ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

1km resolution gridded meteorological variables over Great Britain for the years 1961-2015. This dataset contains time series of daily mean values of air temperature (K), specific humidity (kg kg-1), wind speed (m s-1), downward longwave radiation (W m-2), downward shortwave radiation (W m-2), precipitation (kg m-2 s-2) and air pressure (Pa), plus daily temperature range (K). These are the variables required to run the JULES land surface model [1] with daily disaggregation. The precipitation data were obtained by scaling the Gridded estimates of daily and monthly areal rainfall (CEH-GEAR) daily rainfall estimates [2,3] to the units required for JULES input. Other variables were interpolated from coarser resolution datasets, taking into account topographic information. This release supersedes the previous version [4], doi:10.5285/10874370-bc58-4d23-a118-ea07df8a07f2, as it corrects errors in the air pressure and daily temperature range files for 2013-2015. [1] Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes, Geoscientific Model Development, 4, 677-699. https://doi.org/10.5194/gmd-4-677-2011, 2011. [2] Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D. G.; Keller, V. D. J. (2016). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2015) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/33604ea0-c238-4488-813d-0ad9ab7c51ca [3] Keller,V. D. J., Tanguy, M. , Prosdocimi, I. , Terry, J. A. , Hitt, O., Cole, S. J. , Fry, M., Morris, D. G., Dixon, H. (2015) CEH-GEAR: 1km resolution daily and monthly areal rainfall estimates for the UK for hydrological use. Earth Syst. Sci. Data Discuss., 8, 83-112. https://doi.org/10.5194/essdd-8-83-2015. [4] Robinson, E.L., Blyth, E., Clark, D.B., Comyn-Platt, E., Finch, J. , Rudd, A.C. (2016). Climate hydrology and ecology research support system meteorology dataset for Great Britain (1961-2015) [CHESS-met]. NERC Environmental Information Data Centre. https://doi.org/10.5285/10874370-bc58-4d23-a118-ea07df8a07f2

The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). Version 1.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2016. Data sources of W5E5 are version 1.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2019), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003). Variables (with short names and units in brackets) included in the W5E5 dataset are Near Surface Relative Humidity (hurs, %), Near Surface Specific Humidity (huss, kg kg-1), Precipitation (pr, kg m-2 s-1), Snowfall Flux (prsn, kg m-2 s-1), Surface Air Pressure (ps, Pa), Sea Level Pressure (psl, Pa), Surface Downwelling Longwave Radiation (rlds, W m-2), Surface Downwelling Shortwave Radiation (rsds, W m-2), Near Surface Wind Speed (sfcWind, m s-1), Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and WFDE5-ERA5 Mask (mask, 1). W5E5 is a merged dataset. It combines WFDE5 data over land with ERA5 data over the ocean. The mask used for the merge is included in the dataset. The mask is equal to 1 over land and equal to 0 over the ocean. Over land, orog is the surface altitude used for elevation corrections in WFDE5. For all other variables already included in WFDE5 (huss, prsn, ps, rlds, rsds, sfcWind, tas), W5E5 data over land are equal to the daily mean values of the corresponding hourly WFDE5 data. W5E5 hurs over land is the daily mean of hourly hurs computed from hourly WFDE5 huss, ps, and tas using the equations of Buck (1981) as described in Weedon et al. (2010). W5E5 pr over land is the daily mean of the sum of hourly WFDE5 rainfall and snowfall. Note that W5E5 pr and prsn over land are based on WFDE5 rainfall and snowfall bias-adjusted using GPCC monthly precipitation totals. W5E5 psl over land is the daily mean of hourly psl computed from hourly WFDE5 orog, ps, and tas according to psl = ps * exp((g * orog) / (r * tas)), where g is gravity, and r is the specific gas constant of dry air. Lastly, W5E5 tasmax and tasmin over land are the daily maximum and minimum, respectively, of hourly WFDE5 tas. Over the ocean, W5E5 data are based on temporally (from hourly to daily resolution) and spatially (from 0.25° to 0.5° horizontal resolution) aggregated ERA5 data. The spatial aggregation using first-order conservative remapping was always done after the temporal aggregation. For tasmax and tasmin, hourly tas values were aggregated to daily maximum and minimum values, respectively. For all other variables, hourly values were aggregated to daily mean values. Variables unavailable in ERA5 (huss, hurs, sfcWind, orog) were first derived from available variables at hourly temporal and 0.25° horizontal resolution and then aggregated like all other variables. huss and hurs were derived from Near Surface Dewpoint Temperature, ps, and tas using the equations of Buck (1981) as described in Buck (2010). sfcWind was derived from Eastward Near-Surface Wind (uas) and Northward Near-Surface Wind (vas) according to sfcWind = sqrt(uas * uas + vas * vas). orog is equal to surface geopotential divided by gravity. Lastly, pr and prsn were bias-adjusted such that monthly W5E5 precipitation totals match GPCP version 2.3 values over the ocean. Monthly rescaling factors used for this purpose were computed following the scale-selective rescaling procedure described by Balsamo et al. (2010).

The CRU CY3.21 dataset consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables in 289 countries for the period Jan. 1901 to Dec. 2012. It was produced in 2013 by the Climatic Research Unit (CRU) at the University of East Anglia. Spatial averages are calculated using area-weighted means. Variables include cloud cover (cld), diurnal temperature range (dtr), frost day frequency (frs), precipitation (pre), daily mean temperature (tmp), monthly average daily maximum (tmx) and minimum (tmn) temperature, vapour pressure (vap), Potential Evapo-transpiration (pet) and wet day frequency (wet). CRU CY3.21 is derived directly from the CRU TS3.21 dataset. Version numbering is matched between the two datasets. The data are available as text files with the extension '.per' and can be opened by most text editors. To understand the CRU-CY3.21 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS3.21. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014). CRU CY data are available for download to all CEDA users.

The Climatic Research Unit (CRU) Country (CY) data version 4.03 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency; including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and potential evapotranspiration. This version uses the updated set of country definitions, please see the appropriate Release Notes. This dataset was produced in 2019 by CRU at the University of East Anglia and extends the CRU CY4.02 data to include 2018. The data are available as text files with the extension '.per' and can be opened by most text editors. Spatial averages are calculated using area-weighted means. CRU CY4.03 is derived directly from the CRU time series (TS) 4.03 dataset. CRU CY version 4.03 spans the period 1901-2018 for 292 countries. To understand the CRU CY4.03 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.03. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.03 release notes listed in the online documentation on this record. CRU CY data are available for download to all CEDA users.

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.01 data are month-by-month variations in climate over the period 1901-2016, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia. The CRU TS4.01 variables are cloud cover, diurnal temperature range, frost day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2016. The CRU TS4.01 data were produced using angular-distance weighting (ADW) interpolation. All version 3 releases used triangulation routines in IDL. Please see the release notes for full details of this version update. CRU TS4.01 is a full release, differing only in methodology from the parallel release, v3.25. Both are released concurrently to support comparative evaluations between these two versions, however, this will be the last release of version 3. The CRU TS4.01 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999. All CRU TS output files are actual values - NOT anomalies.

The CRU CY version 3.22 dataset consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables, including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, Potential Evapo-transpiration and wet day frequency. This dataset was produced in 2014 by the Climatic Research Unit (CRU) at the University of East Anglia. The data are available as text files with the extension '.per' and can be opened by most text editors. Spatial averages are calculated using area-weighted means. CRU CY3.22 is derived directly from the CRU TS3.22 dataset. CRU CY version 3.22 spans the period 1901-2013 for 289 countries. To understand the CRU-CY3.22 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS3.22. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014). CRU CY data are available for download to all CEDA users.

The gridded CRU TS (time-series) 3.26 data are month-by-month variations in climate over the period 1901-2017, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia. This version of CRU TS supersedes version 3.25, additionally however these data are superseded by the CRU TS 4.02 data which has a new processing methodology. This concurrent release of CRU TS 3.26 and CRU TS 4.02 is made to support users during the transition to the CRU TS version 4 data. No further releases of version 3 are planned. CRU TS 3.26 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period Jan. 1901 - Dec. 2017. CRU TS 3.26 data were produced using the same methodology as for the 3.21, 3.22, 3.23, 3.24.01, 3.26 datasets. This version contains updates the dataset with 2016 data, some new stations have been added for TMP and PRE only. This release is the latest release of the CRU TS data. Known issues predating this release remain. The CRU TS 3.26 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. All CRU TS output files are actual values - NOT anomalies.

The Climatic Research Unit (CRU) Country (CY) data version 4.02 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency; including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and potential evapotranspiration. This version uses the updated set of country definitions, please see the appropriate Release Notes. This dataset was produced in 2018 by CRU at the University of East Anglia and extends the CRU CY4.01 data to include 2017. CRU CY4.02 is a full release, differing only in methodology from the existing current version 3 release, v3.26. Both are released concurrently to support comparative evaluations between these two versions, however, this will be the last release of version 3. The data are available as text files with the extension '.per' and can be opened by most text editors. Spatial averages are calculated using area-weighted means. CRU CY4.02 is derived directly from the CRU TS4.02 dataset. CRU CY version 4.02 spans the period 1901-2017 for 289 countries. To understand the CRU CY4.02 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.02. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.02 release notes listed in the online documentation on this record. CRU CY data are available for download to all CEDA users.

Early instrumental observations are an important tool to understand multidecadal climate variability or put in context specific extreme phenomena. This paper provides early instrumental data recovered in Latin-America and the Caribbean. Data have been retrieved from 20 countries (Argentina, Bahamas, Belize, Brazil, British Guiana, Chile, Colombia, Costa Rica, Cuba, Ecuador, France (Martinique and Guadalupe), Guatemala, Jamaica, Mexico, Nicaragua, Panama, Peru, Puerto Rico, El Salvador and Suriname) and they cover the 18th and 19th centuries. The main meteorological variables retrieved are air temperature, atmospheric pressure and precipitation but other variables, such as humidity, wind direction, or state of the sky have been retrieved when possible. In total, more than 300 000 early instrumental observations have been rescued (96% with daily resolution). Special effort has been done to document all the available metadata (instruments, observers, methodology of observation...) in order to allow further post processing. The compilation is far from being exhaustive but the data set will contribute to a better understanding of the climate variability in the region and to enlarge the overlapping period between instrumental data and natural and documentary proxies.

Potential evapotranspiration (PET), and reference evapotranspiration (ETo) are estimated at an approximately 1-kilometer spatial resolution and daily time-step from January 1, 2021 to December 31, 2021 for Florida, Alabama, Georgia, South Carolina, and parts of Mississippi, North Carolina, and Tennessee. PET and ETo were computed on the basis of solar radiation, meteorological data (min/max temperature, mean actual and saturation vapor pressure, and mean wind speed at 2-meter height), and shortwave blue-sky albedo data. Solar radiation was computed from Geostationary Operational Environmental Satellite (GOES) sensor data, blue-sky albedo was computed from the Moderate Resolution Imaging Spectrometer (MODIS) MCD43A1 BRDF/Albedo data product, and meteorological data were simulated using a high-resolution Weather Research and Forecasting (WRF) model. Actual and saturation vapor pressure are calculated from the WRF-simulated 2-meter air temperature ("T2") and water vapor mixing ratio ("Q2") output variables. PET and ETo estimates were bias corrected using evapotranspiration computed from meteorological data at 122 weather stations in Florida and Georgia. Open-source tools for managing the NetCDF files in this data release can be found at https://code.usgs.gov/jbellino/florida-goes-et.

The gridded CRU TS (time-series) 3.22 data are month-by-month variations in climate over the period 1901-2013, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.

CRU TS 3.22 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and wet day frequency for the period Jan. 1901 - Dec. 2013.

CRU TS 3.22 data were produced using the same methodology as for the 3.21 datasets. In addition to updating the dataset with 2013 data, the v3.22 release corrects an error in the v3.21 dataset. This is summarised in the document, CRU_Advisory_v3.2x_NE_Africa.txt, and affects PRE and WET variables only. There are several known issues with the current dataset which cannot be resolved in the timeframe of this release; they will be addressed in the future. This directory also contains an advisory note regarding an issue with 35 Mozambique stations that were new. After an investigation by the CRU, the comparison plots show that the only countries affected in a possibly significant way are Egypt and Eritrea. The details of these can be found in this directory.

The CRU TS 3.22 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and netcdf data files both contain monthly mean values for the various parameters.

All CRU TS output files are actual values - NOT anomalies.

CRU TS data are available for download to all CEDA users. The CEDA Web Processing Service (WPS) may be used to extract a subset of the data (please see link to WPS below).

The gridded CRU TS (time-series) 3.10 data are month-by-month variations in climate over the period 1901-2009, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia. CRU TS 3.10 includes variables such as cloud cover, diurnal temperature range, PET, daily mean temperature, monthly average daily minimun/maximum temperature, and vapour pressure for the period 1901-2009. Note that a corrected run of precipitation data, based on the v3.10 precipitation station data are available (e.g cru_ts_3_10_01.1901.2009.pre.dat). CRU provided the BADC with software to generate the CRU datasets in 2010, and this was used to produce CRU TS 3.10 at the BADC in early 2011. CRU TS 3.10 data were produced using the same methodology as for the 3.00 dataset. The main differences is that the 3.10 dataset extends from 1901-2009, and all of the data in this period can now be used. Slight differences may be noticed between the results for a given time/location between the 3.00 and 3.10 versions, due to additional data now being available. CRU have examined the 3.10 dataset in detail and are confident that such differences are not significant. The CRU TS 3.10 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and netcdf data files both contain monthly mean values for the various parameters. All CRU TS output files are actual values - NOT anomalies. CRU TS data are available for download to all CEDA users.

The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.00 data are month-by-month variations in climate over the period 1901-2015, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia. The CRU TS4.00 variables are cloud cover, diurnal temperature range, frost day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2015. The CRU TS4.00 data were produced using angular-distance weighting (ADW) interpolation. All version 3 releases used triangulation routines in IDL. Please see the release notes for full details of this version update. CRU TS4.00 is a full release, differing only in methodology from the existing current release, v3.24.01. Both are released concurrently to support comparative evaluations between these two versions. The CRU TS4.00 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. All CRU TS output files are actual values - NOT anomalies.

Shergyla Mountain meteorological data, Record the surface near Linzhi(1.2-1.5m) conventional meteorological observation.The dataset records the meteorological data at the eastern slope of Shergyla Mountain from 2005 to 2016, and North-facing slope from 2005 to 2012.Including daily average data of temperature, relative humidity, precipitation. Data collected near the eastern slope timberline of Shergyla Mountain, Latitude:29°39′25.2″N; Longitude:94°42′25.62″E; Altitude:4390m, and collected near the north-facing slope of Shergyla Mountain, Latitude:29°35′50.9″N; Longitude:94°36′42.7″E; Altitude:4390m. Collector: Campbell Co CR1000. Collection time interval：30min. Digital automatic data collection, daily average value of artificial calculation. It includes the following basic meteorological parameters: North-facing slope data: Wind speed，Unit m/s Temperature，Unit ℃ Relative Humidity，Unit % Atmospheric pressure，Unit hPa Global radiation，Unit w/m2 Soil heat flux，Unit w/m2 Soil temperature，Unit ℃ Soil moisture，Unit % Precipitation，Unit mm Thickness of snow, Unit cm

Ecology station data: Temperature，Unit ℃ Relative Humidity，Unit % Atmospheric pressure，Unit hPa Wind speed，Unit m/s Precipitation，Unit mm Snow Depth，Unit cm Radiation，Unit w/m2 Soil moisture content，Unit % Soil heat flux，Unit w/m2

This dataset contains the data of the meteorological element gradient observation system of the Sidaoqiao superstation downstream of the Heihe Hydrometeorological Observation Network from January 1, 2014 to December 31, 2014. The site is located in Sidaoqiao, Dalaihu Town, Ejin Banner, Inner Mongolia. The underlying surface is Tamarix. The latitude and longitude of the observation point is 101.1374E, 42.0012N, and the altitude is 873m. The air temperature, relative humidity and wind speed sensors are respectively set at 5m, 7m, 10m, 15m, 20m and 28m, with 6 layers facing the north; the wind direction sensor is set at 15m, facing the north; the barometer is installed in the waterproof box. The tipping bucket rain gauge is installed at 28m; the four-component radiometer is installed at 10m, facing south; two infrared thermometers are installed at 10m, facing south, the probe orientation is vertically downward; two photosynthetically active radiometers are installed At 10m, facing south, and the probe is vertically upward and downward respectively; the soil moisture sensor is installed 2m on the south side of the tower body, and the soil heat flow plates (self-correcting type) (3 pieces) are buried in turn in the ground 6cm deep; The average soil temperature sensor TCAV is buried in the ground 2cm, 4cm; the soil temperature probe is buried in the ground surface 0cm and underground 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm; soil moisture sensors are buried in the underground 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm. Observed items include: wind speed (WS_5m, WS_7m, WS_10m, WS_15m, WS_20m, WS_28m) (unit: m/s), wind direction (WD_15m) (unit: degree), air temperature and humidity (Ta_5m, Ta_7m, Ta_10m, Ta_15m, Ta_20m, Ta_28m and RH_5m, RH_7m, RH_10m, RH_15m, RH_20m, RH_28m) (unit: centigrade, percentage), pressure (unit: hectopascal), precipitation (Rain) (unit: mm), four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts/square meter), surface radiation temperature (IRT_1, IRT_2) (unit: centigrade), up and down photosynthetically active radiation (PAR_U_up, PAR_U_down) (unit: micromol/square Msec), average soil temperature (TCAV) (unit: centigrade), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/square meter), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm) , Ms_120cm, Ms_160cm) (unit: volumetric water content, percentage), soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: centigrade). Processing and quality control of the observation data: (1) ensure 144 data per day (every 10 minutes), when there is missing data, it is marked by -6999; From September 8, 2014 to November 8, due to the sensor problems, the data is missing; on May 9, 2014, the soil moisture probe was re-buried, and the data before and after is inconsistent; (2) eliminate the moment with duplicate records; (3) delete the data that is obviously beyond the physical meaning or the range of the instrument; (5) the format of date and time is uniform, and the date and time are in the same column. For example, the time is: 2014-9-10 10:30; (6) the naming rules are: AWS+ site name. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

This data set contains a mean monthly climatology for several climate variables averaged over the period from 1961 to 1990, and constructed from a data set of station 1961-1990 climatological normals, numbering between 19,800 (precipitation) and 3,615 (windspeed; see New et al, 1999 for details). The station data were interpolated as a function of latitude, longitude and elevation using thin-plate splines. The data comprise a suite of climate elements: precipitation, mean, maximum, and minimum temperature, frost frequency, diurnal temperature range, radiation, wet-day frequency, vapor pressure, wind, and cloud cover. There are 23 files in this data set provided at 0.5 and 1.0 degree spatial resolutions.