With increasing computer power, operational forecast systems have begun to resolve convective scales, yet many hazards are still sub-grid scale phenomena relying on crude parameterizations. However, the promising horizon uncovered by Artificial Intelligence (AI) techniques suggests fruitful synergies between classical computational models and AI to improve severe weather phenomena forecasts.
Furthermore, as our climate changes, certain hazards are likely to become more common and as such an in-depth understanding of how climate change impacts atmospheric hazards is needed.
This session welcomes contributions which increase our understanding of mesoscale and microscale atmospheric processes that might represent a hazard for people, property and the environment. Studies devoted to enhancing our physical and dynamical understanding of severe weather phenomena and their hazards are of particular interest as are contributions incorporating conceptual, observational and modelling research.
Topics of interest include but are not limited to:
1. Deep convection and related hazards: hail, lightning, tornadoes, waterspouts, derechos and downbursts.
2. Mesoscale cyclones (polar lows, medicanes, tropical-like cyclones, mediterranean cyclones) and related hazards: Flash-floods and heavy rain events, strong winds, floods etc.
3. Orographic flows and related hazards: severe gap, barrier, katabatic and foehn winds
4. Cold season hazards: Freezing rain, icing, intense snow falls, cold extremes, fog
5. Warm season hazards: severe droughts, heatwaves
The U.S. National Weather Service’s (NWS) mission includes providing impact-based decision support services to its core partners for the protection of lives and property. Unlike traditional forecast products that focus on accumulation totals, the NWS Weather Prediction Center (WPC) has developed a cutting-edge impact-based winter forecast product – the Winter Storm Severity Index (WSSI) – to translate the forecast to societal impacts. The WSSI is a suite of operational forecast tools that integrates the official gridded forecast data with climatological and non-meteorological information to create a situational awareness forecast tool that communicates the location, timing, and severity of anticipated societal impacts from winter hazards.
WSSI classifies impacts for several winter hazards including snow and ice accumulations, blowing snow, and snow load. Impacts captured in the WSSI include but are not limited to: impacts to transportation, damages to vegetation, disruptions to utilities, damages to infrastructure, and the overall disruption to daily life. Accurate depiction of the societal impacts from winter weather hazards involves an interdisciplinary approach that leverages not only improved forecasts but new and existing research on how winter hazards interact with the natural and built environment. This presentation will include: 1) An overview of the WSSI suite of products (i.e., the deterministic and probabilistic operational versions, in addition to hourly and probabilistic hourly products in development), 2) Examples of how the WSSI scientists and developers approach improvements and additions to the underlying algorithms, and 3) Examples of how the WSSI product is used in the field with forecasters and core partners.
How to cite: Tobin, D., Harnos, K., and Nelson, J.: The Winter Storm Severity Index: Forecasting and Communicating Winter Hazards, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-243, https://doi.org/10.5194/ems2026-243, 2026.
Impact models for various high-risk weather phenomena can be run as downstream models using output of a numerical weather prediction (NWP) model. Using specialised algorithms, the impact models can further refine risk level estimates of potentially dangerous weather phenomena, of which frost events are a prime example. These events, occurring in Europe mostly in April and May, can cause significant damage in the agricultural sector. The vegetation is often activated during a transient period of relatively high temperatures in early spring and then can be heavily damaged during subsequent periods of cold air advection. The impact can be further enhanced by radiative cooling and cold-air pooling at night-time. The damage can be reduced by a multitude of measures, provided a reliable forecast with sufficient spatial resolution is available. Here, we present a new frost events impact model. The impact model estimates real-time development phase of several crops using up-to-date air temperature sums. These phases can be linked to specific temperature thresholds for frost damage. The thresholds are then compared with a forecast of minimum air temperature from a hectometric-scale configuration of an NWP model. This workflow was tested during two frost events – a pronounced event in April 2024 in Spain and a recent April 2025 event in the Czech Republic. The performance of three NWP models was evaluated using minimum air temperature measurements by the Czech Hydrometeorological Institute and the Spanish meteorological service AEMET. While the models agreed well with observations at some cases, elevated bias was found at some regions. Using Meteosat Third Generation imagery, it was revealed that the bias can be partly attributed to overestimated low-level cloud cover. In addition, despite very-high horizontal resolution of the NWP models, some small-scale temperature variability seems to remain unresolved and can also contribute to model inaccuracies. To minimize the bias, the best performing models were identified and are suggested for operational use. The newly developed method can also be used to evaluate historical climatological data to determine areas with elevated frost risk for agriculture.
How to cite: Matějka, M., Hájková, L., Možný, M., Musilová, A., and Vlach, V.: Design and validation of a risk assessment model for vegetation frost damage, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-294, https://doi.org/10.5194/ems2026-294, 2026.
As AI-based weather models are rapidly closing the gap with physics-based systems in average forecast skill, their ability to predict extreme events (where accurate process representation may be critical) remains an open question. AIFS (ECMWF's global artificial intelligence model) has shown clear improvements in average forecast skill for synoptic conditions and surface variables compared to ECMWF's physics-based IFS. But do these improvements extend to the prediction of extreme events? Or do high horizontal resolution and the explicit representation of physical processes remain more important factors for accurately predicting severe events? This presentation addresses these questions by comparing AIFS and the experimental IFS at 4.4 km horizontal resolution in their ability to predict extremes of 24-hour accumulated precipitation, 10 m wind speed, and 2 m temperature over the extratropical regions of the Northern Hemisphere compared to the current operational IFS 9 km resolution model. AIFS-ENS (AIFS ensemble) is also compared against the operational IFS-ENS to evaluate their performance in predicting extreme events in a probabilistic framework.The analysis focuses on the added value and limitations of each system, with the aim of guiding users on which approach offers better performance under different extreme scenarios, and how AI-based and physics-based forecasts can complement each other.
The evaluation uses the new "scorecards for extremes" framework to quantify the skill of the high-resolution IFS and the AIFS in predicting severe events, benchmarked against operational IFS forecasts at 9 km (model cycle 49r1). It allows to define extremes using both percentile-based thresholds derived from the SYNOP station climatology or fixed absolute thresholds, enabling the identification of events that are climatologically rare as well as those with high societal impact. Forecast skill is assessed across multiple lead times, seasons, and orographic complexities (flat and mountainous terrain). Selected case studies illustrate model behaviour during specific extreme events.
How to cite: Gascón, E., Haiden, T., and Vannière, B.: Forecasting extremes: what AIFS and the physics-based km-scale global IFS model can (and can’t) do , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-212, https://doi.org/10.5194/ems2026-212, 2026.
Understanding and forecasting severe weather events requires atmospheric models that not only reliably predict the circulation on the planetary and synoptic scales but also adequately represent meso- and micro-scale processes. Within extratropical cyclones, for instance, severe surface winds can occur along the bent-back extension of the warm front. In many cases, these are accompanied by a short-lived meso-scale vigorous jet, the so-called sting jet, whose underlying processes are an active subject of research. Much of the cyclone-induced heavy precipitation, in turn, is attributable to the frontal zones outside of the cyclone center. While a grid spacing of a few kilometers is typically sufficient to capture the occurrence of such events, models at higher resolution are needed to pinpoint the most disruptive wind gusts and precipitation peaks.
The Portable Model for multi-scale Atmospheric Prediction (PMAP) is a flexible physics-based framework that supports horizontal grid spacings from a few kilometers down to tens of meters. This Python-based model is currently under active development at ECMWF, ETH Zürich, and CSCS. PMAP solves the non-hydrostatic and fully compressible equations using a bespoke finite-volume, 3D semi-implicit dynamical core coupled to state-of-the-art physical parametrizations. The key strengths of the model are its portability across diverse hardware architectures and its high computational performance, both enabled by the programming implementation with the GridTools for Python (GT4Py) domain-specific library.
To evaluate the model representation of a real weather event, we simulate the passage of cyclone Goretti along the English Channel on 8 January 2026. This storm serves as a prime case study since Goretti induced strong wind gusts over the UK and France associated with a sting jet, as well as disruptive snow accumulation in Northern Germany. Forced by kilometer-scale DestinE forecasts at the lateral boundaries, PMAP is run over a regional domain at hectometer scale including nested large-eddy simulations. We benchmark PMAP using near-surface wind observations in the vicinity of the sting jet at the southern coast of the UK, and radar data covering Northern Germany. Beyond model evaluation, we leverage these simulations to investigate sting jet formation mechanisms and the structure of frontal precipitation at high spatial and temporal resolution.
How to cite: Zibell, J., Papritz, L., Krieger, N., Kühnlein, C., Ehrengruber, T., Faghih-Naini, S., Ubbiali, S., Vollenweider, G., and Wernli, H.: Investigating synoptic- and meso-scale severe weather phenomena using PMAP at sub-kilometer resolution, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-401, https://doi.org/10.5194/ems2026-401, 2026.
Approximation of vertical turbulence depends on the ability of the forecast model to resolve as many vertical levels as possible. There might be times when forecast inaccuracy relies on a certain atmospheric level, especially, under complex terrain cases such as orographic flows. Therefore, this is the point when vertical resolution becomes vital in predicting phenomena such as turbulence, convective transport and clouds that are partially resolved otherwise by coarser vertical resolution.
It is here investigated how by altering the number of vertical levels at specific altitudes above earth surface impacts forecast accuracy. The global non-hydrostatic Conformal Cubic Atmospheric Model (CCAM) is used, with a global stretched grid targeting high resolution over part of North Greece (1-km) and the wider region (8-60-km). The grid configuration in CCAM allows the placement of a high-resolution face centred over the domain of interest with gradually reduced resolution away from this region. This more seamless grid configuration allows coupling between the global and regional spatial scales on the same grid and may provide benefits for the representation of storms as they approach the domain of interest. CCAM offers the ability of selecting different number of vertical levels ranging between 0-43,900m from surface.
The importance of vertical levels resolution was made obvious during an extreme rainfall event in Northern Greece when coarse approximation failed to predict the heavy rain event but only became evident when the number of vertical levels was improved. The area is characterized by complex morphology with orographic flows adjacent to sea breezes from Aegean Sea. In the first instance 27 vertical levels (0-35,000m) were selected underestimating intense rainfall of the following three days. On the contrary, the 54 vertical levels (0-43,700m) run predicted efficiently the rain event due to finer vertical resolution at certain altitudes. This was proved by comparing daily and hourly rain levels by differing forecast intervals before the event.
Acknowledgements: Marcus Thatcher,, Commonwealth Scientific and Industrial Research Organisation (CSIRO) Oceans and Atmosphere, Aspendale, VIC, Australia
How to cite: Zoras, S.: Understanding the importance of vertical levels resolution in forecasting an orographic heavy rainfall event, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-770, https://doi.org/10.5194/ems2026-770, 2026.
This study analyzed the integration of Local Climate Zones (LCZ) with machine learning (ML) has advanced urban heat research and quantifying intra-zonal heatwave variability within built LCZ classes (1–10). However, most studies remain constrained to single-city analyses and treat LCZ classes as internally homogeneous. Heatwave events (2013–2025) were identified from the weather stations data through applying a WMO definition for Bari and KNMI local criteria for Amsterdam. LCZ maps for 2025 were derived at 500 m resolution, while morphological subclasses were defined at 100 m and aggregated to capture intra-zonal heterogeneity using a WUDAPT-GIS approach. For Amsterdam, quantify intra-zonal thermal heterogeneity in built-up LCZ classes, parameterized by key parameters like built-up index, impervious surface fraction, and surface albedo. In parallel, the heatwave-driven thermal behaviour and relative UHI dominance of natural LCZ subclasses were explicitly analysed to capture vegetation-mediated cooling dynamics and their interaction with urban heat island and heatwaves events. A Random Forest model estimated LCZ-specific land surface temperature (LST) during heatwaves, with SHAP analysis used to quantify predictor importance.
We find that model performance is robust (R² = 0.79 to 0.85) in different LCZs during heatwaves events in the both cities. In Bari, LCZ-based modelling demonstrates strong performance (R² ≈ 0.81; RMSE ≈ 2.0 °C), with compact classes showing pronounced heat storage and nocturnal heat retention. In Amsterdam, the inclusion of LCZ subclasses reveals additional intra-zonal variability (up to 0.7–2 °C) and alters the spatial extent of high-temperature areas (15% to 20%). These results indicate that LCZ classes are not thermally homogeneous and that incorporating intra-zonal morphological detail improves to understand the heatwave hazards in built-up areas and spatial heat-risk assessment.
How to cite: Nadeem, F., Milošević, D., Berardi, U., and Steeneveld, G.-J.: Quantifying Intra-Zonal Heatwave Variability Using Local Climate Zone Subclasses and Machine Learning: A Cross-Climate Study of Bari (Italy) and Amsterdam (Netherlands), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-662, https://doi.org/10.5194/ems2026-662, 2026.
The rapid urbanization occurring in the Maritime Continent—an expansive geographical domain encompassing Southeast Asia and Oceania—has intensified the exposure of population centers to extreme heat. However, the seasonal drivers and physiological consequences of this thermal stress exhibit spatial heterogeneity. This study investigates the seasonal variation of 2-m air temperature and human biometeorological indices across three major Indonesian cities—Medan, Jakarta, and Surabaya—representing diverse geographical conditions, monsoon onsets, and other climatological regimes.
Meteorological surface observations reveal a distinct bimodal temperature distribution in all three cities, with peak heat stress intensities typically occurring during the monsoonal transition periods in April and October. However, the magnitude and primary peak month vary substantially due to regional climatological influences. In Medan (Northern Sumatra), April is identified as the hottest month, where high ambient temperatures are exacerbated by high specific humidity. In contrast, Surabaya (East Java) experiences its maximum thermal intensity in October, coinciding with the late dry season's clear-sky conditions. Meanwhile, Jakarta (Western Java) exhibits nearly symmetrical peaks during both transitional periods, reflecting its intermediate position relative to the shifting monsoonal air masses.
To quantify the localized impact, canyon layer Urban Heat Island (UHI) intensities are estimated by comparing urban-rural station pairs. Furthermore, human thermal comfort is evaluated using two robust biometeorological indices: the Universal Thermal Climate Index (UTCI) and the Physiologically Equivalent Temperature (PET). By employing both indices, the study estimates day and night-time heat stress levels and the resulting physiological strain on the urban population. Preliminary results indicate that high humidity levels in cities like Medan and Jakarta substantially sustain elevated UTCI and PET values into the night, effectively hindering nocturnal cooling. These findings underscore the importance of incorporating accurate meteorological factors for localized heat-health warning systems and urban planning to effectively mitigate the escalating risks of heat-related disasters in tropical environments.
How to cite: Subasita, N., Ferdiansyah, M. R., Milošević, D., and Steeneveld, G.-J.: Understanding of the Atmospheric Dynamics of Extreme Heat Events in Tropical Cities of Indonesia: An Analysis from Observation Networks in Medan, Jakarta, and Surabaya, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-531, https://doi.org/10.5194/ems2026-531, 2026.
Extreme precipitation in Northeast China is often influenced by the Northeast China Cold Vortex (NCCV), yet the microphysical processes operating within such systems remain poorly characterized. In this study, we investigate an NCCV-associated squall line that occurred over Liaoning Province by integrating S-band polarimetric radar with ground-based disdrometer measurements. The analysis focuses on raindrop size distribution (DSD) characteristics and three-dimensional microphysical structure in both convective and stratiform regimes. A comparison is also performed between this squall line and a Mei-yu frontal event, with emphasis on DSD differences and the underlying mechanisms. Observations indicate that convective precipitation within the NCCV squall line exhibits a continental-type DSD, marked by relatively low drop concentrations but larger raindrops when compared with other heavy rainfall regimes across China. In contrast, the Mei-yu frontal convection displays a transitional DSD, falling between maritime and continental types, characterized by smaller but more numerous raindrops. Vertically, the mature squall line features well-defined columns of differential reflectivity (ZDR) and specific differential phase (KDP) extending above the melting level within convective regions, signaling vigorous riming growth of graupel and hail sustained by strong updrafts. Meanwhile, the stratiform region is dominated by ice crystals and aggregates, formed primarily through deposition and aggregation. As these ice-phase particles melt, subsequent collision-coalescence and evaporation-driven size sorting collectively shape the observed surface DSD, which is large in size yet sparse in number. In contrast to the Mei-yu frontal system, the NCCV squall line develops under drier and more unstable atmospheric conditions that favor deep convection and active ice-phase microphysics. The Mei-yu environment, by contrast, is relatively moist and stable, promoting shallower convection where warm-rain processes prevail. These differences in thermodynamic settings directly account for the distinct DSD signatures observed between the two systems. Future research involving multi-case analyses with integrated observational datasets will be essential to quantitatively assess how environmental and aerosol factors modulate these heavy precipitation events.
How to cite: liu, L.: Microphysical Characteristics of a Northeast China Cold Vortex Squall Line and Its Contrast with Meiyu-Front Precipitation: A Polarimetric Radar and Disdrometer Study, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-129, https://doi.org/10.5194/ems2026-129, 2026.
In this paper, conventional observation data, Fengyun-4 satellite, wind profile radar, S-band dual-polarization Doppler radar, lightning positioning system data, automatic weather stations combined with radar four-dimensional variational assimilation system are used to comprehensively analyze a supercell storm in Beijing on 4 June, 2022. The results show that this supercell is triggered by a cold front. Based on the fact that the observation data has the forecasting potential for the occurrence of severe convective weather, the triggering mechanism and development process of supercell is analyzed by using multi-source data. Vertical wind shear and the change of warm and cold advection in vertical direction based on wind profile radar can help forecasters understand the change characteristics of mesoscale environment before the occurrence of strong convection.S-band dual polarization radar can effectively identify the types and characteristics of severe convection. The simulation of different stages of the occurrence and development of supercell by Variational Doppler Radar Analysis System (VDRAS for short) shows that the collision between thunderstorm outflow and ambient warm and humid air leads to the rapid development of supercell and the right shift feature.The analysis of vdras data can effectively judge the characteristics of thunderstorm structure and development stage, which provides an important reference for the future occurrence and development trend of thunderstorm. The increase time of lightning frequency detected by lightning positioning system is advanced to the occurrence time of severe convective weather, with the advance is 7 min to 58min. The joint application of the above multi-source data has a good guiding significance for the nowcasting and early warning of sudden severe convective weather.
How to cite: He, N., Yang, Y., Tian, Y., and Liu, K.: Integrated Analysis of Multi-Source Observations during a Supercell Storm Event in Beijing, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-221, https://doi.org/10.5194/ems2026-221, 2026.
Understanding the spatial organization and transitions of convective cloud systems over complex terrain remains a major challenge for both observation-based analysis and numerical weather prediction (NWP). This limitation affects our ability to accurately represent and predict extreme precipitation and hail events, which are expected to intensify over the Alpine region under climate change.
In this work, we present a data-driven framework based on self-supervised learning (SSL) to characterize convective cloud structures and their transitions using long-term geostationary satellite data records. By learning representations directly from infrared imagery without manual labeling, the model organizes cloud scenes based on their spatial and morphological similarities, providing a physically interpretable feature space of mesoscale cloud features. This learned representation is exploited in two complementary ways.
First, it enables the analysis of convective cloud structures and their transitions associated with severe precipitation events by identifying dominant pathways linked to intense rainfall and hail over the Alpine region. The most severe precipitation is predominantly observed either in association with long-lasting deep convective systems or during transitions from early-stage convection to a more mature regime, highlighting two key modes linked to extreme precipitation.
Second, the framework provides a novel tool for evaluating convection-permitting NWP simulations. By projecting synthetic satellite channels into the same feature space, it enables a direct comparison between modeled and observed cloud structures, both in terms of continuous embeddings and discrete cloud classes. Preliminary results indicate that ICON tends to overestimate the spatial extent of convective cells during their early development stages, suggesting biases in the representation of convective initiation and growth.
Overall, this approach demonstrates the potential of self-supervised learning to bridge satellite observations and model simulations, providing a flexible framework for studying convective processes and assessing their representation in high-resolution weather models over complex terrain.
How to cite: Corradini, D., Cattani, E., Chatterjee, D., and Acquistapace, C.: Learning Convective Cloud Structures from Satellite Data for Severe Weather Analysis and Model Evaluation over the Alps, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-176, https://doi.org/10.5194/ems2026-176, 2026.
The deployment of the Lightning Imager (LI) aboard Europe’s Meteosat Third Generation (MTG) satellite has opened new opportunities for monitoring mixed convection over tropical–subtropical transitional maritime zones. The Red Sea experiences localized severe winter convection from November to April annually, characterized by short-duration thunderstorms and strong winds concentrated in its northern and central basins. This winter convection substantially affects maritime safety, coastal infrastructure, and regional climate regulation [1]. Frequent lightning with low precipitation efficiency (including dry thunderstorms) and frequent dust activity typify this region [2]. Focusing on winter convective systems in the Red Sea climatic transition zone (12°N–28°N), this study develops a specialized identification and tracking algorithm integrating MTG-LI lightning observations with cloud parameters from the MTG Flexible Combined Imager (FCI).
Key convective parameters are examined: Convective Available Potential Energy (CAPE), cloud-top temperature (CTT), cloud-top height, lightning frequency, and cloud-top cooling rate (CTC)—the latter quantifying CTT change over 10–30-minute intervals to capture convective development dynamics. For storm tracking, we establish nonlinear response thresholds for the lightning frequency–CTC relationship specific to the Red Sea transition zone, constructing a multi-threshold collaborative model. Convective storms are identified when CAPE > 1000 J kg⁻¹, CTT < −50°C, and lightning frequency exceeds 10 flashes per 10 minutes. We then integrate optical flow with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for dynamic storm cell tracking. Optical flow computes displacement vectors of cloud-top features between consecutive time steps to derive storm motion vectors, while DBSCAN clusters spatially dense lightning regions to pinpoint storm core locations and extents.
This study establishes a winter convective identification algorithm for the Red Sea, advancing methodologies for tropical–subtropical transition zones. The multi‑parameter fusion approach reduces false‑alarm rates inherent in single‑indicator systems, with transferable applications for other global transition seas.
Acknowledgment
This work was jointly supported by the Open Project of Key Laboratory of Intelligent Meteorological Observation Technology of China Meteorological Administration under Grant ZNGC2025MS26, and the Science Funds of Changdao National Climatic Observatory under Grant 2025cdkfz05.
References
[1] Almazroui M. Climatology of the Red Sea tropical cyclones from 1975 to 2020[J]. International Journal of Climatology, 2021, 41(8): 4039-4054.
[2] Virts K S, Wallace J M. Seasonal and regional variations in the lightning diurnal cycle over the Red Sea[J]. Journal of Geophysical Research: Atmospheres, 2020, 125(8): e2019JD032005.
How to cite: Xu, M., Fullekrug, M., Qie, X., Gu, C., Liang, L., Sun, H., Gao, C., Wang, J., and Qin, S.: Winter Convective Identification and Tracking in the Red Sea: An Algorithm Based on MTG-LI Lightning Observations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-799, https://doi.org/10.5194/ems2026-799, 2026.
Mesoscale convective systems (MCSs) experience complex changes in intensity as they move from land to the ocean. Predicting the offshore evolution of these systems poses a significant forecasting challenge. Using radar observations and ERA5 reanalysis data from the warm seasons of 2016–2018, this study investigates the characteristics and environmental controlling factors of land-to-sea (LS) MCSs over the Yangtze-Huai River Basin (YHRB). These systems are classified into offshore-intensifying and offshore-weakening types, accounting for approximately 40% and 60% of the total cases, respectively. Spatiotemporally, intensifying MCSs predominantly occur in spring (April–May; 56%) at lower latitudes, whereas weakening MCSs peak in summer (June–July; 45%) at higher latitudes. Composite analysis reveals that intensifying MCSs occur in environments with weaker thermodynamic instability but stronger dynamic forcing. Specifically, most unstable convective available potential energy (MUCAPE) is ~43% lower with reduced total column water (TCW), yet they are characterized by enhanced upper- and lower-level jets and greater vertical wind shear (VWS). A critical factor facilitating intensification is the cooler marine atmospheric boundary layer (MABL), which promotes enhanced isentropic lifting and lowers the lifting condensation level (LCL) via increased relative humidity. Crucially, offshore intensity evolution is governed by environmental change rather than absolute coastal conditions. Weakening MCSs originate in thermodynamically and dynamically superior environments over land but undergo considerable deterioration in thermodynamic and moisture conditions (MUCAPE drops ~47%, TCW decreases) upon moving offshore. Conversely, intensifying MCSs experience modest MUCAPE reduction (~26%) but benefit from significant enhancement of dynamic conditions (low-level VWS strengthening) and moisture enhancement (TCW increases in 60% of cases) during transit, enabling their intensification over the ocean despite lower absolute instability at the coast. These findings provide crucial insights into how coastal environments alter storm intensity, and offer practical insights to improve offshore weather forecasting.
How to cite: Wei, Y. and Xu, X.: Offshore Transition of Mesoscale Convective Systems over the Yangtze-Huai River Basin: Intensity Change and Environmental Controls, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-121, https://doi.org/10.5194/ems2026-121, 2026.
Extreme convective events are increasingly challenging aviation operations, particularly in vulnerable regions such as the central Mediterranean. This study investigates the atmospheric conditions associated with the severe late-autumn thunderstorm that disrupted operations at Rome–Fiumicino Airport on 13 December 2024. Using a conditional analogue-based framework applied to ERA5 reanalysis data, we identify past events characterized by similar synoptic and mesoscale circulation patterns over the satellite era (1979–present), and compare their associated environmental conditions between a historical baseline (1979–2000) and a recent period (2002–2023).
Our results suggest that the large-scale dynamical structure of circulation patterns has broadly remained unchanged over time. However, the thermodynamic background has undergone significant changes, marked by increased near-surface air and warmer sea surface temperatures across the Mediterranean basin. These shifts favor low-level moisture availability and atmospheric instability, leading to more favorable conditions for intense convection. Correspondingly, similar events in the recent period are associated with increased precipitation, stronger wind shear, and higher convective potential. Furthermore, observations at Rome–Fiumicino Airport reveal statistically significant increases in mean wind speed and maximum wind gusts, along with higher dew-point temperatures and reduced visibility in recent decades. These factors are critical for aviation safety, as they can affect aircraft handling and increase the likelihood of operational disruptions. Finally, the absence of significant changes in large-scale climate variability modes suggests that these trends are primarily driven by gradual thermodynamic warming rather than internal variability.
Overall, the findings support the hypothesis that climate change is amplifying the impacts of convective events by intensifying the environmental conditions in which they develop, even when the underlying circulation patterns remain similar. This has important implications for aviation risk assessment and for understanding the evolving nature of late-season convection in the Mediterranean region.
Acknowledgements
This research has been carried out with funding from Ministero dell'Università e della Ricerca under the call Fondo Italiano per la Scienza 2022-2023 (FIS-2) for the project "Mediterranean Extreme Events and Tipping elements in a changing climate on multiple spatiotemporal scales", grant number FIS-2023-00159, CUP: D53C24005450001.
How to cite: Alberti, T. and Faranda, D.: Mediterranean warming amplified the convective environment during the December 2024 severe thunderstorm at Rome-Fiumicino Airport, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-125, https://doi.org/10.5194/ems2026-125, 2026.
Bow echoes, identifiable by their distinct bow-like shape on radar, are often associated with intense specific convective processes. The T-initiation Mesoscale Convective System (MCS) exemplifies this phenomenon, appearing as a quasi-linear bow echo with downstream nascent leading convection (LC), forming a characteristic 'T' shape on radar displays. The LC presents significant challenges for operational forecasts and can lead to severe weather conditions, including damaging straight-line winds and extreme precipitation, particularly when they rapidly merge with the parent bow echoes. Focusing on a high-impact T-initiation MCS over the Bohai Sea in China, this study investigates the underlying physical mechanisms governing LC initiation in a coastal environment. Utilizing high-resolution, cloud-resolving simulations from the WRF-ARW model, backward trajectory tracking within a Lagrangian framework and detailed vertical momentum budget analysis are employed to explore the dynamic factors driving the uplift of air parcels within the LC cells from their pre-convective state. Results reveal that a mid-level mesoscale anticyclonic vortex, associated with the bow echo’s upper-level outflow, significantly modulated the local environmental wind field. This resulted in stronger vertical wind shear between 2.5–5 km AGL downstream of the bow-echo. Interactions between this locally enhanced shear and pre-existing vertical motion led to low perturbation pressures at midlevels. This effect subsequently generated an upward-directed nonhydrostatic vertical perturbation pressure gradient force that aided parcel ascent, directly contributing to the initiation of new LC cells. Thus, this study helps elucidate the complex self-organizing and sustaining processes of bow echoes by identifying a novel, dynamically driven initiation mechanism for LC cells in a T-initiation MCS.
How to cite: Huang, L., Zhang, S., and Xu, X.: Dynamics Governing a T-initiation Mesoscale Convective System, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-166, https://doi.org/10.5194/ems2026-166, 2026.
Intense extratropical cyclones contain a range of mesoscale features capable of producing hazardous surface wind gusts. Well‑established examples include the cold conveyor belt, the evocatively named sting jet, and lines of locally enhanced convection along narrow cold frontal zones. These features are now routinely represented in convection‑permitting numerical weather prediction models, whose horizontal resolution approaches kilometre scale, and have been extensively studied using both idealised simulations and case‑study analyses.
While such models are commonly used in research to investigate storm structure and underlying physical mechanisms, operational meteorologists are uniquely placed to identify emerging or poorly documented features through continual real‑time interrogation of high‑resolution forecasts. In this study, a previously under‑recognised wind‑producing feature is identified within operational forecasts from the UKV, the Met Office’s kilometre‑scale limited area model, during Shapiro–Keyser type extratropical cyclones.
This feature occurs within a region of the cyclone traditionally considered to be of relatively low wind hazard, and is therefore not typically a focus of operational attention. Enhanced surface gusts associated with this feature may come as a surprise, particularly as it is not well represented in coarser‑resolution global models. As a result, there is potential for mistimed wind warnings and insufficient preparedness if reliance is placed solely on conceptual models or lower‑resolution guidance. Evidence for the existence of this feature is supported by surface and near‑surface observations during recent high‑impact windstorms.
The identification of this feature, together with subsequent investigation of its dynamical mechanisms, frequency of occurrence and predictability, has clear potential to improve the timing and targeting of wind warnings for future storms. More broadly, this work highlights the value of operational experience in complementing research‑driven model analysis and advancing understanding of high‑impact weather.
How to cite: Ramsdale, S., Flack, D., Ackerley, D., Volonte, A., and Gray, S.: The Shallow Convective Zone – enhanced gusts in operational UKV forecasts of developing mid latitude windstorms., EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-700, https://doi.org/10.5194/ems2026-700, 2026.
Convective wind gusts, driven by the intense downdrafts and cold pool dynamics of thunderstorm systems, represent a significant hazard, especially in the warm part of the year. Yet, their climatological characteristics and response to a warming atmosphere remain scarcely investigated and poorly understood.
The modeling framework used here is HARMONIE-Climate (HCLIM) with two nested grids. The EC-Earth2 global climate model provides initial and boundary data to the hydrostatic HCLIM configuration with 12 km horizontal grid spacing, and the 12 km run is then further downscaled using a non-hydrostatic convection permitting HCLIM configuration with 3 km horizontal grid spacing. For the analysis, the historical (1996 - 2005) period and the end-of-century (2090 - 2099) period for the RCP8.5 scenario are used for the broader Croatian domain.
The convective wind gusts are detected using a two-step filter. First, the presence of convective activity using a cloud condensed water path and ice water path is ensured. After that, the maximum wind speed and wind gust are identified from the model output in the vicinity of the convective cloud.
The described approach performs well in detecting convective wind gusts in a convection-permitting climate model output. While HCLIM shows an increase of extreme convective precipitation by around 18% from the historical to future climate, the projected change in convective wind gust intensity is negligible. Further, the role and intensity of cold pools in extreme convective wind gust events are investigated, and whether they could be used as a proxy for the detection of such events. Finally, the connection to large-scale dynamics will be discussed.
How to cite: Jureša, P. and Belušić, D.: No sign of convective wind gust change despite stronger convective storms in future climate, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-521, https://doi.org/10.5194/ems2026-521, 2026.
This talk will focus on the devastating small-scale cyclonic windstorm “Kristin” that hit central Portugal in the early hours of 28th January 2026. This was reportedly the most costly European windstorm since “Lothar” hit France 26 years ago.
After describing the dynamical setting, including critical juxtaposition of an upper level jet and the surface feature, and the storm‘s morphology, including strong radar-based evidence of a sting jet, the surface impacts in both Portugal and Spain will be highlighted. These included 15 fatalities, Portugal’s biggest ever power outage, with over 5000 km of power lines damaged or destroyed, 15 million trees uprooted or snapped and an estimated €7.2 billion in losses in Iberia.
Next the handling by various forecast models, and reanalysis products, will be elaborated. Due in part to Kristin’s small size there was a strong link between the accuracy of model output and model spatial resolution. But even at higher resolution other problems are apparent, such as the generation of some unrealistic initial conditions in mean sea level pressure via the ECMWF EDA (ensemble of data assimilations). Also there was high uncertainty in cyclone track, such that red warnings issued by the Portuguese Met Service (IPMA) initially covered an area much larger than the area eventually impacted.
Machine learning model output, from ECMWF’s AIFS systems, will be included in discussion, highlighting the extra challenges of using such systems. These include temporal discretization (currently 6h), lack of a gust diagnostic and the potential for noisy output.
The practical implications of all these issues, for forecasting in a life-critical operational setting, will be discussed.
Similarities between storm Kristin and other notable small-scale European windstorms, such as Xola from 2009, will also be touched on.
How to cite: Hewson, T., Prates, F., Gascón, E., Vanniere, B., and Pinto, P.: Extreme Windstorm “Kristin”: Representation across the model spectrum, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-745, https://doi.org/10.5194/ems2026-745, 2026.
On 28 January 2026, a rapidly intensifying cyclone named “Kristin” struck mainland Portugal, causing widespread damage, particularly in the central region of the country. Extreme wind gusts associated with the storm’s sting jet destroyed houses and industrial facilities, caused at least 13 fatalities, damaged key electrical and communication infrastructures, and severely impacted the national economy. This event ranks among the most extreme ever recorded in mainland Portugal in terms of wind gusts, setting new records that exceeded 200 km/h at weather observation stations.
A sting jet is a narrow, transient airstream that descends from the cloud head, accelerating towards the surface and generating damaging winds. These events are often challenging to forecast due to their short duration, small spatial scale, and rapid intensification. In this context, we present an overview of different numerical weather prediction (NWP) models to forecast this storm: IFS at 9km resolution (ECMWF’s HRES), IFS at 4.4km resolution (the DestinE initiative’s global Digital Twin), AROME at 2.5km (IPMA’s operational limited area model), and AROME at 500m resolution (the DestinE initiative’s regional component, produced by project DE_330, the so-called Destination Earth on Demand Extremes, or DEODE).
All models capture the rapid deepening of the cyclone, with central pressure decreasing by more than ~10 hPa in approximately 12 hours. However, marked differences are detected in both the timing and magnitude of the minimum pressure. The DEODE simulation predicts the deepest cyclone, whereas AROME estimates a weaker cyclone, not necessarily in terms of deepening rates or wind speeds, but rather in minimum MSLP values at the center of the cyclone.
There is a general agreement among the forecasts regarding wind gust values and the accumulated precipitation pattern associated with both the cloud head and the cold front. However, AROME better captures the location of the observed maximum gusts, whereas both IFS and DEODE predict the extreme winds further north than observed at the weather stations. In contrast, AROME tends to overestimate the accumulated precipitation, while both IFS configurations better represent precipitation amounts over the affected regions.
The intercomparison between these NWP configurations and observations provides valuable insights into the challenges of forecasting extreme events, highlighting the importance of high-resolution modelling to better represent storms such as “Kristin”.
How to cite: Ermitão, T., Pardal, M., Serpa, P., and Belo-Pereira, M.: Forecasting Sting-Jet Storm Kristin over mainland Portugal: A Multi-Resolution and Multi-Model Perspective, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-673, https://doi.org/10.5194/ems2026-673, 2026.
On 29 October 2024, an extreme precipitation event affected eastern Spain, triggered by a cut-off low, with daily rainfall totals exceeding annual climatological values in several locations. The province of Valencia was particularly impacted, where widespread accumulations surpassed 300 mm and local maxima reached up to 771 mm. Given the magnitude of the event, its dynamical and thermodynamical drivers, as well as the potential influence of climate change, have been the subject of extensive investigation. Several media reports and recent studies (Campos et al., 2025) have highlighted the possible role of an upper-level tropospheric moisture plume resembling an atmospheric river (hereafter, AR-like structure) connecting the Mediterranean region with the tropical Atlantic via North Africa, suggesting a potential contribution to the event’s intensification. However, the quantitative contribution of this structure to the observed precipitation remains unclear.
To address this question, we employ the WRF with Water Vapor Tracers (WRF-WVTs) model, which enables tracking moisture from predefined source regions to precipitation while fully resolving the event dynamics. Our results indicate that moisture evaporated from the Mediterranean Sea constitutes the dominant contribution, whereas moisture associated with the AR-like structure accounts for approximately 20-30% of the total precipitation. To further assess the role of this remote moisture transport, we introduce a methodology to quantify its indirect impact via enhanced latent heat release and the associated increase in atmospheric instability. This methodology is further applied to several additional events in the western Mediterranean characterized by the coexistence of a cut-off low and an AR-like structure producing substantial precipitation. Preliminary results indicate that this indirect mechanism is considerably more important than the direct moisture contribution, highlighting the key role of mid-tropospheric moisture supplied by these structures in intensifying such events.
Campos, D. A., Grayson, K., Saurral, R. I., Beyer, S., John, A., Olmo, M., and Doblas-Reyes, F.: The October 2024 Extreme Precipitation Event over Valencia: Storyline Attribution of the Synoptic-Scale Thermodynamic Drivers, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-5929, 2025.
How to cite: Crespo-Otero, A., Insua-Costa, D., and Míguez-Macho, G.: The role of AR-like moisture plumes in the October 2024 Valencia precipitation event and other western Mediterranean extremes, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-523, https://doi.org/10.5194/ems2026-523, 2026.
Thunderstorms with climatologically extreme rainfall rates produced flash flooding in the city of San Diego and adjacent suburban areas to the east during the mid-late morning of 22 January 2024. Local rainfall rates from ALERT and ASOS precipitation gauges between 50 and 75 mm in one hour resulted in rainfall return intervals (based on NOAA Atlas 14) of 500 to 1000 years. This resulted in reported damage to around 1000 homes and businesses, numerous road closures and nearly 200 swift-water rescues. An atmospheric river along and just south of the US-Mexico border, associated with a jet stream which circumnavigated the entire North Pacific had estimated synoptic-scale integrated water vapor transport (IVT) reaching the coast of 400 kg m-1s-1. However, a southerly low-level jet over San Diego with 850 hPa winds of nearly 20 ms-1 and associated with precipitable water of 28-30 mm brought locally higher IVT of over 600 kg m-1s-1. The low-level jet also combined with west to southwest winds of similar speeds at 700 hPa to bring strong shear in the unstable environment. Thunderstorms trained along an approximate west to east line from Coronado through southern San Diego to Spring Valley, maximizing rainfall amounts locally to 100 mm during a 3-hour period.
While details of an extreme precipitation event like this are difficult to predict, a forecaster can detect and communicate the possibility of this occurring. For this event, high-resolution models, including the HRRR and a WRF-ARW model run locally by the National Weather Service in San Diego, showed local hourly rainfall amounts of 50 mm or more in one hour up to 36 hours in advance of the rainfall, though not in the exact location. Ensemble models also showed a high probability of heavy rainfall rates. As a result, forecasters at the National Weather Service Forecast Office in San Diego were able to issue a flood watch the day before the event and produce messaging for emergency managers and other National Weather Service partners mentioning a risk of heavy rain five days before the event.
How to cite: Maxwell, B.: Extreme Rainfall Rates and Flash Flooding in San Diego, California, USA on 22 January 2024, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-255, https://doi.org/10.5194/ems2026-255, 2026.
Accurate precipitation data plays a central role in ensuring the reliability of flood prediction, especially in catchments where observation networks are sparse. When relying on a single measurement source such as rain gauges, weather radar or satellite products surface runoff predictions often become less reliable. This limitation largely arises from topographic effects such as radar beam blockages as well as the inherent constraints of each sensor in capturing extreme rainfall events.
To address these challenges, this study proposes a multisensor precipitation data fusion framework based on the Random Forest machine learning algorithm with the aim of improving flood prediction lead times in the Busan city, South Korea. The approach combines weather radar observations, GPM IMERG satellite estimates, and ground-based gauge data along with topographic information derived from a Digital Elevation Model.
The resulting fused precipitation product was evaluated using a range of statistical metrics and further tested within a hydrological modeling framework. The results show that integrating remote sensing data with ground observations can substantially improve predictive performance. In particular, the Random Forest model demonstrates strong capability in capturing complex nonlinear relationships and in reducing orographic biases, leading to more accurate spatial rainfall estimates.
When applied to hydrological simulations, the multisensor framework enables earlier detection of upstream rainfall signals, which in turn supports longer and more reliable evacuation lead times. This improvement is also reflected in more stable simulations of hydrograph peak timing. Overall, the findings highlight the practical value of multisensor data integration for supporting more informed and timely decision-making in flood risk management, particularly in regions with complex terrain.
Acknowledgments: The research for this paper was carried out under the KICT Research Program (Project no. 20260161–001, Development of Digital Urban Flood Control Technology for the Realization of Flood Safety City) funded by the Ministry of Science and ICT.
How to cite: Nugraha, B., Seong-Sim, Y., and Dong Sop, R.: Multisensor Precipitation Fusion Based on Random Forest for Enhancing Flood Prediction Lead Time, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-437, https://doi.org/10.5194/ems2026-437, 2026.
Wave-flow interactions within tropical cyclones are closely linked to their structural evolution and intensity variability. Using ERA5 reanalysis data and Tropical Regional Assimilation Model for the South China Sea (TRAMS) numerical simulation data, this study adopts wave-flow separation and Fourier decomposition techniques to investigate the interaction between the basic-state vortex and asymmetric disturbance of Typhoon Hato. Cross-scale energy transfer processes are analyzed via energy spectra and energy budget analyses. The results are shown as follows.
Typhoon Hato intensified primarily due to strong southwesterly and southeasterly moisture convergence, which advected water vapor toward the inner core and facilitated upward vertical transport, sustaining condensation latent heat release and the subsequent intensifying. Vigorous updrafts created favorable dynamic conditions for latent heat release during the rapid intensification (RI) stage, whereas downdrafts dominated the stagnation stage, markedly suppressing latent heat release processes.
Kinetic energy transfer within Typhoon Hato is closely coupled to its intensity evolution and exhibits distinct radial heterogeneity. During the RI stage, a forward (positive) energy cascade from the symmetric basic-state vortex to asymmetric disturbances dominates in the lower troposphere (~1.5 km), while an inverse (negative) energy cascade from asymmetric scales back to the symmetric vortex prevails in the middle troposphere (~5 km). At the peak of RI, a complete energy transfer chain forms in the upper troposphere (~8 km), with energy sequentially conveyed from the symmetric vortex to outer asymmetric small-scale disturbances and then to spiral rainbands. In the lower stratosphere (~13 km), continuous conversion of asymmetric-scale available potential energy to kinetic energy serves as the dominant process.
The direction of kinetic energy cascade reverses periodically with fluctuations in typhoon intensity, especially at intensity extrema. Forward cascades dominate the RI stage, while inverse cascades prevail during the re-intensification stage. Compared with the inner core region, kinetic energy transfer in the outer region is relatively simpler, characterized by the predominance of asymmetric-scale kinetic energy.This study elucidates the coupled mechanism between abrupt typhoon intensity changes and multi-scale kinetic energy redistribution, in which nonlinear energy transfer at the 8-km level plays a critical regulatory role in overall energy repartitioning.
The Heat redistribution induced by hydrometeor phase transitions and the drag effect associated with hydrometeor sedimentation enhance multi-scale energy transport within the typhoon at the same time, thereby modulating tropical cyclone intensity. These results may provide a sound theoretical basis for understanding the physical mechanism by which hydrometeor phase changes in spiral rainbands trigger asymmetric disturbances and further regulate the overall intensity of tropical cyclones.
How to cite: Huang, H., Wang, J., Li, X., Wang, T., Gu, S., and Jiang, C.: Multi-scale Energy Transfer during the Offshore Intensification of Typhoon Hato, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-315, https://doi.org/10.5194/ems2026-315, 2026.
In this study, numerical experiments with different initial radius of maximum wind (RMW) are performed to study the effects of tropical cyclone (TC) size combined with land-sea contrast on TC motion and low-level wind structure before landfall. By idealized numerical simulations, we found that when TCs approach the coastline, the translation speed of them will accelerate. The larger TC arrived coastline earlier than smaller TC, when they started moving from the same position. This is because that the larger TCs not only accelerate earlier but also have greater movement speed than smaller TCs when they approach the coastline. The mechanism responsible for this is that the edge of large TCs reach coastline earlier, thus their movement speed accelerated earlier than small TCs, due to the asymmetries in diabatic heating and radial flow generated by the land-sea contrast. Moreover, when TCs in three experiments all affected by the land-sea contrast, the stronger asymmetries generated in larger TC, thus resulting in faster movement in larger TC. The stronger inflow in western quadrant and weaker inflow (even outflow) in eastern quadrant of larger TC deduced apparently difference in vertical motion and diabatic heating between western and eastern quadrant of TC before landfall. An analysis of potential vorticity tendency proved that the diabatic heating terms were important and considered in determining the TC landward drift because asymmetries in vertical motion and relative vorticity developed due to asymmetric flow. This achievement can deepen the understanding of the evolution of TC structures in coastal areas and provide certain reference value for the accurate prediction of TC landing time over land.
How to cite: Liu, L. and Wang, H.: The effect of tropical cyclone size on its movement as TC approach the coastline, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-89, https://doi.org/10.5194/ems2026-89, 2026.
Tropical cyclone (TC), among the most devastating natural disasters globally, represents one of the most critical and challenging research foci in meteorology, particularly for intensity forecasting. A pivotal source of uncertainty lies in the rapid intensification (RI) process, defined as an increase in maximum sustained winds of at least 15 m/s within 24 hr. The TC RI events become more frequent in recent years, but their forecasts still remain challenging. Better understanding of the physical processes associated with RI of TCs would essentially improve its forecasting capability. The cloud dynamical and microphysical processes, especially their interactions that respond to RI are not well explored. In this study, the cloud macro and micro characteristics associated with RI of TC Nanmadol (2022) over the western Pacific are investigated using multiple-satellites observations. The storm underwent RI during 15–16 September 2022, and it has wreaked havoc on Japan's most cities as it moved across the Japanese island afterward with a track length of about 1,120 km. It is found inside Nanmadol as well as other TCs that a few particularly-large particles tend to occur in the outer rainbands during RI process. We further found that the rapidly-intensifying TCs possess a distinct upper-level outflow structure, which would attract cloud particles to accumulate and grow in the outer rainbands. This suggests that the large particles form in the outer rainbands due to the interaction of cloud dynamical and microphysical processes, which likely play a more substantial role in the RI process than previously acknowledged. Moreover, such unique features of particle distribution and upper-level outflow could be useful indicators for TC RI.
How to cite: Wu, Z.: Interaction of Cloud Dynamics and Microphysics During Tropical Cyclone Rapid Intensification, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-34, https://doi.org/10.5194/ems2026-34, 2026.
Accurate multi-step typhoon track and intensity prediction remains a critical yet hightly challenging task due to complex nonlinear atmospheric dynamics and strong spatiotemporal variability of typhoon evolution. Traditional statistical methods and numerical modeling approaches often struggle to sustain predictive accuracy, particularly for medium- to long-range forecasts. In this study, we propose a Transformer-based deep learning approach to jointly predict typhoon trajectories and maximum wind speed using multi-source meteorological data. The model is trained on historical best-track of typhoon datasets across China and reanalysis data from 1949 to 2023, incorporating key environmental variables to characterize large-scale atmospheric conditions that modulate typhoon development. All input features are subjected to rigorous quality control, normalization, and temporal alignment, and are structured sequential samples to capture the temporal evolutionary dynamics of typhoon systems. The Transformer architecture is adopted to effectively model long-range dependencies and complex spatiotemporal interactions inherent in typhoon evolution.
To quantitatively evaluate the performance of the proposed approach, two baseline models, XGBoost and Long Short-Term Memory (LSTM), are implemented for comparison. Experimental results demonstrate that the proposed model significantly outperforms both baselines across all evaluated forecast lead times. At short lead times, the model achieves RMSE values of 200–244 km for track prediction, corresponding to a 30% reduction relative to LSTM and a 58% reduction compared to XGBoost. At longer lead times (48h), the model maintains robust and competitive performance, with track forecasting RMSE ranging from 273–480 km, achieving up to 26% lower errors than LSTM and 52% reduction relative to XGBoost. For intensity prediction, the model also shows strong performance, with an MAE of 2.37 m/s, RMSE of 3.12 m/s, and R² of 0.68, indicating strong agreement with best-track observational records.
Overall, our results confirm that the proposed framework effectively captures the complex spatiotemporal dynamics of typhoon systems, providing marked improvements in predictive accuracy, stability, and robustness. This framework holds strong application potential for operational typhoon forecasting and typhoon-induced disaster risk management.
How to cite: Lin, Z., Wang, Y., Li, W., Ma, H., and Zhang, G.: An improved Transformer-based deep learning approach enhances typhoon track and intensity prediction across China , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-459, https://doi.org/10.5194/ems2026-459, 2026.
Mesoscale coherent vortex structures (CVSs), including polar lows and tropical cyclones, play a central role in generating high-impact weather such as heavy precipitation, strong winds, and extreme air–sea fluxes. However, a consistent and physically interpretable climatology of these systems across different vortex types and regions remains limited, constraining our ability to assess associated atmospheric hazards.
This study presents a unified framework for identifying, tracking, and clustering mesoscale CVSs in the atmosphere. The analysis uses high-resolution numerical simulations over the North Atlantic and the Arctic. Vortex structures are identified with the Eulerian Rortex criterion, which isolates the rigid-body rotation component of the flow in three-dimensional velocity fields. Identified vortices are then tracked with a self-developed algorithm and subsequently classified by K-means clustering based on a minimal set of physically motivated features describing vortex dynamics, thermodynamics, and geometry.
First, to ensure the framework's physical interpretability, clustering is applied to reference sets of vortex tracks manually identified for 2010 (North Atlantic) and 2019 (Arctic) using the same Rortex-based detection. These tracks are linked to known vortex types using existing best-track datasets for tropical cyclones and polar mesocyclones, providing a physically grounded reference for the most intense and best-studied systems. Clustering the manual tracks enables evaluation of how known vortex types are represented in feature space, assessment of class separability, and identification of additional vortex types with distinct dynamical and thermodynamic characteristics. The resulting classes are further analyzed using a range of physically relevant metrics — including intensity, lifetime, and associated surface heat fluxes — allowing comparison of their relative contributions to hazardous weather and air–sea interaction processes.
The validated feature space and clustering strategy are then applied to a 40-year automatically tracked dataset, enabling a consistent large-scale climatological analysis. The results reveal changes in the relative occurrence and dominance of different vortex types over the past four decades, suggesting links to large-scale circulation variability and ongoing climate change. This approach improves the physical interpretability and robustness of mesoscale vortex climatologies and supports more reliable assessment of associated atmospheric hazards.
How to cite: Koshkina, V., Gavrikov, A., Ezhova, E., Nikitenko-Valiakhmetov, M., Kuznetsov, G., and Gulev, S.: Automatic identification, tracking, and classification of mesoscale atmospheric vortices in high-resolution numerical model data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-719, https://doi.org/10.5194/ems2026-719, 2026.
The Northeast China Cold Vortex (NCCV) is the primary synoptic system causing severe weather in North China and more than 70% of convective events associated with the NCCV occur in its southeast and southwest sides; however, forecasting first convection initiation (first CI) in its rear side remains highly challenging. This study investigates the underlying CI triggering mechanisms and the causes of low forecast skill in this regime. Using 2016–2021 mosaic reflectivity from seven operational Doppler radars and a modified convective tracking algorithm, we map first-CI distributions and evaluate Numerical Weather Prediction model (NWP) simulations to pinpoint low-forecasting-skill regions (the plain region between Beijing and Tianjin). A representative case (8 June 2019) is then diagnosed using a high-resolution rapid-update (3-km, 12-min) analysis assimilating radar and dense-surface-network observations.
Results show that CI is driven by complex interactions among a cold, dry northerly flow, a warm, moist southerly flow, and a modified sea-breeze front (SBF). Near 850 hPa, the converging northerly and southerly flows generate localized weak convergence and high humidity. Simultaneously, the topographically deflected, inland-propagating SBF forces intense near-surface convergence with the prevailing southerly flow. This combined dynamic forcing establishes a deep updraft channel that successfully initiates convection.
Diagnostics reveal that NWP forecast failures primarily stem from inaccuracies in simulating nocturnal boundary layer wind fields. Although the model similarly captures synoptic-scale forcing and the moisture environment, it produces an anomalously strong low-layer northerly flow. This overestimation blocks the inland penetration of the SBF, thereby eliminating the critical near-surface convergence zone. Absent this essential dynamic trigger, accumulated convective instability cannot be released, resulting in missed CI forecasts. This study addresses the critical challenge of forecasting CI events associated with the R-NCCV, which are notoriously difficult to predict due to their weak synoptic forcing. We have identified the key multiscale factors of a representative event and elucidated how their misrepresentation compromises prediction accuracy.
How to cite: xiao, X.: Key Multi-scale Factors Determining Convection Initiation and Forecast Skill on the Rear Side of the Northeast China Cold Vortex, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-98, https://doi.org/10.5194/ems2026-98, 2026.
This study investigates the impact of a scale-aware convective parameterization scheme (CPS) on the simulation of typhoon track and intensity through a series of experiments using the Global-to-Regional Integrated forecast SysTem (GRIST) model. Through a series of experiments using the GRIST model, we investigate the impact of scale-aware CPS on the simulation of typhoon track and intensity. The results of four typhoon cases show that scale-aware CPS helps to reduce the track error by about 15 km and the intensity error by about 10%, demonstrating the benefits of scale-aware CPS on typhoon modeling. The Lekima case is then used as an example to illustrate the reasons behind the improved typhoon intensity with scale-aware CPS. By analyzing the budget equation of surface pressure tendency contributed by different physical processes, we found the pressure depression due to CPS heating is about 0.6 hPa h-1 weaker when scale-aware CPS is applied. However, the microphysics process takes up the convective instability left over by CPS and outweighs the reduction in parameterized convection, yielding a net pressure depression of about 1 hPa h-1. This suggests the suppression of sub-grid convection favors the stimulation of stronger microphysics heating due to stronger grid-scale ascending. Further examination of the inner-core precipitation validates the assertion. Indeed, when the scale-aware CPS is applied, the microphysics precipitation and corresponding diabatic heating in the inner-core region increase by about 9.5% and 13.8% respectively, which more than offsets the decrease in precipitation and diabatic heating of the CPS component. This is accompanied by stronger grid-scale ascending and accelerated radial winds, with the low-level inflow and upper-level outflow increased by about 2 m s-1. In summary, by suppressing sub-grid convection and enhancing microphysics process, the scale-aware CPS intensifies the secondary circulation, producing stronger diabatic heating and thus enhanced typhoon intensity.
How to cite: Wang, X.: Mechanistic Impacts of a Scale-Aware Convection Scheme on Typhoon Intensity, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-10, https://doi.org/10.5194/ems2026-10, 2026.
Based on observational data, ERA5 hourly reanalysis data, and s-band dual-polarization radar data, the extremely severe convective weather happening in Shandong Peninsula on 1 October 2021 is analyzed. The results show that: The process was affected by the upper-level cold vortex, and extremely severe convective weather happened from west to east in the northern area of Shandong Peninsula. Extra-large hail occurred in this process. The upper-level cold vortex provided the synoptic-scale dynamic forcing and unstable thermal stratification conditions, which were conducive to the occurrence of severe convective weather. About 2400 J•kg-1 sufficient CAPE and 30.2 m•s-1 strong vertical wind shear between 0 and 6 km altitude prompted this severe convective weather to arise in October. Due to the special coastal geographical environment of Shandong Peninsula, the surface sea-breeze front triggered this severe convection process, and the movement of the surface convergence line stimulated the occurrence and development of convection nearby upstream and downstream. The mean values of the maximum reflectivity (DBZM), cells-vertical integrated liquid (C-VIL), the top height of the strong reflectivity center (HT) and the top height of the cell (TOP) of the supercell storm which lasting for about 3 h were 70.7 dBZ, 68.9 kg•m-2, 3.2 km, and 11.4 km respectively. The three-body scatter spike (TBBS) and bounded weak echo region (BWER) of the storm were very significant. The value of ZDR is lower below 0℃ layer than that above 0℃ layer，meanwhile the values of CC and KDP is higher below 0℃ layer than that above 0℃ layer. There were two ZDR columns on the east and west sides in the supercell storm and the height of east ZDR column top exceeds that of the -20 °C layer. The thickness of the east ZDR column (about 3.2 km) was thicker than that of west ZDR column (about 2.2 km). These all indicate that the storm had a deep updraft, which was conducive to the formation and growth of hail. There were also two KDP columns on the east and west sides of the BWER, which had almost the same thickness about 3.2 km, and both extended beyond the height of the -20°C layer. The thickness of the strong reflectivity above 65 dBZ reaches 7 km, and the thickness of the ZDR column and KDP column reaches 2-3 km can be used as a reference for predicting extra-large hail in autumn short-term forecasting.
How to cite: Sun, S., Sun, Y., Yang, C., Diao, X., Ren, B., and Guan, H.: Analysis of Observational Characteristics of Extra-large Hail in Shandong Peninsula on 1 October 2021, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-72, https://doi.org/10.5194/ems2026-72, 2026.
The combined effects of topography and the urban heat island in Beijing create a complex thermodynamic and dynamic structure in the environmental field, leading to significant uncertainty in snowfall forecasting. This study examines a snowfall event that occurred on January 25–26, which was notably absent over the urban plain, with precipitation instead observed in the surrounding mountains and plains. Based on numerical simulations and observational data, the roles of gravity wave drag and the urban heat island effect are investigated through sensitivity experiments. Results indicate that the unique spatial distribution of snowfall over Beijing is primarily attributable to gravity wave drag, while the urban heat island effect played only a minor role. Gravity wave drag influenced the formation and distribution of snowfall primarily through the following dynamic and moisture-related mechanisms. It reduced near-surface wind speeds and shifted the dominant wind direction over the plain from southwesterly/southerly to northwesterly, resulting in a substantial decrease in the southerly wind component. By weakening the southerly moisture transport in the lower atmosphere, it diminished the water vapor flux. This reduced moisture supply, in turn, hindered the development and intensification of cloud systems and snow formation. Secondly, gravity wave drag induced warming in the lower atmosphere and cooling in the upper atmosphere, which stabilized the atmospheric stratification and suppressed turbulent development. This lower-level warming led to a decrease in relative humidity, which on one hand suppressed ice-phase processes such as condensation and sublimation, and on the other hand promoted evaporation. This combined effect prevented snowflakes from reaching the ground, resulting in the virga distribution in Beijing.
How to cite: Jia, X.: The Impacts of Gravity Wave Drag and Urban Heat Island on Snowfall in Beijing, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-73, https://doi.org/10.5194/ems2026-73, 2026.
On August 14, 2018, Typhoon Yagi (2018) moved northward and impacted Shandong Province of China, resulting in widespread rainstorm with a round-shaped heavy rain distribution. Specifically, an outer spiral rainband appeared on the typhoon periphery in southeastern Shandong, bringing short-term heavy rainfall and local heavy rainstorms. To study the mechanisms of the outer spiral rainbands, the characteristics and causes of the spiral rainbands are investigated in this study by using radar data and the observations from ground-based stations, radiosonde stations and aircraft. Numerical experiments are also conducted based on the Advanced Research WRF (Weather Research and Forecasting) model and its Hybrid-3DVAR (three-dimensional variational) data assimilation system. The model adopts 12 km and 4 km one-way nested grids, with 44 vertical layers. The initial ensemble perturbation fields are generated by using a stochastic perturbation method, and the Ensemble Transform Kalman Filter (ETKF) method is used for the bias correction of ensemble forecast, providing flow dependent background errors for the Hybrid-3DVAR assimilation module.
The results indicate that the outer spiral rainbands are formed by the merging and development of several linear mesoscale convective systems (MCSs). The outer spiral rainbands exhibit distinct characteristics of the linear MCSs with leading stratiform precipitation. There are several stronger linear MCSs merging laterally into other linear MCSs. Broad stratiform echoes appear in the front (eastern part) of the linear MCS in its maturity stage, and the convection develops up to 10 km or more. Short-term heavy rainfall occurs along the linear MCS at the maturity stage. The water vapor of heavy rainfall mainly comes from the near-surface layer (below 850 hPa) around the typhoon, and the water vapor flux convergence is mainly concentrated near the wind field convergence line. Before convection initiation, the middle and lower levels over Shandong are thermally unstable with high temperature and high humidity, and the wind rotates clockwise with height, which favor the development of convective systems. As the typhoon slowly moves northward, downward intrusion of cold air appears at 500 hPa. Below 900 hPa, on the southeast of the typhoon over central Shandong there are local convergence between southwesterly wind and southerly wind, and between southerly wind and southeasterly wind. The convergence-induced dynamic uplift triggers the release of unstable energy, stimulating several local linear MCSs. The MCSs develop northward along the steering flow. The linear MCSs merge and strengthen for several times, and finally the elongated spiral rainbands occur. At the mature stage of the convective systems, dry and cold downdrafts appear in the lower levels in the front of the MCS. Convective systems at the heights above 600 hPa move rapidly eastward with the upper-air steering flow, leading to the gradual weakening and dissipation of the linear MCS.
How to cite: Sheng, C., Fan, S., Qu, Q., Liu, S., and Zhu, W.: Causes of the Outer Spiral Rainbands Induced by Typhoon Yagi (2018) in Shandong Province of China, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-75, https://doi.org/10.5194/ems2026-75, 2026.
A total of 13 severe convective weather events occurred in Shandong Province in May 2020, including 8 hailstorm events. Hail was observed at 81 stations (65.9%) in 15 cities (93.8%), with a maximum of 4 station-times of hail occurrence. Ten events produced thunderstorm gales above level 10, and 5 events brought short-duration heavy rainfall. The frequency, coverage, intensity, and associated disasters of severe convection were rarely seen in the past decade. Among them, the severe convective event on 17 May was the most intense, with an extremely large hail-affected area. The maximum hail diameter reached 4.5 cm, the maximum wind speed was 36.6 m·s⁻¹ (level 12), and the maximum hourly rainfall intensity was 56.9 mm.
Based on ERA5 reanalysis data, encrypted automatic weather station observations, Doppler weather radar data, and lightning location data, this study analyzes the characteristics and causes of frequent severe convective weather in Shandong in May 2020. Taking the extreme severe convective event on 17 May as a typical case, the radar echo characteristics, vertical motions within storms, and maintenance mechanism of the long-lived squall line are investigated. The results show that:(1) The subtropical high was stronger than normal. On the one hand, it favored the transport of warm and moist southwesterly flows to Shandong; on the other hand, it blocked westerly systems, resulting in a strong forward-tilting trough that persisted over Shandong for a long time. Cold air carried by northwesterly flows ahead of an anomalously strong warm ridge on 500 hPa overlaid a strong warm temperature ridge on 850 hPa, maintaining persistent convective instability over Shandong. (2) Under the above favorable synoptic background, abundant moisture and large CAPE existed over Shandong. Strong cyclonic convergence occurred at the junction of Hebei, Shandong, and Henan provinces, and a persistent convergence line was maintained over central Shandong, which readily triggered severe convection and led to frequent severe convective weather in May. (3) Highly organized convective storms, regional supercell clusters, and a strong squall line exceeding 500 km in length were the direct causes of the 17 May extreme severe convection. The updraft velocity inside convective storms reached up to 28 m·s⁻¹. (4) Sustained low-level warm and moist advection transported warm and moist air into Shandong, which acted as the mechanism for CAPE reconstruction and the main energy source supporting the long-term maintenance of supercell clusters and the long-lived squall line.
How to cite: Hou, S., Shi, Q., Cui, J., Guo, Z., Cao, Q., and Wan, F.: Causes of Frequent Severe Convective Weather in Shandong Province, China, in May 2020 and Analysis of the Typical Case, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-84, https://doi.org/10.5194/ems2026-84, 2026.
Supercells represent the most intense and vigorous type of convective storms, often associated with severe weather phenomena such as large hail, damaging winds, and tornadoes. This study investigates a supercell embedded within a quasi‑linear convective system (QLCS) that occurred over Beijing on 12 June 2022. This storm produced large hail with diameters of 3–5 cm in Miyun and Shunyi Districts, resulting in significant economic losses. By assimilating high spatiotemporal‑resolution observations from a C‑band phased array radar (PAR) into a four‑dimensional variational data assimilation system, we examine the dynamical processes responsible for the supercell’s development and its associated mesocyclone.
Our findings reveal that prior to convective initiation, a pronounced convergence zone formed west of the terrain, generating several meso‑γ‑scale vortices near the surface. During the vertical merger of the convective cell with the QLCS, a strong QLCS-driven downdraft enhanced low‑level horizontal convergence, stretching the embedded vortices and markedly increasing vertical vorticity. In the mature stage, a mesocyclone developed with its rotational center reaching 4.5 km altitude and a maximum rotational velocity of 20 m s⁻¹.
The analysis demonstrates that the surface convergence lines and the meso‑γ‑vortices along them strengthen low‑level convergence and generate strong updrafts, triggering the initial storm. These intense updrafts effectively convert horizontal vorticity into vertical vorticity and transport it upward. Furthermore, the process of convective merging reinforces low‑level horizontal convergence, which forcibly stretches the mesovortex, enhancing vertical vorticity. These mechanisms enable the convective storm to develop in a strong, organized manner, ultimately leading to the formation of the supercell storm.
Overall, this study highlights the critical role of terrain‑induced convergence, meso‑γ‑scale vortices, and convective merging in the initiation and intensification of supercell storms within QLCS environments. The high‑resolution PAR observations and assimilation techniques employed here provide valuable insights into the multiscale interactions that govern severe convective weather, with important implications for operational forecasting and early warning systems.
How to cite: Liu, R., Chen, M., and Wu, J.: Revealing Key Dynamical Mechanisms of a Severe Supercell within a QLCS Using Rapid Update 4DVar Assimilation of C‐band Phased Array Weather Radar Data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-106, https://doi.org/10.5194/ems2026-106, 2026.
Accurate forecasting of atmospheric stability is critical for predicting convection and associated severe weather hazards, including thunderstorms, hail, and damaging wind gusts. Atmospheric stability indices (SIs) are routinely used to assess convective potential, yet their reliability across different data sources remains incompletely understood. This study evaluates the forecast skill of stability indices derived from two operational numerical weather prediction (NWP) models: the Integrated Forecast System (IFS, ECMWF) and the Global Forecast System (GFS, NCEP) alongside satellite-derived estimates from the NOAA-LAP product, using high-resolution Global Climate Observing System Reference Upper Air Network (GRUAN) radiosonde observations at Lamont (USA) as reference truth. The analysis focuses on five commonly employed indices: K-Index, Total Totals, Lifted Index, Convective Available Potential Energy (CAPE), and Precipitable Water. Results demonstrate that forecast accuracy varies significantly across indices. Precipitable Water exhibits the highest correlation and lowest normalized RMSE, whereas CAPE proves most challenging to predict, with substantially higher errors across all data sources. Among the three products, IFS consistently delivers superior performance, achieving the highest correlation coefficients and lowest errors for nearly all indices. Notably, the satellite-based NOAA-LAP nowcasting product, despite its higher temporal refresh rate and spatial resolution, does not surpass the hourly forecasts from the global NWP models. This underscores the intrinsic uncertainty in estimating atmospheric stability from either approach. The IFS advantage is likely attributable to its finer vertical resolution (137 hybrid levels versus 20 standard pressure levels in GFS), which better captures the thermodynamic structure essential for parcel-based indices. These findings have important implications for severe weather forecasting and early warning systems. While stability indices remain valuable operational tools, their limitations in predicting convection suggest that reliance on conventional scalar metrics alone is insufficient. Future work should explore richer representations of the atmospheric column or probabilistic frameworks that preserve more thermodynamic information for convective onset prediction.
How to cite: Kurzrock, F., Schuhbauer, D., Caron, C., and Schmutz, N.: Comparative assessment of stability indices from NWP models and satellite nowcasting, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-147, https://doi.org/10.5194/ems2026-147, 2026.
Post-tropical cyclones is the name given to tropical cyclones that have completed extratropical transition (ET), a process by which their tropical characteristics are replaced by those of extratropical cyclones. These storms pose a hazard for areas outside the typical tropical cyclone extent, as they pivot to draw energy from the sharp gradients surrounding the midlatitude jet stream. They bring high winds and large amounts of precipitation to areas unused to such fury. However, the lack of data on these storms is relatively scarce due to their infrequent occurrence (in the North Atlantic basin ~50% of the ~14 tropical cyclones that form annually undergo ET). With increasing global warming, the risks to areas such as Europe will increase, as increasing sea surface temperatures allow the storms to maintain more of their tropical characteristics in areas previously incapable of supporting tropical convection.
The HURRIFIC project aims to create a model capable of generating a global dataset of transitioning storms so the risks that they pose can be accurately calculated. This model, called STORM-EX, will be built as an extension of STORM (Synthetic Tropical cyclOne generRation Model) which has had success in generating synthetic tropical cyclone tracks and pressure profiles. Using data from a combination of observations, model simulations, and literature, we will simulate ET start and end, jet stream interactions, transition pathways, wind speed, and storm lysis. As a statistical model cannot capture all the complex interactions taking place during ET, this involves creative solutions to parametrize these cyclone features. Initial work has shown the model can reproduce ET start patterns and frequencies with skill, as well as reproduce basic interactions with the jet stream.
How to cite: Ribberink, M., Bloemendaal, N., Coumou, D., and Hemmati, M.: STORM-EX: Statistical modelling of post-tropical cyclones, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-248, https://doi.org/10.5194/ems2026-248, 2026.
From July 26th to 27th, 2025, Beijing experienced a rainfall of 315.3 millimeters within 12 hours. Due to the combined effect of a series of local precipitation processes over a period of time, severe floods and casualties occurred in the Miyun area. This paper compared the forecasting performance of five operational numerical weather prediction models for this process, and analyzed the impact of the improvement of physical parameterization schemes and data assimilation on precipitation forecasting based on the CMA-BJv3.0 system. The results showed that the CMA-BJv3.0 forecast was consistent with the spatial distribution and overall timing of the observed precipitation, but there were issues of a northward bias in the precipitation area and a smaller precipitation amount. The other four models underestimated the average precipitation in the target area, and there was a significant underprediction. The numerical sensitivity experiments showed that the coordinated optimization of physical processes and assimilation strategies could improve the forecast of precipitation area and intensity, enhancing precipitation in the northern part of Beijing and shifting the precipitation area southward, and reducing the overprediction in the northern part of Hebei. After optimization, the convergence of the northward and southward winds in the lower layer, the convergence and upward movement of the east-west wind field, and the abundant water vapor convergence and large initial CAPE provided favorable conditions for precipitation. The north wind and east wind in the lower layer may play an important role in this extreme precipitation event. A single scheme optimization could not achieve the above improvement effects.
How to cite: Xue, Y. and Jia, X.: "25•7" Beijing Severe Rainstorm: Basic Characteristics and Simululation with CMA-BJv3.0 Model, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-274, https://doi.org/10.5194/ems2026-274, 2026.
Flash droughts represent a rapidly intensifying form of drought with significant impacts on ecosystems, agriculture, and water resources. Unlike conventional droughts, these events develop over short timescales, often within weeks, leading to abrupt soil moisture depletion. In the Iberian Peninsula (IP), their relevance has increased in recent decades, particularly in southern and southeastern regions, where climate change is enhancing aridity and atmospheric evaporative demand.
This study evaluates the capability of the Weather Research and Forecasting (WRF) model to reproduce the occurrence, duration, and intensity of flash droughts over the IP for the period 1976–2022. Four standardized drought indices have been used: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Evaporative Demand Drought Index (EDDI), and the Standardized Soil Moisture Index (SSI). Flash drought events are identified based on the occurrence of dry conditions, the rapid development of drought conditions, and the persistence over time.
Model outputs have been evaluated against observational and satellite-based reference datasets (AEMET precipitation and GLEAM evapotranspiration and soil moisture). A statistical regionalization based on principal component analysis and k-means clustering identified five homogeneous climatic regions across the IP, enabling a detailed regional assessment.
Results shown that WRF generally captures the spatial and temporal variability of flash drought characteristics, particularly for SPI and SPEI. The model reproduces interannual variability and long-term trends reasonably well, especially in northern and central regions. However, it tends to underestimate event frequency for EDDI and shows limited skill for SSI. An increasing trend in frequency, duration, and intensity is detected for indices incorporating evaporative demand (SPEI and EDDI), particularly after the early 2000s, highlighting the role of rising atmospheric demand in flash drought dynamics.
Overall, the study demonstrates that WRF is a valuable tool for simulating flash droughts in the IP, although its performance depends on the selected drought index. These findings contribute to improving the understanding and modeling of rapidly developing droughts under current climate conditions.
Acknowledgements: This research has been carried out within the framework of project PID2021-126401OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU.
How to cite: Donaire-Montaño, D., García-Valdecasas Ojeda, M., Tacoronte, N., Castro-Díez, Y., Esteban-Parra, M. J., and Gámiz-Fortis, S. R.: Assessment of WRF performance in reproducing flash drought dynamics over the Iberian Peninsula, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-298, https://doi.org/10.5194/ems2026-298, 2026.
The increasing frequency of localized heavy rainfall driven by climate change has intensified urban flood damage, demanding situational awareness systems capable of capturing the complete flood lifecycle—from pre-event onset through spatial inundation propagation—in real time. This study presents a GNN-based urban flood situational awareness framework that integrates radar rainfall forecasting and real-time operational sensor observations to simultaneously predict flood onset timing, flow pathways, and inundated areas across a complex urban drainage network.
In the rainfall forecasting component, hazard-triggering rainfall is classified using the morphological characteristics of radar-observed precipitation fields and storm motion vectors, after which a deep learning-based short-range prediction model (KICT-RAIN-AI) generates high-resolution gridded rainfall fields as forecast inputs. Within the GNN model, the urban drainage system is reconstructed as a topological network in which manholes and sewer pipes are represented as nodes and links, respectively. The forecast rainfall is incorporated as dynamic nodes at each time step, enabling the model to learn spatiotemporal correlations between predicted rainfall and real-time sensor measurements, including sewer water levels and road surface inundation depths. Training data are derived from physics-based inundation scenarios generated by a coupled one-dimensional SWMM and two-dimensional GIAM framework, comprising 76 rainfall events and 1,296 time-series inundation maps, followed by domain-adaptive fine-tuning using field sensor records.
A key distinction of the proposed framework lies in its explicit encoding of drainage network topology within the learning architecture, which enables the model to trace inundation propagation pathways along hydraulically connected structures—a capability that conventional grid-based deep learning approaches cannot resolve. The study area is the Gwanak-gu district of Seoul, a topographically vulnerable catchment characterized by a valley-type terrain, high impervious surface ratio, and dense pipe network, where multiple historical flood events including the August 2022 extreme rainfall episode will be used for validation. The proposed system is expected to provide flood onset predictions with a lead time exceeding 30 minutes, supporting more accurate and timely decision-making during extreme weather events. By unifying radar rainfall forecasting, drainage network topology learning, and real-time sensor fusion into a single operational framework, this study provides a critical technological foundation for proactive urban flood management and early warning.
Acknowledgments: The research for this paper was carried out under the KICT Research Program (Project no. 20260161–001, Development of Digital Urban Flood Control Technology for the Realization of Flood Safety City) funded by the Ministry of Science and ICT.
How to cite: Yoon, S., Choi, G., and Rhee, D. S.: A GNN-Based Urban Flood Situational Awareness Framework Integrating Radar Rainfall Forecasting and Real-Time Sensor Observations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-341, https://doi.org/10.5194/ems2026-341, 2026.
Accurate representation of near-surface wind fields in complex terrain is essential for understanding and forecasting atmospheric hazards such as strong wind events, gap flows, and local wind extremes. In this study, we assess the capability of two diagnostic models to generate high-resolution wind fields over the Basque Country, a region characterized by complex orography and heterogeneous land use.
Euskalmet’s operational numerical weather prediction (NWP) systems currently operate at horizontal resolutions of up to 1 km. However, resolving microscale wind features relevant for hazard assessment requires finer spatial detail, often at a high computational cost. To address this limitation, CALMET and GRAMM are evaluated as computationally efficient downscaling tools capable of producing wind fields at 100 m resolution, with potential for further refinement. Both models, widely used within the CALPUFF and GRAL modelling frameworks, adjust meteorological input fields using terrain elevation, land use, and observational data, enabling an enhanced representation of local wind patterns. Simulations were performed on a 100 × 100 m grid using wind observations from automatic weather stations under different dominant wind regimes. The temporal evolution of hourly mean wind fields was analysed to assess the models’ ability to reproduce local flow structures.
Special attention is given to extreme wind situations, including episodes of strong synoptic forcing and locally enhanced flows associated with complex terrain features such as valleys and coastal gaps. Comparative analyses were carried out for selected high-impact events to evaluate the performance of both models in capturing wind intensity, spatial gradients, and the location of local maxima. These results provide insight into the strengths and limitations of each approach under adverse conditions, where accurate wind representation is particularly critical for risk assessment and early warning systems. A preliminary validation was conducted using standard statistical metrics, including bias, root mean square error, and correlation coefficient, complemented by graphical diagnostics such as scatterplots and Taylor diagrams. Results indicate that both models capture the main spatial patterns of wind flow, with differences in their sensitivity to terrain-induced effects and local variability, particularly under extreme conditions.
The proposed methodology is transferable to other regions with complex terrain and can be driven by either observational data or NWP outputs. These results highlight the potential of diagnostic downscaling approaches to improve the representation of wind-related hazards at microscale, supporting both forecasting and risk assessment applications.
How to cite: Diaz de Arcaya, A., Arrillaga, J. A., R. Gelpi, I., and Gaztelumendi, S.: Assessment of High-Resolution Wind Fields in the Basque Country Using Diagnostic Models, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-607, https://doi.org/10.5194/ems2026-607, 2026.
When certain atmospheric conditions are met, deep moist convection becomes prone to severe weather. Identifying these convective environmental conditions is a central objective in severe weather research. This study focuses on the environments associated with severe convective weather over Spain, derived from vertical profiles extracted from the ERA5 reanalysis and combined with cloud‑to‑ground lightning data and public reports of downbursts ( > 80 km/h), large hail (> 5 cm), flash heavy precipitation (> 60 mm/h), and tornadoes. We evaluated the skill of more than 200 convective parameters using threshold‑based binary classifications between each hazard type and non‑severe thunderstorms, assessed through ROC curves. Composite thermodynamic diagrams and hodographs were also constructed for each severe‑weather hazard, permitting a more visual comparison that might be useful to forecasters.
Summer downburst environments are noticeably warmer than those of non‑severe thunderstorms, with DCAPE and freezing‑level height emerging as the most discriminating parameters. Outside summer, downbursts occur in environments with enhanced wind throughout the profile, although wind strength remains below that of High‑Shear–Low‑CAPE severe environments described in the literature. The increased hodograph length in non-summer downbursts is primarily due to strong 0–3 km winds. Large‑hail events occur mostly in summer and are associated with elevated CAPE and wind shear, making them well distinguished from non‑severe thunderstorms by the WMAXSHEAR compound index. Flash heavy precipitation is favored by high parcel moisture content and low‑level saturation. Their hodographs indicate slow storm motions, reflected in reduced Bunkers right‑moving vector, and exhibit a rapid turning in the lowest 3 km. Tornadoes and waterspouts are challenging to distinguish from non‑severe thunderstorms based on convective parameters, showing only modest increases in 0–3 km CAPE, saturation, and slightly lower lapse rates in upper-levels. Composite thermodynamic diagrams and hodographs do not indicate any major distinguishing characteristic.
This study provides a reference framework for identifying the most relevant environmental indicators for future severe‑weather research in the region. It also delivers the first convective‑parameter‑based characterization of downburst environments in Spain, highlighting differences from studies in other regions where alternative parameters have proven more suitable or have shown other typical values.
How to cite: Barrio-Martin, A., Calvo-Sancho, C., Plaza-Martin, N. P., and Azorin-Molina, C.: Convective Environments Prone to Downbursts, Large Hail, Flash Heavy Precipitation and Tornadoes in Spain, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-676, https://doi.org/10.5194/ems2026-676, 2026.
Derechos are large windstorms that cause significant damage to forests, vegetation, and infrastructure not only due to their scale but also their intensity. Although only a few such events typically occur each year in Central Europe, forecasting these windstorms remains a major challenge for meteorologists.
In our study, we examined the convective environment associated with derechos from 1999 to 2024 using the ERA5 reanalysis during the full life cycle of each event. The path of each derecho was refined both spatially and temporally by combining data from the European Severe Weather Database (ESWD) with weather radar observations and other sources (mainly case studies). We also analyzed vertical profiles across modelled conditions one to two hours prior to each derecho occurrence, ensuring they were not contaminated by ongoing convection. In addition, we investigated anomalies of selected variables derived from the ERA5 reanalysis using non-exceedance probability and return periods related to the derecho events.
Each derecho path was divided into segments based on storm evolution: regions of intensification, mature phase, and weakening. For each segment, we assessed environmental precursors of convection such as CAPE, vertical wind shear, or helicity, along with composite parameters, moisture characteristics, and characteristics of lapse rate.
Our findings reveal notable differences in key precursor parameters (such as CAPE, CIN, and wind shear) between the intensifying and dissipating phases of derechos. Despite low CAPE and vertical wind shear during the dissipating phase, some derechos still produced damaging winds, particularly where surface moisture and the low-level lapse rate remained favourable. In some cases, this extended the area of impact by up to 200 km.
We also analyzed vertical profiles of air temperature, humidity, and wind prior to derecho onset using median and mean Skew-T diagrams and hodographs derived from the ERA5 reanalysis. The profiles suggest that relative humidity in the lower and mid-troposphere over Central Europe is fairly uniform, in contrast to U.S. derecho environments, where low-level humidity tends to be higher and mid-level humidity lower. Hodographs show slight curvature in the 0–3 km layer, suggesting a link between derechos, bow echoes, and supercells.
Warm-season derechos in Central Europe typically develop under a positive 500 hPa geopotential height anomaly over the Balkans and a negative one over the British Isles. Between these anomalies, warm, moist air flows into Central and Eastern Europe. Results, including non-exceedance probability and return periods, will also be presented in detail in our poster.
How to cite: Staněk, M., Müller, M., Kašpar, M., and Rýva, D.: Conditions during the formation of warm-season derechos in Central Europe during last 25 years, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-797, https://doi.org/10.5194/ems2026-797, 2026.
Characterizing pre‑convective environments is fundamental to research on deep moist convection, yet the most direct observations (radiosondes) are spatially sparse and typically available only twice per day. For this reason, reanalysis datasets, such as ERA5, are widely used, although their limitations must be considered. This study evaluates the reliability of ERA5 in representing convective environments, focusing on proximity soundings of thunderstorms and severe convective weather. The analysis is conducted over Spain, a region in the western Mediterranean.
Multiple convective parameters derived from ERA5 were compared with those obtained from radiosonde observations. The poorest agreement occurs for boundary‑layer‑related and parcel‑based parameters, particularly convective inhibition (CIN). The strongest bias affects extreme CAPE values, which ERA5 consistently fails to capture (missing 70% of events with CAPE > 3000 J kg-1) due to its inability to represent extreme temperature and moisture conditions in the boundary layer.
In contrast, many parameters show strong consistency between ERA5 and soundings, including kinematic variables (e.g., vertical wind shear, mean wind, Bunkers storm motion) and several thermodynamic indicators (e.g., freezing‑level height, low‑level relative humidity, DCAPE, equivalent potential temperature). Severe‑weather environments as a whole (hail, downbursts, heavy precipitation and tornadoes) exhibit biases similar to those of thunderstorm environments not showing these hazards. However, very‑large‑hail cases are associated with extreme CAPE, and thus are particularly affected by its underestimation.
The systematic omission of extreme CAPE, especially along the Mediterranean coast, may have important implications for future climatological studies and hazard‑assessment applications. The agreement between our findings and earlier studies in Europe and North America suggests that several of the identified biases are likely systematic features of ERA5 rather than region‑specific issues.
How to cite: Barrio-Martin, A., Calvo-Sancho, C., Plaza-Martin, N. P., and Azorin-Molina, C.: Evaluation of ERA5 in Severe Convective Environments against soundings in Spain, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-680, https://doi.org/10.5194/ems2026-680, 2026.
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