This dataset contains the original files for a life-cycle definition of 7+1 year-round North Atlantic-European weather regimes following the definition of Grams et al. (2017, https://doi.org/10.1038/nclimate3338), updated to ECMWF ERA5 reanalysis as described in Hauser et al. (2024, https://doi.org/10.5194/wcd-5-633-2024). It is supplementary data to the publication Grams (2025, in preparation for Weather Clim. Dynam.) which will contain an in-depth technical explanation and analysis of key characteristics and trend in these regimes. A key novelty in the regime definition are the objectively identified regime life cycles which allow process-oriented predictability studies (e.g. Hauser et al. 2024). The dataset has been originally computed for the data period 1979-2019. Using the backward extension of ERA5 (Soci et al., 2024, https://doi.org/10.1002/qj.4803), it has been extended to 1950. Using the near-realtime update it is extended forward until 20250726_21 in release V1.0. For the lifetime of ERA5 it is planned to continously update the data irregularly at least once per year and upon request. Future updates will ensure full backward compatibility.For the methodological documentation we refer to Grams (2025) once available. This dataset also contains a Juypter notebook with examples of simple data analysis in python. The notebook aims to facilitate an easy start of the work with the data. Christian thanks Seraphine Hauser and Dominik Büeler for help with coding this ipynb and reformatting the data for easier accessibility. Users are advised to get familiar with this Readme.md prior to using this data. Contact: [email protected] (Christian Grams)Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., et al.: The ERA5 global reanalysis: Preliminary extension to 1950. Q J R Meteorol Soc, 147(741), 4186–4227, https://doi.org/10.1002/qj.4174, 2021.Grams, C. M.: A life-cycle definition of year-round weather regimes: characteristics and trends in the North-Atlantic European region, in preparation for Weather Clim. Dynam., 2025.Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I., and Wernli, H.: Balancing Europe's wind power output through spatial deployment informed by weather regimes, Nat. Clim. Change, 7, 557–562, https://doi.org/10.1038/nclimate3338, 2017.Hauser, S., Teubler, F., Riemer, M., Knippertz, P., and Grams, C. M.: Life cycle dynamics of Greenland blocking from a potential vorticity perspective, Weather Clim. Dynam., 5, 633–658, https://doi.org/10.5194/wcd-5-633-2024, 2024. Hersbach H, Bell B, Berrisford P, et al.: The ERA5 global reanalysis. Q J R Meteorol Soc, 146: 1999–2049. https://doi.org/10.1002/qj.3803, 2020. Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., et al.: The ERA5 global reanalysis from 1940 to 2022. Q J R Meteorol Soc, 150(764), 4014–4048, https://doi.org/10.1002/qj.4803, 2024.

This dataset contains the original files for a life-cycle definition of 7+1 year-round North Atlantic-European weather regimes following the definition of Grams et al. (2017, https://doi.org/10.1038/nclimate3338), updated to ECMWF ERA5 reanalysis as described in Hauser et al. (2024, https://doi.org/10.5194/wcd-5-633-2024). It is supplementary data to the publication Grams (2025, in preparation for Weather Clim. Dynam.) which will contain an in-depth technical explanation and analysis of key characteristics and trend in these regimes. A key novelty in the regime definition are the objectively identified regime life cycles which allow process-oriented predictability studies (e.g. Hauser et al. 2024). The dataset has been originally computed for the data period 1979-2019. Using the backward extension of ERA5 (Soci et al., 2024, https://doi.org/10.1002/qj.4803), it has been extended to 1950. Using the near-realtime update it is extended forward until 20250726_21 in release V1.0. For the lifetime of ERA5 it is planned to continously update the data irregularly at least once per year and upon request. Future updates will ensure full backward compatibility.For the methodological documentation we refer to Grams (2025) once available. This dataset also contains a Juypter notebook with examples of simple data analysis in python. The notebook aims to facilitate an easy start of the work with the data. Christian thanks Seraphine Hauser and Dominik Büeler for help with coding this ipynb and reformatting the data for easier accessibility. Users are advised to get familiar with this Readme.md prior to using this data. Contact: [email protected] (Christian Grams)Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., et al.: The ERA5 global reanalysis: Preliminary extension to 1950. Q J R Meteorol Soc, 147(741), 4186–4227, https://doi.org/10.1002/qj.4174, 2021.Grams, C. M.: A life-cycle definition of year-round weather regimes: characteristics and trends in the North-Atlantic European region, in preparation for Weather Clim. Dynam., 2025.Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I., and Wernli, H.: Balancing Europe's wind power output through spatial deployment informed by weather regimes, Nat. Clim. Change, 7, 557–562, https://doi.org/10.1038/nclimate3338, 2017.Hauser, S., Teubler, F., Riemer, M., Knippertz, P., and Grams, C. M.: Life cycle dynamics of Greenland blocking from a potential vorticity perspective, Weather Clim. Dynam., 5, 633–658, https://doi.org/10.5194/wcd-5-633-2024, 2024. Hersbach H, Bell B, Berrisford P, et al.: The ERA5 global reanalysis. Q J R Meteorol Soc, 146: 1999–2049. https://doi.org/10.1002/qj.3803, 2020. Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., et al.: The ERA5 global reanalysis from 1940 to 2022. Q J R Meteorol Soc, 150(764), 4014–4048, https://doi.org/10.1002/qj.4803, 2024.

Synthetic hail experimentsThe data used for assessing the performance of drone-based hail photogrammetry.Three types of hail objects were used:EPS (extended polystyrene)Glass pebblesIce from a consumer-grade ice-makerThe experiments were performed on different types of grass, such as soccer fields, lawns and meadows. The models are trained on a leave-one-out cross-validation (LOOCV) scheme, where the test set is based on one surface, while training and validation sets are based on the rest (randomly split). This is defined as an experiment configuration (a-e), where the name refers to the surface of the test set.CodeThe code used to create and analyze the data can be found on github: https://github.com/MeteoSwiss/ehw24-hail-photogrammetry

Synthetic hail experimentsThe data used for assessing the performance of drone-based hail photogrammetry.Three types of hail objects were used:EPS (extended polystyrene)Glass pebblesIce from a consumer-grade ice-makerThe experiments were performed on different types of grass, such as soccer fields, lawns and meadows. The models are trained on a leave-one-out cross-validation (LOOCV) scheme, where the test set is based on one surface, while training and validation sets are based on the rest (randomly split). This is defined as an experiment configuration (a-e), where the name refers to the surface of the test set.CodeThe code used to create and analyze the data can be found on github: https://github.com/MeteoSwiss/ehw24-hail-photogrammetry

ContentOutput files from the break detection and subsequent homogenisation stages.Figures (mean, max, media original vs homogenised) are in the plots subdirectory.The batch _i directories contain the reference series for that particular homogenisation batch. The batches are explained in the technical report with is currently under revision.Homogenised series are in the combined file: ho_data.rdaAssociated resourcesInput files can be found here: zenodo.15386644R-code can be found here: zenodo.15386663Technical report: under revision

ContentOutput files from the break detection and subsequent homogenisation stages.Figures (mean, max, media original vs homogenised) are in the plots subdirectory.The batch _i directories contain the reference series for that particular homogenisation batch. The batches are explained in the technical report with is currently under revision.Homogenised series are in the combined file: ho_data.rdaAssociated resourcesInput files can be found here: zenodo.15386644R-code can be found here: zenodo.15386663Technical report: under revision

Input data for the homogenisation of Swiss snow depth seriesThe data consists of daily manual snow depth and height of new snow data collected from SLF and MeteoSwiss. Data is present from November to April for each hydrological year.

Input data for the homogenisation of Swiss snow depth seriesThe data consists of daily manual snow depth and height of new snow data collected from SLF and MeteoSwiss. Data is present from November to April for each hydrological year.

Combined risk index (including tropical cyclone and sea level rise risk factors) for mangroves and their services under SSPs 245, 370, and 585 by 2100. Please consult Huelsen_et_al_TC_mangrove_risk_data_overview.pdf for an overview of the provided datasets and their contents.

Combined risk index (including tropical cyclone and sea level rise risk factors) for mangroves and their services under SSPs 245, 370, and 585 by 2100. Please consult Huelsen_et_al_TC_mangrove_risk_data_overview.pdf for an overview of the provided datasets and their contents.