| 04 Mar 2026
EMO-1: an improved version of the high-resolution multi-variable gridded meteorological dataset for Europe
Abstract. High-quality, gridded meteorological datasets are essential for continental-scale hydrological modelling. This paper introduces EMO-1, an advanced version of the European Meteorological Observations (EMO) dataset, developed to support the operational European Flood Awareness System (EFAS) of the Copernicus Emergency Management Service. EMO-1 provides daily and 6-hourly meteorological fields across Europe at a high spatial resolution of 1 arc-minute (~1.5 km) covering the period from 1990 to 2024. The dataset represents a substantial upgrade over its predecessor, EMO-5, integrating observations from 47 data providers and increasing significantly the number of stations used for interpolation. It harmonizes heterogeneous data from in-situ stations and integrates "virtual stations" from high-resolution regional grids and ERA5-Land to minimize data gaps in regions with very low station density. The dataset covers eight variables: daily and 6-hourly total precipitation, minimum and maximum air temperature, 6-hourly average air temperature, daily mean wind speed, solar radiation, and water vapor pressure. A rigorous, two-tier quality control system is applied to filter erroneous observations before processing. For interpolation EMO-1 uses an open-source implementation of the Angular Distance Weighting (ADW) scheme. Cross-validation results demonstrate that ADW maintains interpolation skill comparable to other deterministic methods while offering superior computational efficiency and reproducibility. EMO-1 prioritizes station density and timeliness to serve operational forecasting needs, although the resulting spatial-temporal heterogeneity makes it less suitable for long-term trend analysis. The dataset is openly available under a Creative Commons Attribution 4.0 license via the Joint Research Centre Data Catalogue, with foreseen annual updates and accessible open-source interpolation software to foster transparency and community collaboration.
The presented manuscript contains information concerning development of a sub-daily grided meteorological product for Europe. The product itself is interesting, and certainly will find some applications. However, the manuscript contains a serious shortcoming which has to corrected before publication.
My major concern is that the manuscript lacks validation. There is a sort of validation presented in Table 4 and appendix B, which gives an overview of what errors can be expected for 56 rainy days, however, this cannot be used as a proper, unbiased, fair validation of the dataset. Since only rainy days were chosen, the error values are clearly biased towards rainy conditions. 56 days is also a too short record to conclude about the performance in the 1990-2024 period. Nonetheless, Authors clearly state in the manuscript that those errors were used to justify the selection of the interpolation algorithm (’We assessed the three interpolation methods applying the repeated stratified k-fold cross validation method’) i.e. not to validate entire dataset. Authors also argue that the precipitation was the most important variable in the dataset, hence the results in the main part of the manuscript consider only precipitation. Maybe precipitation is the most important for the Authors use case, but in general I do not agree with that, as other users may have different use cases, especially that the potential evapotranspiration (derived from remaining variables in the dataset) is often greater in some parts of Europe than precipitation. In this scope, the sentence ’Cross-validation results demonstrate that ADW maintains interpolation skill comparable to other deterministic methods while offering superior computational efficiency and reproducibility’ in the abstract is misleading as it suggest a fair validation was done.
To summarize, the manuscript has to be updated with the following points before publication:
Another point is that Authors are mixing points from gridded reanalysis products with in situ observations. Please better justify this approach, especially in scope of homogeneity of the data from different source types. Please include experimental results to support this.
Finally, the study lacks comparisons with other datasets, such as E-OBS, or different national products. While this point is not obligatory, inclusion of such a comparison would greatly increase the manuscript impact and reduce a workload of future users (who would have to perform such analysis on their own to find out which dataset to use). Authors state that: ’In a future study we aim to provide a comparison of the new EMO-1 dataset with other gridded meteorological datasets such as the […]’, however, such an analysis should be performed now - it should be a part of the data descriptor paper.
Minor points:
Please include x and y axis labels in all relevant plots. Remember about units.
Please include legend titles where applicable. Remember about units.
Figures A9-A16 - remove letter (a) - (h) from the plotting area. Remove letters a) - h) from the plot caption.
Figure 3 and figures A9-A16 use sequential color scale - now the figures are extremely difficult to parse. Remember about units in the legend. The best if you would use a perceptually uniform sequential color scale.
Tables in appendix B - all are missing units.
Acknowledgements - usually meteorological organizations require mentioning the data source in the acknowledgements - please make sure you comply.