bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis and application of CNN to improve deterministic optical flow nowcasting at DWD
Authors: Friedrich Ulrich
Published: March 2023

Optical flow based nowcasting is a powerful technique to compute forecasts for small lead times up to a couple of hours. Forecasts of radar data are very useful as standalone products and serve as input data for multiple operational products, e.g., for the seamless combination of radar and NWP data and for cell-based analysis and prediction products. However, the optical flow technique has several drawbacks. It assumes stationarity in both the data values as well as the advection information. Further, it is a deterministic technique and the nowcasts have no dynamic properties. Recently, machine learning techniques have shown promising results for producing nowcasts with dynamic properties. However, for radar reflectivity and precipitation data, the predictions often lack high-intensity values and tend to become blurry for larger lead times.

In the current work we explore the potential of deterministic convolutional neural networks (CNN) to improve the operational optical flow nowcasting at DWD. A two-year dataset consisting of radar, NWP and orography data is used for training modified UNet based neural networks. Each network predicts radar reflectivity composites for a specific lead time, in 5-minute steps. Several optimization techniques are combined, both for the input data and the network architecture. The input data contains optical flow based nowcasts of previous radar timesteps that are mapped to the target lead time. The NWP input parameters are chosen for their known importance in convective processes. To understand their impact in this application, an ablation study is performed. The network architecture is optimized. The classic UNet architecture is augmented with additional horizontal computation blocks. This adds more nonlinearity to the finer scales of the network and reduces the validation error. Individual encoders are used for the radar and NWP data and combined with affine linear transformations. Experiments with classical pointwise loss functions as well as losses with spatial context (e.g., FSS) are conducted. The new forecasts are compared with the operational nowcasting at DWD as well as a closed-loop NN approach (RainNet).
Evaluation of ESTOFEX convective outlooks from 2007 to 2021. Part 2: climatology and reliability of threat level polygons 
Authors: Taszarek Mateusz , Groenemeijer Pieter , Pucik Tomas , van der Velde Oscar , Dafis Stavros
Published: March 2023

The European Storm Forecast Experiment (ESTOFEX) is a team of volunteer forecasters that have been providing experimental convective outlooks for Europe since 2002. Probabilistic storm forecasts issued by ESTOFEX address threats posed by severe convective storms, i.e. lightning, large hail, severe wind gusts, tornadoes and excessive precipitation. ESTOFEX also serves as a platform for exchange of knowledge about forecasting severe convective storms with a goal of improving their understanding among both members of ESTOFEX and others. While not official, ESTOFEX products have been widely used by national meteorological services, severe storm communities and the public. ESTOFEX forecasters have regularly contributed to the ESSL Testbeds and are using an ingredients based forecasting methodology to forecast severe storms. Consistently improving severe storm reporting in the European Severe Weather Database (ESWD) and availability of ground-based lightning detection measurements over the last decade enabled the verification of a large number of ESTOFEX forecasts. Thus, in this work we evaluate 4019 convective outlooks issued by ESTOFEX forecasters since 2007. Our goals are to detect spatiotemporal patterns in convective outlooks and test the reliability of issued threat level polygons, i.e. for a low and high probability of lightning, and an increasing probabilities of severe weather: level 1, level 2 and level 3. We performed the verification by applying a number of methods, including contingency table statistics, receiver operating characteristic curves, practically perfect hindcasts and by calculating spatial coverage of detected lightning (ATDnet network) and local storm reports (ESWD) within issued polygons. Results indicate that products issued by ESTOFEX over the last 15 years, when combined together, are consistent with convective climatologies based on reanalyses and lightning detection data. However, we note that forecasters tend to issue outlooks relatively more often for severe weather outbreaks across western and central Europe. We found that while 95% of the issued lightning probability areas fulfilled the required criterion of coverage, this was only true for 40% of the severe weather probability areas. One reason is that while lightning observations are relatively homogeneous across the forecast domain, the same cannot be said about severe weather observations. These are lacking in regions such as southeastern or eastern Europe, while forecasters calibrated themselves to the higher observed coverage in western and central Europe. The reliability of ESTOFEX forecasts increased over the time, but we found underestimation of lightning probabilities over southern Europe and an overestimation of lightning probabilities over British Isles and Scandinavia.
A conceptual model for the development of tornado in the Po Valley 
Authors: De Martin Francesco , Davolio Silvio , Miglietta Mario Marcello , Levizzani Vincenzo
Published: March 2023

Why is the Po Valley a hot spot in Europe for tornadoes? In this study the authors propose an explanation to this issue, studying a tornado outbreak that affected the Po Valley on 19 September 2021. During that event seven tornadoes (four of them ranked as F2 according to the Fujita scale) developed between Lombardia and Emilia-Romagna regions in a few hours. Although tornadoes are not rare in Italy, so many tornadoes in such a short time are an unusual event. 

The case study was analysed by means of observations and numerical simulations obtained with the convection permitting MOLOCH model. Observations showed that during the event there were two low-level boundaries in the Po Valley: a cold front coming from the Alps and a dry line generated by the downslope winds from the Apennines. These two boundaries created a triple point, like those observed during tornado outbreaks in the US MidWest, but on a smaller scale. Observations proved a strong correlation between tornado developments and low-level boundaries.

Numerical simulations with 500 m grid spacing showed that a warm and moist air tongue from the Adriatic Sea played a fundamental role in generating the supercells, causing an advection of vorticity and favouring instability conditions. Moreover, through numerical experiments, it has been proved that this moist air tongue was sensitive to the Froude number of the south-westerly flow from the Apennines: the greater the Froude number, the further north and narrower was the tongue of air, with impacts on the development of supercells. Along the cold front large amounts of streamwise vorticity were generated by the buoyancy gradient. Furthermore, the dry line played a key role in the generation of tornadoes, creating locally large amounts of instability and strong wind veering near the surface: kinematic and windshear parameters were comparable to those observed in US-tornado events only along a narrow path near the dry line. 

Comparing these results with previous papers, the presence of thermal boundaries and dry lines represents a typical pattern during tornado-events in the region. Then, in conclusion, a conceptual model also useful for forecasting applications is proposed for the development of tornadoes in the Po Valley, which explains why tornadoes are relatively common in Northern Italy.
The Impact of Convective Storms on Global Atmospheric Transport Processes
Authors: Wang Pao K.
Published: March 2023

In addition to their tremendous power display and serious damages they often cause, convective storms also play important roles in the global atmospheric transport processes. In this presentation, I would like to discuss two aspects of these processes: (1) the cross-tropopause transport of matter and (2) the transport of momentum and energy to the upper atmosphere. The cross-tropopause transport of matter is manifested by the phenomenon of the above-anvil cirrus plumes (AACP) discovered more than two decades ago by Martin Setvak and others via satellite data. I will review both satellite observations and theoretical modeling of this and related storm top phenomena. The transport of momentum and energy to the upper atmosphere by convective storms is manifested by the presence of concentric airglow patterns at the mesopause whose centers are collocated with storm activities in the troposphere. The wave-like airglow patterns have been observed by Suomi/NPP VIIRs imageries and we have performed numerical simulations to show that they form such patterns due to the propagation of storm-generated gravity waves from the lower atmosphere to the mesopause level.
The extreme phenomena from the end of June 2021 and the variations of the planetary boundary layer height, in Moldavia region
Authors: Cosmina Apetroaie , Ingrid-Mihaela Miclăuș , Diana-Corina Bostan , Adrian Timofte , Marius-Mihai Cazacu
Published: March 2023

On June 30th, 2021, a low-pressure formed in the center of Europe, causing the advection of warm air and the infiltration of cold air from the rear sector of the low-pressure field. This resulted in the formation of a strong convergence line in the Moldavia region, which generated a multicellular system. We analyzed the forecast atmospheric sounding for this date at 12 UTC in various locations in Moldavia, using the European Centre for Medium-Range Weather Forecasts (ECMWF) to identify areas with high convective potential. The planetary boundary layer (PBL) was determined through the analysis of selected atmospheric sounding, and a well-mixed PBL was observed. As the air particle rises, it cools adiabatically, carrying moisture with it. However, slight variations in the height of PBL were noted at different locations, with the northern-central part of Moldavia experiencing more convection than the southern part, which was less affected by severe weather. The region of the atmosphere closest to the ground surface that is directly influenced by it in terms of turbulence, wind direction and speed, moisture, heat, and convective activity is known as PBL. Most of the water vapor and aerosols in the atmosphere are concentrated in this layer, where turbulent mixing occurs and atmospheric fronts and their associated phenomena are manifested. It is important to analyze the height of PBL and how it varies, as it does not have the same height everywhere and changes over time and space. In our work we determined the height of  PBL through the use of atmospheric sounding.

 

Keywords: forecast, convection, PBL, atmospheric sounding
Climatology of convective dust storms in Poland
Authors: Skop Filip
Published: March 2023

Dust storms are an unusual and understudied type of severe weather phenomena in Europe, causing low horizontal visibility, hazardous to human health particulate matter concentrations and economic losses. Despite occurring mostly in arid and semiarid climates, dust storms are also being reported occasionally in Poland during severe drought periods. Regions most prone to dust storms in Poland include Greater Poland, Masovian, Kuyavian-Pomeranian and West Pomeranian Voivodeships. A significant horizontal gradient of sea level pressure, as well as convective phenomena, are considered the main causees of dust storms in Poland. Apart from strong wind, low near-surface level relative humidity, low soil humidity and negative Standarised Precipitation-Evaporation Index (SPEI) values also support dust storms' development. Based on a comparison between particulate matter concentration data, obtained from air quality measurement stations and meteorological data obtained from synoptic stations, dozen of convective dust storm days were identified in the Polish warm period (April-September) between 2003 and 2020. Recorded dust events formed most commonly as a cause of thunderstorm's outflow, connected to cold fronts and low tropospheric convergence zones. ERA5 reanalysis combined with atmospheric soundings data were used  in order to determine the environment supportive for convective systems likely to produce a dust storm.  High Lifted Condensation Level values and low humidity in a lower troposphere strongly supported this type of events. 
Reconstruction of violent tornado environments in Europe: High-resolution dynamical downscaling of ERA5
Authors: Pilguj Natalia , Taszarek Mateusz , Kryza Maciej , Brooks Harold
Published: March 2023

Around 2–3 violent tornadoes hit Europe per decade. Although they are less frequent compared to the Great Plains of the United States, Europe has a population density higher by the order of the magnitude, thus extreme convective events typically produce large societal impacts and considerable damage to infrastructure. As operational prediction of such events is of high priority to emergency managers and forecasters, in this study we reconstruct environmental conditions for selected 12 violent tornadoes from the period between 1950 and 2021. Because numerical modeling of severe weather events requires convection-permitting resolutions, we used the Weather Research and Forecasting model for dynamical downscaling of ERA5 reanalysis. Results show that out of 12 high-resolution simulations (3 km), in 10 cases the modelled updraft helicity (UH) tracks cross the neighborhood of tornado reports, however, in 2 cases the timing of convection was more than 3 hours away from the time of the reports. Analysis of vertical profiles show that all simulations represented favorable tornado environments with cyclonically curved hodographs, comparable to significant tornadoes on the Great Plains of the United States. The combination of UH tracks with convective environments offers promising results for operational forecasting in Europe, and should be explored in future studies.
Estimates of the relationship among characteristics of severe convective storms, their cloud-top properties, and environmental parameters in Northern Eurasia
Authors: Chernokulsky Alexander V. , Shikhov Andrey , Yarinich Yulia , Sprygin Alexander
Published: March 2023

Severe convective storms that produced tornadoes and straight-line winds usually develop under peculiar environmental conditions and have specific signatures on the cloud tops associated with intense updrafts. In this study, we performed a comparative analysis of satellite-derived characteristics, with a focus on cloud-top properties, and ERA5-based environmental parameters of convective storms in forested regions of the western part of Northern Eurasia in 2006–2021. The analyzed sample includes 128 different convective storms that produced 138 tornadoes and 143 linear windstorms. We found most of the tornadoes and linear windstorms are generated by quasi-linear convective storms or supercells. Such supercells form under lower convective instability and precipitable water content compared to those for other types of storms. We found a significant negative correlation of minimum temperature on the storm cloud top with instability parameters. In its turn, the longevity of convective storms significantly correlates with wind shear and storm-relative helicity. About half of the tornadoes and 2/3 of linear windstorms are associated with the presence of cloud top signatures, like overshooting tops, cold-ring, or cold U/V features. The events associated with such signatures are formed under high values of instability parameters. Our results can be used for further analysis of the peculiarities of a tornado and linear windstorm formation and to enhance the predictability of such severe events, especially in regions with a lack of weather radar coverage. 
On the Use of Helicity in Tornado Forecasting
Authors: Davies-Jones Robert
Published: March 2023

Hodographs in proximity to violent tornadoes and idealized ones with different shapes and shear distributions are assessed for tornado threat, which is measured by the dynamical part of the significant tornado parameter (STP), namely, storm-relative helicity (SRH) from 0 to 1 km times the 0-6 km bulk wind difference (BWD).  The STP is a skillful predictor of tornadoes.  Here the SRH is computed across ground-based layers of different depths to assess the importance of different layers to updraft rotation.  The idealized hodographs are purely straight, straight apart from a right-angle bend at 1 km, and semicircular.  Storm motion for a right-moving supercell is customarily estimated as the mean 0-6 km wind plus a fixed propagation to the right of the 0-6 km shear vector of 7.5 m s-1.  The propagation speed is amended herein to make it scale invariant, but this change has little bearing on the conclusions.

BWD is kept nearly constant at a high value to support supercells.  It is independent of storm motion so the effects of propagation on the STP are confined to the SRH factor.  Introduction of the storm-motion formula into the SRH expression decomposes SRH into a mean-wind helicity (MWH) and a propagation helicity (PH).  Limiting the SRH to the 0-1 km layer leaves PH mostly unrepresented in the STP for semicircular, bent, and proximity hodographs alike.  A storm need not propagate away from the mean wind for the updraft to ingest air with abundant helicity from the lowest kilometer of inflow.  Purely straight hodographs, which have no MWH and are unreal anyway, have the least tornado potential; semicircular hodographs with shear declining with height hold the greatest threat.  For semicircular hodographs, PH is smaller than MWH for all ground-based layers up to 6 km deep. In the most dangerous environments, updrafts would still rotate even if they moved simply with the mean wind and did not propagate at all.

The main contributor to 0-1 km SRH is MWH rather than PH.  This is true even for the bent hodographs, which appear almost straight.  The bend in the hodograph is crucial as it associates with near-ground streamwise vorticity and helicity in both the mean-wind and storm reference frames.  As far as the current tornado prediction method is concerned, propagation seems almost inconsequential.
The effects of warm and cold core eddy processes on cyclone activity
Authors: Barbosa Humberto , Buriti Catarina , Buriti Catarina
Published: March 2023

It is a well-established that the Tropical Cyclones (TC) often fuel from the thermal energy stored in the ocean top layer. As the ocean mixed layer temperature is able to modulate the air-sea interactions between ocean and tropical cyclones, the identified changes/dominant processes in mixed layer heat budget could have notable impact on the cyclone intensity and propagation. The present study examines the role of distinct oceanic mixed layer processes over warm and cold core eddies in impacting the tropical cyclones. Three severe cyclones are studied. We have examined the relationship between mixed layer heat budget terms and the cyclone genesis potential parameters, with a focus on cyclones approaching and sustained time over warm-core eddy regions. To further examine the role of different Ocean variables on atmospheric counterparts of cyclones, we have computed the area-averaged values of these Ocean variables and the Genesis Potential Parameter (GPP) index for warm-core eddy region. The temperature tendency shows highest magnitude of negative correlation with GPP. Among all the parameters that determines net heat flux, the net latent heat flux showing high response/influence on GPP. The relatively moderate correlation values between latent heat flux and GPP is evident during the active period of cyclones. The findings found in the present study has vital future scope in the area or studies related to air-sea interactions particularly for the case of tropical cyclones and adaptation of such knowledge in forecast models could enhance the predictions.
Observing deep convection with NASA’s TROPICS mission
Authors: Bennartz Ralf , Chen Ruiyao , Blackwell Bill , Leslie Vince
Published: March 2023

NASA’s ‘Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats’ (TROPICS) mission will consist of four 3U CubeSats, likely to be launched in May 2023. TROPICS will enable passive microwave observations of tropical cyclones with an unprecedented temporal resolution. Each satellite is equipped with cross-track scanning microwave sounders covering the 118 GHz oxygen and 183 GHz water vapor absorption bands as well as channels at 92 and 204 GHz. As a precursor to the full mission, the TROPICS Pathfinder satellite was launched in June 2021 and has provided valuable science data since August 2021.

We will present a short overview on NASA’s TROPICS mission as well as a novel K-D-tree-based approach to derive temperature and moisture profiles alongside estimates of surface precipitation and ice water path over deep convection from TROPICS Pathfinder. In addition to the retrieved atmospheric parameters, the novel retrieval approach allows for an estimate of how likely it is for a given retrieval to be representative and for rigorous Bayesian uncertainty estimates associated with each parameter. Results will be presented for various tropical storms and compared against established precipitation products such as NASA’s Global Precipitation Measurement Mission (GPM) and the Global Precipitation Climatology Project (GPCP).
Object-based Ensemble Prediction System KONRAD3D-EPS 
Authors: Josipovic Lukas , Pante Gregor , Schnoor Isabel , Brechtel Andreas , Blahak Ulrich
Published: March 2023

The precise forecast of convective cells is essential for meteorological services as they can be accompanied by life-threatening severe hail, wind gusts, or heavy rain. However, state-of-the-art NWP models usually possess update frequencies of several hours so that forecasters must use predictions that are outdated when new thunderstorm cells develop. NWP models do often accurately simulate the intensity of convective cells, but with shifts in space and time. Object-based nowcasting algorithms with higher update frequencies became necessary to deliver information on the evolution of convective storms for the first two hours since observation. Furthermore, the combination of nowcasting and model data enables the relocation of simulated cells towards observed cells.

Many deterministic object-based nowcasting tools as DWD’s KONRAD3D algorithm assume that detected cells will have persistent intensity. Within the SINFONY (Seamless INtegrated FOrecastiNg sYstem) project at DWD, we aim at modelling the life-cycles of storm cells in a truthful way and capturing the uncertainties of object-based nowcasts. Hence, we extended our nowcasting algorithm towards an ensemble prediction system called KONRAD3D-EPS. Each ensemble member is initialized by drawing from parameterized distributions of storm lifetime and maximum severity. Inspired by previous studies, e.g. Wapler (2021), KONRAD3D-EPS uses a set of horizontally flipped parabolas to model the life-cycle of convective cells in terms of their severity. In case of redetection of a convective cell, the algorithm corrects the previously estimated lifetime and severity maxima. Thus, the parabolas can be adapted individually for any convective storm in any weather condition.

Besides life-cycle predictions, KONRAD3D-EPS delivers information on the probability of thunderstorm occurrence for the next 2 hours depending on detected cells and their severity. In order to condense the ensemble data, we also provide the representative member for each convective cell. This is done by applying the pseudomember algorithm by Johnson et al. (2020) to the ensemble data.

We will give an overview of our probabilistic object-based nowcasting algorithm KONRAD3D-EPS and present its predictions for prominent example cases. Moreover, we will show first verification results.
Estimation of storm life-cycle by machine learning to enhance ensemble nowcasting of cell objects
Authors: Josipovic Lukas , Berndt Christian , Blahak Ulrich
Published: March 2023

Object-based cell detection and tracking algorithms are widely used to assist meteorological forecasters in their decision-making regarding severe weather warnings. In many approaches, the extrapolation of current storm movement delivers a forecast of future cell position for lead times from five to 120 minutes. However, most operational nowcasting algorithms do not make predictions about the life cycle of storms, although information about cell weakening, intensification, or the general lifetime might be beneficial with regard to meteorological warnings. Prediction of storm life-cycle is not straightforward and involves large uncertainties, as recent research has shown. We aim at using probabilistic machine learning approaches to learn life-cycle information from past storm data and incorporate their prediction and its corresponding uncertainty into an object-based ensemble prediction system.

In a first concept, we identified maximum future storm severity as well as storm lifetime as the important characteristics to be included in the object-based ensemble nowcasting approach. We carried out a cross validation based on a 5-year recalculation of DWD’s object-based nowcasting system KONRAD3D including atmospheric storm environment from NWP model data. Cross validation shows that prediction techniques based on decision trees, i.e. random forest (RF) and gradient boosting (GB) are able to deliver a reasonable performance. For maximum storm severity, both methods do outperform a persistence forecast assuming no future change of current cell severity. Due to the sparse occurrence of long-lasting storms, prediction of cell lifetime is more difficult and the corresponding uncertainty is larger. Quantile regression variants of RF and GB are useful to quantify uncertainty based on current storm attributes and atmospheric environmental conditions and might be a useful base for the generation of an object-based ensemble nowcasting system.

KONRAD3D-EPS, the current ensemble prediction system under development within the project SINFONY (Seamless INtegrated FOrecastiNg sYstem) at the German Meteorological Service, uses a horizontally flipped parabola to model the life-cycle of storm cells in terms of their severity. Each member is initialized by drawing from parameterized distributions of storm lifetime and maximum severity. The improvement presented here uses quantile regression GB predictions to initialize the ensemble in order to adjust the spread depending on the given meteorological situation. In addition to machine learning results, we will present basic functionalities of KONRAD3D-EPS and show the conceptual overview on how to combine it with machine learning predictions.
An early warning system for urban fluvial floods based on a predefined library of hazard maps and rainfall depth-duration thresholds: the case study of Palermo (Italy)
Authors: Pumo Dario , Avanti Marco , Francipane Antonio , Noto Leonardo
Published: March 2023

The growing expansion of urban areas has profoundly modified the processes of generation and transferring of surface runoff; the increase in the density of infrastructures, human activities and population in urban settlements also implies a considerable rise of the elements exposed to flood risk. In the last years, climate change has induced an intensification of the hydro-climatic extreme events, and it has been globally observed an increase in the frequency of water related catastrophic events in urban areas, such as fluvial and pluvial floods.

Among the possible different approaches to mitigate hydraulic risk, non structural mitigation measures probably offer the highest potentialities in the short term. This study proposes a prototypal Early Warning System (EWS) for fluvial flood risk developed over a densely populated subarea of Palermo (Italy) threatened by the presence of the Oreto River. The proposed EWS is capable of determining a relationship between possible event scenarios and some potential precursors. Each event scenario identifies potential first points of flooding, flooding areas and water levels, providing hazard maps specific for people, vehicles and infrastructures. Forecast rainfall, antecedent soil moisture and initial river stage conditions are considered as event precursors. Rainfall characteristics considered are the 24/48 h forecast of the expected cumulative depth and duration, retrieved from the National Surveillance Bulletins based on a Sicily Local Area Model. Water levels monitored in real-time by the Oreto a Ponte Parco hydrometric station are used to derive a proxy measure for the antecedent soil moisture and the initial river stage conditions.

The system is based on numerical rainfall depth-duration thresholds previously defined for the basin under analysis that, for fixed antecedent moisture condition and rainfall hyetotype, provide a family of isocritical discharge curves. From such curves, once the characteristics of the expected rainfall event are known, it is possible to estimate the expected hydrograph peak. Coupling this information with the initial river stage, the EWS is able to retrieve a corresponding event scenario, exploiting a pre-built library of event scenarios previously developed offline through a coupled hydrological-hydraulic model, considering several project hydrographs under different initial river stage conditions. The computation domain was previously accurately defined, starting from a 2m DEM, opportunely corrected to account for the exact geometry of bridges and other infrastructures. The EWS was tested with respect to an historical flood event occurred in 2018, demonstrating satisfactory performances in terms of predicted flooding area and water levels.
Severe hail in France: reconstruction of Storm Ela’s and June 2022 hailstorms
Authors: Panosetti Davide , Allen Christopher , Obaidullah Yaqubi , Thollon Orane
Published: March 2023

Hail is by far the greatest contributor to insured losses from severe convective storms on an annual basis. Individual severe convective storm outbreaks can cause hail losses well above EUR 1 bn. On 19-22 June 2022 a series of such events impacted France, and in particular the large metropolitan region of Ile-de-France. There were many reports of large hailstones, causing significant damage to property and motor vehicle. Total insured hail loss estimates in France alone exceeded EUR 2.4 bn, of which EUR 1.34 bn of property loss and EUR 1.08 bn of motor vehicle loss. These were the largest hail events in France in terms of losses since Storm Ela’s, which on 9-10 June 2014 resulted in insured hail losses in excess of 900 mn in 2021 EUR.

Common denominator to these two impactful events were persistent meteorological situations conducive to large-scale severe convective storms for several consecutive days. These compounded with local conditions favorable for the development of severe hail. Maximum hailstone sizes of 12 cm in diameter were observed in the administrative regions of Centre-Val-De-Loire (Ela) and Occitanie (June 2022). In this study we present a reconstruction of these events based on eye-witness event reports from ESWD and a local provider cross-referenced with weather radar data. We analyze the synoptic configurations and pre-convective environments that characterized them, with focus on those properties and features that are peculiar to severe hail-forming thunderstorms. We discuss in particular different formulations of CAPE and vertical wind shear, as well as the performance of the Significant Hail Parameter (SHiP) as predictor for severe hail occurrence. These event reconstructions are part of our effort to construct a Realistic Disaster Scenario (RDS) model for France and Belgium to stress test both individual client portfolios and the market as a whole.
A New Ensemble System at DWD for Seamless Short-Term Forecasts of Convective Precipitation
Authors: Rempel Martin , Schultze Markus , Blahak Ulrich
Published: March 2023

Reliable and accurate forecasts in the short-term range are essential for precise and consistent warnings that help to increase the lead time for decision makers in emergency services. In the current operational weather forecasting, these warnings are commonly based on nowcasting techniques and numerical weather prediction (NWP). Both forecast systems are able to provide valuable warnings guidance, albeit for different lead-time ranges. Especially in convective environments, the skill of nowcasting techniques based on Lagrangian persistence is often limited due to large dynamical uncertainties. On the other hand, NWP forecast quality may be affected by incorrect initial conditions, outdated boundary conditions or by model spin-up effects.

The ongoing DWD project SINFONY (Seamless INtegrated FOrecastiNg sYstem) developes an integrated short-term ensemble forecasting system on the convective scale. As a first step towards the combination of precipitation and NWP, both techniques have been further enhanced. The previously purely advective precipitation nowcasting was replaced by STEPS-DWD, providing ensemble extrapolations every 5min. It represents an adaption of the well-known STEPS (e.g. Seed 2003, Bowler et al., 2006). On the part of the NWP, the new rapid update cycle (RUC) of ICON-D2 provides hourly initialized ensemble forecasts running 8h ahead with a horizontal resolution of 2.2 x 2.2km². Consistent with the radar scanning strategy, output of precipitation variables and synthetic radar reflectivities is given every 5 minutes.

To condense information from both described forecast systems and to provide an improved basis also for hydrologic warnings, the combined ensemble forecasting system INTENSE (Integration of NWP Ensembles and Extrapolations) is introduced. INTENSE adapts the Bayesian combination approach according to Nerini et al., 2019 by utilizing the ensemble Kalman filter in a dimension-reduced space. It provides combined forecasts up to 6h ahead with a spatial and temporal resolution of 5min and 1 x 1km², respectively.

This contribution will give an overview about the adaption of the combination approach and will discuss additional modifications. Further, a verification study for the summer 2022 will be shown, highlighting the benefit of combined forecasts for several severe convective events over Germany. In addition, results of an evaluation carried out by forecasters in the same period will be presented.
Analysis of Hail Characteristics and Detection of Hail Pre-Signal based on 3-dimensional Radar Data
Authors: Jung Woomi , Ye Bo-Young , Son MyoungJae , Suk Mi-Kyung , Cho Ik-Hyun
Published: March 2023

Hail is generated within strong convective cells. In particular, hail from strong convective cells that develop rapidly can occur within 30 minutes. In that case, hail is difficult to be predicted and can cause a lot of damage in many part such as agriculture, aviation fields, and human life, etc. Pre-signal detection of developing hail will contribute to predicting hail and reducing damage. Therefore, Weather Radar Center of KMA (Korea Meteorological Administration) analyzed the characteristics of hail and developed an algorithm to detect hail pre-signal using the results of analysis.
In this study, 3-dimensional gridded radar reflectivity and temperature data were used to detect hail pre-signals. The results of pre-signal detection have 2 types of areas: “hail possible” and “hail warning”. The “hail possible” area represents the area where hail can develop before hail exists in the upper atmosphere. The “hail warning” area represents the area with a possibility of hail size of 2cm or larger. And the “hail warning” area is included in the “hail possible” area. For signal classification, 3-dimensional data based VIL (Vertically Integrated Liquid), UVIL (Upper-level VIL, -10~-40 degrees Celsius), 35 dBZ Echo top, freezing level height, and hail ground observation information such as hail size, observed time and location were used. Hail ground observation cases for 5 years were analyzed to determine thresholds for signal classification variables and verify this algorithm.
As a result of analyzing hail cases observed on 78 days, at 231 sites through this algorithm, all cases but 4 days were detected (ACC=0.81, POFD=0.22), and the hail signals were detected in advance of an average of about 40 minutes. And all 18 cases in which hail with a size of 2 cm or larger was observed were detected as “hail warning” signals. Comparing with the severe hail indices (SHI, POSH, MEHS) of previous studies, the “hail warning” areas in most of the cases were similar.

 

ACKNOWLEDGEMENTS
This research was supported by “Development of integrated radar analysis and customized radar technology (KMA2021-03021)” of “Development of integrated application technology for Korea weather radar” project funded by the Weather Radar Center, Korea Meteorological Administration.
Identification of convective precipitation events through lightning data in a Mediterranean area
Authors: Cipolla Giuseppe , Treppiedi Dario , Francipane Antonio , Noto Leonardo Valerio
Published: March 2023

Nowadays, studying heavy rainfall events, characterized by significant rainfall depth concentrated in short durations, and by the presence of lightning, downbursts, and hail, is extremely important. The increasing attention to these phenomena is due to the fact that they may determine serious impacts on the population, economic activities, and the environment. Among heavy rainfall events, high-intensity and short-duration ones, are usually associated with the occurrence of convective cells.

Since these events have been occurring in a more frequent way over the last two decades as a climate change effect and the Mediterranean area is considered one of the most prone areas to this type of event, this study focuses on the identification of heavy rainfall over Sicily, i.e., the biggest island of the Mediterranean Sea. The high-resolution rainfall time series (i.e., 10 minutes) here analyzed have been collected by the rain gauge network of the Servizio Informativo Agrometeorologico Siciliano (SIAS) within the period 2002 - 2021.

Given that convective cells are usually characterized by high lightning activity, their detection has been carried out by means of a lightning dataset of Blitzortung, providing the location and time of lightning strikes for all of Europe on a daily scale since 2015. To reach this goal, different searching radii centred on the rain gauges and some conditions to weigh the distance between lightning strikes to the gauge, have been considered. This allowed exploring how far the lightning activity developed from the rain gauge, where rainfall is recorded.

The detection of convective precipitation through lightning data has been then improved by using some reanalysis data, such as the Convective Available Potential Energy (CAPE), and the K-Index, from the ERA-5 database of the European Centre for Medium-Range Weather Forecasts (ECMWF). 
Generation of AI-based proxy radar data from GK2A/AMI data in East Asia
Authors: Park Ki-Hong , Lee Heeyong , Sohn Eunha
Published: March 2023

Remote sensing data such as weather satellites and radars with the high spatio-temporal resolution are importantly used for severe weather monitoring. Geostationary meteorological satellites can observe clouds in a wide area, and the weather radar can obtain rainfall intensity information close to the ground truth through raindrop particle observation. These remote sensing data are collected as real-time images, which are suitable for application to image convolution-based artificial intelligence (AI) models. In this study, we developed an AI-based model for the generation of proxy radar data from the Geo-Kompsat-2A (GK2A) satellite data. As an AI model structure, an image-to-image translation technique called Pix2PixCC which is based on a conditional Generative Adversarial Network (cGAN) was used. For the training model, GK2A satellite data and composite data of radar reflectivity with Korea, Japan, and China were used. In order to solve the memory issue of AI model learning using high-performance GPU, the model was trained by dividing the image into 256x256 patch sizes. This AI model made it possible to produce real-time proxy radar data without gaps in the East Asian region, and this proxy data showed a similar shape to the real radar data compared to precipitation information from geostationary meteorological satellites in traditional methods. In future studies, we plan to improve the accuracy by applying a custom loss function that applies a high weight to the high reflectivity of radar.
Ability of the new high-resolution atmospheric reanalysis ALADIN/PERUN to simulate heavy convective precipitation
Authors: Bliznak Vojtech , Zacharov Petr
Published: March 2023

Atmospheric reanalysis are powerful tool to obtain information about the past state of the atmosphere by combination of the observations and numerical weather prediction (NWP) modelling through various data assimilation schemes. Major developments in NWP modelling have recently enabled to increase model resolution by nesting the regional reanalysis into the global reanalysis using a limited-area model. However, the relatively small area limits the use of those reanalyses for further meteorological, climatological and/or hydrological applications. A new atmospheric reanalysis ALADIN/PERUN was recently calculated to simulate various meteorological variables at high horizontal (2.3 km) and temporal (1 h) resolution over most of Europe in the 1989–2020 time span. Due to the high-resolution of the reanalysed data, precipitation fields can well capture the small-scale processes and thus well reproduce heavy convective precipitation. The contribution will evaluate this ability using adjusted radar-derived precipitation estimates as ground truth in the warm parts of the years 2002-2019 over the Czech Republic. Heavy convective precipitation will be selected from the dataset using defined criteria and their location and total amount will be evaluated for two different NWP model runs. The first one (A) includes the full assimilation of the observed data every 6 hours using the 4D-VAR assimilation scheme. The second one (B) uses only the boundary conditions from the ERA-5 global reanalysis and the prognostic data are not modified in any way. Comparing the two runs will provide us with information about the level of physical description in the NWP model as well as the effect of assimilation on the resulting precipitation fields.
A Verification of Hail Proxies with Insurance Data
Authors: Fischer Jannick , Kunz Michael
Published: March 2023

Information about the hail threat of a thunderstorm is typically limited to rather indirect data from remote sensing or reanalysis. Ground observations of maximum hail diameters provide a more accurate assessment but often suffer from other problems, such as limited or non-uniform coverage. Despite these shortcomings, the above data sources are commonly used for nowcasting of hail producing storms and in hail climatologies. However, only very few studies have actually compared the skill of these different proxies. One reason for this is the lack of ground truth data which could be used to verify whether damaging hail was falling. This is because of the described issues with hail reports, and the fact that insurance datasets, which could provide a more reliable confirmation of hail damage, are usually not made available for research.

To fill this gap, this study uses a 5-year dataset of crop damage claims of a German agricultural insurance. These insurance claims cover large parts of Germany and most accurately reflect hail damage during the growing season of crops from May to August, which is also the time of year with the strongest thunderstorm activity. The damage claims are used to verify and compare common proxies for hail, which include the radar-based (I) column maximum reflectivity and (II) hail tracks using the more refined TRACE3D tracking algorithm, (III) lightning density, (IV) European Severe Weather Database hail reports, (V) observed overshooting tops from geostationary satellites, and (VI) microwave imager hail signatures from polar-orbiting satellites. Their skill in predicting damaging hail is assessed by categorical verification with probability of detection, false-alarm rate, and Heidke Skill Score, including a sensitivity analysis to varying thresholds.

Preliminary results based on 30 events in 2014 indicate that none of the proxies alone can predict hail damage with high accuracy. However, all of them show at least some skill, except for the microwave imager. The radar-based predictors show the largest skill on average. If these findings can be confirmed over more cases, while also including null cases, this would support the use of proxies I-V for hail climatologies but discourage nowcasts of hail for an individual thunderstorm with one of the proxies alone.
Severe Weather Impact Database and Impact-based Forecasts by Utilizing Machine-Learning Technology
Authors: Punkka Ari-Juhani , Kämäräinen Matti
Published: March 2023

The availability of various digitized data sources has been steeply increasing during the last 15 years. However, in Finland next to no efforts have been made to gather and normalize weather-related impact data into a single data storage. To fill this gap of information a two-part project called SILVA was carried out in 2020-2022 to scan, collect and store weather-related impact data. In all, a dozen different data sources were identified ranging from electricity supply disruptions and road traffic accidents to airport traffic and train traffic flow anomalies. Most data series covered the whole country and ranged several years back in time. All datasets included time, location, and magnitude or strength of the impact events.

Once the impact events had been stored into the SILVA database they were utilized in various ways. First, some real-time impact data were combined with conventional meteorological data to offer quick impact views for nowcasting purposes (electricity supply disruptions, observed wind gusts and lightning observations etc.). Second, archived impact data and historical ECMWF weather forecasts were used for the training of gradient boosting machine-learning methodology. As a result, seven-day impact-based forecasts for the amount of damage clearance tasks, wildfire fighting tasks, traffic accidents and pedestrian slipping accidents were generated for each Finnish administrative region.

During the latter half of the project a seven-month pilot phase was carried out. More than 30 organizations attended the pilot, and they were provided with real-time weather observations, weather warnings, severe weather outlooks as well as novel impact-based forecasts. During the pilot phase severe weather forecasters gave video briefings ahead of each potentially interesting weather episode to make the users familiar with the pilot products. Briefings were held ten times, prior to blizzard, severe thunderstorm, wildfire, and synoptic-scale windstorm cases.

Preliminary verification results and the feedback gathered from the pilot users have been encouraging. The impact-based forecasts have been warmly welcomed by the users although some expected forecast pitfalls have been reported especially in relation to severe thunderstorm impacts. The users gave an overall rating of 4/5 on the project and rating of 4.3/5 on the future development potential.

Product example: 5-day impact-based forecast on the amount of wind damage clearance tasks for the Finnish administrative regions. Forecast issued on the 6th of August 2022 ahead of an severe thunderstorm event.