Chairpersons: Victoria Sinclair, Sabrina Wahl
Rapid changes of wind speed, wind gusts, can harm the structural safety of wind turbines, reduce their energy output, lead to a shorter rotor blade life cycle due to fatigue, and reduce the lifetime of other wind turbine components. Besides damages to infrastructure, the oscillations and / or ramping events can result in a fast fluctuation of the grid voltage and add additional burden to the power grid.
Thus, accurate forecasting of wind gusts and gustiness for power grid balancing, trading, can become a crucial factor. However, often no wind gust measurements are available from the wind farm operators (SCADA, mast if available) as only 10-minute wind speed averages can be used. Often, the environmental conditions surrounding a wind farm, e.g. roughness length, are unknown, too. Thus, a very simple gust estimation algorithm based on the 10-minute SCADA wind speed averages is needed.
In the past, several attempts have been made to estimate the wind gusts based on very simple assumptions (Wieringa, 1973, Cvitan, 2004, Harper et al., 2010). However, those algorithms all have their flaws.
To account for gustiness a very simple gust estimation algorithm was developed and tested for different heights above ground and locations (flat, mountainous), and observation qualities such as standard synop-sites, mast measurements, and SCADA data. The algorithm was developed in a way that it can be used in a deterministic and probabilistic setup. A verification of the algorithm against measurements showed that it is able to outperform other, similarly simple algorithms and that it is able to provide a good estimate of gustiness for those locations.
How to cite: Schicker, I. and Meirold-Mautner, I.: A very simple gust estimation algorithm for wind energy applications, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-370, https://doi.org/10.5194/ems2022-370, 2022.
Gaining a deeper physical understanding of the high-impact weather events repeatedly occurring over the Croatian coast is highly needed to reduce the casualties and economic impacts due to these highly localized and hardly predictable phenomena. Recently obtained national hail climatology in Croatia revealed that parts of the Croatian coast are frequently struck by early morning hailstorms with the local maximum of the daily activity occurring approximately at 08:00 h local time (CET). While the afternoon maximums of hail activity could be attributed to the daily heating cycle over land, the mechanisms responsible for the occurrence of the early morning maximum of hail activity are still unknown. This high uncertainty regarding the mechanisms associated with early morning hail formation stem from the scarcity of high-resolution modeling studies and observations of such events further lowering their forecasting ability.
Benefiting from the advantages associated with the convection-permitting high-resolution numerical modeling, this work aims to inspect the triggering mechanism and evolution of such hailstorms. Moreover, a special focus is on the role that synoptic, mesoscale, but also local conditions such as orography or the shape of the coastline, play in the formation of the convergence zone responsible for severe weather effects. Several cases of early morning hailstorms are selected for simulations using Weather Research and Forecasting (WRF) model alongside HAILCAST and Lightning Potential Index (LPI) parameterizations. HAILCAST is a one-dimensional hail growth model that forecasts the maximum hail diameter at the ground. Similarly, LPI highlights the areas with the potential for developing lightning activity. Using these recently developed tools alongside a highly valuable data set of hail observations from Croatian meteorological stations, Croatian hailpad network, and lightning observations from the LINET network, the ability of HAILCAST and LPI to reproduce the observed hail and lightning activity is assessed.
By utilizing a process-oriented approach for analyzing such high impact events, a deeper understanding of the synoptic, mesoscale and local conditions that benefit the development of hailstorms is obtained. This directly benefits the forecasting ability of early morning hail over the Croatian coast, but also highlights the ability and potential improvements of the high-resolution modeling systems and specific tools to represent such highly localized severe weather phenomena.
How to cite: Malečić, B., Jelić, D., Blašković, L., Križanac, A.-M., Horvath, K., and Telišman Prtenjak, M.: Triggering and evolution of early morning hail over the Croatian coast – convection-permitting modeling study, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-341, https://doi.org/10.5194/ems2022-341, 2022.
Sub-hourly heavy precipitation events (SHHPs) frequently underlie major meteorological hazards. This study uses a 71-weather-station dataset for 2000–2020 to diagnose SHHPs corresponding to 10-minute precipitation events of at least 5.0 mm (above the 99th percentile of the precipitation days) and identify their associated synoptic-scale conditions, using a meridional pressure gradient (MPG index) defined on an hourly timescale. This index revealed two main synoptic-scale weather types: remote and regional low-pressure systems (RemL and RegL, respectively). RegL events show two pronounced maxima in spring and autumn, whereas RemL events have a single maximum in autumn. Moreover, RegL events are largely concentrated in the afternoon and evening, while RemL events are much more uniformly distributed during the day, despite some bias toward mid-day and early afternoon. These features suggest that RegL events tend to be thermally driven, whereas RemL events are associated with frontal systems.
Moreover, a preliminary linkage between the SHHPs and mesoscale convective systems is established by detecting sudden surface pressure surges, which are manifestations of mesohighs associated with downbursts. Finally, two case studies of downburst events are presented. The first event occurred on 23 December 2009 when a rapidly deepening extra-tropical cyclone crossed Mainland Portugal. During this event, several power towers were destroyed by downburst winds associated with a mesovortex, observed in a bow echo line triggered by an upper cold front.
The second event occurred on 17 June 2017 and contributed to the rapid spreading and intensification of unprecedented forest fires in Portugal, with the loss of many human lives. On 17 June, the distinctive markers of mesohighs, cold pools and the associated downbursts, were identified by weather stations and these signatures were also present in the AROME forecasts. These downbursts were caused by an MCS (visible in satellite images and radar reflectivity), which developed in an environment characterized by weak synoptic forcing, accompanied by moderate CAPE and an inverted V-profile, with a very warm and dry layer from the surface to 650 hPa, which is typical of hybrid downbursts.
How to cite: Belo-Pereira, M., Santos, J., and Pinto, P.: Sub-Hourly Precipitation Extremes in Mainland Portugal and downburst events, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-305, https://doi.org/10.5194/ems2022-305, 2022.
Heavy precipitation events (HPEs) frequently occur in Andalusia (south of Spain), causing major economic losses and affecting human stability. Variations in the precipitation patterns are expected under a changing climate. Hence, a correct characterization and prediction is crucial to better understand this kind of events.
In this region, HPEs are frequently related to deep convection; convection-permitting models (CPMs) can generate more reliable information on small scales due to their high spatial resolution of ≤ 4 km. At this scale, deep convection can be explicitly resolved. Therefore, precipitation features (e.g., patters of diurnal cycle) could be better represented.
In this framework, this study aims to assess the Weather Research and Forecasting (WRF) model version 4.3.3 in convection-permitting mode, analyzing its sensitivity to different parameterization schemes. For this purpose, WRF has been configured using two one-way nested domains: a coarser domain (d01) covering the Iberian Peninsula (IP) at 5 km of spatial resolution, and a finer domain (d02) centered over the Sierra Nevada (SN) mountain range with 1 km spatial resolution. To analyze the model performance under different precipitation conditions, WRF was separately run for two one-year periods, both using as lateral and boundary conditions the ERA5 reanalysis and using one year of spin-up; a wet year, and a dry year. Specifically, the analysis was based on combining two planetary boundary layer options (YSU and ACM2), two microphysics (WSM-6 and Thompson), two radiation (RRTMG and CAM), and two cumulus (Kain-Fritsch and Grell 3D) schemes for the 5-km domain. Additionally, this coarser domain was also tested with the convection scheme switched off.
Sub-daily outputs of precipitation have been compared with different observational products in order to select the best combination of parameterization schemes and to determine whether the model is able to incorporate the effects of topographical features at this spatial resolution. This provides physically consistent results for this region.
Keywords: sensitivity study, convection-permitting climate simulations, Andalusia, heavy precipitation patterns, Weather Research and Forecasting model.
Acknowledgments: This research has been carried out in the framework of the project P20_00035, funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades, the Spanish Ministry of Science and Innovation (LifeWatch-2019-10-UGR-01), project CGL2017-89836-R, funded by the Spanish Ministry of Economy and Competitiveness with additional FEDER funds, and project B-RNM-336-UGR18, funded by FEDER / Junta de Andalucía - Consejería de Economía y Conocimiento. F. Solano Farías acknowledges the Mexican National Defense Secretary for the predoctoral fellowship.
How to cite: Solano-Farías, F., García-Valdecasas Ojeda, M., Rosa-Canóvas, J. J., Toro-Ortiz, Y. M., Romero-Jiménez, E., Gámiz-Fortis, S. R., Castro-Díez, Y., and Esteban-Parra, M. J.: A sensitivity study of the Weather Research and Forecasting model to different physics schemes in convection-permitting mode over Southern Spain, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-432, https://doi.org/10.5194/ems2022-432, 2022.
The Spanish Mediterranean Basin (SMB) have experienced an increase of urbanization resulting from the socioeconomic growth of the last five decades. Consequently, flood prone areas have been intensively occupied and have become more vulnerable to this hazard. In this framework, we analyze the intensity and distribution of precipitation associated to floods occurred in the coastal municipalities of the region between 1960 and 2012, according to the SMC-Flood database (https://doi.org/10.5194/nhess-19-1955-2019).
Our study uses this database and a Principal Component Analysis technique to identify significant patterns and correlations between the damage produced by the floods, the main synoptic patterns during those days (https://doi.org/10.1016/j.scitotenv.2021.150777) obtained from the 20th Century Reanalysis V3 project and the precipitation from the SPREAD database (https://doi.org/10.5194/essd-9-721-2017).
Regarding the synoptic analysis, 12 different patterns explain most of the variance of the surface pressure. Additionally, the provinces from Girona to Almería are more commonly affected by eastern airflows in autumn, mainly owing to the warm and moist Mediterranean air transportation; whereas the prevailing wind in the most southwestern provinces (Cádiz, Málaga and Granada) is from the south and it is frequently associated to Atlantic low-pressure centres. Yet, it is found that the northern provinces of the SMB can be also affected by the latter configuration.
Regarding the precipitation, 9 different patterns associated to different regions along the SMB are obtained. The matching between both types of patterns (synoptic and precipitation) is performed according to the flood events statistically related to each of them.
Finally, from the analysis it can be concluded that the provinces of Granada, Almería and Barcelona are more exposed to this hazard, as low average rainfall values and high mean severity indexes are identified; and also, that the patterns presenting an Atlantic trough tend to provide more precipitation and more severe events.
How to cite: Cuixart, M., Peña, J. C., Gil-Guirado, S., Pérez-Morales, A., Serrano-Notivoli, R., and Pino, D.: Analyzing precipitation distribution and intensity related to floods in the Spanish Mediterranean basin since 1960, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-359, https://doi.org/10.5194/ems2022-359, 2022.
Lightning in western Europe follows mostly a distinct annual cycle with the majority of lightning occurring in summer or fall reflecting the importance of CAPE thunderstorms (thunderstorms accompanied by high CAPE and moisture values) or, in the Mediterranean, the influence of high sea surface temperatures. But lightning occurs also without the typical favorable conditions so other meteorological processes become relevant. These rare thunderstorm types can be very destructive as the high hit rate to elevated infrastructure in winter shows: even in regions with a pronounced annual lightning cycle, tower striking lightning has no seasonality. Neglecting rare thunderstorm types can lead to insufficient lightning risk assessments, especially concerning elevated infrastructure such as radio towers and wind turbines. A proper understanding of the various thunderstorm types as presented in this study helps to find good places for wind farms and to construct more resilient wind turbines.
We use lightning observations from the EUCLID network along dozens of ERA5 variables with a resolution of 0.25 degree longitude/latitude and one hour. Each found thunderstorm type is described by its driving ERA5 variables, which are in turn categorized based on their meteorological characteristics: Mass/temperature field variables, wind field variables, cloud physics variables, moisture variables, and surface exchange variables. As some thunderstorm types may only occur in specific regions, we exert our analysis on several rather homogeneous subdomains in Europe representing the sea, as well as flat and mountainous domains. Further, we assure that lightning from each season is equally represented in our data set to better capture rare lightning conditions in unfavorable seasons.
This comprehensive data-driven description of various thunderstorm types in Europe is based on unsupervised learning methods. Cluster analysis groups observations with and without lightning based on their accompanying meteorological values working out various thunderstorm groups and clearly distinct from them groups without lightning. The thunderstorm groups are then meteorologically described using their cluster means, principal component analysis, and their prevalent weather patterns.
First results show that in addition to the well-known CAPE thunderstorm type there are at least cloud-physics thunderstorms and wind-field thunderstorms. Cloud-physics thunderstorms are associated with large amounts of cloud particles, strong updrafts, thick clouds, and high precipitation amounts. Wind-field thunderstorms occur together with large wind speeds and high wind shear and in the absence of CAPE.
The presented description of various thunderstorm types in Europe improves the conceptual understanding of thunderstorms and provides a basis to better evaluate lightning risk in Europe.
How to cite: Morgenstern, D., Stucke, I., Simon, T., Mayr, G. J., and Zeileis, A.: Meteorological thunderstorm types in Europe, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-201, https://doi.org/10.5194/ems2022-201, 2022.
Mediterranean Tropical-like Cyclones, or commonly named as medicanes are a special type of cyclone over the Mediterranean Sea. These cyclones are quite similar to tropical cyclones, although smaller. Their development process is the same as that of subtropical or tropical cyclones forming over the higher latitude subtropical areas of the Atlantic Ocean: they mainly originate from extratropical lows that cut off from the main westerly flow and gradually acquire subtropical and then tropical characteristics if they remain long enough over relatively warm waters. This type of cyclones is officially classified in the Atlantic region by the National Hurricane Center (USA), and nowadays some meteorological services, for example the UK Met Office already admit the medicanes as subtropical or tropical cyclones, too.
In September 2020, a unique cyclone developed over the central Mediterranean Sea, named 'Ianos' after the Greek Meteorological Service. The genesis occurred over warm water of 26-27 °C from a larger cluster of thunderstorms and it did not have extratropical precursor. Later, the cyclone gradually strengthened and built up a well-defined tropical-like structure with sustained deep convection and occasionally with an eye. In our work, we investigated this cyclone based on available surface and satellite-based measurements and the ECMWF ERA-5 reanalysis data with 0.75° resolution to determine whether it is really a tropical cyclone. We analysed the low-level, vertical and high-level structure of the cyclone and compared reanalysis parameters that typically differ between tropical and extratropical cyclones. The results show that Ianos had more tropical than subtropical characteristics and did not show any extratropical sign, so it could be classified as a tropical cyclone. Furthermore, surface measurements confirmed that the cyclone reached at least the middle part of the Category 1 intensity interval on the Saffir-Simpson hurricane wind scale before it made landfall in Greece, and it is also possible that it had been a Category 1 hurricane for a short period the previous day, too.
How to cite: Hérincs, D. and Dezső, Z.: Synoptic analysis of Cyclone Ianos via surface, satellite and reanalysis data, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-388, https://doi.org/10.5194/ems2022-388, 2022.
The aim of this work is to study the triggering mechanism and the other factors involved in the development of the Medicane ‘Ianos’ over the Ionian Sea, occurred during 15-20 September 2020. Ianos was one of the most intense Tropical-Like cyclones(TLC) observed on the Mediterranean. It was characterized by a minimum pressure of 984 hPa and generated winds of over 120 km/h and up to 5 meters of significant waves height. It also caused massive flooding along the Greek coast and rain accumulations of more than 500 mm in inland areas. The triggering and the physical process for intensifying the cyclone has been studied using satellite and the convection permitting numerical simulation at 1 km resolution. Factors such as dry air intrusion due to the stratosphere-troposphere exchange partially helped in the formation and stabilization of the eye of the cyclone. Moreover, convection was initially triggered by the cold north-easterly advection that de-stabilized the atmosphere in Gulf of Sidra, helping in the deep convection and rainfall in the region during 15 September. Further, the formation of the low pressure system in Libya that later interacted with this convective system along with the dry air intrusion led to the formation of the cyclone. The latent heat release mostly sustains and intensified the storm processes. This can also be deduced (in conjunction with the marine phenomena of mixing, waves, etc.) from the decrease in Sea Surface Temperature along the TLC track. Understanding the mechanism of the storm initiation process is an important task for improving the forecast in the future in order to improve forecasts but also to limit the impacts of these extreme phenomena, in a complex context of climate change.
How to cite: Neduncheran, A., Ricchi, A., and Ferretti, R.: Case study of Medicane Ianos: Investigation into its triggering mechanism, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-127, https://doi.org/10.5194/ems2022-127, 2022.
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.
The Mediterranean Sea is a mid-latitude fairly temperate marine basin, strongly influenced by the North-Atlantic atmospheric circulations. In this semi-enclosed basin, a wide variety of cyclogenesis mechanisms are known to develop, including baroclinic waves coming from the Atlantic, Mediterranean cyclogenesis originating from the cut-off of baroclinic waves, Tropical-Like Cyclones (TLC), Rapid-Cyclogenesis (RC) and Intense Mediterranean Cyclones (IMC). Depending on the cyclone type, the characteristic frequency of appearance can vary, ranging from tens per month to 1.5 per year, as in the TLC case. RCs are among the rarest and probably most intense and destructive cyclogenesis events that can develop within the Mediterranean basin; they usually originate at high latitudes, during wintertime, and mainly over the sea, preferring areas with high Sea Surface Temperature (SST) gradients. It is generally accepted that these events are determined by 12 different parameters, among which the most relevant one is the quick drop of pressure, close to 1hPa/hr for 24 hours, within the eye of the cyclone. RCs formation is an extremely complicated process, and in the Mediterranean basin it is mostly driven by air intrusions from the stratosphere and by the presence of Atmospheric Rivers. Using ERA5 dataset, we firstly conducted a physical and dynamical analysis of the 30 most intense cyclogenesis events occurred in the Mediterranean basin in the period 1979-2020, identifying factors which triggered and generated cyclones and contributed to the intensification of such events; this allowed to classify them as TLC, RC or IMC. Further analysis has been undertaken to determine the cyclones' phase and their main morphological characteristics, as well as their statistical distribution, seasonality and correlation with relevant indexes such as NAO, EA and SCAND, as well as SST anomalies exhibited by the Central Mediterranean Basin.
How to cite: Carniel, C. E., Ferretti, R., Ricchi, A., Curci, G., Serafini, P., Wellmeyer, E. D., and Zardi, D.: Statistical analysis and classification of cyclogenesis events in the Mediterranean, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-485, https://doi.org/10.5194/ems2022-485, 2022.
Windstorms constitute nowadays one of the most dangerous hazards worldwide, representing the costliest natural hazard in Europe between 1980 and 2015 and ranking second for overall losses and fourth in terms of the number of human casualties. Among all the extreme phenomena associated to intense deep convective storms, they have the largest impacts on human health, structures, natural environments, as well as transport and energy infrastructures. Local non-synoptic phenomena as tornadoes and thunderstorm outflows are often responsible for the strongest winds, and according to IPCC projections these episodes are expected to further worsen soon in many parts of the world because of global warming. The warming of the Earth’s surface temperature, indeed, is expected to enhance in the future the convective activity at the base of thunderstorms, and therefore some studies project increasing intensity, for example in the Mediterranean region, with a forecast trend of more frequent and more severe synoptic and convective windstorms. However, given the great unpredictability of thunderstorm events, the study of their behaviour is still a challenge, especially from a numerical point of view. In this context, a study based on the explicit cloud modelling of thunderstorms is performed to investigate the mechanisms that underlie deep convective development and intensification in the Ligurian Sea, which is one of the areas in the Mediterranean most prone to severe convective episodes, particularly during the autumn. High-resolution simulations, performed using the Bryan Cloud Model Version 1 (CM1), are focused on the intense thunderstorm event and its associated downburst that hit the city of Genoa in the morning of the 14th of August 2018. The processes responsible for its development and that triggered its intensification are studied, as well as the forcing due to the complex topography, which is thought to have played a role in the enhancement of the thunderstorm outflow at the ground. Preliminary results show some discrepancies between idealized simulations without and with the orography of this area, in terms of intensity and duration of the event. Additionally, the possible role of the sea surface temperature (SST) discontinuities in the convection triggering, through the downward momentum mixing (DMM) mechanism, is investigated as a contributing factor to the thunderstorm development, together with the SST anomalies, observed warmer than average during the occurrence of this severe weather episode by satellite measurements.
How to cite: Hourngir, D. and Burlando, M.: Thunderstorm formation in the Mediterranean: a cloud modelling sensitivity analysis to SST anomalies, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-393, https://doi.org/10.5194/ems2022-393, 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.
The Multi-sensor Approach for Satellite Hail Advection (MASHA) is a new satellite hybrid technique conceived for the real time detection and advection of hail clouds. MASHA is based on a machine learning algorithm able to identify hail clouds from satellite measurements and predict the evolution of hail-bearing systems every 5 min. The machine Learning techniques represent a valuable tool to address this problem. In particular, the use of deep learning model allows to automatically combine low level data and providing accurate predictions. Operationally, MASHA combines the strengths of the MWCC-H method to detect hail through the whole GPM constellation (Laviola et al., 2020a-b) with the high temporal rate of the Meteosat Rapid Scan Service (MSG-RSS). The novelty of this approach is offering the unprecedented possibility to advect hail-bearing systems in real-time and at very high spatial resolution. This opens the way to the operational applications of MASHA method by offering an unprecedented support to the nowcasting of hailstorms and to regional numerical weather predictions. Recent applications experimented the ingestion of lightning strikes and radar hail indices in order to improve the reconstruction of hail fields when the GPM-C overpasses are missing. The result is a near-real time, more consistent, high-resolution hail map.
References
Laviola S., V. Levizzani, R. R. Ferraro, and J. Beauchamp: Hailstorm Detection by Satellite Microwave Radiometers. Remote Sens. 2020a, 12(4), 621; https://doi.org/10.3390/rs12040621
Laviola S., G. Monte, V. Levizzani, R. R. Ferraro, and J. Beauchamp: A new method for hail detection from the GPM constellation. A prospective for a global hailstorm climatology. Remote Sens. 2020b, 12(21), 3553; https://doi.org/10.3390/rs12213553
How to cite: Laviola, S., Vermi, F., Guarascio, M., Monte, G., Folino, G., and Levizzani, V.: The Multi-sensor Approach for Satellite Hail Advection (MASHA): a new technique for nowcasting applications, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-571, https://doi.org/10.5194/ems2022-571, 2022.
Windstorms associated with extratropical cyclones are destructive natural hazards due to the resulting surface gusts mostly. Processes governing the formation of gusts are thus crucial for the societal impact of windstorms but are not well understood and too small scale to be explicitly represented in numerical weather prediction models. The ANR project WINDGUST aims to better understand the formation of wind gusts through innovative numerical simulations with the Meso-NH atmospheric model. Accurately modeling windstorms requires high resolution over a large domain to represent both fine-scale structures and mesoscale dynamics, as well as advanced physical parameterizations and coupling with surface models to capture complex interactions. Running such simulations is a computational challenge, as is the analysis of the resulting deluge of data.
First results are presented for the intense Mediterranean cyclone Adrian that hit Corsica on 29 October 2018 with gusts above 50 m/s and extended damages. While state-of-the-art meteorological simulations with kilometer-scale resolution are able to develop strong mesoscale winds associated with the cold conveyor belt of the cyclone, large-eddy simulations with hectometer-scale resolution are necessary to explicitly represent their boundary-layer organization. The latter reveal the presence of coherent structures as convective rolls align with the main wind direction over the warm Mediterranean sea. Lagrangian tracers computed online during the model integration highlight their crucial role in the downward transport of momentum to the surface. The characteristics of convective rolls depend on the horizontal grid spacing but are also strongly sensitive to the representation of surface fluxes over sea, which are poorly constrained under winds above 20–25 m/s. These results suggest a large uncertainty in numerical weather predictions of surface gusts using kilometer-scale models.
Future work will extend the methodology to Atlantic cyclones involving different mesoscale features and using full atmosphere-wave-ocean coupling in order to accurately represent air-sea interactions under windstorm conditions.
How to cite: Pantillon, F., Lfarh, W., and Chaboureau, J.-P.: Understanding processes leading to surface gusts by modeling windstorms at very high resolution, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-640, https://doi.org/10.5194/ems2022-640, 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.
Natural and artificial tornadoes take a special form called “multiple vortex structure” where two or more secondary vortices revolve around the eye of their parent vortex. The structure is seen when the Swirl ratio, one of the three dimensionless number governing the system, is larger than 0.3 – 0.4. In the meanwhile, around the lower limit of the Swirl ratio to give the multiple vortex structure, two secondary vortices intertwined with each other emerge to form double helical configuration similar to DNA. We reproduced such a vortex in our laboratory tornado simulator at National Defense Academy (NDA), Japan, and attempt to reveal its structure through a velocity measurement.
For this purpose, we adopt a Sonic anemometer-thermometer (SAT) developed for indoor use (Sonic/Kaijo WA-790) instead of conventional anemometers for laboratory experiments, i.e. Pitot tube, Hot-wire anemometer, Laser Doppler Velocimetry (LDV), etc. This is because wind field in the interior of the vortex is unsettled especially when two or more secondary vortices are embedded within it, and then it is geometrically impossible to measure the wind from the rear side of the probe by Pitot tube, even omnidirectional one, and a Hot-wire anemometer, even two- or three-dimensional one, respectively. Similarly, it is difficult to provide optical axes for LDV in a tornado simulator with complex configuration. In this respect, the indoor SAT is desirable since it can measure three components of wind velocity without any restrictions on wind direction. The probe of WA-790 consists of three set of transduces disposed 3 cm apart with each other. Thus, although the probe is considerably larger than that for Pitot tube or Hot-wire anemometer, it is sufficiently small compared with the size of the vortex spawned in our tornado simulator at NDA, which is about 20 cm in diameter.
We carried out velocity measurement at 87 points on the vertical cross section of 40 cm high and 30 cm wide set in the vortex simulator, which has concentric cylindrical structure with inner diameter of 130 cm and outer diameter of 190 cm, respectively and the height of 95 cm. Each run of velocity measurement is set to be 4 min long and three component of wind speed is obtained with 10 Hz. While the parent vortex is revealed on the average wind fields, the structure of the secondary vortices and their momentum transport are examined statistically on the variance and covariance fields, respectively. In addition, to show their time variations, the spectra and co-spectra of wind speed are also investigated.
How to cite: Itano, T. and Okuyama, K.: Measurement of a Vortex with Double-Helical Structure in a Laboratory Tornado Simulator by Sonic Anemometer-Thermometer, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-264, https://doi.org/10.5194/ems2022-264, 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.
