The long-time series data set of extreme precipitation index in the arid region of Central Asia contains 10 extreme precipitation index long-time series data of 49 stations. Based on the daily precipitation data of the global daily climate historical data network (ghcn-d), the data quality control and outlier elimination were used to select the stations that meet the extreme precipitation index calculation. Ten extreme precipitation indexes (prcptot, SDII, rx1day, rx5day, r95ptot, r99ptot, R10, R20) defined by the joint expert group on climate change detection and index (etccdi) were calculated 、CWD、CDD）。 Among them, there are 15 time series from 1925 to 2005. This data set can be used to detect and analyze the frequency and trend of extreme precipitation events in the arid region of Central Asia under global climate change, and can also be used as basic data to explore the impact of extreme precipitation events on agricultural production and life and property losses.
YAO Junqiang CHEN Jing LI Jiangang
This dataset is the high-resolution downscaled results of three global circulation models (CCSM4, HadGEM2-ES, and MPI-ESM-MR) from CMIP5. The regional climate model applied is the WRF model. The domain of this dataset covers the five countries of Central Asia. Its horizontal resolution is 9km. The future (reference) period is 2031-2050 (1986-2005), which includes the 10 years under 1.5-2℃ global warming. The carbon emission scenario is RCP4.5. The variances are annual mean temperature at 2m and precipitation (cumulus and grid-scale precipitation). This dataset can be used to project the climate in Central Asia.
Gwadar deepwater port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from the Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and the Red Sea in the West. It is a port with a strategic position far away from Muscat, the capital of Oman. This data is the measured meteorological data of Gwadar Port meteorological station (62.329494e, 25.233308n). The data time range is 2014-2015, and the data time resolution is one day.
Coupled Model Intercomparison Project Phase 5 (CMIP5) provides a multiple climate model environment, which can be used to predict the future climate change in the key nodes in the Belts and Road to deal with the environmental and climate problems. Key nodes in the Belt and Road are taken as the study regions of this dataset. The ability of 43 climate models in CMIP5 to predict the future climate change in the study regions was assessed and the optimal models under different scenarios were selected according to the RMSE between the prediction results and real observations. This dataset is composed of the prediciton results of precipitation and near-surface air temperature between 2006 and 2065 using the optimal models in monthly temporal frequncy. The spatial resolution of the dataset has been downscaled to 10 km using statistical downscaling method. Data of each period has three bands, namely maximum near-surface air temperature, minimum near-surface air temperature and precipitation. In this data set, the unit of precipitation is kg / (m ^ 2 * s), and the unit of near-surface air temperature is K. This dataset provides data basis for solving environmental and climate problems of the key nodes in the Belts and Road.
LI Xinyan LING Feng
The data set includes the start time (year, month), location (longitude and latitude), duration (month), drought intensity and vulnerability data of vegetation response to drought in Central Asia from 1982 to 2015, with a spatial resolution of 1 / 12 °. The drought events were identified by the standardized precipitation evapotranspiration index at the time scale of 12 months (spei12) < - 1.0. The specific algorithm of drought characteristics and vegetation vulnerability is detailed in the citation. The dataset has been applied in the study of vegetation vulnerability to drought in Central Asia, and has application prospects in the research fields of spatial-temporal characteristics of drought events, drought-vegetation interaction mechanism, drought risk assessment and so on.
1) Data content (including elements and significance): the data includes daily values of temperature (℃), precipitation (mm), relative humidity (%) and wind speed (M / s) 2) Data source and processing method; air temperature, relative humidity and wind speed are daily mean values, precipitation is daily cumulative value; data collection location is 29 ° 39 ′ 25.2 ″ n; 94 ° 42 ′ 25.62 ″ E; 4390m; underlying surface is natural grassland; collector model Campbell Co CR1000, collection time: 10 minutes. Digital automatic data acquisition. The temperature and relative humidity instrument probe is hmp155a; the wind speed sensor is 05103; the precipitation is te525mm; 3) Data quality description; the original data of temperature, relative humidity and wind speed are the average value of 10 minutes, and the precipitation is the cumulative value of 10 minutes; the daily average temperature, relative humidity, precipitation and wind speed are obtained by arithmetic average or summation. Due to the limitation of sensors, there may be some errors in winter precipitation. 4) In addition, it is convenient for scientists to update the atmospheric data in the future. This data is updated from time to time every year.
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.
The China-Mongolia-Russia Economic Corridor is confronted with security problems related with global warming, mostly including the increasingly serious of degradation of permafrost and land desertification. On one hand, frozen soil degradation has caused frequent disasters such as debris flow, flood, ice and snow damage along the China-Mongolia-Russia transportation and pipeline, which will cause water and soil erosion followed by exposed pipes in frozen soil, in particular in summer. On the other hand, desertification will drive the ecological environment more vulnerable with the compound hazards of soil erosion and sandstorms occurring frequently. Therefore, this dataset will hopefully provide basic climate data for the research on the climate change and its impacts on permafrost and desertification for the China-Mongolia-Russia Economic Corridor. The original data is extracted from ERA5- Land surface climate reanalysis data (ERA5 – Land) (source: https://cds.climate.copernicus.eu). We adopted the inverse distance weight (IDW) method to interpolate the original data with the spatial resolution of 10 km. Based on this dataset, the spatial and temporal distribution pattern of climatic factors are outlined over the past 40 years for the corridor.
Effective evaluation of future climate change, especially prediction of future precipitation, is an important basis for formulating adaptation strategies. This data is based on the RegCM4.6 model, which is compatible with multi-model and different carbon emission scenarios: CanEMS2 (RCP 45 and RCP85), GFDL-ESM3M (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), HadGEM2-ES (RCP2.6, RCP4.5 And RCP8.5), IPSL-CM5A-LR (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), MIROC5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The future climate data (2007-2099) has 21 sets, with a spatial resolution at 0.25 degrees and the temporal resolution at 3 hours, daily and yearly scales.
PAN Xiaoduo ZHANG Lei
The data set is the daily precipitation stable isotope data (δ 18O, δ D, d-excess) from Satkhira, Barisal and sylhet3 stations in Bangladesh from 2017 to 2018. The data set was collected by Bangladesh Atomic Energy Commission (BAEC) and measured by picarro l2130i wavelength scanning cavity ring down spectrometer in the Key Laboratory of environment and surface processes, Institute of Qinghai Tibet Plateau, Chinese Academy of Sciences. Sampling location and time of three observation points: Satkhira ：2017.03.11-2018.07.16 Barisal：2017.03.05-2018.07.02 Sylhet : 2017.02.20-2018.09.04
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.
The stable oxygen isotope ratio (δ 18O) in precipitation is a comprehensive tracer of global atmospheric processes. Since the 1990s, efforts have been made to study the isotopic composition of precipitation at more than 20 stations located on the TP of the Tibetan Plateau, which are located at the air mass intersection between westerlies and monsoons. In this paper, we establish a database of monthly precipitation δ 18O over the Tibetan Plateau and use different models to evaluate the climate control of precipitation δ 18O over TP. The spatiotemporal pattern of precipitation δ 18O and its relationship with temperature and precipitation reveal three different domains, which are respectively related to westerly wind (North TP), Indian monsoon (South TP) and their transition.
The Frequency distribution improved and wind-induced undercatch corrected gridded precipitation in Tibetan Plateau(1980-2009) is a dataset suitable for the Tibetan Plateau . It considers the measurement undercatch caused by wind and optimizes the precipitation frequency distribution by adopting an advanced interpolation method. The data is in NETCDF format, with a temporal resolution of 1 day and a horizontal spatial resolution of 10km. The data can be used as a reference data source for numerical model precipitation frequency correction. This dataset uses daily observations from the China Meteorological Administration and GSOD at 164 stations as the data sources. The construction of the dataset is divided into four steps :(1) firstly, quality control is carried out on the gauge data, including the removal of abnormal values and bad values.(2) Doing wind-induced undercatch correction for every precipitation record.(3) A thin-plate splines interpolation algorithm considering altitude as a covariate is used to interpolate the monthly total precipitation, and the ratio of daily and monthly precipitation was interpolated by the Ordinary Kriging method. The dataset with a spatial resolution of 1km was obtained by multiplying the monthly total precipitation and day to month ratio. (4) Aggregating the 1km dataset to 10km spatial resolution to obtain the final data. Compared with the similar international gridded precipitation dataset, this data highlights for it’s wind-induced undercatch correction of gauge precipitation and the optimized interpolation method to make itself have more accurate frequency distribution. The data is suitable for correction of statistical deviation of precipitation output by numerical model or analysis of precipitation frequency characteristics at grid-box. y. It is more suitable for correcting the statistical deviation of precipitation output by numerical model or analyzing the precipitation frequency characteristics on gridded points.
MA Jiapei LI Hongyi
1) Data content (including elements and significance): 21 stations (Southeast Tibet station, Namucuo station, Zhufeng station, mustag station, Ali station, Naqu station, Shuanghu station, Geermu station, Tianshan station, Qilianshan station, Ruoergai station (northwest courtyard), Yulong Xueshan station, Naqu station (hanhansuo), Haibei Station, Sanjiangyuan station, Shenzha station, gonggashan station, Ruoergai station（ Chengdu Institute of biology, Naqu station (Institute of Geography), Lhasa station, Qinghai Lake Station) 2018 Qinghai Tibet Plateau meteorological observation data set (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) 2) Data source and processing method: field observation at Excel stations in 21 formats 3) Data quality description: daily resolution of the site 4) Data application results and prospects: Based on long-term observation data of various cold stations in the Alpine Network and overseas stations in the pan-third pole region, a series of datasets of meteorological, hydrological and ecological elements in the pan-third pole region were established; Strengthen observation and sample site and sample point verification, complete the inversion of meteorological elements, lake water quantity and quality, above-ground vegetation biomass, glacial frozen soil change and other data products; based on the Internet of Things technology, develop and establish multi-station networked meteorological, hydrological, Ecological data management platform, real-time acquisition and remote control and sharing of networked data.
ZHU Liping PENG Ping
Precipitation estimates with ﬁne quality and spatio-temporal resolutions play signiﬁcant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at ﬁne resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. Focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (ﬁnished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0◦/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1◦/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25◦/daily) at the daily scale for the Asian applications. Considering the advantages from both satellite-based precipitation estimates and the ground observations, AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China.
1) This data includes the basic meteorological data of Kathmandu center for research and education,CAS-TU in 2019; the parameters are: temperature ℃, relative humidity%, atmospheric pressure kPa, precipitation mm, radiation w / m2, wind speed M / s. Table 2 is a description of the weather station, including the geographical location and underlying surface. 2) Data sources and processing methods: the data are from the hourly data of Kathmandu science and education center, Chinese Academy of Sciences, daily average of temperature, air pressure, radiation and wind speed, and daily sum of rainfall. 3) Data quality description: among these parameters, the quality of air pressure data is poor, and there are many missing data due to instrument failure from June to August in 2019 4) Compared with the data of different regions in South Asia, the meteorological data can be used for postgraduates and scientists with atmospheric science, hydrology, climatology, physical geography and ecology.
(1) This data set is the carbon flux data set of Shenzha alpine wetland from 2016 to 2019, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
Vulnerability assessment dataset of hectometre level for 34 key nodes assessment the flood risk of key nodes in the Belt and Road under the extreme precipitation events, in order to provide basis for decision-making for the local government department, at the same time before flood disaster early warning, which may take the disaster prevention and mitigation measures for the precious time, reduce people's lives and property damage brought by the flood. Based on the data of GDP, population, land ues, road density and river density in the Belt and Road, this dataset combined with the methods of spatial analysis of ArcGIS, assigning different weights to each indicator and building assessment 34 key nodes under the condition of extreme precipitation in flood vulnerability level, which was divided into 5 levels by using natural break point method, representing no vulnerability, low vulnerability, middle vulnerability, high vulnerability, extreme high vulnerability, respectively.
GE Yong LI Qiangzi LI Yi
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Guazhou Station from January 1 to December 31, 2019. The site (95.673E, 41.405N) was located on a desert in the Liuyuan Guazhou, which is near Jiuquan city, Gansu Province. The elevation is 2016 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, 8, 16, 32, and 48 m, towards north), wind speed and direction profile (windsonic; 2, 4, 8, 16, 32, and 48 m, towards north), air pressure (1.5 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile -0.05, -0.1m, -0.2m, -0.4m, -0.6m and -0.8m in south of tower), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_2 m, Ta_4 m, Ta_8 m, Ta_16 m, Ta_32 m, and Ta_48 m; RH_2 m, RH_4 m, RH_8 m, RH_16 m, RH_32 m, and RH_48 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, Ws_8 m, Ws_16 m, Ws_32 m, and Ws_48 m) (m/s), wind direction (WD_2 m, WD_4 m, WD_8 m, WD_16 m, WD_32 m, and WD_48 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s m^2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_80 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_80 cm) (%, volumetric water content),soil water potential (SWP_5cm, SWP_10cm, SWP_20cm, SWP_40cm, SWP_60cm, and SWP_80cm)(kpa), soil conductivity (Ec_5cm, Ec_10cm, Ec_20cm, Ec_40cm, Ec_60cm, and Ec_80cm)(μs/cm), sun time (h). 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 data during August 3 to 24 were missing because the power supply failure; From April 4, 2019, 2m air temperature and humidity sensor failure; from May.10, 2019, 48m wind speed and direction sensor failure; from July, 2019, 10cm soil moisture sensor failure. (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: 2019-6-10 10:30.
ZHAO Changming ZHANG Renyi
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Linze Station from January 1 to December 31, 2019. The site (100.060° E, 39.237° N) was located on a cropland (maize surface) in the Guzhai Xinghua, which is near Zhangye city, Gansu Province. The elevation is 1400 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation; -0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4m), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_3 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 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 long wave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5cm, Gs_10cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential(SWP_5cm, SWP_10cm), soil conductivity (Ec_5cm,Ec_10cm) (μs/cm), sun time(h). 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 precipitation and the air humidity data were rejected due to program error. (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: 2019-6-10 10:30.
ZHAO Changming ZHANG Renyi