Current Browsing: Atmosphere


Data set of spatial and temporal distribution of water resources in Indus from 2001 to 2017

This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.

2022-11-21

High resolution atmosphere-hydrologic simulation dataset over the Tibetan Plateau (2000-2010)

The high-resolution atmosphere-hydrologic simulation dataset over Tibetan Plateau is prepared by WRFv4.1.1 model with grids of 191 * 355 and spatial resolution of 9 km, and a spatial range covering the entire plateau. The main physics schemes are configured with Thompson microphysics scheme, the rapid radiative transfer model (RRTM), and the Dudhia scheme for longwave and shortwave radiative flux calculations, respectively, the Mellor-Yamada-Janjic (MYJ) TKE scheme for the planetary boundary layer and the Unified Noah Land Surface Model. The time resolution is 3h and the time span is 2000-2010. Variables include: precipitation (Rain), temperature (T2) and water vapor (Q2) at 2m height on the ground, surface skin temperature (TSK), ground pressure (PSFC), zonal component (U10) and meridional component (V10) at 10m heigh on the ground, downward long-wave flux (GLW) and downward short-wave flux (SWDOWN) at surface, ground heat flux (GRDFLX), sensible heat flux (HFX), latent heat flux (LH), surface runoff (SFROFF) and underground runoff (UDROFF). The data can effectively support the study of regional climate characteristics, climate change and its impact over the Tibet Plateau, which will provide scientific basis for the sustainable development of the TP under the background of climate change.

2022-11-09

Spatial distribution of global mean annual temperature simulated by multi-model ensemble under different climate scenarios (2006-2100)

According to the data of three future scenarios of CMIP5 (RCP2.6、RCP4.5、RCP8.5), the spatial variation characteristics and temporal variation trend of the global mean annual air temperature from 2006 to 2100 are analyzed. Under rcp2.6 scenario, the mean annual air temperature shows an increasing trend, with the growth rate ranging from 0.0 ° c/decade to 0.2 ° c/decade (P<0.05), the growth in high latitude regions is faster, ranging from 0.1 ° c/decade to 0.2 ° C / decade. Based on the spatial and temporal characteristics of the mean annual air temperature in the northern hemisphere in the 21st century, under different scenarios, the mean annual air temperature shows a warming trend, and the high latitudes show a more sensitive and rapid growth.

2022-10-23

Data of aerosol types in the three polar region V2.0 (2006-2021)

The triple pole aerosol type data product is an aerosol type result obtained through a series of data pre-processing, quality control, statistical analysis and comparative analysis processes by comprehensively using MEERA 2 assimilation data and active satellite CALIPSO products. The key of the aerosol type fusion algorithm is to judge the aerosol type of CALIPSO. During the data fusion of aerosol type, the final aerosol type data (12 types in total) and quality control results in the three polar regions are obtained according to the types and quality control of CALIPSO aerosol types and referring to MERRA 2 aerosol types. The data product fully considers the vertical and spatial distribution of aerosols, and has a high spatial resolution (0.625 ° × 0.5 °) and time resolution (month).

2022-10-20

Meteorological observation data at grassland site of Ngoring Lake basin from 2017 to 2020

This data set is the conventional meteorological observation data of the Ngoring Lake Grassland Observation site (GS) in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity(specific humidity in 2020), air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.

2022-10-20

Meteorological observation data at Maqu grassland site from 2017 to 2020

This data set is the conventional meteorological observation data of Maqu grassland observation site in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.

2022-10-20

Meteorological observation data at Zoige wetland site in the source region of the Yellow River from 2017 to 2019

Zoige Wetland observation point is located at Huahu wetland (102 ° 49 ′ 09 ″ E, 33 ° 55 ′ 09 ″ N) in Zoige County, Sichuan Province, with an initial altitude of 3435 m. The underlying surface is the alpine peat wetland, with well-developed vegetation, water and peat layer. This data set is the meteorological observation data of Zoige Wetland observation point from 2017 to 2019. It is obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments. The time resolution is half an hour, mainly including wind speed, wind direction, air temperature, relative humidity, air pressure, downward short wave radiation, downward long wave radiation.

2022-10-20

Aerosol optical properties based on ground observation data in Arctic Alaska (1998-2020)

The aerosol optical thickness data of the Arctic Alaska station is formed based on the observation data products of the US Department of Energy's atmospheric radiation observation program at the Arctic Alaska station. The data coverage time is from 1998 to 2020, the time resolution is hourly, the coverage site is the Arctic Alaska station, and the longitude and latitude coordinates are (71 ° 19 ′ 22.8 ″ N, 156 ° 36 ′ 32.4 ″ W). The observation data is obtained from the inversion of the radiation data observed by MFRSR instrument. The optical characteristic variable is aerosol optical thickness, and the observation inversion error range is about 15%. The data format is nc format.

2022-10-19

Aerosol optical depth in the polar regions in 2000-2020

The 0.1 º aerosol optical thickness dataset (also known as the "Poles AOD Collection 1.0" aerosol optical thickness (AOD) dataset) in the polar regions from 2000 to 2020 was produced by combining Merra-2 mode data and MODIS satellite sensor AOD. The data covers the period from 2000 to 2020, with a daily time resolution, covering the "tri polar" (Antarctic, Arctic and Qinghai Tibet Plateau) region, and a spatial resolution of 0.1 degree. The verification of the measured stations shows that the relative deviation of the data is within 35%, which can effectively improve the coverage and accuracy of AOD in the polar region.

2022-10-17

Yulong snow mountain glacier No.1, 4 506 m altitude the daily average meteorological observation dataset (2014-2018)

1. Data content: air temperature, relative humidity, precipitation, air pressure, wind speed, average total radiation, total net radiation value and daily average water vapor pressure data. 2. Data source and processing method: Observed by American campel high-altitude automatic weather station, air temperature and humidity sensor model HMP155A; wind speed and wind direction model: 05103-45; net radiometer: CNR 4 Net Radiometer four component; atmospheric pressure sensor: CS106; Rain gauge: TE525MM. The automatic weather station automatically collects data every 10 minutes, and collects daily statistical data to obtain daily average weather data. 3. Data quality description: Data is automatically acquired continuously. 4. Data application results and prospects: The weather station is located in the middle of the glacier, and the meteorological data can provide data guarantee for simulating the response of oceanic glacier changes to global climate change in the context of future climate change.

2022-09-30