Object-based analysis of simulated thunderstorms in Switzerland: application and validation of automated thunderstorm tracking with simulation data

We present a feasibility study for an object-based method to characterise thunderstorm properties in simulation data from convection-permitting weather models. An existing thunderstorm tracker, the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm, was applied to thunderstorms simulated by the Advanced Research Weather Research and Forecasting (AR-WRF) weather model at convection-permitting resolution for a domain centred on Switzerland. Three WRF microphysics parameterisations were tested. The results are compared to independent radar-based observations of thunderstorms derived using the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm. TRT was specifically designed to track thunderstorms over the complex Alpine topography of Switzerland. The object-based approach produces statistics on the simulated thunderstorms that can be compared to object-based observation data. The results indicate that the simulations underestimated the occurrence of severe and very large hail compared to the observations. Other properties, including the number of storm cells per day, geographical storm hotspots, thunderstorm diurnal cycles, and storm movement directions and velocities, provide a reasonable match to the observations, which shows the feasibility of the technique for characterisation of simulated thunderstorms over complex terrain.

Identifying Storm Hotspots and the Most Unsettled Areas in Barcelona by Analysing Significant Rainfall Episodes from 2013 to 2018

Urban floods repeatedly threaten Barcelona, damaging the city infrastructure and endangering the safety of the population. The urban planning of the city, the socioeconomic distribution, its topography, and the characteristics of precipitation systems translate into these flood events having a heterogeneous effect across the city. It means that the coping capacity has a strong dependence on local factors that must be considered when management plans are developed by the municipality. This work aims to contribute to the better knowledge of precipitation structures associated with heavy rainfall events and floods in Barcelona based on radar data and an urban rain gauge network. Radar data have been provided by the Meteorological Service of Catalonia (SMC), while precipitation data, impact data, and early warnings, have been provided by Barcelona Cicle de l’Aigua S.A. (BCASA), for the period 2013–2018. A new radar-based methodology has been developed to identify convective rainfall structures from radar reflectivity volumes (CAPPI and TOP products) to make the analysis easier. The high computing speed of the procedure allows efficient analysis of a large set of convective cells without scarifying temporal resolution of radar data. Both rainfall fields (radar and rain gauge, respectively) have been compared. Then through the identified rainfall convective structures, thunderstorm hotspots have been identified. Considering an alert indicator from BCASA and the reported incidents, episodes with the highest impact have been analysed in depth. Results show 207 significant rainfall episodes in the ROI for the six years, which are mainly concentrated between September and November. The fact that significant episodes are usually produced by highly convective rain corroborates the advantage of using radar images as a tool to detect any maxima even when no rain gauge is there. In 64 of the episodes, the level of pre-alert was achieved with a maximum frequency between August and September. The proposed algorithm shows more than 8000 centroids of convective cells from 189 cases. Whilst maximum surface reflectivity over 45 dBZ is more prone to occur near the coastline, the centroids of storm cells tend to concentrate more inland. The final objective is to improve the actions taken by the organisation responsible for managing urban floods, which have seen Barcelona recognised as a model city for flood resilience by the United Nations.

Object-based tracking and nowcasting of gust fronts

<p>Thunderstorm gust fronts threaten human safety and property, especially in industries such as aviation and construction. The ability to predict the precise time and location of gust front arrivals would mitigate risk and reduce damage. </p><p>Existing methods for nowcasting gust front locations are based on detecting the gust fronts from individual Doppler weather radars or scanning lidars. Even though these methods are locally effective, they have so far not been applied to large-scale radar mosaics to generate forecasts that could benefit society at large. To address this gap, an object-based method is proposed for nowcasting gust fronts by any number of ground-based Doppler weather radars.  </p><p>The gust fronts are first detected from the radar measurements and presented as objects consisting of spline curves. Given the one-dimensional geometry of the curves, existing object-based tracking methods, designed for tracking thunderstorms and based on two-dimensional polygons, cannot be applied to the gust front objects. Instead, a tracking method is formulated that matches multiple observations of the same gust front based on the location and length of the curves. The tracking considers possible splitting and merging of the gust front objects. After matching the gust front instances between consecutive timesteps, the location of the gust front is nowcast with a Kalman filter algorithm.  </p><p>The methodology is demonstrated with case studies of gust fronts related to mesoscale convective systems (MCS) in Finland. MCSs occur frequently in Finland during summer and cause significant wind and other storm-related damage. Spatially and temporally accurate forecasting of MCS events would aid preparedness and reduce the risk posed to society. The methodology presented in this work can be used to nowcast the gust front trajectory and thus increase preparedness especially for the wind damage related to MCS events. The methodology can also be combined with existing object-based methods for nowcasting convective storm cells, to create comprehensive hazard forecasting systems for thunderstorms.</p>

Evaluation of WRF microphysics schemes in the simulation of a squall line over IRAN using radar and reanalysis data

A squall line was recorded in Dayyer port over southwest of Iran, on 19 Mar 2017. In the present paper, we have simulated the characteristic features associated with the squall line by Weather Research and Forecasting (WRF) model using five different microphysics (MP) schemes. For validating the simulated characteristics of the squall line, the latitude-height and longitude-height cross section reflectivity and precipitation value derived from observed reflectivity gathered by Doppler Weather Radar at Bushehr, synoptic weather station data at Dayyer port along with NCEP-NCAR and ERA-INTERIM reanalyzes data were used. To verify the simulated precipitation, the Fractions Skill Score (FSS) curve was calculated. Examining the simulation results for geopotential and sea level pressure show that the model simulations using different MP schemes, agree well with the verifying reanalyzes. Also, the spatial rainfall distribution of simulations and verifying observations did not show big differences. However, there are significant differences in the details of simulations such as the maximum reflectivity of the convective cells, vertical extent of the storm cells, speed and direction of the wind, rainfall values and FSS curves. Though, all of the simulations have shown convective cells over Dayyer port at the time of occurrence of the squall line, but, only the model simulation using Lin MP scheme is consistent with the corresponding radar reflectivity and vertical extent. The FSS chart showed that the skill changes with spatial scale. Results using Lin microphysics scheme crossed the FSSuniform line at lower scales when compared to other MP schemes.

Object-based analysis of simulated thunderstorms in Switzerland: application and validation of automated thunderstorm tracking on simulation data

We present a feasibility study for an object-based method to characterise thunderstorm properties in simulation data from convection-permitting weather models. An existing thunderstorm tracker, the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm, was applied to thunderstorms simulated by the Advanced Research Weather Research and Forecasting (AR-WRF) weather model at convection-permitting resolution for a domain centred on Switzerland. Three WRF microphysics parameterisations were tested. The results are compared to independent radar-based observations of thunderstorms derived using the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm. TRT was specifically designed to track thunderstorms over the complex Alpine topography of Switzerland. The object-based approach produces statistics on the simulated thunderstorms that can be compared to object-based observation data. The results indicate that the simulations underestimated the occurrence of severe and very large hail compared to the observations. Other properties, including the number of storm cells per day, geographical storm hotspots, thunderstorm diurnal cycles, and storm movement directions and velocities, provide a reasonable match to the observations, which shows the feasibility of the technique for characterisation of simulated thunderstorms over complex terrain.

Large-Sample Application of Radar Reflectivity Object-Based Verification to Evaluate HRRR Warm-Season Forecasts

The Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR from April – September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern US during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE.HRRR tends to over-forecast all objects, but substantially over-forecasts both small objects at low reflectivity thresholds and large objects at high reflectivity thresholds. HRRR tends to either under-forecast objects in the southern and central Plains or has a correct frequency bias there, whereas it over-forecasts objects across the southern and eastern US. Attribute comparisons reveal the inability of the HRRR to fully resolve convective scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts.Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke Skill Score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.

Dynamic rainfall mapping using multi-radar multi-gauge observations in changing synoptic environments

<p>It is well known that the performance of radar-derived quantitative precipitation estimates greatly relies on the physical model of the raindrop size distribution (DSD) and the relation between the physical model and radar parameters. However, incorporating changing precipitation microphysics to dynamically adjust the radar reflectivity (Z) and rain rate (R) relations can be challenging for real-time applications. In this study, two adaptive radar rainfall approaches are developed based on the radar-gauge feedback mechanism using 16 S-band Doppler weather radars and 4579 surface rain gauges deployed over the Eastern JiangHuai River Basin (EJRB) in China. Although the Z–R relations in both approaches are dynamically adjusted within a single precipitation system, one is using a single global optimal (SGO) Z–R relation, whereas the other is using different Z–R relations for different storm cells identified by a storm cell identification and tracking (SCIT) algorithm. Four precipitation events featured by different rainfall microphysical characteristics are investigated to demonstrate the performances of these two rainfall mapping methodologies. In addition, the short-term vertical profile of reflectivity (VPR) clusters are extensively analyzed to resolve the storm-scale characteristics of different storm cells. The verification results based on independent gauge observations show that both rainfall estimation approaches with dynamic Z–R relations perform much better than fixed Z–R relations. The adaptive approach incorporating the SCIT algorithm and real-time gauge measurements performs best since it can better capture the spatial variability and temporal evolution of precipitation.</p>

C-Band Dual-Doppler Retrievals in Complex Terrain: Improving the Knowledge of Severe Storm Dynamics in Catalonia

Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn, with severe weather occurring 33 days per year on average. In some cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ propagation mechanisms. Partly due to the local nature of the events, numerical weather prediction models are not able to accurately nowcast the complex mesoscale mechanisms (i.e., local influence of topography). This directly impacts the retrieved position and motion of the storms, and consequently, the likely associated storm severity. Although a successful warning system based on lightning and radar observations has been developed, there remains a lack of knowledge of storm dynamics that could lead to forecast improvements. The present study explores the capabilities of the radar network at the Meteorological Service of Catalonia to retrieve dual-Doppler wind fields to study the dynamics of Catalan thunderstorms. A severe thunderstorm that splits and a tornado-producing supercell that is channeled through a valley are used to demonstrate the capabilities of an advanced open source technique that retrieves dynamical variables from C-band operational radars in complex terrain. For the first time in the Iberian Peninsula, complete 3D storm-relative winds are obtained, providing information about the internal dynamics of the storms. This aids in the analyses of the interaction between different storm cells within a system and/or the interaction of the cells with the local topography.

Blob Extraction Algorithm in Detection of Convective Cells for Data Fusion

Earth’s atmosphere is monitored by a multitude of sensors. It is the troposphere that is of crucial importance for human activity, as it is there that the weather phenomena take place. Weather observations are performed by surface sensors monitoring, inter alia, humidity, temperature and winds. In order to observe the developments taking place in the atmosphere, especially in the clouds, weather radars are commonly used. They monitor severe weather that is associated with storm clouds, cumulonimbuses, which create precipitation visible on radar screens. Therefore, radar images can be utilized to track storm clouds in a data fusion system. In this paper an algorithm is developed for the extraction of blobs (interesting areas in radar imagery) used within data fusion systems to track storm cells. The algorithm has been tested with the use of real data sourced from a weather radar network. 100% of convection cells were detected, with 90% of them being actual thunderstorms.