Meteorological variables for Agriculture: daily time series for the Italian Area (MADIA daily)

Abstract

The MADIA daily gridded dataset provides the series of the main agro-meteorological variables derived from ERA5 hourly surface data, with a spatial resolution of 0.25 degrees, across the Italian domain for the period 1981-2022. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. Data is provided at daily temporal resolution and in csv format (every cell is identified by the latitude/longitude coordinates of its centre). The dataset is annotated with discovery and description metadata. A vector file is included with the ERA5 cell polygons covering the Italian country for visualizing and mapping csv data. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file which reports the cell weight in terms of fraction covered of each administrative unit considered, as well as its altitude, is provided in:

Parisse Barbara, Alilla Roberta, Pepe Antonio Gerardo, & De Natale Flora. (2022). Meteorological variables for Agriculture: a dataset for the Italian Area (MADIA) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6868944 

Further details on methods applied for data processing are available in:

Parisse B., Alilla R., Pepe A.G., De Natale F., MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area, Data in Brief, 46 (2023), 108843, 10.1016/j.dib.2022.108843, (https://www.sciencedirect.com/science/article/pii/S2352340922010460)

The MADIA daily dataset will be periodically updated.

Attached content

A ZIP archive composed by the following folders:

csv_data: daily time series for each year from 1981 to 2022 in csv format

metadata: discovery and description metadata

shp_data:  a complementary vector layer with the ERA5 cell polygons for Italy

Acknowledgments

This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)