). 4. excel table of drought index (1655-2005) in central Hengduan Mountains of China based on tree rotation, with attribute fields including year and reconstruction of drought index in spring (March-May). 5. pdf file of growth dynamic graph of leaves and branches. it records the growth dynamic trend line and leaf dynamic trend graph of plants with s-type, f-type, intermediate-type and S+SD-type branches from March 22, 2004 to April 8, 2005. 6.32 Phenological Summary Tables of Woody Plants (word Document: Specific Name, Number of Observed Plants/Branches, Type of Branch Extension, Leaf Phenology, Length of Current Year Branches (cm), Total Leaves on Branches, Leaf Area (cm2), Non-leaf Period (Months), Flowering Period, Fruit Ripening Period and Fruit Type) 7. Seasonal Changes of Relative Water Content of Plant Leaves in Yuanjiang Dry-hot Valley (March 2003-February 2004) Excel Table 8. Seasonal Changes of Photosynthesis of 6 Representative Plants in Yuanjiang Dry-hot Valley (Maximum Photosynthetic Rate, Stomatal Conductance, Water Use Efficiency, Maximum Subefficiency of photosystem II) excle Table (2003-2005) 9. excle Table of Long-term Water Use Efficiency (Isotope) Data of Representative Plants in Yuanjiang Dry-hot Valley (Water Use Efficiency in Dry and Wet Seasons of Shrimp Flower, Red-skin Water Brocade Tree, Three-leaf Lacquer, Phyllanthus emblica, Pearl Tree, Dried Sky Fruit, Cyclobalanopsis glauca, West China Small Stone Accumulation, Geranium, Tiger thorn, Willow and Pigexcrement Bean) 10. word Document of List of Plants in Mandan Qianshan, Yuanjiang
CAO Kunfang
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