ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land provides a consistent view of the water and energy cycles at surface level during several decades. It contains a detailed record from 1950 onwards, with a temporal resolution of 1 hour. The native spatial resolution of the ERA5-Land reanalysis dataset is 9km on a reduced Gaussian grid (TCo1279). The data in the CDS has been regridded to a regular lat-lon grid of 0.1x0.1 degrees. The data presented here is a post-processed subset of the full ERA5-Land dataset. Monthly-mean averages have been pre-calculated to facilitate many applications requiring easy and fast access to the data, when sub-monthly fields are not required.

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 pressure levels from 1940 to present".

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 MONTHLY provides aggregated values for each month for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, monthly minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Monthly total precipitation values are given as monthly sums. All other parameters are provided as monthly averages. ERA5 data is available from 1940 to three months from real-time, the version in the EE Data Catalog is available from 1979. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Monthly aggregates have been calculated based on the ERA5 hourly values of each parameter.

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

Abstract

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. The full dataset is available from 1940 onwards at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview. This version only contains hourly measures of solar radiation, temperature and wind speeds, as well as monthly measures for sea surface temperature for 1950-2020.

Methodology

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.

Usage

Downloaded Using: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form

These datasets contains ERA-5 data for the entirety for CONUS for the following temporal resolutions and fields:

Hourly Resolution Data

The following fields are available at an hourly resolution.

1. solar_radiation - Surface solar radiation downwards

2. temperature - 2m temperature

3. wind_speeds - 100m u-component of wind and 100m v-component of wind

Note:- Within each field xxxx.nc denotes the hourly data for xxxx year. The data span from 1950-2020.

###Monthly Resolution Data###

1. sst - Available at two resolutions.

preliminary_sst --%3E Data from 1950-1978.

sst --%3E Data from 1979-2020.

Additionally the sst field contains Sea Surface Temperature across the globe.

The dataset of net primary productivity (NPP) distribution of vegetation in the China Nepal transportation corridor includes NPP grids within a 2-kilometer range on both sides of the corridor. The data is in Tif format, with a temporal resolution of quarters (divided into four quarters: January March, April June, July September, and October December) and a spatial resolution of 500 meters. Processing method: ① Calculate the monthly NDVI based on the red and near-infrared light of LandsatTM/ETM+/OLI images, and resample to 500 meters; ② Download the weather reanalysis dataset (ERA5 Land hourly data from 1950 to present) provided by ECMWF (European Centre for Medium Range Weather Forecasts), https://cds.climate.copernicus.eu/cdsapp# !/dataset/reanalysis-era5-land? tab=overview）， Interpolate the monthly temperature, solar radiation, actual evapotranspiration, potential evapotranspiration, and other data in the dataset to a resolution of 500 meters Download land cover data from the Chinese Academy of Sciences' data sharing service system（ https://data.casearth.cn/sdo/detail/5fbc7904819aec1ea2dd7061 ）Resample to a resolution of 500 meters, and use the CASA model to calculate monthly NPP based on the above data. Sum up quarterly NPP. The data unit is gc/m2

The Hydrometeorological Sandbox - École de technologie supérieure (HYSETS) is a rich, general and large-scale database for hydrological modelling covering 14425 watersheds in North America. The database includes data covering the period 1950-2023 depending on the type and source of data:

• Watershed properties including boundaries, area, elevation slope, land use, soil properties and other physiographic information;
• Hydrometric gauging station discharge time-series;
• Maximum and minimum daily air temperatures and precipitation from weather gauges, NRCan and Livneh gridded interpolated products, SCDNA infilled-station data and ERA5 / ERA5-Land reanalyses;
• ERA5 and ERA5-Land reanalysis also contain dewpoint temperature, 10m U- and V- components of wind, evaporation, runoff, snow water equivalent, snow evaporation, snowfall, snowmelt, surface net solar radiation, surface net thermal radiation, surface pressure, surface runoff, surface solar radiation downwards, surface thermal radiation downwards and total cloud cover (only in ERA5 as itdoes not exist in ERA5-Land)
• SNODAS and ERA5-Land snow water equivalent;
• Other diagnostic variables;
• Monthly aggregation of meteorological variables;
• Elevation band data for each catchment in 100m increments.

All data has been processed and averaged at the watershed scale, and provides a solid basis for hydrological modelling, climate change impact studies, model calibration assessment, regionalization method evaluation and essentially any study requiring access to large amounts of spatiotemporally varied hydrometeorological data.

Paper citation: Arsenault, R., Brissette, F., Martel, J. L., Troin, M., Lévesque, G., Davidson-Chaput, J., ... & Poulin, A. (2020). A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds. Scientific Data, 7(1), 1-12. https://doi.org/10.1038/s41597-020-00583-2

Monthly crop water use simulated for period 1981-2021

Description of the dataset

The dataset provides monthly crop water uses for 26 crop types and period 1981-2021 simulated at 5 arc-minute resolution using the Global Crop Water Model GCWM (Siebert and Döll, 2010; Meza et al., 2021) distinguishing irrigation water use (blue water) and use of precipitation water stored in the soil (green water). Water uses are provided for 26 irrigated and rainfed crops in m³.

GCWM simulates for each crop a daily soil water balance to estimate evapotranspiration and runoff. Evapotranspiration is calculated according to the Penman-Monteith approach as implemeted in the FAO Irrigation and Drainage Paper 56 (Allen et al., 1998) using ERA5 global reanalysis data (Hersbach et al., 2020; Copernicus Climate Change Service, Climate Data Store, 2023) as climate input. The original hourly ERA5 data were first aggregated to daily data and then disaggregated from 15 arc-minute resolution to 5 arc-minute resolution by using a digital elevation modell and assuming a temperature difference of 0.65 K per 100 m altitude difference. The land use is according to the MIRCA2000 dataset (Portmann et al., 2010) distinguishing 26 irrigated and rainfed crop types. Specific sub-crop classes were used to account for multiple cropping (e.g. different growing seasons of rice) or different growing seasons of temperate cerelas (e.g. spring barley and winter barley). Consequently, the model setup is considering climate variability and climate change but assumes a static land use centered around year 2000. The green water use of irrigated and rainfed crops is the amount of water evapotranspirated under rainfed conditions reflecting the impact of drought on actual evapotranspiration. Blue water use of irrigated crops is the amount of water that would be needed to maintain potential evapotranspiration of irrigated crops.

Technical specifications

The data available for download consist of ascii-grids at global extent and 5 arc-minute resolution (4320 columns x 2160 rows) with a header of 6 lines. For each of the 26 crops a separate zip-archive is provided to reduce the size of the single files. In total, 1476 ascii-grids are provided for each crop (41 years x 12 months x 3 water use types). Three water use types are distinguished: blue water use of irrigated crops, green water use of irrigated crops and green water use of rainfed crops. When a crop is not growing in a specific grid cell or month, water use will be 0. Water uses are reported in m³ per grid cell. To convert water uses from m³ per grid cell to mm per grid cell please use the ascii-grid containg the cell area of the grid cells in ha and the following equation:

Water use (mm) = 0.1 * Water use (m³) / Cell area (ha).

More details with regard to file names and crop types are provided in the readme file available in the download section.

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to present. It is produced by the Copernicus Climate Change Service (C3S) at ECMWF and provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes an ensemble component at half the resolution to provide information on synoptic uncertainty of its products. ERA5.1 is a dedicated product with the same horizontal and vertical resolution that was produced for the years 2000 to 2006 inclusive to significantly improve a discontinuity in global-mean temperature in the stratosphere and uppermost troposphere that ERA5 suffers from during that period. Users that are interested in this part of the atmosphere in this era are advised to access ERA5.1 rather than ERA5. ERA5 and ERA5.1 use a state-of-the-art numerical weather prediction model to assimilate a variety of observations, including satellite and ground-based measurements, and produces a comprehensive and consistent view of the Earth's atmosphere. These products are widely used by researchers and practitioners in various fields, including climate science, weather forecasting, energy production and machine learning among others, to understand and analyse past and current weather and climate conditions.

**Description of GlobalRx Dataset:

204,517 records of prescribed burns in 16 countries. In the information below, the name of the variable's column within the dataset is given in parentheses () in code font. For example, the column with the Drought Code data is titled DC.

For each record, the following general information (derived from the original burn records sources) is included, where available:

• Latitude (Latitude)
• Longitude (Longitude)
• Year (Year)
• Month (Month)
• Day (Day)
• Time* (Time)
• DOY (DOY)
• Date (Date)
• Country (Country)
• State/Province (State/Province)
• Agency/Organisation (Agency/Organisation)
• Burn Objective* (Burn Objective)
• Area Burned (Ha)* (Area Burned (Ha))
• Data Repository (Data Repository)
• Citation (Citation)

* Not available for every record

For each record, the following meteorological information (derived from the ERA5 single levels reanalysis product) is also included:

• Daily total accumulated precipitation (PPT_tot)
• Daily minimum and mean relative humidity (RH_min, RH_mean)*
• Daily maximum 2-meter temperature (T_max,T_mean)
• Daily maximum and mean 10-meter wind speed (Wind_max, Wind_mean)
• Daily minimum boundary layer height (BLH_min)
• C-Haines Index (CHI)*
• Vapor pressure deficit (VPD)*

* Computed from other ERA5 meteorological variables.

For each record, the following fire weather indices and components (derived from ERA5 fire weather reanalysis product) are also included:

• Canadian fire weather index (FWI)
• Fine fuel moisture code (FFMC)
• Drought moisture code (DMC)
• Drought code (DC)
• McArthur forest fire danger index (FFDI)
• Keetch-Byram drought index (KBDI)
• US burning index (USBI)

For each record, the following environmental information (derived from various sources, see paper for more information) is also included:

• Ecoregion (Olson et al. 2001) (Ecoregion (Olson))
• Biome (Olson et al. 2001) (Biome (Olson))
• Koppen Climate (Beck et al. 2023) (Koppen Climate)
• Topography (Danielson and Gesch 2011) (Topography)
• Fuelbed Classification (GFD-FCCS) (Pettinari and Chuvieco 2016) (Fuelbed Classification (GFD-FCCS))
• Fuelbed Group, broader groupings of the Fuelbed Classification used in the summary tables above (Fuelbed Group)
• WDPA Name (WDPA 2024) (WDPA Name)
• WDPA Governance (WDPA 2024) (WDPA Governance)
• WDPA Ownership (WDPA 2024) (WDPA Ownership)
• WDPA Designation (WDPA 2024) (WDPA Designation)
• WDPA IUCN Category (WDPA 2024) (WDPA IUCN Category)

This dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models. Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model. The data was produced on behalf of the Copernicus Climate Change Service.

This dataset provides gridded modelled daily hydrological time series forced with meteorological reanalysis data. The data set is a product of the Global Flood Awareness System (GloFAS) and offers a consistent representation of key hydrological variables across the global domain including:

River discharge Soil wetness index (root zone) Snow water equivalent Runoff water equivalent (surface plus subsurface)

Also provided are two ancillary files for interpretation, one containing upstream area data and the other containing elevation data (see the table of related variables and the associated link in the documentation). This dataset was produced by forcing the open-source LISFLOOD hydrological model with ERA5 meteorological reanalysis data, interpolated to the GloFAS resolution, produced at a 24-hourly timestep. Two variations of the ERA5 forcing data are used, resulting in two types of hydrological data: intermediate and consolidated. Intermediate hydrological data is produced using ERA5 Near Real Time (ERA5T) data and is updated daily, whilst consolidated hydrological data is produced using the consolidated ERA5 reanalysis and is updated monthly. Companion datasets, also available through the EWDS, are forecasts for users who are looking for medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users specifically interested in European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All the GloFAS and EFAS datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre.

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.