2.2 ERA5

    ERA5 is a dataset created by the European Centre for Medium-Range Weather Forecasts (ECMWF) and managed by Copernicus Climate Change Services (C3S). To produce a more precise spatial and temporal resolution compared to ERA-Interim, ERA5 uses advanced techniques like 4D-Var and a high-resolution numerical weather model (Hersbach  et al., (2020)). ERA5 assimilates a broad range of observational data, including satellite measurements, ground-based weather stations, and ocean buoys, thus, improving the accuracy of the initial conditions used in weather models. This dataset plays a significant role in weather forecasting by assimilating observational data, offering high-resolution information, maintaining consistency in data records, providing global coverage, and aiding in model validation. All of these factors contribute to the accuracy and reliability of temperature forecasts (Hersbach et al., (2020) ; Yu  et al., (2021) ; McNicholl et al., (2022)).

    2.3 Air Temperature and ERA5

    Several studies have assessed ERA5 efficiency both in terms of air temperature data and air temperature trends (Almeida and Coelho, (2023) ; Yilmaz, (2023)). According to them, ERA5 has a tendency to slightly underestimate air temperature in some regions, possesses  a greater accuracy with simulations across flatter areas in contrast to locations of high altitude and complex, uneven terrain patterns (Almeida and Coelho, (2023)). While it may be best to be cautious for short term environmental studies, it is overall really effective to describe air temperature in Europe (Almeida and Coelho, (2023)). Focusing more on temperature trends, ERA5 is shown to be consistent with observed trends with a better accuracy over long term period, its trends can be on average slightly higher than observed but to a negligible level of difference (Yilmaz, (2023)). Factors such as time period, location of study, biases in ground observation and inhomogeneities can introduce trends and variability in the dataset that are inconsistent with observed values (Almeida and Coelho, (2023)). In light of these points, Almeida and Coelho (2023) suggest carrying out assessments of reanalysis datasets under different climatic conditions to eliminate as much uncertainty as possible,  however, all in all, studies still agree that ERA5 can be highly trusted with air temperature.
    The air temperature data used in this study were collected on the 7 of December 2023 on the following website : https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview. These data were collected from a two-meter altitude, hourly from September 2013 to September 2014, in between latitude 49–50° and longitude 8–9°. 

2.4 Code used

 

2. Data Description and development

A description of the experimental set-up for the acquisition of the data. This will vary according to what the dataset consists of. For example, if the dataset is model outputs, then this section will describe or reference the model. If the dataset consists of observational records, you need to explain how the data were obtained and the quality control methods used. You may also want to include details about particular software and code developed to build the dataset, to ensure provenance and reusability.

2.1. Subheading

A subheading within this section would look like the above. You may want to consider a subsection on limitations of the dataset.

3. Dataset Access

Description of location, format and accessibility of the dataset. If there are any registration requirements to access the data, please explicitly mention this. Here you should also mention any possible updates or extensions to the dataset that might happen in the future. You can also include details about useful software for data analysis.  

4. Potential data set use and reuse

Short (~200–500 word) overview of actual and potential uses for the dataset for future, multidisciplinary users. Please also mention any limitations to the dataset here to ensure meaningful data reuse.

5. Conclusions

If appropriate, include a short conclusion summarising the development, limitations and potential value of the dataset described.