Regional reanalysis data sets are becoming more used and requested for a broad spectrum of users, from climate adaptation and mitigation, to agricultural and economical applications (e.g. energy meteorology). While the development of a reanalysis system mainly relies on an existing numerical weather prediction (NWP) model and corresponding data assimilation scheme, it involves a large amount of testing from both a computational and technical perspective.

The talk will present preliminary results of the development for a new pan-European regional reanalysis at the Deutscher Wetterdienst (DWD). The framework involves the ICON NWP model in its global configuration with a two-way coupled nest over Europe and its operational data assimilation method run at 3-hours intervals, namely EnVar for the deterministic run at 13 km global resolution (6.5 km over Europe) and Localized Ensemble Transform Kalman Filter (LETKF) for the 20 ensemble members at 40 km global resolution (20 km over Europe). 

The reanalyses cover the 2010-2022 period, extended to current, and it is run split in 3 streams (June 2009, January 2015, and January 2018) with forward overlap. The first stream start date includes a 6-months initial spin-up, that in the other two streams is dealt through the forward overlap. The spin-up phase is necessary for the soil to reach the equilibrium, and in our case is shorter than what usually seen in free-running simulations because our system performs a surface analysis for soil-moisture every day at 00UTC.

First of all, we present that the number of active observations has a similar magnitude to what shown for ERA5. Then we proceed showing the evolution of the profiles averaged over Europe of first-guess and analyses departures w.r.t. observations. 

Preliminary results of the first year of the 2018 stream are evaluated in observation space on a 6-day average over the European domain. First we see that first-guess bias for the radiosondes and dropsondes (TEMP) temperature of at most -0.12K below 500 hPa and +0.3 K above it. The bias magnitude is reduced by at least half when the analysis is used.  The SYNOP biases for the 2-meter temperature and relative humidity are on average zero, with spatial variability maxima of 0.2 K/0.01 (first guess) and 0.1 K/0.005 (analysis).

How to cite: Valmassoi, A., Keller, J. D., Potthast, R., Anlauf, H., and Cress, A.: Towards a new generation of regional reanalyses for Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-27, https://doi.org/10.5194/ems2024-27, 2024.

The development of regional reanalyses aims at the provision of high-resolution data sets that are suitable for climate applications and climate services. As the desired high-resolution information can barely be provided by either synoptic or remote sensing observation data, a growing interest in high-quality regional reanalyses is recognisable. Particular demand arises from the renewable energy sector. Further quality gains are expected by using an ensemble approach, e.g. by making available the desired uncertainty information when moving towards higher resolution. Within the framework of the Innovation Programme for applied Researches and Developments (IAFE) at Germany's national meteorological service (DWD) our project aims to develop and evaluate an operational ensemble-based regional reanalysis system incorporating the current NWP model of DWD (ICON). One final goal of the project is to provide a basic framework for user-oriented verification.  

For our new reanalysis system only observation data of the ECMWF ERA5 reanalysis will be used. To provide these data to the operational DWD assimilation scheme a converter has been developed. Test experiments have been performed to evaluate whether the current system performance can be improved by using ERA5 observation datasets instead of the operationally used observations from DWDs data base. For testing we use the Basic Cycling Environment (BACY) of DWD being characterized by different modules for each cycling step (e.g boundary data, ICON run, assimilation scheme). BACY enables users to perform cycling over multiple time steps (automatization) as well as repeated tests of a single module for debugging or binary checks. Here we present the BACY specifications chosen for the reanalysis production as well as results of the assimilation experiments.

How to cite: Kelbch, A., Valmassoi, A., Spangehl, T., and Borsche, M.: Ensemble-based regional reanalysis system for Central Europe: Assimilation experiments with ERA5 observations, data conversion, results and outlook, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-179, https://doi.org/10.5194/ems2024-179, 2024.

The development and provision of regional reanalyses requires a coordinated quality assessment targeted to user needs. Regional reanalyses with higher temporal and spatial resolution are of particular importance for applications in the energy sector. Detailed information is required on meteorological parameters such as wind speed and direction at hub heights of modern wind turbines (offshore and onshore), global radiation and near-surface temperature. In addition, regional reanalyses are of interest for climate applications dealing with climate extremes such as strong wind, heat waves, heavy precipitation and droughts.

The regional reanalysis COSMO-R6G2 is currently produced at DWD as a successor of COSMO-REA6. It is based on a newer version of the COSMO model and ERA5 boundary conditions, which enables continuous updating. The nudging technique (Newtonian relaxation) is used for the assimilation of conventional observations stemming from radiosondes, SYNOP stations, ships, buoys or aircrafts. No satellite data is assimilated.

The quality is assessed using in-situ measurements of offshore FINO research platforms, tower and lidar measurements over Germany (companion paper by Bär et al., EMS 2024), DWD’s station network for Germany and satellite-based surface irradiance data of CM SAF as reference. Uncertainties are addressed by a comprehensive comparison to other state-of-the-art reanalyses and derived products.

The temperature and radiation biases seen in COSMO-REA6 are still similarly present in COSMO-R6G2. However, there is evidence that the representation of reduced shortwave transmissivity related to the effect of optically thick clouds is more realistic in COSMO-R6G2 compared to COSMO-REA6 where post-processing methods have already been proven for bias adjustment of non-cloudy cases.

How to cite: Spangehl, T., Borsche, M., Niermann, D., Bär, F., Drücke, J., Kelbch, A., Kaspar, F., Imbery, F., and Becker, A.: Evaluation framework for regional reanalyses at DWD: results and outlook, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1020, https://doi.org/10.5194/ems2024-1020, 2024.

Wind speed data from reanalysis models is used for a wide range of industrial and research applications in the wind energy sector. These range from resource estimation in the development phase to planning of the future (wind) energy system and the integration of wind energy into electrical grids. Validation of reanalysis models for offshore conditions far away from the coast is relatively straightforward, for which very good agreement between reanalysis models and wind speed measurements has been demonstrated. In contrast, validation in heterogeneous and complex terrain is much more difficult.

This study uses an extensive measurement dataset with high-quality wind speed measurements at heights relevant for modern wind energy applications (100 – 200 m) to evaluate and compare a set of reanalysis models, i.e. ERA5, COSMO-REA6, CERRA, and NEWA. The evaluation dataset in this study comprises approximately 100 lidar and mast measurements, mainly carried out for wind park developments, distributed over Germany and covering a wide range of topographic conditions.

The analysis focuses on identifying local topographic effects and indicators that influence the quality of the agreement between the reanalysis models and the measurements (i.e. correlation) as well as systematic deviations (biases). The investigated topographic effects include orographic exposure, land /forest cover, and sub-grid scale orography. In a preliminary analysis based on 44 measurement stations, a clear correlation between the bias in the reanalysis models (compared to the measurements) and the orographic exposure of the measurement location could be demonstrated. At measurement locations with ground heights exceeding the height of the grid cell of the reanalysis model, the model data underestimated the observed wind speeds. The opposite could be observed at measurement location below grid height.

These findings are especially important for wind energy applications, as wind farm developments tend to concentrate on areas with high exposure and specific land use types. Moreover, the observed relationships with local geography potentially provide the possibility to empirically correct and downscale wind speeds from reanalysis models. This allows to better represent the wind resource available for wind parks and to estimate the uncertainties based on local geographical conditions.

How to cite: Pauscher, L., Geiger, D., Yuan, D., Bär, F., Good, G., Spangehl, T., Kaspar, F., Weber, H., and Callies, D.: An evaluation and comparison of wind speeds from different reanalysis models in the context of wind energy – the influence of topography, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-957, https://doi.org/10.5194/ems2024-957, 2024.

How to cite: Geiger, D., Pfennig, M., Callies, D., Pape, C., and Pauscher, L.: Assessing the performance of reanalysis models for wind power modelling in Germany , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1038, https://doi.org/10.5194/ems2024-1038, 2024.

In recent years, attention has intensified on the interplay between environmental stressors, notably poor air quality, heatwaves, droughts, and fires, as exacerbated by climate change. These stressors, once viewed in isolation, are now recognized as interconnected phenomena forming a complex web of impacts with global repercussions. The compounding effects of these events have significant implications for ecosystems, economies, and public health worldwide. In this work, we present a comprehensive analysis of compound events of poor air quality, heatwaves, droughts, and fires on a global scale, shedding light on their interconnected nature, underlying drivers, and implications for ecosystems and societies worldwide.

We use meteorological data from ERA5 to compute heatwaves and droughts (using the Standardized Precipitation-Evapotranspiration Index – SPEI), Fire Radiative Power (FRP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua sun-synchronous orbit satellites, and air quality data (particulate matter PM2.5) retrieved from the Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis (EAC4). The horizontal resolution is preset at 0.75º × 0.75º, and the time window is between 2003 and 2022 at a daily basis. Compound events were identified based on the detection of pollution events, heatwaves, droughts, or FRP in various combinations, ranging from two to four events, within each cell of space and time.

Analysis revealed hotspots of compound events distributed across different regions worldwide. For instance, pollution and heatwaves were predominantly observed in India, the Arabian Peninsula, and eastern China, whereas heatwaves and fires were prevalent in the Brazilian Cerrado, northern Australia, and South African Savannas. The impacts of single and concurrent hot, dry, and fire events on particulate matter PM2.5 levels varied significantly by continent. Notably, North America and Asia exhibited pronounced pollution levels during simultaneous occurrence of these events compared to isolated pollution events.

The interaction between compound hot and dry events and wildfires poses a critical public health challenge, underscoring the interconnectedness of climate change, extreme weather events, and air pollution. Addressing these complex interrelationships necessitates comprehensive strategies integrating climate resilience, wildfire management, and air quality regulations to safeguard human health and well-being amidst a changing climate.

This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020), UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020). This work was performed under the scope of project https://doi.org/10.54499/2022.09185.PTDC (DHEFEUS) and supported by national funds through FCT. DL and AR acknowledge FCT I.P./MCTES (Fundação para a Ciência e a Tecnologia) for the FCT https://doi.org/10.54499/2022.03183.CEECIND/CP1715/CT0004 and https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006, respectively.

How to cite: Bento, V. A., Lima, D. C. A., Careto, J. A., and Russo, A.: Global Assessment of the Compound Effects of Hot, Dry Conditions and Fires on PM2.5 Levels using ERA5 and CAMS reanalysis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1085, https://doi.org/10.5194/ems2024-1085, 2024.

Reanalyses are currently a major source of information about the state of the troposphere in many meteorological and climatological investigations. However, since they are a product of numerical modeling, they contain some systematic errors, which occur mainly due to the discretisation of a domain, parametrisation of physical processes and numerical approximations. The errors are particularly apparent for meteorological variables which cumulate over time and have great spatial heterogeneity. One example of such a variable is snow depth. In this work, snow depth from ERA5 and ERA5-Land reanalyses was evaluated against observations from over 300 stations in Poland, Czech Republic and Slovakia in winter seasons from 2001 to 2021. Additionally, an attempt was made to reduce existing biases and produce the field in finer resolution based on terrain characteristics derived from a digital elevation model. Verification results show that the difference between modelled and real elevation, together with station rank, are major factors contributing to systematic errors. In complex terrain, bias occurs nearly at every station, while in the lowlands, it is roughly neutral at synoptic stations as their data are assimilated during the production of the reanalyses. For lower-ranked stations, errors are apparently higher. On average, ERA5-Land has lower Root Mean Square Error (RMSE) than ERA5, however, there are stations with greater positive and negative bias. Despite higher resolution, ERA5-Land performs worse than ERA5 for around 30% of stations. Machine learning methods are capable of reducing systematic errors existing in the reanalyses. The greatest improvement occurs especially for sites in complex terrain. Statistical downscaling provides some useful insight in spatial distribution of snow depth, however, its physical consistency needs to be enhanced, e.g., by using physically-constrained machine learning.

How to cite: Stachura, G.: Evaluation and machine-learning-based downscaling of ERA5 snow depth in Central Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-168, https://doi.org/10.5194/ems2024-168, 2024.

Meteorological reanalyses are used for multiple applications, from understanding past climate to assessing extreme weather events. In this study, an extensive validation of high-resolution regional reanalyses was performed, evaluating their capability to reproduce precipitation fields over Italy, which is characterized by complex orography and land-sea interactions.

In this study, we carried out an inter-comparison of nine different reanalysis products, using the ECMWF ERA5 product as the global reanalysis reference. Some of the reanalyses considered cover Europe (BOLAM, COSMO-REA6, CERRA), while others are specifically designed for Italy (MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT), using a variety of different atmospheric models and parametrizations.

The precipitation fields inter-comparison employed wavelet decomposition techniques and frequency distribution analysis. Then, a validation against independent observations involved both observation products interpolated onto regular grids and punctual data from stations’ series.

The wavelet decomposition permitted assessing the effective information of every reanalysis at each spatial scale, clustering the products into global, regional, and convection-permitting ones. On the other hand, the frequency distribution of daily rainfall amounts allowed proving the capability of higher-resolution reanalyses to depict the frequency of occurrence of extreme precipitation events better than the ERA5 global product.

Moreover, the comparison of the reanalyses fields with observations was performed by calculating the relative bias to assess climatology reliability and the Stable Equitable Error in Probability Space (SEEPS) to evaluate the ability to discriminate among dry, light rain, and heavy rain days. The results indicated wet biases over the Alps, especially during spring and summer, and dry biases over Liguria, Tuscany, the Apennines, and Southern Italy during autumn and winter. Significant differences were found among reanalysis products, with MOLOCH and MERIDA-HRES showing the best performances.

Finally, the trend analysis revealed a long-term signal in the reanalysis precipitation bias, with a significant increase in the annual amount in most reanalyses (with the only exception being VHR-REA_IT), suggesting caution when using these products for climate change studies in this area.

How to cite: Cavalleri, F., Lussana, C., Brunetti, M., Viterbo, F., Bonanno, R., Manara, V., Lacavalla, M., and Maugeri, M.: Multi-scale assessment of regional high-resolution reanalyses precipitation fields over Italy, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-9, https://doi.org/10.5194/ems2024-9, 2024.

The interest towards the development of regional high-resolution retrospective datasets, allowing an enhanced representation of past meteorological states, has been progressively increasing. Convection-permitting (CP) datasets (i.e., hindcasts, based on historical model integrations, or reanalyses, including the additional assimilation of historical observational data) have demonstrated to improve the representation of precipitation compared to convection-parameterized counterparts. The benefits involve the spatial structures of rainfall fields, the timing and peak of the diurnal cycle of summer precipitation, and the frequency of wet days/hours. This is of particular relevance for enhancing the characterization of severe precipitation events with the aim to prevent and minimize their impacts on terrestrial ecosystems, and on human and animal life. However, the simulation of convective-related phenomena is highly model-dependent, implying the inability to sample the full range of natural variability with single-model experiments. This is exacerbated for km-scale simulations owing to the intrinsic chaotic behavior underlying convective processes.

Recently, the development of Multi-Model Ensembles (MMEs) of CP regional climate models over Europe has demonstrated to efficiently tackle this issue and reduce the simulation error associated with single model outputs. This approach could benefit also retrospective estimates in order to retrieve a complete, homogeneous, and optimized assessment of past atmospheric states. In case of precipitation, this could be valuable also for potential downstream modeling applications such as forcing hydrological forecasting systems to obtain improved historical series of high-impact flood events.

This work presents the first MME of retrospective CP datasets over Italy based on four reanalyses/hindcasts recently produced, with the aim to assess the added value of their joint employment. The datasets are obtained by dynamically downscaling the global reanalysis ERA5 using different numerical models: MERIDA-HRES (based on WRF-ARW), the hindcast based on the model MOLOCH, and SPHERA and VHR_REA-IT (both based on COSMO). The reference dataset for comparison is GRIPHO, the first Italian pluviometer-based hourly analysis. The investigation over a decade (2007-2016) includes various aspects such as the annual and seasonal dependence of daily and hourly mean rainfall intensity and frequency, heavy precipitation occurrences, and their summer diurnal cycles. The results indicate that a superior dataset performing always best is not detected, while large inter-model variability characterizes summer precipitation. The ensemble aggregation systematically improves rainfall estimates over single datasets, resulting in more adherent spatial fields and lower root-mean-squared errors and relative biases when compared to the observations, at the expense of reduced spatial variability of the distributions.

How to cite: Giordani, A., Ruggieri, P., and Di Sabatino, S.: Added value of a multi-model ensemble of convection-permitting rainfall re-forecasts over Italy, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-761, https://doi.org/10.5194/ems2024-761, 2024.