Dataset of surface inundation caused by historical extreme precipitation for The 34 critical nodes of the pan third pole (2014-2018)

Data set of surface inundation caused by historical extreme precipitation evaluated the surface inundation range of One Belt And One Road key areas under extreme precipitation, providing a basis and reference for the decision-making of local government departments, so as to give early warning before the occurrence of extreme precipitation and reduce the loss of life and property caused by extreme precipitation.This data set to the extreme precipitation threshold set "and" the extreme precipitation recognition "as the foundation, to confirm the extreme precipitation time node and the area, and then to NASA's web site to download the submerged range products corresponding to the time and region, combining ArcGIS spatial analysis was used to connect the above data, build the data sets of historical extreme precipitation caused surface submerged range for 34 key nodes. The data mainly includes 34 key nodes (Vientiane, China-Myanmar oil and gas pipeline, China-Laos Thai-Cambodia railway, Alexandria, Yangon, Kwantan, Kolkata, Warsaw, Karachi, Yekaterinburg, Yekaterinburg and other regions).

0 2020-06-17

The precipitation dataset of the Third Pole region (1951-2010)

The precipitation dataset of the Third Pole region mainly contains two EXCEL files: (1) Daily precipitation data in China in the Third Pole region, named as China_daily.xlsx. The precipitation data in China were obtained from the China Meteorological Administration-National Meteorological Information Center (http://data.cma.gov.cn/site/index.html). (2) Daily precipitation data in other countries in the Third Pole region, named as Foreign_daily.xlsx. The precipitation data in other countries were obtained from NCDC International Climatic Data Center - NOAA Satellite Information Service Center (http://www7.ncdc.noaa.gov/CDO/country), Pakistan Meteorological Administration, Nepal Meteorological Administration, etc. There are seven variables in these two EXCEL data files: precipitation, corrected precipitation, correction factor, wind-induced loss, evaporation loss, wet loss, and trace precipitation. The detail characteristics of TPE stations were described in an EXCEL file either, named as "TPE station and gauge type.xls". The raw data has been strictly quality controlled by the relevant meteorological departments and has been applied in relevant academic papers.

0 2020-06-17

Data set of meteorological observation day, month and year of Pan third critical node area stations (2000-2016)

The site's daily and monthly statistical data sets are the key parameters reflecting the weather conditions of the site, and are the GSOM data. Meteorology plays an important role in the lithosphere, biosphere, soil circle and the atmosphere, providing a basis for assessing the regional contribution and response of climate factors to the world. This data set takes 34 key node regions of the pan third pole as the study area (Abbas, Astana, Bangkok, etc.),based on the site climate data from 2000 to 2016, the meteorological factors in different regions were counted, and the data series of meteorological observations in key nodes were obtained. The main parameters are: annual average maximum value, average minimum value and average temperature; monthly total precipitation and snowfall.

0 2020-06-17

Daily value dataset of 10m meteorological tower at Laohugou Glacier No.12 in the Qilian Mountains of China(V1.0) (2014-2018)

This data is the log data set of the meteorological tower in 2014-2018 in Laohugou base camp of Qilian Mountains. "The 10 meter meteorological tower of Laohugou 12 glacier is located in the base camp, with an altitude of 4200 meters. Its observation elements include temperature, precipitation, wind speed, wind direction, relative humidity, air pressure, downward radiation, upward radiation, downward long wave radiation and upward long wave radiation, with a resolution of daily value. The meteorological instrument passes through China's meteorology After calibration and commissioning one belt, one road is connected with the CR1000 (Campbell), the -55 (CR1000) data collector. The data quality is complete. The data of many articles are all derived from this data. The Hexi Corridor nurtured by glacier water and melting glacier in Qilian Mountains is an important channel for the national strategic "one belt and one road". The study of its changes has great influence on Gansu, the whole country and the whole country. Therefore, this data has great research value and application value. "

0 2020-06-11

Spatial distribution data set of extreme precipitation disaster risk (2014-2018)

Based on the world surface water data (wod) from 1984 to 2018, this data set selects several indexes of precipitation, topography and land use type, and combines with the spatial analysis method in ArcGIS, constructs and evaluates the risk level of flood disaster in 34 key nodes under extreme precipitation conditions. One belt, one road, 34 critical nodes, is evaluated for the risk of flooding in the key areas along the extreme precipitation events. It provides a basis for local government departments to make decisions and early warning before the flood. Thus, we can gain valuable time to take measures to prevent and reduce disasters, and to reduce people's lives and property losses caused by floods. Loss.

0 2020-06-11

Vulnerability assessment data set of extreme precipitation disaster (2019)

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.

0 2020-06-11

Extreme precipitation disaster risk assessment data set (2019)

Based on 100m risk assessment data set and 100m vulnerability assessment data set, this data set respectively gives different weights to the risk and vulnerability (the risk weight is 0.8, and the vulnerability weight is 0.2), and 34 key node 100m risk assessment data sets are obtained by adding. One belt, one road area, is evaluated for flood risk in extreme areas. The data provide basis for local government departments to make decisions, and early warning before flood disasters, so that we can gain valuable time to take measures to prevent and reduce disasters, and to reduce the loss of lives and property of people caused by floods.

0 2020-06-11

North american multi-model ensemble forecast (1982-2010)

The North American Multi-Model Ensemble (NMME) Forecast is a multi-modal ensemble seasonal forecasting system jointly published by the US Model Center (including NOAA/NCEP, NOAA/GFDL, IRI, NCAR, and NASA) and the Canadian Meteorological Centre. The data include retrieval data from 1982 to 2010 and real-time weather forecast data from 2011 to the present. The forecasting system covers the whole world with a temporal resolution of one month and a horizontal spatial resolution of 1°. NMME has nine climate forecasting models, and each contains 6-28 ensemble members, with a forecasting period of 9-12 months. The name, source, ensemble members, and forecasting period of the climate models are as follows: 1) CMC1-CanCM3, Environment Canada, 10 models, 12 months 2) CMC2-CanCM4, Environment Canada, 10 models, 12 months 3) COLA-RSMAS-CCSM3, National Center for Atmospheric Research, 6 models, 12 months 4) COLA-RSMAS-CCSM34, National Center for Atmospheric Research, 10 models, 12 months 5) GFDL-CM2p1-aer04, NOAA Geophysical Fluid Dynamics Laboratory, 10 models, 12 months 6) GFDL-CM2p5-FLOR-A06, NOAA Geophysical Fluid Dynamics Laboratory, 12 models, 12 months 7) GFDL-CM2p5-FLOR-B01, NOAA Geophysical Fluid Dynamics Laboratory, 12 models, 12 months 8) NASA-GMAO-062012, NASA Global Modeling and Assimilation Office, 12 models, 9 months 9) NCEP-CFSv2, NOAA National Centers for Environmental Prediction, 24/28 models, 10 months With the exception of the CFSv2 model (which includes only precipitation and average temperature), the variables of other models include precipitation, average temperature, maximum temperature, and minimum temperature. Each model ensemble member stores one NC file every month for each variable. The meteorological elements, variable names, units, and physical meanings of each variable are as follows: 1) Average temperature, tref, K, monthly average near-surface (2-m) average air temperature 2) Maximum temperature, tmax, K, monthly average near-surface (2-m) maximum air temperature 3) Minimum temperature, tmin, K, monthly average near-surface (2-m) minimum air temperature 4) Precipitation, prec, mm/day, monthly average precipitation. The dataset has been widely applied in climate forecasting, hydrological forecasting, and quantitatively estimating model forecasting uncertainty.

0 2020-06-03

Dataset of gridded daily precipitation in China (Version 2.0) (1961-2013)

The National Meteorological Information Center Meteorological Data Room has detected, controlled and corrected the quality of 2474 national-level ground stations' basic meteorological data and formed a set of high-quality, national and provincial ground-based basic data files. On the basis of the basic ground data of the precipitation data files, the thin-plate spline method is used, introducing the digital elevation data to eliminate the influence of the elevation on the precipitation precision under the unique terrain conditions in China. A dataset of 0.5°×0.5° grid values for the surface precipitation in China since 1961 is established. It provides a data basis for accurately describing the trends and magnitudes of precipitation changes in China. One of two data sources for the development of “Dataset of Gridded Daily Precipitation in China (Version 2.0)” was 1) the monthly and daily precipitation data of 2474 national-level stations in the country archived by the Meteorological Data Room for nearly 50 years. The information comes from the monthly information of the “Monthly Report of the Surface Meteorological Record” reported by the climate data processing departments of all the provinces, municipalities and autonomous regions. That information is collected, organized and strictly checked and reviewed by the National Meteorological Information Center. Since the establishment of the station, many stations in the country have undergone historical changes such as business reform and station migration. In 1961, the total number of stations had stabilized above 2,000, and the number of backstage stations in the late 1970s reached 2,400. 2) The second data source was a Chinese range of 0.5°×0.5° digital elevation model data DEMs generated by GTOP030 data (resolution 30′′×30′′) resampling. For the quantitative analysis and evaluation of the data, please see the Dataset of Gridded Daily Precipitation in China - Data Specification.

0 2020-06-03

Automatic weather station dataset from Guoluo station (2017)

The data set contains meteorological observations from Guoluo Station from January 1, 2017, to December 31, 2017, and includes temperature (Ta_1_AVG), relative humidity (RH_1_AVG), vapour pressure (Pvapor_1_AVG), average wind speed (WS_AVG), atmospheric pressure (P_1), average downward longwave radiation (DLR_5_AVG), average upward longwave radiation (ULR_5_AVG), average net radiation (Rn_5_AVG), average soil temperature (Ts_TCAV_AVG), soil water content (Smoist_AVG), total precipitation (Rain_7_TOT), downward longwave radiation (CG3_down_Avg), upward longwave radiation (CGR3_up_Avg), average photosynthetically active radiation (Par_Avg), etc. The temporal resolution is 1 hour. Missing observations have been assigned a value of -99999.

0 2020-06-03