Current Browsing: Precipitation

Standard weather station diurnal data of the Yellow River’s Upstream (1952-2011)

Ⅰ. Overview This dataset contains daily meteorological data for the upper Yellow River and its surroundings from 1952 to 2011. Standard station data includes 15 elements: average pressure, maximum pressure, minimum pressure, average temperature, maximum temperature, minimum temperature, average relative humidity, minimum relative humidity, precipitation, average wind speed, maximum wind speed, maximum wind speed and direction, Maximum wind speed, maximum wind speed and direction and sunshine hours. Ⅱ. Data processing description The data is stored as integers, the temperature unit is (0.1 ° C) value, the precipitation unit is (0.1 mm), and it is stored as an ASCII text file. Ⅲ. Data content description Standard station data. All meteorological elements are stored in one text. V0100 indicates the station number, v04001 indicates the year, v04002 indicates the month, v04003 indicates the day, v10004 indicates the average pressure, v10201 indicates the maximum pressure, v10202 indicates the minimum pressure, and v12001 indicates the average temperature. v12052 indicates the highest temperature, v12053 indicates the lowest temperature, v13003 indicates the average relative humidity, v13007 indicates the minimum relative humidity, v13201 indicates the precipitation, v11002 indicates the average wind speed, v11042 maximum wind speed, v11212 indicates the maximum wind speed and direction, v11041 indicates the maximum wind speed, and v11043 indicates Extreme wind speed and direction, v14032 represents sunshine hours. Ⅳ. Data usage description In terms of resources and environment, meteorological data is used to simulate the regional climate change and runoff, sediment, water and soil loss and vegetation change in the basin, and it is also a necessary input condition for remote sensing inversion.


Nonstandard weather station diurnal data of Inner Mongolia Reach of the Yellow River’s Upstream (1956-2006)

I. Overview This data set contains daily meteorological data from the Inner Mongolia section of the Yellow River from Wuhai to Dalat Banner from 1952 to 2006. Non-standard station data includes two elements, namely: temperature and precipitation. Ⅱ. Data processing description The data is stored as integers, the temperature unit is (0.1 ° C) value, the precipitation unit is (0.1 mm), and it is stored as an ASCII text file. Ⅲ. Data content description Standard station data, temperature and precipitation are stored separately, which are temperature file and precipitation file. Ⅳ. Data usage description In terms of resources and environment, meteorological data is used to simulate the regional climate change and runoff, sediment, water and soil loss and vegetation changes in the basin, and is also a necessary input condition for remote sensing inversion.


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.


NCEP/NCAR reanalysis 1.0 (1948-2017)

NCEP/NCAR Reanalysis 1 is an assimilation of data from the past (1948-recent). It was developed by the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP–NCAR) in the US to act as an advanced analysis and prediction system. Most of the data are from the original daily average data of the PSD (Physical Sciences Division). However, the data from 1948 to 1957 are slightly different because these data are conventional (non-Gaussian) grid data. The information published on the official website is generally from 1948 to the present, and the latest information is generally updated every two days. For data on an isostatic surface, the general vertical resolution is 17 layers, from 1000 hPa to 10 hPa. The horizontal resolution is typically 2.5° x 2.5°. The NCEP reanalysis data are systematically comparable among international atmospheric science reanalysis data sets. Compared with the reanalysis data of the European Center, the initial year is earlier, and the latest data updates are more frequent. These two sets of reanalysis data are currently the most widely used data sets in the world. For details of the data, please visit the following website:


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.


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.


The simulated meteorology data by using WRF model on the Tibetan Plateau and its surronding area (2004-2013)

This data set is output from WRF model. The data include ‘LU_INDEX’ (land use category), ‘ZNU’(eta values on half (mass) levels), ‘ZNW’(eta values on full (w) levels),’ZS’(depths of centers of soil layers), ‘DZS’ (thicknesses of soil layers), ‘VAR_SSO’ (variance of subgrid-scale orography), ‘U’(x-wind component), ‘V’(y-wind component),’W’(z-wind component),’T’(perturbation potential temperature (theta-t0)), ‘Q2’ ('QV at 2 M), ‘T2’ (TEMP at 2 M), ‘TH2’ ('POT TEMP at 2 M), ‘PSFC’ (SFC pressure), ‘U10’ (U at 10 M), ‘V10’ (V at 10 M), ‘QVAPOR’ (Water vapor mixing ratio), ‘QLOUD’ (Cloud water mixing ratio),’QRAIN’ (Rain water mixing ratio), ‘QICE’ (Ice mixing ratio), ‘QSNOW’ (Snow mixing ratio), ‘SHDMAX’ (annual max veg fraction), ‘SHDMIN’ (annual min veg fraction), ‘SNOALB’ (annual max snow albedo in fraction), ‘TSLB’ (soil temperature), ‘SMOIS’ (soil moisture), ‘GRDFLX’ (ground heat flux), ‘LAI’ (Leaf area index),’ HGT’ (Terrain Height), ‘TSK’ (surface skin temperature), ‘SWDOWN’ (downward short wave flux at ground surface), ‘GLW’ (downward long wave flux at ground surface), ‘HFX’ (upward heat flux at the surface), ‘QFX’ (upward moisture flux at the surface), ‘LH’ (latent heat flux at the surface), ‘SNOWC’ (flag indicating snow coverage (1 for snow cover)), and so on. The data is in netCDF format with a spatial resolution of 10 km.


China regional atmospheric driving dataset based on geostationary satellites and reanalysis data (2005-2010)

Based on the geostationary satellites and reanalysis data, the China Regional Atmospheric Driving Dataset is a set of atmospheric driving data sets with high spatiotemporal resolution prepared by the China Meteorological Administration, with a spatial resolution of 0.1 ° × 0.1 ° and a temporal resolution of 1 Hours, covering a range of 75 ° -135 ° east longitude and 15 ° -55 ° north latitude, include 6 elements of near-surface temperature, relative humidity, ground pressure, near-surface wind speed, incident solar radiation on the ground, and ground precipitation rate. The preparation process of precipitation products is as follows: The 6-hour cumulative precipitation estimated from the multi-channel data of the China Fengyun-2 geostationary satellite is integrated with the 6-hour cumulative precipitation from conventional ground observations to obtain 6-hour cumulative precipitation spatial distribution data, and then use the high-resolution cloud classification information retrieved from the multi-channel inversion of the geostationary satellites determines the interpolation time weight of the cumulative precipitation and obtains an estimated one-hour cumulative precipitation. The preparation process of the radiation data is as follows: The surface incident solar radiation based on FY-2C, uses the radiation transmission model DISORT (Discrete Ordinates Radiative Transfer Program for a Multi-Layered Plane-parallel Medium) to calculate the radiation transmission and obtains the data of surface incident solar radiation in China. Preparation process of other elements: The space and time interpolation method is used for the NCEP reanalysis data of 1.0 ° × 1.0 ° to obtain driving factors such as near-surface air temperature, relative humidity, ground pressure, and near-surface wind speed of 0.1 ° × 0.1 ° per hour. Physical meaning of each variable: Meteorological Elements || Variable Name || Unit || Physical Meaning | Surface temperature || TBOT || K || Surface temperature (2m) | Surface pressure || PSRF || Pa || Surface pressure | Relative humidity on the ground || RH || kg / kg || Relative humidity near the ground (2m) | Wind speed on the ground || WIND || m / s || Wind speed near the ground (anemometer height) | Surface incident solar radiation || FSDS || W / m2 || Surface incident solar radiation | Precipitation Rate || PRECTmms || mm / hr || Precipitation Rate For more information, see the data documentation published with the data.


Reanalysis data for surface meteorological elements for western China (2002)

The research project on land surface data assimilation system in western China belongs to the major research plan of "environment and ecological science in western China" of the national natural science foundation. the person in charge is Li Xin, researcher of the institute of environment and engineering in cold and arid regions of the Chinese academy of sciences. the project runs from January 2003 to December 2005. One of the data collected in this project is the reanalysis data of surface climate factors in western China in 2002. This data set is generated based on the daily 1 × 1 provided by the National Environmental Prediction Center (NCEP). However, the re-analysis of the data has the following problems: (1) the temporal and spatial resolution is not high enough (the horizontal resolution is 1 degree and the time is 6 hours); (2) The low-level errors in plateau areas are large; (3) The data are standard isosurface data and need interpolation. The 2002 reanalysis data set of surface climate elements in western China was generated by combining NCEP reanalysis data and MM5 model by Dr. Longxiao and Professor Qiu Chongjian of Lanzhou University using Newton relaxation data assimilation method (Nudging), including 10m horizontal and vertical wind speed (m/s), 2m air temperature (k), 2m mixing ratio, surface pressure (Pa), upstream and downstream short wave and long wave radiation (w/m2), convective precipitation and large scale precipitation (mm/s) at 0.25 degree per hour throughout 2002. I. preparation background The quality of the driving data seriously affects the ability of the land surface model to simulate the land surface state, so a very important component of the land surface modeling research is the driving data used to drive the land surface model. No matter how realistic these models are in describing the surface process, no matter how accurate the boundary and initial conditions they input, if the driving data are not accurate, they cannot get the results close to reality. Land surface models are so dependent on the quality of externally provided data that any error in these externally provided data will seriously affect the ability of land surface models to simulate soil moisture, runoff, snow cover and latent heat flux. These externally provided data include: precipitation, radiation, temperature, wind field, humidity and pressure. The 2002 reanalysis data set of surface climate elements in western China uses Newton relaxation data assimilation method (Nudging) to combine NCEP reanalysis data and MM5 model to generate driving data with higher spatial and temporal resolution suitable for complex terrain in western China. Second, the basic parameters of the operation mode 1. Using the US PSU/NCAR mesoscale model MM5 as a simulation model; The selection of simulation grid domain: center (32°N, 90°E), grid distance of 36km, number of horizontal grid points of 131*151, vertical resolution of 25 layers, and mode top of 100hPa;; 2. The data used for initialization are 1 * 1 GRIB grid data of NCEP in the United States. 3. The time step is 120s. Third, the physical process 1. physical process treatment of cloud and precipitation: Grell cumulus cloud parameterization scheme is adopted for sub-grid scale precipitation, and Reisner mixed phase microphysical explicit scheme is adopted for distinguishable scale precipitation; 2. MRF parameterization scheme is adopted for planetary boundary layer process. 3. the radiation process adopts CCM2 radiation scheme. IV. File Format and Naming It is stored in a monthly folder and contains 24 hours of data every day. The naming rules are as follows: 2002***&.forc, where * * * is Julian day and 2002***& is time (in hours), where. forc is the file extension. V. data format Stored in binary floating point type, each data takes up 4 bytes.


Temperature and precipitation dataset of WRF model in Northwest China (1979-2013)

This data is the NCEP/DOE reanalysis data of 6h interval nested downscaling by WRF model in northwest China to a horizontal resolution of 12km, 364 grid points in the east-west direction, 251 grid points in the north-south direction and 31 layers in the vertical direction. The simulation time starts from 1979-01-01,06:00:00 and ends at 2013-12-31,23:00:00. The parameterization schemes of the model are as follows: Kain Frisch cumulus convection scheme, WSM3 cloud microphysics scheme, RRTM long wave scheme, Dudhia short wave scheme, Noah land surface model, YSU planetary boundary layer scheme. The file naming rules in the data set are: and, where YYYY is the annual abbreviation, t2 is the 2m temperature (unit ℃), and rain is the total surface precipitation (unit mm).