Overview: 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. Total precipitation: Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step. The original ERA5-Land dataset (period: 2000 - 2020) has been reprocessed to: - aggregate ERA5-Land hourly data to daily data (minimum, mean, maximum) - while increasing the resolution from the native ERA5-Land resolution of 0.1 degree (~ 9 km) to 30 arc-sec (~ 1 km) by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate proportion of ERA5-Land / aggregated CHELSA 3. interpolate proportion with a Gaussian filter to 30 arc seconds 4. multiply the interpolated proportions with CHELSA Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA. Data available is the daily sum of precipitation. Software used: GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief) Original ERA5-Land dataset license: https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

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".

Overview:
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.

Total precipitation:
Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

Processing steps:
The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:
1. spatially aggregate CHELSA to the resolution of ERA5-Land
2. calculate proportion of ERA5-Land / aggregated CHELSA
3. interpolate proportion with a Gaussian filter to 30 arc seconds
4. multiply the interpolated proportions with CHELSA
Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.

The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.
Data available is the weekly average of daily sums and the weekly sum of daily sums of total precipitation.

File naming:
Average of daily sum: era5_land_prectot_avg_weekly_YYYY_MM_DD.tif
Sum of daily sum: era5_land_prectot_sum_weekly_YYYY_MM_DD.tif

The date in the file name determines the start day of the week (Saturday).

Pixel values:
mm * 10
Example: Value 218 = 21.8 mm

Coordinate reference system:
ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)

Spatial extent:
north: 82:00:30N
south: 18N
west: 32:00:30W
east: 70E

Spatial resolution:
1km

Temporal resolution:
weekly

Period:
01/01/2016 - 12/31/2020

Lineage:
Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Original ERA5-Land dataset license:
https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

CHELSA climatologies (V1.2):
Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4
Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Other resources:
https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b

Format: GeoTIFF

Representation type: Grid

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Contact:
mundialis GmbH & Co. KG, info@mundialis.de

ERA5-Land total precipitation monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)

Source data: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.

Total precipitation:Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

Processing steps:The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate proportion of ERA5-Land / aggregated CHELSA 3. interpolate proportion with a Gaussian filter to 30 arc seconds 4. multiply the interpolated proportions with CHELSA Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.

The spatially enhanced daily ERA5-Land data has been aggregated to monthly resolution, by calculating the sum of the precipitation per pixel over each month.

File naming:ERA5_land_monthly_prectot_sum_30sec_YYYY_MM_01T00_00_00_int.tif e.g.:ERA5_land_monthly_prectot_sum_30sec_2023_12_01T00_00_00_int.tif

The date within the filename is year and month of aggregated timestamp.

Pixel values:mm * 10Scaled to Integer, example: value 218 = 21.8 mm

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:north: 28:18Nsouth: 14:42Nwest: 17:05Weast: 4:49W

Temporal extent:January 2019 - December 2023

Spatial resolution:30 arc seconds (approx. 1000 m)

Temporal resolution:monthly

Lineage:Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Software used:GRASS GIS 8.3.2

Format: GeoTIFF

Original ERA5-Land dataset license:https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Representation type: Grid

Processed by:mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Contact: mundialis GmbH & Co. KG, info@mundialis.de

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 DAILY provides aggregated values for each day 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, daily minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Daily total precipitation values are given as daily sums. All other parameters are provided as daily averages. ERA5 data is available from 1979 to three months from real-time. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Daily aggregates have been calculated based on the ERA5 hourly values of each parameter.

This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:

ERA5: Total Precipitation

The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2022.

Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2010 - 2022. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium-Range Weather Forecasting . for 2010 - 2022. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algroithms. The daily data have been aggregated into dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.

Process:

Image values were extracted from ERA5 ( Total precipitation) 5 km imagery from 2010 to 2022. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408) Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic (WGS84) and later nibbled in arcmap to transfer to 1km resolution by reducing zeros in mask areas. The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.

This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)

File names:

The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 22 refers to the year timeline of 2010-2022.The next two characters identify the channel:20 Monthly Total PrecipitationThe last two characters of each file name denote the output from Fourier processing:a0 - meanmn - minimummx - maximuma1 - amplitude of annual cyclea2 - amplitude of bi-annual cyclea3 - amplitude of tri-annual cyclep1 - phase of annual cyclep2 - phase of bi-annual cyclep3 - phase of tri-annual cycled1 - variance in annual cycled2 - variance in bi-annual cycled3 - variance in tri-annual cycleda - combined variance in annual, bi-annual, and tri-annual cyclesvr - variance in raw dataParameter Fourier Variable Image values areERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mmALL D1,D2,D3,Da PercentagesALL E1,E2,E3 PercentagesALL P1,P2.P3 Months*100. (Jan=100)

This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:

ERA5: Total Precipitation

The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for whole worldThis series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2022.

Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2010 - 2022. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2010 - 2022 . The original data is at 0.25 degree resolution and wasdownscaled by ERA extraction algroithms. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.

Process:

Image values were extracted from ERA5 ( Total precipitation) 5 km imagery from 2010 to 2022. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408) Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic (WGS84) .The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.

This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.

Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326)

File names:

The wd at the start of each file name indicates that the image covers the whole world in the E4warning and is in geographic projection. 22 refers to the year timeline of 2010-2022.The next two characters identify the channel:20 - Monthly Total PrecipitationThe last two characters of each file name denote the output from Fourier processing:a0 - meanmn - minimummx - maximuma1 - amplitude of annual cyclea2 - amplitude of bi-annual cyclea3 - amplitude of tri-annual cyclep1 - phase of annual cyclep2 - phase of bi-annual cyclep3 - phase of tri-annual cycled1 - variance in annual cycled2 - variance in bi-annual cycled3 - variance in tri-annual cycleda - combined variance in annual, bi-annual, and tri-annual cyclesvr - variance in raw dataParameter Fourier Variable Image values areERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mmALL D1,D2,D3,Da PercentagesALL E1,E2,E3 PercentagesALL P1,P2.P3 Months*100. (Jan=100)

Daily total precipitation for area of South Sudan for period 2012-2019 from ERA5 Land ECMWF Copercnicus dataset

ERA5-Land daily: Total precipitation, daily time series for Europe at 30 arc seconds (ca. 1000 meter) resolution (2000 - 2020)

Source data:
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.

Total precipitation:
Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.

Processing steps:
The original hourly ERA5-Land data (period 2000 - 2020) has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:
1. spatially aggregate CHELSA to the resolution of ERA5-Land
2. calculate proportion of ERA5-Land / aggregated CHELSA
3. interpolate proportion with a Gaussian filter to 30 arc seconds
4. multiply the interpolated proportions with CHELSA
Using proportions ensures that areas without precipitation remain areas without precipitation. Only if there was actual precipitation in a given area, precipitation was redistributed according to the spatial detail of CHELSA.

Data available is the daily sum of precipitation.
File naming:
era5_land_daily_prectot_YYYYMMDD_sum_30sec.tif
e.g.:era5_land_daily_prectot_20200418_sum_30sec.tif

The date within the filename is Year, Month and Day of timestamp.

Pixel values:
mm * 10
Scaled to Integer, example: value 218 = 21.8 mm

Projection + EPSG code:
Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:
north: 82:00:30N
south: 18:00:00N
west: 32:00:30W
east: 70:00:00E

Temporal extent:
01.01.2000 - 31.12.2020
NOTE: Due to file size, only 2020 data are available here. Data for other years are available on request.

Spatial resolution:
30 arc seconds (approx. 1000 m)

Temporal resolution:
daily

Lineage:
Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Format: GeoTIFF

Original ERA5-Land dataset license:
https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf

CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4
Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Representation type: Grid

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Contact:
mundialis GmbH & Co. KG, info@mundialis.de

Abstract: Monthly Precipitation form the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2001 - 2019. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algroithms to 5km. This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data: ERA5: Total Precipitation The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for whole worldThis series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2001 to 2019. Precipitation from the ERA5 reanalysis archive supplied by the European Centre ofr Medium Range Weather Forecasting for 2001 - 2019. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2001 - 2019 . The original data is at 0.25 degree resolution and wasdownscaled by ERA extraction algroithms. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets. Process: Image values were extracted from ERA5 ( Total precipitation) 5 km imagery from 2001 to 2019. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408) Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic (WGS84). The E4Warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility. Projection + EPSG code:Latitude-Longitude/WGS84 (EPSG: 4326) File names: The wd at the start of each file name indicates that the image covers Globally and ER refers to Europe, North Africa, Eurasia in the E4warning and is in geographic projection. 19 refers to the year timeline of 2001-2019.The next two characters identify the channel:20 - Monthly Total PrecipitationThe last two characters of each file name denote the output from Fourier processing:a0 - meanmn - minimummx - maximuma1 - amplitude of annual cyclea2 - amplitude of bi-annual cyclea3 - amplitude of tri-annual cyclep1 - phase of annual cyclep2 - phase of bi-annual cyclep3 - phase of tri-annual cycled1 - variance in annual cycled2 - variance in bi-annual cycled3 - variance in tri-annual cycleda - combined variance in annual, bi-annual, and tri-annual cyclesvr - variance in raw dataParameter Fourier Variable Image values areERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mmALL D1,D2,D3,Da PercentagesALL E1,E2,E3 PercentagesALL P1,P2.P3 Months*100. (Jan=100)

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 uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

The historical climate reanalysis data from ERA5 are offered at 0.25 x 0.25-degree resolution over the entire globe. ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to the present. ERA5 uses a broad collection of observational data, including various satellite-derived products in multivariate data assimilation mode to capture global variability and change. The data are offered through the Copernicus Climate Change Service (C3S) as a public good and are updated operationally. Data are updated annually.

Presented at monthly, seasonal, and annual scale Spatial resolution: 0.25o x 0.25o Historical Climatologies (20-year or 30-year periods used for climatologies and natural variability): 1986-2005, 1991-2020, 1995-2014
Decadal trends calculated for: 1951-2020, 1971-2020, 1991-2020

Recommended Use: ERA5 is considered one of the top reanalysis products. It provides consistent coverage of all variables found in climate models, making it a valuable reference. In areas with good station coverage, ERA5 closely aligns with CRU data, while in regions lacking stations, it offers reliable estimates and minimizes false trends from short satellite records. Temperature data from ERA5 is highly reliable, but for precipitation, it’s recommended to use multiple datasets due to the challenges in accurately measuring and modeling it.

If you use this dataset please cite the accompanying paper (Lea et al., 2024)

Maps of key (bio-)climatic variables derived from all currently available ERA5-Land reanalysis data (Muñoz Sabater et al., 2019). These have been calculated for:

1. Annual timescales from 1951-2022 (this dataset); and

2. All possible World Meteorological Organisation (WMO) 30 year climate baseline periods, including: 1951 to 1980; 1961 to 1990; 1971 to 2000; 1981 to 2010; and 1991 to 2020 (see link).

Annual timescale data are calculated using monthly statistics using calendar months that account for leap years. WMO baseline maps are calculated by taking the mean of all annual timescale ERALClim maps that fall within the time periods stated above (inclusive). Image bands are named to map onto equivalent BioClim variables (Fick and Hijmans, 2017).

Global data are provided here in GeoTIFF format as multiband images (where each band represents a different year/variable depending on the data downloaded) at a spatial scale of 0.1 degrees within a WGS84 grid (EPSG:4326). If users require data from point locations and/or subset regions for a specific time range or for a custom range of variables, these can be easily accessed using the Google Earth Engine Climate Tool (GEEClimT; Lea et al.). Access to this tool requires a Google Earth Engine account, and is free to use for academic research and education purposes. If you use any data extracted using this tool, please cite Lea et al., 2024.

Descriptions of each band within the dataset are listed below:

bio1 - Mean 2 m air temperature derived from hourly data (units: degrees C).

bio2 - Annual mean of monthly mean diurnal 2 m air temperature ranges (units: degrees C).

bio3 - Isothermality (100 * bio2 / bio7) (no units).

bio4 - Standard deviation of monthly mean 2 m air temperatures (units: degrees C).

bio5 - Mean of maximum 2 m air temperature for the warmest month (units: degrees C).

bio6 - Mean of minimum 2 m air temperature for the coldest month (units: degrees C).

bio7 - Annual range of 2 m air temperature (bio5 - bio6) (units: degrees C).

bio8 - Mean 2 m air temperature of wettest 3 month period (units: degrees C).

bio9 - Mean 2 m air temperature of driest 3 month period (units: degrees C).

bio10 - Mean 2 m air temperature of warmest 3 month period (units: degrees C).

bio11 - Mean 2 m air temperature of coldest 3 month period (units: degrees C).

bio12 - Total annual precipitation (units: mm).

bio13 - Total precipitation of wettest month (units: mm).

bio14 - Total precipitation of driest month (units: mm).

bio15 - Precipitation Seasonality (Coefficient of Variation, based on monthly total precipitation data) (no units).

bio16 - Total precipitation in wettest 3 month period (units: mm).

bio17 - Total precipitation in driest 3 month period (units: mm).

bio18 - Total precipitation in warmest 3 month period (units: mm).

bio19 - Total precipitation in coldest 3 month period (units: mm).

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.

This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:

ERA5: Total Precipitation

The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the whole world
This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2024.

Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting for 2010 - 2022. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algorithms to 1km resolution, then downloaded. The daily data have been aggregated to dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.

Process:

Image values were extracted from ERA5 ( Total precipitation) at 1 km resolution imagery from 2010 to 2024. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and errors measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)
Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility. Sea pixels were masked with a VIIRS land/sea layer.

This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way.

Projection + EPSG code:

Latitude-Longitude/WGS84 (EPSG: 4326)

Extent -180.0000000000000000,-90.0000000000000000 : 179.9999999999998295,89.9999999999999147

File names:

The wg at the start of each file name indicates that the image covers the whole world in the E4warning and is in geographic projection. 04 refers to the year timeline of 2010-2024.

The next two characters identify the channel:
20 - Monthly Total Precipitation

The last two characters of each file name denote the output from Fourier processing:
a0 - mean
mn - minimum
mx - maximum
a1 - amplitude of annual cycle
a2 - amplitude of bi-annual cycle
a3 - amplitude of tri-annual cycle
p1 - phase of annual cycle
p2 - phase of bi-annual cycle
p3 - phase of tri-annual cycle
d1 - variance in annual cycle
d2 - variance in bi-annual cycle
d3 - variance in tri-annual cycle
da - combined variance in annual, bi-annual, and tri-annual cycles
vr - variance in raw data

Parameter Fourier Variable Image values are
ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm
ALL D1,D2,D3,Da Percentages
ALL E1,E2,E3 Percentages
ALL P1,P2.P3 Months*100. (Jan=100)

Note: a new time-series dataset from ERA5 has been published — this one won't be updated/maintained anymore

Country averages of meteorological variables generated using the R routines available in the package panas based on the Copernicus Climate Change ERA5 reanalyses. The time-series are at hourly resolution and the included variables are:

• 2-meter temperature (t2m),
• snow depth (snow_depth),
• mean sea-level pressure (mslp),
• runoff,
• surface solar radiation (ssrd),
• surface solar radiation with clear-sky (ssrdc),
• temperature at 850hPa (t850),
• total precipitation (total_prec),
• zonal (west-east direction) wind speed at 10m (u10) and 100m (u100),
• meridional (north-sud) wind speed at 10m (v10) and 100m (v100),
• dew point temperature (dew)

The original gridded data has been averaged considered the national borders of the following countries (European 2-letter country codes are used, i.e. ISO 3166 alpha-2 codes with the exception of GB->UK and GR->EL): AL, AT, BA, BE, BG, BY, CH, CY, CZ, DE, DK, DZ, EE, EL, ES, FI, FR, HR, HU, IE, IS, IT, LT, LU, LV, MD, ME, MK, NL, NO, PL, PT, RO, RS, SE, SI, SK, UA, UK.

The unit measures here used are listed in the official page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/era5-hourly-data-on-single-levels-from-2000-to-2017?tab=overview

The script used to generate the files is available on github here

Bias-Corrected Precipitation data over South Siberia (CPSS 1.2) contains monthly precipitation data for the area within the coordinates 50-65 N, 60-120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and precipitation data records from ground weather stations (Il'in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results. Data provided in NetCDF (Network Common Data Form) format. Copernicus Climate Change Service (C3S), 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.

The NA-ISD2ERA is a station-based gridded dataset of hourly 10-m wind speed, surface total precipitation, sea-level pressure, and 2-m air and dew point temperature observations interpolated on the regular 0.25° latitude-longitude ERA5 grid over North America for the 1990-2021 period. Station observations are from the Integrated Surface Database (ISD) developed by the National Centers for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA) (Smith et al. 2011). It includes over 35,000 weather stations around the world of hourly to sub-hourly in situ observations for numerous variables such as wind speed, precipitation, sea-level pressure, air and dew point temperature. The NCEI ISD dataset is available at https://www.ncei.noaa.gov. ERA5 is the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (Hersbach et al., 2020). Quality checks implemented in ISD are used to select reliable observations. For each ERA5 grid cell and at each hour, the data are interpolated by taking the nearest available ISD observation to the grid cell center that is located within the targeted grid cell.

Please note: Please use ds633.0 to access RDA maintained ERA-5 data, see ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid) [https://rda.ucar.edu/datasets/ds633.0], RDA dataset ds633.0. This dataset is no longer being updated, and web access has been removed.

After many years of research and technical preparation, the production of a new ECMWF climate reanalysis to replace ERA-Interim is in progress. ERA5 is the fifth generation of ECMWF atmospheric reanalyses of the global climate, which started with the FGGE reanalyses produced in the 1980s, followed by ERA-15, ERA-40 and most recently ERA-Interim. ERA5 will cover the period January 1950 to near real time, though the first segment of data to be released will span the period 2010-2016.

ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (18 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. There are a number of forecast parameters, e.g. mean rates and accumulations, that are not available from the analyses.

Improvements to ERA5, compared to ERA-Interim, include use of HadISST.2, reprocessed ECMWF climate data records (CDR), and implementation of RTTOV11 radiative transfer. Variational bias corrections have not only been applied to satellite radiances, but also ozone retrievals, aircraft observations, surface pressure, and radiosonde profiles.

NCAR's Data Support Section (DSS) is performing and supplying a grid transformed version of ERA5, in which variables originally represented as spectral coefficients or archived on a reduced Gaussian grid are transformed to a regular 1280 longitude by 640 latitude N320 Gaussian grid. In addition, DSS is also computing horizontal winds (u-component, v-component) from spectral vorticity and divergence where these are available. Finally, the data is reprocessed into single parameter time series.

Please note: As of November 2017, DSS is also producing a CF 1.6 compliant netCDF-4/HDF5 version of ERA5 for CISL RDA at NCAR. The netCDF-4/HDF5 version is the de facto RDA ERA5 online data format. The GRIB1 data format is only available via NCAR's High Performance Storage System (HPSS). We encourage users to evaluate the netCDF-4/HDF5 version for their work, and to use the currently existing GRIB1 files as a reference and basis of comparison. To ease this transition, there is a one-to-one correspondence between the netCDF-4/HDF5 and GRIB1 files, with as much GRIB1 metadata as possible incorporated into the attributes of the netCDF-4/HDF5 counterpart.

Bias-Corrected Precipitation data over South Siberia (CPSS 1.2) contains monthly precipitation data for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and precipitation data records from ground weather stations (Il’in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results. Data provided in NetCDF (Network Common Data Form) format.

Copernicus Climate Change Service (C3S), 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), Available at https://cds.climate.copernicus.eu/cdsapp#!/home

Il’yin, B.M., Bulygina, O.N., Bogdanova, E.G, Veselov, V.M. and Gavrilova, S.Y., 2013. Dataset of monthly precipitation totals, with the elimination of systematic errors of precipitation gauges. Available at http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov