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ERA5-Land selected indicators daily aggregates for Africa, 1977

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1977. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation. Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for Africa, 1979

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1979. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation. Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for Africa, 1978

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1978. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation. Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for Africa, 1974

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1974. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation. Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for Africa, 1970

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1970. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1973

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1973. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation. Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for Africa, 1971

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1971. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation. Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

ERA5-Land selected indicators daily aggregates for Africa, 1972

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1972. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation. Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.

opencc-by-sa-4.0Nov 2023View details →

Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data

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. Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m 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) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). 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 difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds 4. add the interpolated differences to CHELSA Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023. Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997): <code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code> <code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code> <code>relative humidity = actual water pressure / maximum water pressure</code> The resulting relative humidity has been aggregated to monthly averages. Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000]. File naming scheme (YYYY = year; MM = month): <code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code> Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E Spatial resolution: 30 arc seconds (approx. 1000 m) Temporal resolution: Monthly Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %) Software used: GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2 Original ERA5-Land dataset license: <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a> 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. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a> 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. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a> Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>) Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a>

opencc-by-4.0Jul 2024View details →

2015_2020_ERA5_MonthlyPrecipitation_10k

Monthly Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2015-2020  Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2015 - 2020. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algorithms to 10km.   File naming scheme:  Filenames =  mo+ era5precet+ month+ year : moera5prectSep-15.TIF    More information can be found in this file: ERA5-Land monthly averaged data from 1981 to present.pdf   Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent   -32.0999979564505225,17.9499999999999886 : 68.9999956073235836,82.0499999999999972 Spatial resolution: 10 km  Pixel values: unit: meter Source:  The European Centre of Medium Range Weather Forecasting (ERA5)  Software used: ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) <a href="https://ergoonline.co.uk/" target="_blank" rel="noopener">https://ergoonline.co.uk/</a> for the H2020 MOOD project

opencc-by-4.0Jul 2024View details →

2001_2019_ERA5_MonthlyPrecipitation_FourierProcessed

This is a set of images produced by temporal Fourier analysis of Monthly precipitation provided by the ERA5 dataset for 2001-2019 from the European Centre for Medium-Range Weather Forecasting. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area.  Abstract: Monthly precipitation values were extracted from ERA5 files for the years 2019 through 2020, and 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 outputs 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 MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format  to give data users more flexibility   File naming scheme:  The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection.    The next two characters identify the channel:  20 for precipitation and 19 refers to the year timeline of 2001-2019.    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       Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Temporal resolution: 2001-2019 Pixel values: Parameter Fourier Variable Image values are   A0, A1, A2, A3, Index Value * 10  ALL D1,D2,D3,Da Percentages  ALL E1,E2,E3 Percentages  ALL P1,P2.P3 Months*100. (Jan=100)   Source:   Monthly Precipitation for ERA5 from the European Centre for Medium-Range Weather Forecasting (ECMWF) Software used: Codes for modelling are in Python and C++ The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

opencc-by-4.0Jul 2024View details →

2010_2022_ERA5_MonthlyPrecipitation_5k

Precipitation from the ERA5 reanalysis archive supplied by the European Centre of 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 algorithms. The daily data have been aggregated into decadal, 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.   File naming scheme:   Monthly Precipitation:    2022    <a href="../api/records/13122971/draft/files/moeraprecmmmonthly2022.zip/content" target="_blank" rel="noopener noreferrer">moeraprecmmmonthly2022.zip</a>        ;  2010 to 2021 <a href="../api/records/13122971/draft/files/moeraprecmmmonthly20102021.zip/content" target="_blank" rel="noopener noreferrer">moeraprecmmmonthly20102021.zip</a> Daily, decadal, and annual precipitation can be found <a href="https://drive.google.com/drive/folders/1HzVeyfGSTms_IRYW5QntD-QqHk1Vyea8?usp=sharing">here. </a>   Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.25 (5000m)  Temporal resolution: Monthly  from 2010 to 2022 Pixel values Precipitation in meter Source:  ERA5 Precipitation by the European Centre for Medium-Range Weather Forecasting Software used:   The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

opencc-by-4.0Aug 2024View details →

ERA5-Land selected indicators daily aggregates for Africa, 1969

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1969. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1964

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1964. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1965

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1965. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1966

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1966. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1968

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1968. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

ERA5-Land selected indicators daily aggregates for Africa, 1967

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1967. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.

opencc-by-4.0Oct 2024View details →

ERA5-Land selected indicators daily aggregates for Africa, 1959

This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1959. Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates. For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.