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Mediterranean basin could be occasionally it can be affected by tropical-like cyclonic phenomena. These are called out Mediterranean Tropical-Like Cyclones (TLC) or Medicanes. Their frequency is about 1.5 per year and they can reach category 1 intensities. Previous studies focusing could beon past TLCs events have found that SST play a fundamental role in modulating the intense air-sea exchange of latent and sensible heat fluxes, hence controlling both development and evolution of TLCs. However, given the connection between ocean mixed layer, ocean heat content and temperature, it is important to explore also the role of the mixed layer depth (MLD). In this study we investigated the role of both SST and MLD on genesis, intensification and evolution of a recent record-breaking TLC “IANOS”.  IANOS cyclone it originated over the southern Ionian Sea around 14 Sept 2020, moved over the Central Ionian Sea from south-west to North-East, and made landfall around 19 Sept 2020 over Greece mainland coast, reaching atmospheric pressure values of 984 hpa and wind intensity of over 120-130 km/h. It developed over a basin where a positive SST anomaly up to 4 °C was detected, which coincided with the sea area where it reached the maximum intensity. We conducted a series of experiments using an atmospheric model (WRF - Weather Research and Forecasting system) driven by underlying SST (standalone configuration) with daily update or coupled to a simple mixed-layer ocean model (SLAB ocean), with SST calculated at every time step using the SLAB ocean for a given value of the MLD. WRF was implemented with 3 km grid spacing, IFS Analysis (at 9km resolution), while SST or MLD initialization, for standalone or coupled runs, respectively, are provided by the MFS-CMEMs Copernicus dataset at 4 km of horizontal resolution. For the studied TLC, the mean MLD is modified by increasing or decreasing its depth by 10 m, 30 m, 50 m, 70 m, 100 m. At first the MLD thickness was characterized  for the days in which the cyclone developed using ocean modeling data. Then we identified possible past and future climatological scenarios of MLD thickness. Starting from these data, we simulated the impact of the MLD, and consequently of the Ocean Heat Content, on the TLC. The preliminary results show that the MLD influences not only the intensity of the cyclone but also the structure of the precipitation field both in terms of magnitude and location. The results deserve further investigation in particular in the content of climate change scenarios.

How to cite: Ricchi, A., Liguori, G., Cavicchia, L., Miglietta, M. M., Bonaldo, D., Carniel, S., and Ferretti, R.: On the role of Ocean Mixed Layer and Sea Surface Temperature Anomaly in the genesis, intensification and evolution of the Mediterranean Tropical-Like cyclones “IANOS”., EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-312, https://doi.org/10.5194/ems2022-312, 2022.

Lightning flashes are locally rare albeit hazardous events. Cloud-to-ground lightning strikes may injure or kill people and damage infrastructure or start wild fires. Having reliable climatologies of lightning thus aids the assessment of all these risks.

Despite this scarcity, generalized additive models (GAMs) succeed in producing a climatology of lightning occurrence for the eastern Alps and surrounding lowlands at an unprecedented resolution of 1km² for each day of April through September with data from the Austrian lightning detection and information system (ALDIS) and the digital elevation model TanDEM-X.  ALDIS provides data for eleven years 2010-2020.  The GAM adds the effects of seasonality, jaggedness of the terrain, and seasonally varying effects of elevation and region, thus combining information from analysis cells sharing similar characteristics. Therefore the GAM approach enables more flexibility and the inclusion of more explanatory information than the commonly used "cell-count" method that allows only for smoothing over cells which are close in space and/or time to each other.

The results of the GAM climatology are summarised as follows: At the beginning of the season the probability of a cloud-to-ground discharge over 1km² on a given day is typically less than 1% with a rapid increase in spring, followed by a plateau and a gentler tapering-off in fall. Probabilities are lower at high elevations early in the season but increase once their snow cover is gone.  Regional patterns of lightning also vary with season with an overall southward shift later in the year but more complex details. Grid cells with jagged topography have a higher probability of lightning.

How to cite: Simon, T. and Mayr, G. J.: Daily-resolved lightning climatology of the eastern Alpine region at the kilometer scale, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-61, https://doi.org/10.5194/ems2022-61, 2022.

Hail is one of the most hazardous perils for agriculture, infrastructures, and economy among severe weather events related to deep moist convection. The frequency and severity of severe hailstorms is increasing throughout all Europe with the highest potential to worsen expected over Italy. Hence it is becoming increasingly important to enhance our knowledge of hailstorms to limit and prevent major damages in the most affected areas. However, due to the intrinsic difficulties in systematically observing and simulating hail events, their scientific understanding is still very limited. While direct hail observations are strongly heterogeneous, temporally limited and scarce, numerical simulations lack a sufficient level of detail to properly represent strongly-localized and rapidly-evolving high-impact weather events. Furthermore, the very complex dynamics governing hailstorms prevent the simulation of direct model estimates for hail. Hail probability can be indirectly assessed considering a set of meteorological parameters describing dynamical and thermodynamical characteristics of convective environments prone to hail development. For these reasons the new high-resolution reanalysis dataset SPHERA (High rEsolution ReAnalysis over Italy), developed at ARPAE-SIMC, is considered for investigating hail-favouring environments over Italy. Produced as a dynamical downscaling of the global reanalysis ERA5 (ECMWF), SPHERA is based on the model COSMO at the convection-permitting horizontal resolution of 2.2 km, and provides hourly meteorological products. The high level of detail of SPHERA is expected to enhance the representation of the key ingredients describing hailstorm potential compared to coarser and convection-parametrized datasets that have been employed up to now. Anyhow, these convective parameters alone can not be sufficient for reliably retrieving hail probability, but they must be combined with the available hail information coming from observed data. A major source of information are remote sensing observations, especially Overshooting cloud Top (OT) satellite detections, that recently have proven to be of great potential in discerning hail occurrence, as well as lightning strikes data presenting an intensification in the flash rates (i.e. Lightning Jump (LJ)). In this study OT detections from the geostationary Meteosat Second Generation SEVIRI infrared images are considered, while the LJ index data are obtained from the LAMPINET lightning detection dataset over Italy. Based on the numerical proxies describing hailstorm environments, a filter is built and tuned to retain OT and LJ occurrences related to hail presence on the ground. These are subsequently validated by considering a set of direct hail observations coming from ESWD (European Severe Weather Database) and claims to national insurance companies for hail damage. This method has already provided useful new insights in spatial hail climatologies over Europe, Australia and South Africa, and is expected to benefit even more when considering new advances in numerical modeling and automatic OT detection algorithms, and including additional lightning strikes data.

How to cite: Giordani, A., Kunz, M., Bedka, K. M., Punge, H. J., Paccagnella, T., and Di Sabatino, S.: Hail hazard estimation over Italy with a combination of high-resolution reanalysis, overshooting top detections and lightning data, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-528, https://doi.org/10.5194/ems2022-528, 2022.

Doppler lidar systems allow for a reliable determination of the profiles of wind speed and wind direction in the Atmospheric Boundary Layer (ABL) based on classical measurement strategies such as a VAD scan (Velocity Azimuth Display, e.g. Päschke et al., 2015, Atmos. Meas. Tech. 8, 2251–2266). For many practical applications, however, short-term fluctuations of the wind, such as those that occur in connection with wind gusts, are of great interest in addition to the mean wind profile.

A study by Suomi et al. (2017, Q.J.R. Meteorol. Soc. 143, 2061-2072) has shown that it is, in principle, possible to derive wind gusts from Doppler lidar measurements. However, the high temporal resolution in the determination of the wind vector required for this is not achieved with usual measurement strategies. The authors therefore introduced a correction of the gust values derived from the lidar data based on a scaling approach using in-situ wind measurements.

In our study, an alternative measurement strategy for Doppler lidar systems of the type "Streamline" (Halo Photonics) was developed and tested over several months in 2020/21 at the boundary layer measurement field site (GM) Falkenberg of the German Meteorological Service (DWD). The gust derivation is based on a so-called continuous scan mode (CSM) where the radial velocity measurements taken continuously during a complete rotation of the lidar scan head are assigned to 10-11 beam directions and the wind vector for each rotation is determined using the VAD method. The duration of a scan cycle is about 3.4s, thus a time resolution can be achieved that corresponds to the widely-accepted definition of a wind gust (3s moving average; WMO (2018)).

This new configuration brings challenges to the data processing. In the fast CSM, comparatively few lidar pulses per measurement beam have to be used, so that classical approaches for data filtering (signal-to-noise thresholding, consensus filtering) cannot be used. An alternative method for processing the raw lidar data is proposed. The results of deriving both the mean wind vector and the respective maximum wind gust for a 10-minute averaging interval are compared with sonic measurements at 90m height for a two-month period during the FESSTVaL experiment (Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg, www.fesstval.de). Further measurements were realised in Hamburg in spring 2022 in order to make comparisons with sonic measurements on a tower at higher levels (up to 250m). First results from this 4-week experiment will also be presented.

World Meteorological Organization (WMO) (2018): Measurement of surface wind. In Guide to Meteorological Instruments and Methods of Observation, Volume I -Measurement of Meteorological Variables, No.8: 196–213, URL: https://library.wmo.int/doc_num.php?explnum_id=10616 (accessed April 2022)

How to cite: Detring, C., Päschke, E., Kayser, M., Leinweber, R., and Beyrich, F.: Deriving wind gusts from Doppler lidar measurements, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-184, https://doi.org/10.5194/ems2022-184, 2022.

Strong winds associated with extratropical cyclones are one of the most dangerous natural hazards in Europe. These high winds are mostly connected with five mesoscale dynamical features, namely the warm (conveyor belt) jet (WJ), the cold (conveyor belt) jet (CJ), (post) cold-frontal convective gusts (CFC), strong cold sector winds (CS) and – at least in some storms – the sting jet (SJ). While all these have strong winds in common, the timing, location and some further characteristics tend to differ and hence likely also the forecast errors occurring in association with them.

Here we present a novel objective identification approach for the features listed above, based on a probabilistic random forest using each feature’s most important characteristics in wind, rainfall, pressure and temperature evolution. However, as CJ and SJ turn out to be difficult to distinguish in surface observations alone, we decided to consider the two features together. This identification can then be used to generate a climatology for Central Europe, to analyse forecast errors specific to individual features, and to ultimately improve forecasts of high wind events through feature-dependent statistical post-processing. To achieve this, we strive to identify the features in irregularly spaced surface observations and in gridded analyses and forecasts in a consistent way, thus making it independent of spatial dependencies and gradients.

To train the probabilistic random forest, we subjectively identify the four storm features in twelve winter storm cases between 2015 and 2020 in both hourly surface observations and high-resolution reanalyses of the German COSMO model over Europe, using an interactive data analysis and visualisation tool. Results show that mean sea-level pressure (tendency), potential temperature, precipitation amount and wind direction are most important for the distinction between the features. From the random forest we get occurrence probabilities for each feature at every station, which can be converted into areal information using Kriging.

The results show a satisfactory identification for all features, especially for WJ and CFC. We encounter, however, some difficulties to clearly distinguish the CJ and CS, which are dynamically similar. A climatology is currently being compiled for both surface observations and the reanalyses over a period of around 20 years using the trained probabilistic random forests and further for high-resolution COSMO ensemble forecasts, for which we want to analyse forecast errors and develop feature-dependent postprocessing procedures.

How to cite: Eisenstein, L., Schulz, B., Qadir, G. A., Knippertz, P., and Pinto, J. G.: Identifying high-wind features within extratropical cyclones using a probabilistic random forest, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-438, https://doi.org/10.5194/ems2022-438, 2022.

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings, e.g. in European winter storms. First, we provide a comprehensive review and systematic comparison of several statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing, then we assess the performance of selected methods within winter storms. The methods can be divided in three groups: State of the art postprocessing techniques from statistics (ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression), established machine learning methods (gradient-boosting extended EMOS, quantile regression forests) and neural network-based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The different approaches are systematically compared using six years of data from a high-resolution, convection-permitting ensemble prediction system run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts as well as estimating locally adaptive neural networks leads to significant improvements in forecast skill. Assessing the performance of EMOS and neural network-based postprocessing for selected winter storms, we find that the networks better adapt to the extreme conditions than the statistical benchmark and thus yield a superior predictive performance. However, results suggest that the performance can still be further improved, e.g. via regime-dependent postprocessing.

How to cite: Schulz, B. and Lerch, S.: Machine learning for postprocessing ensemble forecasts of wind gusts with a focus on European winter storms, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-271, https://doi.org/10.5194/ems2022-271, 2022.

The dynamic and thermodynamic drivers of spatially extended climate extremes are difficult to disentangle due to the earth system’s the high dimensionality, interconnectedness and non-linear relationships between individual processes. One approach in the literature relies on carefully selected case studies and uses dynamical models to obtain physical insights into the development of individual extreme episodes. Other studies focus on the statistical relationship between a class of extreme events and one specific driving mechanism. We aim to complement both of these approaches with a machine learning framework in three steps: firstly, the dimensionality of the predictand and a wide range of potential predictor variables is reduced using an appropriate change of basis functions. Secondly, their relationship is modeled by a statistical learner of intermediate complexity — powerful enough to represent the non-linear relationships but simple enough to allow for fast training and extensive experimentation. Lastly, the contribution of each variable to the overall model performance, and to the representation of individual events is assessed using recently developed methods of explainable machine learning.

As a first example application, we model European heatwaves in ERA5 data based on potential explanatory variables including geopotential, sea level pressure, soil moisture and the wind components of the jet stream. The predictors are summarized by classic principal component analysis (PCA); for the heatwave fields we rely on a specialized PCA for binary data. A simple neural network is capable of representing a large part of the variability in the reduced space. With the help of Shapley values, we can then quantitatively asses how much information on heatwaves is contained in each variable, and how individual heatwave events differ in terms of the variables by which the model recognizes them. This type of explanation explicitly allows for predictors with overlapping information and nonlinear interactions.  One advantage of our framework is its ability to represent the impact of all suspected drivers on any arbitrarily defined binary event in a single model.

How to cite: Buschow, S., Keller, J., and Wahl, S.: Explaining Heatwaves with Machine Learning, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-425, https://doi.org/10.5194/ems2022-425, 2022.