EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe

Abstract

Abstract. In this paper we present EMO-5 (“European Meteorological Observations”, spatial resolution of 5 km), a European high-resolution, (sub-)daily, multi-variable meteorological dataset built on historical and real-time observations obtained by integrating data from 18 964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG – INCA, EURO4M-APGD, and CarpatClim), and one global reanalysis (ERA-Interim/Land). EMO-5 includes the following at daily resolution: total precipitation, temperatures (minimum and maximum), wind speed, solar radiation, and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature data. The raw observations from the ground weather stations underwent a set of quality controls before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5×5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high-resolution datasets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high-resolution dataset. The availability of the uncertainty fields increases the transparency of the dataset and hence the possible usage. EMO-5 (version 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen with version 2. As a product of Copernicus, the EU's Earth Observation Programme, the EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2020).

Non‐Linear Seismic Velocity Variations Observed During a Seismic Swarm in the Alto Tiberina Low Angle Normal Fault From Ambient Noise Correlation Measurements

“…Model 3 considers regional climatological factors while Model 4 addresses the potential climatological variations that may manifest at the level of individual stations. To that end, we use a higher resolution environmental data set, for both the daily temperature and the daily rainfall, from the Joint Research Centre Data Catalog—EMO (Thiemig et al., 2022). This data set is characterized by an enhanced spatial resolution of 1.5 km × 1.5 km.…”

“…The rain and the temperature data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program (https://power.larc.nasa.gov/) (Stackhouse, 2021) and from a Joint Research Centre Data Catalog—EMO (Thiemig et al., 2022). The seismic data are available at Instituto Nazionale di Geofisica e Vulcanologia (http://iside.rm.ingv.it/instruments) (INGV, 2005).…”

Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS

“…For EWS driven by physically based models, hydrological simulations based on the same hydrological model configurations are generally preferred over in‐situ hydrological observations to define the initial conditions (especially as many of the state variables of hydrological models do not have observations) to ensure consistency in the modelling chain, regardless of the overall forecasting techniques (e.g., based on statistics, climatology or NWP information; Troin et al, 2021). For large‐scale distributed EWS requiring spatially consistent and continuous information, the hydrological reanalysis can be forced with atmospheric reanalysis (e.g., ERA5T; Hersbach et al, 2020 used in GloFAS), hybrid datasets (e.g., HydroGDF; Berg et al, 2021) or observation‐based datasets (e.g., EMO5 but updated in real‐time used in the CEMS European Flood Awareness System EFAS (Thiemig et al, 2022), or UK Met Office precipitation and potential evaporation used in UK Hydrological Outlook (Prudhomme et al, 2017)).…”

“…The dataset also showed that the river discharge reached record low levels across July and August in the Rhine river basin, just 1 year after record floods on the Rhine in July 2021, causing devastating impacts in public water supply, agriculture, power generation and industry or ecosystem. Whilst for Europe, studies such as that presented in Figure 7b do not necessarily rely on modelled weather reanalysis input thanks to a very dense meteorological observational network (Figure 7b uses the EMO5 dataset (Thiemig et al, 2022) as forcing data), similar rapid assessments are possible globally at any time using datasets such as C3S ERA5T and CEMS GloFAST.…”

“…For more detail on GloFAS v4, please refer to Grimaldi et al (2024). (b): Monthly average river discharge anomalies for June, July and August (JJA) 2022 generated using the EFAS v4 system forced by the EMO5 dataset (Thiemig et al, 2022). The categories ‘exceptionally high (low)’, ‘notably high (low)’, ‘above (below) normal’ and ‘normal’ range relate to the percentile ranges >90 (<10), 75–90 (10–25), 60–75 (25–40) and 40–60 for the 1991–2020 reference period.…”