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

Abstract. 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.

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

10.5194/gmd-2021-105 ◽

2021 ◽

Author(s):

Timothy Hugh Raupach ◽

Andrey Martynov ◽

Luca Nisi ◽

Alessandro Hering ◽

Yannick Barton ◽

...

Keyword(s):

Feasibility Study ◽

Complex Terrain ◽

Weather Research And Forecasting ◽

Observation Data ◽

Simulation Data ◽

Radar Tracking ◽

Diurnal Cycles ◽

Weather Model ◽

Object Based ◽

Storm Cells

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Assessing High-Resolution Weather Research and Forecasting (WRF) Forecasts Using an Object-Based Diagnostic Evaluation

10.21236/ada595323 ◽

2014 ◽

Author(s):

Gail Vaucher ◽

John Raby

Keyword(s):

High Resolution ◽

Diagnostic Evaluation ◽

Weather Research And Forecasting ◽

Object Based ◽

Weather Research

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

Weather and Forecasting ◽

10.1175/waf-d-20-0203.1 ◽

2021 ◽

Author(s):

Jeffrey D. Duda ◽

David D. Turner

Keyword(s):

Warm Season ◽

Skill Score ◽

Central Plains ◽

Attribute Weights ◽

Object Based ◽

Convective Scale ◽

Heidke Skill Score ◽

Sample Application ◽

Storm Cells ◽

The Impact

AbstractThe 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.

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

Remote Sensing ◽

10.3390/rs12182930 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2930 ◽

Cited By ~ 1

Author(s):

Anna del Moral ◽

Tammy M. Weckwerth ◽

Tomeu Rigo ◽

Michael M. Bell ◽

María Carmen Llasat

Keyword(s):

Complex Terrain ◽

Prediction Models ◽

Weather Prediction ◽

Warning System ◽

Wind Fields ◽

Severe Thunderstorm ◽

Radar Network ◽

Storm Dynamics ◽

Storm Cells ◽

First Time

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.

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A Set of Methods to Support Object-Based Distributed Analysis of Large Volumes of Earth Observation Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2016.2636362 ◽

2017 ◽

Vol 10 (2) ◽

pp. 681-690 ◽

Cited By ~ 5

Author(s):

Rodrigo S. Ferreira ◽

Cristiana Bentes ◽

Gilson A. O. P. Costa ◽

Dario A. B. Oliveira ◽

Patrick N. Happ ◽

...

Keyword(s):

Earth Observation ◽

Observation Data ◽

Object Based ◽

Earth Observation Data ◽

Distributed Analysis

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Evaluation of the Weather Research and Forecasting (WRF) model with respect to wind in complex terrain

Journal of Physics Conference Series ◽

10.1088/1742-6596/1102/1/012011 ◽

2018 ◽

Vol 1102 ◽

pp. 012011 ◽

Cited By ~ 3

Author(s):

Kine Solbakken ◽

Yngve Birkelund

Keyword(s):

Complex Terrain ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Weather Research

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A Study on Microscale Wind Simulations with a Coupled WRF–CFD Model in the Chongli Mountain Region of Hebei Province, China

Atmosphere ◽

10.3390/atmos10120731 ◽

2019 ◽

Vol 10 (12) ◽

pp. 731

Author(s):

Shaohui Li ◽

Xuejin Sun ◽

Shan Zhang ◽

Shijun Zhao ◽

Riwei Zhang

Keyword(s):

Wind Field ◽

Complex Terrain ◽

Wrf Model ◽

Mountain Region ◽

Weather Research And Forecasting ◽

Cfd Model ◽

Improved Method ◽

Comprehensive Understanding ◽

Cfd Models ◽

Computational Fluid Dynamics Cfd

To ensure successful hosting of the 2022 Olympic Winter Games, a comprehensive understanding of the wind field characteristics in the Chongli Mountain region is essential. The purpose of this research was to accurately simulate the microscale wind in the Chongli Mountain region. Coupling the Weather Research and Forecasting (WRF) model with a computational fluid dynamics (CFD) model is a method for simulating the microscale wind field over complex terrain. The performance of the WRF-CFD model in the Chongli Mountain region was enhanced from two aspects. First, as WRF offers multiple physical schemes, a sensitivity analysis was performed to evaluate which scheme provided the best boundary condition for CFD. Second, to solve the problem of terrain differences between the WRF and CFD models, an improved method capable of coupling these two models is proposed. The results show that these improvements can enhance the performance of the WRF-CFD model and produce a more accurate microscale simulation of the wind field in the Chongli Mountain region.

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Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-Yu Season of 2008 in Taiwan. Part III: Application of an Object-Oriented Verification Method

Atmosphere ◽

10.3390/atmos11070705 ◽

2020 ◽

Vol 11 (7) ◽

pp. 705

Author(s):

Chung-Chieh Wang ◽

Sahana Paul ◽

Dong-In Lee

Keyword(s):

Object Oriented ◽

Tropical Rainfall Measuring Mission ◽

Weather Research And Forecasting ◽

Total Rainfall ◽

Mesoscale Models ◽

Subjective Analysis ◽

Verification Method ◽

Rain Gauges ◽

Object Based ◽

Central Weather Bureau

In this study, the performances of Mei-yu (May–June) quantitative precipitation forecasts (QPFs) in Taiwan by three mesoscale models: the Cloud-Resolving Storm Simulator (CReSS), the Central Weather Bureau (CWB) Weather Research and Forecasting (WRF), and the CWB Non-hydrostatic Forecast System (NFS) are explored and compared using an newly-developed object-oriented verification method, with particular focus on the various properties or attributes of rainfall objects identified. Against a merged dataset from ~400 rain gauges in Taiwan and the Tropical Rainfall Measuring Mission (TRMM) data in the 2008 season, the object-based analysis is carried out to complement the subjective analysis in a parallel study. The Mei-yu QPF skill is seen to vary with different aspects of rainfall objects among the three models. The CReSS model has a total rainfall production closest to the observation but a large number of smaller objects, resulting in more frequent and concentrated rainfall. In contrast, both WRF and NFS tend to under-forecast the number of objects and total rainfall, but with a higher proportion of bigger objects. Location errors inferred from object centroid locations appear in all three models, as CReSS, NFS, and WRF exhibit a tendency to simulate objects slightly south, east, and northwest with respect to the observation. Most rainfall objects are aligned close to an E–W direction in CReSS, in best agreement with the observation, but many towards the NE–SW direction in both WRF and NFS. For each model, the objects are matched with the observed ones, and the results of the matched pairs are also discussed. Overall, though preliminarily, the CReSS model, with a finer grid size, emerges as best performing model for Mei-yu QPFs.

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Analysis and Estimation of Geographical and Topographic Influencing Factors for Precipitation Distribution over Complex Terrains: A Case of the Northeast Slope of the Qinghai–Tibet Plateau

Atmosphere ◽

10.3390/atmos9090349 ◽

2018 ◽

Vol 9 (9) ◽

pp. 349 ◽

Cited By ~ 3

Author(s):

Weicheng Liu ◽

Qiang Zhang ◽

Zhao Fu ◽

Xiaoyan Chen ◽

Hong Li

Keyword(s):

Complex Terrain ◽

Multivariate Regression ◽

Estimation Method ◽

Precipitation Forecast ◽

Tibet Plateau ◽

Mean Annual Precipitation ◽

Observation Data ◽

Precipitation Distribution ◽

Precipitation Estimation ◽

Qinghai Tibet Plateau

Due to the complex terrain, sparse precipitation observation sites, and uneven distribution of precipitation in the northeastern slope of the Qinghai–Tibet Plateau, it is necessary to establish a precipitation estimation method with strong applicability. In this study, the precipitation observation data from meteorological stations in the northeast slope of the Qinghai–Tibet Plateau and 11 geographical and topographic factors related to precipitation distribution were used to analyze the main factors affecting precipitation distribution. Based on the above, a multivariate linear regression precipitation estimation model was established. The results revealed that precipitation is negatively related to latitude and elevation, but positively related to longitude and slope for stations with an elevation below 1700 m. Meanwhile, precipitation shows positive correlations with both latitude and longitude, and negative correlations with elevation for stations with elevations above 1700 m. The established multivariate regression precipitation estimating model performs better at estimating the mean annual precipitation in autumn, summer, and spring, and is less accurate in winter. In contrast, the multivariate regression mode combined with the residual error correction method can effectively improve the precipitation forecast ability. The model is applicable to the unique natural geographical features of the northeast slope of the Qinghai–Tibet Plateau. The research results are of great significance for analyzing the temporal and spatial distribution pattern of precipitation in complex terrain areas.

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