UniMi/ISAC-CNR Observational Dataset (t2m) on reanalysis grid and elevations

UniMi/ISAC-CNR Observational Dataset (t2m) on reanalysis grid and elevations

This dataset accompanies the articles:

Cavalleri, F., Viterbo, F., Brunetti, M., Bonanno, R., Manara, V., Lussana, C., Lacavalla, M., & Maugeri, M. (2024). Inter-comparison and validation of high-resolution surface air temperature reanalysis fields over Italy. International Journal of Climatology. https://doi.org/10.1002/joc.8475

Viterbo, F., Sperati, S., Vitali, B., D'Amico, F., Cavalleri, F., Bonanno, R., & Lacavalla, M. (2024). MERIDA HRES: A new high-resolution reanalysis dataset for Italy. Meteorological Applications, 31(6), e70011. https://doi.org/10.1002/met.70011

This dataset provides gridded daily minimum and maximum surface air temperature (t2m) observations reconstructed over Italy for the period 1991–2020. The values were obtained by interpolating a dense network of observational stations onto the grids of six different reanalysis products, accounting explicitly for the latitude, longitude, and elevation of each reanalysis grid cell.

The methodology ensures consistency between the observational dataset and the spatial characteristics of each reanalysis, minimizing discrepancies arising from differences between real-world topography and the smoothed model orography typically used in numerical models. This is particularly crucial in regions with complex terrain, such as the Italian Alps and Apennines, where temperature gradients are strongly dependent on elevation.

The dataset is designed for the validation and evaluation of reanalysis products over Italy and directly supports the analyses described in the associated publications. By providing elevation-adjusted gridded observations, this dataset allows for more accurate assessments of the ability of different reanalyses to capture temperature patterns and variability.

The dataset includes daily minimum and maximum temperatures for six reanalysis grids:

• ERA5 (global reanalysis, ~31 km resolution)

• ERA5-Land (global reanalysis, ~9 km resolution, land focus)

• MERIDA (MEteorological Reanalysis Italian DAtaset, ~7 km resolution)

• MERIDA HRES (MEteorological Reanalysis Italian DAtaset High RESolution, ~4 km resolution)

• CERRA (Copernicus European Regional ReAnalysis, ~5.5 km resolution)

• VHR-REA_IT (Very High-Resolution dynamical downscaling of ERA5 REAnalysis over Italy, ~2.2 km resolution)

The interpolation method is based on the anomaly method (Mitchell & Jones, 2005) and the work of Brunetti et al. (2012, 2014), combining climate normals (calculated with local weighted regressions of station temperatures against elevation) with homogenized daily anomalies (using angula weighted regression). Monthly reconstructed values were then disaggregated into daily fields following the methodology of Di Luzio et al. (2008). The result is a set of consistent, high-quality gridded datasets that can be directly compared with reanalysis outputs on their native grids. See indicated references for more details.

This dataset is the result of a collaboration between the University of Milan and the National Research Council of Italy – Institute of Atmospheric Sciences and Climate (CNR-ISAC).

Dataset Characteristics:

• Variables: Daily minimum and maximum surface air temperature (t2m)

• Domain: Italy

• Period covered: 1991–2020 (30 years)

• Spatial reference: Grids of 6 reanalyses (ERA5, ERA5-Land, MERIDA, MERIDA HRES, CERRA, VHR-REA_IT)

• Interpolation method:

  • Climate normals: local weighted linear regression versus elevation;

  • Anomalies: angular weighted regression from homogenised station data.

• Station network: 400–2500 stations, evolving through the study period

• Applications: Validation of reanalysis datasets; studies on temperature variability and climate change impacts in Italy

• Contributors: University of Milan & CNR-ISAC

Technical Notes for Download and Use

• Files are provided in compressed .7z format. Each reanalysis is distributed in a separate set of files:

  • The name of each compressed file begins with the identifier of the corresponding reanalysis (e.g., ERA5_, ERA5Land_, MERIDA_, MERIDA-HRES_, CERRA_, VHR-REA_IT_).

• For each reanalysis, 30 compressed .7z files are available, one for each year in the period 1991–2020.

• To extract the files, use standard tools such as 7-Zip (Windows/Linux) or p7zip (Linux/macOS). For example:

  • Windows: right-click on the .7z file and select Extract here or Extract to folder.

  • Linux/macOS (terminal):  7z x ERA5_1991.7z

• After decompression, each yearly file contains daily fields of minimum and maximum temperature in NetCDF format.

• Alongside the temperature fields, additional flag variables are included. These flags identify the very rare cases where interpolation could not be performed due to insufficient data availability for specific days.

• The impact of these missing values is statistically negligible, as confirmed through analyses performed with the Climate Data Operators (CDO). Nonetheless, the flags allow users to identify the exact days and grid points where data are unavailable.

Citation Requirements

Users of this dataset are kindly requested to cite the associated publication:

Cavalleri, F., Viterbo, F., Brunetti, M., Bonanno, R., Manara, V., Lussana, C., Lacavalla, M., & Maugeri, M. (2024). Inter-comparison and validation of high-resolution surface air temperature reanalysis fields over Italy. International Journal of Climatology. https://doi.org/10.1002/joc.8475

For the methods in obtaining these datasets see:

Brunetti, M., Maugeri, M., Nanni, T., Simolo, C. & Spinoni, J. (2014) High-resolution temperature climatology for Italy: interpolation method intercomparison. International Journal of Climatology, 34(4), 1278–1296.

Brunetti, M., Lentini, G., Maugeri, M., Nanni, T., Simolo, C. & Spinoni, J. (2012) Projecting North Eastern Italy temperature and precipitation secular records onto a high-resolution grid. Physics and Chemistry of the Earth, Parts A/B/C, 40, 9–22.

Mitchell, T.D. & Jones, P.D. (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. International Journal of Climatology: A Journal of the Royal Meteorological Society, 25(6), 693–712.

Di Luzio, M., Johnson, G.L., Daly, C., Eischeid, J.K. & Arnold, J.G. (2008) Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. Journal of Applied Meteorology and Climatology, 47(2), 475–497.