Agrometeorological indicators from 1979 to present derived from reanalysis

This dataset provides daily surface meteorological data from 1979 onward, designed for use in agricultural and agroecological studies. Known as AgERA5, it is based on hourly ECMWF ERA5 surface-level data. The acquisition and preprocessing of the original ERA5 data are complex and specialized tasks. By providing the AgERA5 dataset, users can bypass this process and directly access meaningful information for analysis and modeling.

The variables provided in this dataset are tailored to meet the input requirements of most agricultural and agroecological models. The data has been aggregated into daily intervals in local time zones and corrected for more accurate topography with a spatial resolution of 0.1°. Corrections were made using grid- and variable-specific regression equations applied to the ERA5 dataset, interpolated to the 0.1° grid. These equations were trained on the high-resolution (HRES) ECMWF operational atmospheric model at a resolution of 0.1°. This adjustment ensures the data aligns with the most accurate topography, land-use patterns, and land-sea delineation of the ECMWF HRES model.”

Data

Produced by: Copernicus Climate Change Service

Key Variables

Meteorological variables mentioned:

1. 10m Wind Speed

• Units: m/s
• Description: Mean wind speed at 10 meters above the surface over a 24-hour period.
• Importance: Determines wind energy potential, pollutant dispersion, and impact on crops.

2. 2m Dewpoint Temperature

• Units: K
• Description: Mean dewpoint temperature at 2 meters above the surface over 24 hours. Indicates air saturation with water vapor. Used with air temperature to calculate relative humidity.

3. 2m Relative Humidity

• Units: %
• Description: Relative humidity at 06h, 09h, 12h, 15h, and 18h local time at 2 meters above the surface. Indicates the amount of water vapor in the air compared to the maximum at the same temperature.

4. 2m Temperature

• Units: K
• Description: Mean air temperature at 2 meters above the surface over a 24-hour period.
• Importance: Crucial for understanding thermal conditions affecting crop growth.

5. Cloud Cover

• Units: Dimensionless
• Description: Proportion of hours with cloud cover during a 24-hour period.
• Importance: Helps assess solar radiation received and its impact on temperature and photosynthesis.

6. Liquid Precipitation Duration Fraction

• Units: Dimensionless
• Description: Proportion of hours with liquid precipitation (rain) during a 24-hour period.
• Importance: Quantifies precipitation’s influence on soil moisture and irrigation needs.

7. Precipitation Flux

• Units: mm/day
• Description: Total liquid water precipitated over 24 hours per unit area.
• Importance: Critical for understanding water availability for crops.

8. Snow Thickness

• Units: cm
• Description: Mean snow depth over 24 hours per unit area.
• Importance: Monitors winter conditions affecting agriculture and other sectors.

9. Snow Thickness Liquid Water Equivalent (LWE)

• Units: cm
• Description: Snow depth in liquid water equivalent if all snow melted without infiltration, runoff, or evaporation.

10. Solar Radiation Flux

• Units: J/m²/day
• Description: Total solar energy received at the surface over 24 hours per unit area.
• Importance: Essential for understanding energy availability for plant photosynthesis.

11. Solid Precipitation Duration Fraction

• Units: Dimensionless
• Description: Proportion of hours with solid precipitation (snow, sleet, hail, etc.) during a 24-hour period.

12. Vapor Pressure

• Units: hPa
• Description: Contribution of water vapor to total atmospheric pressure over 24 hours.
• Importance: Quantifies water vapor content, related to relative humidity and condensation potential.

Importance for Modern Agriculture

These variables are critical for modern agriculture, enabling farmers and climatologists to understand and predict weather conditions affecting crop production and sustainable resource management.

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Data – Download

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Preview

Package tidync

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<pre>
<code style="background-color: #eeeeee; border: 1px solid rgb(153, 153, 153); display: block; overflow: auto; padding: 20px;">
library(tidync)
setwd("D:\\geoeasy\\agro")
# Set the path to the NetCDF file
file_nc <- "Temperature-Air-2m-Max-24h_C3S-glob-agric_AgERA5_20240102_final-v1.1.nc"
  <code></code>
</code>
</pre>

<pre>
<code style="background-color: #eeeeee; border: 1px solid rgb(153, 153, 153); display: block; overflow: auto; padding: 20px;">
# Open the NetCDF file with tidync
nc <- tidync(file_nc)
# open data
ht<-hyper_tibble(nc)
str(ht)
  <code></code>
</code>
</pre>

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Summary of steps:

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• Load the library needed to work with NetCDF files.
• Set the working directory to where the NetCDF file is located.
• Specify the NetCDF file that contains the data of interest.
• Open the file using tidync to access its data.
• Extract the data in a convenient format (tibble).
• Visualize the structure of the extracted data

Result

<pre>
<code style="background-color: #eeeeee; border: 1px solid rgb(153, 153, 153); display: block; overflow: auto; padding: 20px;">
tibble[2,353,526 × 4] (S3: tbl_df/tbl/data.frame)
$ Temperature_Air_2m_Max_24h: num[1:2353526] 252 252 252 252 252 ...
$ lon : num[1:2353526] -37.2 -37.1 -37 -36.9 -36.8 ...
$ lat : num[1:2353526] 83.9 83.9 83.9 83.9 83.9 ...
$ time : num[1:2353526] 45291 45291 45291 45291 45291 ...
  <code></code>
</code>
</pre>

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Package raster

<pre>
<code style="background-color: #eeeeee; border: 1px solid rgb(153, 153, 153); display: block; overflow: auto; padding: 20px;">
library(raster)
df<-raster("Temperature-Air-2m-Max-24h_C3S-glob-agric_AgERA5_20240102_final-v1.1.nc",varname = "Temperature_Air_2m_Max_24h")
plot(df, main = "Temperature_Air_2m_Max_24h - 20240102")
  <code></code>
</code>
</pre>

Package ggplot2

<pre>
<code style="background-color: #eeeeee; border: 1px solid rgb(153, 153, 153); display: block; overflow: auto; padding: 20px;">
library(ggplot2)
ggplot(ht, aes(x = lon, y = lat, fill = Temperature_Air_2m_Max_24h)) +
 geom_tile() +
 scale_fill_viridis_c() +
 labs(title = "Heat Map of Maximum Air Temperature at 2m",
 x = "Longitude", y = "Latitude", fill = "Temperature (K)") +
 theme_minimal()
  <code></code>
</code>
</pre>