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.”
Produced by: Copernicus Climate Change Service
These variables are critical for modern agriculture, enabling farmers and climatologists to understand and predict weather conditions affecting crop production and sustainable resource management.
<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>
<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>
<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>
<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>
Ronaldo brings decades of expertise to the field of geotechnology. Now, he's sharing his vast knowledge through exclusive courses and in-depth e-books. Get ready to master spatial and statistical analysis techniques, and raise your professional level.
Sign up for our Newsletter to receive content and tips on Geotechnology and R. 👇
.png)
