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.

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.

Total volume of liquid water (mm3) precipitated over the period 00h-24h local time per unit of area (mm2), per day. Unit: mm day-1. The Precipitation flux variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators 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. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Reference evapotranspiration per month with a spatial resolution of 0.1 degree. Unit: mm month-1. The dataset contains monthly values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators 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. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Minimum air temperature calculated at a height of 2 metres above the surface. Unit: K. The Minimum air temperature variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators 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. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Abstract

The gridded dataset includes the monthly time series of the precipitation, temperature and reference evapotranspiration variables derived from AgERA5 daily and AgERA5_ET0 monthly data, with a spatial resolution of 10 kilometers, covering the area interested by the project, for the period 1979-2022.

Data is provided as .tif files with their corresponding .rts files (SpatRasterTS object in R).

Attached content

The following ZIP archives containing the spatial raster time series are provided:

ag5_2m_temperature_rts_monthly_19792022_EPSG3035.zip

ag5_precipitation_flux_rts_monthly_19792022_EPSG3035.zip

ag5_et0_rts_monthly_19792022_EPSG3035.zip

In addition a document with a short description of data processing is provided.

Relative humidity at 15h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators 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. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

This database contains spatial information with a 0.05° grid resolution of specific agroclimatic indices for maize, dry beans, soybeans, and coffee regions in Angola. In total, the database comprises 13 agroclimatic indices for each crop, grouped as follows: 1. Dry Conditions Indices: • Number of Dry Days • Number of Dry Spells • Average Length of Dry Spells 2. Wet Conditions Indices: • Number of Wet Days • Number of Wet Spells • Average Length of Wet Spells • Total Precipitation 3. Heatwave Indices: • Number of Hot Days • Number of Heatwaves • Maximum Length of Heatwaves 4. Crop Water Requirement Index: • Potential Evapotranspiration (ETo) 5. Water Balance Index: • Standardized Precipitation and Evapotranspiration Index (SPEI) These indices were calculated using historical climatic data for the period 1981 to 2020, considering the typical growth and development periods of each crop of interest, detailed as follows: • Maize: September - April • Beans: November – March • Soybeans: October – April • Coffee: September – August Additionally, six "El Niño" events (1982-1983, 1987-1988, 1991-1992, 1997-1998, 2009-2010, 2015-2016) and six "La Niña" events (1984-1985, 1988-1989, 1998-1999) were considered to characterize the behavior of each indicator under the influence of different phases of the ENSO phenomenon. Metodology:Regarding the climatic data used to calculate each of the indices, the following information is provided: 1. Dry and Wet Conditions Indices: Historical daily rainfall data from the Climate Hazards Group InfraRed Precipitation Measurement (CHIRPS) dataset (https://www.chc.ucsb.edu/data) were used. 2. Heatwave Indices: Historical daily maximum temperature data were obtained from the AgERA5 database (https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-agrometeorological-indicators?tab=overview), and a resampling process was applied to reduce the spatial scale of the original maps from 0.1° to 0.05°. 3. Crop Water Requirement Indices: The Priestley-Taylor equation was used to calculate Potential Evapotranspiration (ETo) due to its simplicity and suitability for tropical conditions. Daily maximum and minimum temperature data, as well as solar radiation, were obtained from the AgERA5 database. A resampling process was also applied to reduce the spatial scale of the original maps from 0.1° to 0.05°. 4. Water Balance Indices: The SPEI indicator calculation was based on daily precipitation data from CHIRPS and ETo calculated using daily maximum and minimum temperature data, as well as solar radiation, from the AgERA5 database. This database provides a valuable tool for understanding and managing agroclimatic aspects in key crop-producing regions in Angola, which can have a significant impact on the country's agriculture and food security.

The dataset consists of spatial information with a grid resolution of 0.05°, providing historical agroclimatic indices related to dry conditions, extreme heat, and water availability during the agricultural seasons (Primera, Canícula, Postrera, and Apante) in Honduras. Spanning from 1981 to 2022, it includes eight indices categorized into Dry Conditions (Number of Dry Days, Number of Dry Spells, Average Length of Dry Spells), Extreme Heat (Number of Heat Days, Number of Heat Nights), and Water Availability (Potential Evapotranspiration, Total Precipitation, Standardized Precipitation and Evapotranspiration Index). To elucidate the impact of climate variability, the dataset considers eight El Niño events (1982, 1987, 1991, 1997, 1998, 2002, 2009, 2015) and seven La Niña events (1988, 1999, 2000, 2007, 2011, 2021, 2022). These events provide a framework for characterizing how each agroclimatic indicator responds under the influence of different phases of the El Niño–Southern Oscillation (ENSO) phenomenon, offering valuable insights for understanding and adapting to climate variations in the agricultural context. Metodology: 1. Dry Conditions Indices: Historical daily rainfall data from the Climate Hazards Group InfraRed Precipitation Measurement (CHIRPS) dataset were used. Index Definitions: Number of dry days: Days when the amount of daily precipitation falls below 1 mm. Number of dry spells: Period of five or more consecutive dry days immediately followed by a wet day (when precipitation falls above 1 mm). Average length of dry spells: Describes the average duration in days of dry spells. 2. Extreme Heat Indices: Historical daily maximum and minimum temperature data were obtained from the AgERA5 database, and a resampling process was applied to reduce the spatial scale of the original maps from 0.1° to 0.05°. Index Definitions: Number of heat days: Days with a maximum temperature greater than 30°C (≈80th percentile). Number of heat nights: Nights with a minimum temperature greater than 20°C (≈80th percentile). 3. Water Availability Indices: The Priestley-Taylor equation was used to calculate Potential Evapotranspiration (ETo) due to its simplicity and suitability for tropical conditions. Daily maximum and minimum temperature data, as well as solar radiation, were obtained from the AgERA5 database. A resampling process was also applied to reduce the spatial scale of the original maps from 0.1° to 0.05°. The SPEI indicator calculation was based on daily precipitation data from CHIRPS and ETo calculated using daily maximum and minimum temperature data, as well as solar radiation, from the AgERA5 database. Index Definitions: Total precipitation:Total amount of precipitation. Potential Evapotranspiration:Potential water loss due to evaporation and plant transpiration. SPEI: The Standardized Precipitation and Evapotranspiration Index computed using a two-month temporal scale.

Mean wind speed at a height of 10 metres above the surface over the period 00h-24h local time. Unit: m s-1. The Wind Speed variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators 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. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

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.

Dataset of daily canopy temperature and meteorological data from the ECMWF’s AgERA5 product for the period 1979 though 2020, and for 785 points belonging to the International Wheat Improvement Network (IWIN). Wheat canopy temperature was estimated from a linear model using maximum air temperature, vapor pressure deficit, and solar radiation as inputs. The model was calibrated using multiple measurements of wheat canopy temperature.

This resource includes eight temperature indices used to assess the accuracy of spatiotemporal variability and trends in temperature extremes in Colombia. T These indices were calculated monthly for the 1997–2016 period.

• Hottest Day (TXx)
• Coldest Night (TNn)
• Diurnal Temperature Range (DTR)
• Mean Temperature (2TM)
• Percentile-Based Threshold Indices, which measure: Number of days below the 10th percentile (Cold Nights—TN10p and Cold Days—TX10p) Number of days above the 90th percentile (Warm Nights—TN90p and Warm Days—TX90p)

To estimate these indices, six high-resolution gridded datasets with varying spatial and temporal resolutions were used:

• ERA5 (0.25°)
• ERA5-Land (0.10°)
• AgERA5 (0.10°)
• MSWX (0.10°)
• CHELSA (0.01°)
• CHIRTS (0.05°)

The performance of these temperature-gridded products in calculating climate extremes was compared with observed data from selected ground weather stations for the period 1997–2016. Initially, long-term climate data for daily minimum (TN) and maximum temperatures (TX) were assessed from 664 and 629 weather stations, respectively. These records, provided by the Colombian Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM), spanned from 1997 to 2016. After filtering out stations with more than 20% missing data, 153 stations remained, which are located at elevations ranging from 0 to 3500 meters above sea level. Most of these stations are situated in the country's interior, particularly in the Andean region (74%), whereas the peripheral areas have limited data availability due to fewer in-situ stations. To estimate the missing data in the observed monthly temperature indices, the nonlinear principal component analysis (NLPCA) method was employed. This technique has been previously applied to estimate missing data in various contexts, including precipitation series, extreme precipitation indices, and streamflow data on daily, monthly, and seasonal scales. NLPCA is a nonlinear extension of principal component analysis that utilizes Artificial Neural Networks (ANN), specifically employing the inverse NLPCA method for its calculations.

This dataset contains daily rainfall and climate data for north Cameroon, that can be used as input of the SARRA-Py spatialized crop simulation model. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.

The archive contains :

• AgERA5 (doi:10.24381/cds.6c68c9bb) climatic data for north Cameroon, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²/d) at 0.1° spatial resolution from 01/01/2020 to 31/12/2022
• CHIRPS v2.0 (doi:10.1038/sdata.2015.66) satellite rainfall estimation data for north Cameroon (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/2020 to 31/12/2022

This data has been extracted from their original sources using the SARRA-data-downloader tool.

The applicable licences are the licences of the respective datasets.

The Crop Water Requirement Tool (CropWat-online) is a tool to explore spatial weather data and use that data to run a crop-water-requirement model called CROPWAT. It gives information on meteorology, reference-evapotranspiration, soil-saturation and crop-water-requirements under different climatic conditions, different soil types and different crop related variables, such as the sowing and the harvesting date. The model uses input data from either CRU CL 2.0 or agERA5, which is a higher resolution version of ECMWF’s ERA5 reanalysis dataset, adapted for agricultural purposes and available between 1979 and today.

The Crop Water Requirement Tool is available at https://aquastat.fao.org/climate-information-tool/

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Reference evapotranspiration per dekade with a spatial resolution of 0.1 degree. Unit: mm dekad-1. The dataset contains dekadal values for global land areas, excluding Antarctica, since 1979. The dataset has been prepared according to the FAO Penman - Monteith method as described in FAO Irrigation and Drainage Paper 56. The input variables are part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators 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. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.