Nowcasting of hailstorms simulated by the NWP model COSMO for the area of the Czech Republic

Sokol, Zbyněk; Bližňák, Vojtěch; Zacharov, Petr; Skripniková, Kateřina

doi:10.1016/j.atmosres.2015.12.006

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…Kunkel et al, ), for the verification of numerical weather prediction (NWP) model output (e.g. Rajeevan et al, ; Spiridonov et al, ; Sokol et al, ), and for applications in the insurance sector (Botzen et al, ).…”

Section: Introductionmentioning

confidence: 99%

A 15‐year hail streak climatology for the Alpine region

Nisi

Hering

Germann

et al. 2018

Quart J Royal Meteoro Soc

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In this study, we present a unique 15‐year hail streak climatology for Switzerland based on volumetric radar reflectivity. Two radar‐based hail detection products and an automatic thunderstorm‐tracking algorithm were reprocessed for the extended convective season (April–September) between 2002 and 2016. More than 1.1 million convective cells were automatically tracked over the full radar domain, and over 191,000 storms and 31,000 hail streaks in the considered subdomain were selected for analysis following consistency and robustness tests. The year‐to‐year variability in the number of hailstorms reveals two types of convective seasons: (a) a few seasons with hail frequency far above the average, and (b) all other years with an average number of hailstorms. A high number of hailstorms in a particular year is not correlated with a higher number of convective storms in general, but is related to a greater fraction of severe storms. Convection initiation, hail initiation, and hail frequency maxima are located along the southern and northern foothills over the pre‐Alpine area and over the Jura mountains. Few hail streaks are present over the Alpine main ridge. Hail streak frequency and location is found to be strongly dependent on the synoptic‐scale weather regimes. This is important for monthly and seasonal outlooks, as well as for climate modelling. Analysis of storm life cycles shows that: (a) the majority of hail swaths contain only a single hail streak, (b) severe storms follow a more rapid evolution during their initial stages than do less severe storms, and (c) severe storms produce more spatially extended hail streaks. Finally, significant seasonal and diurnal cycles are present in most of the considered storm characteristics.

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Section: Introductionmentioning

confidence: 99%

A 15‐year hail streak climatology for the Alpine region

Nisi

Hering

Germann

et al. 2018

Quart J Royal Meteoro Soc

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“…The methods based on NWP can simulate the physical process inside the convective systems, but a long time is required for computing and forecast generation. With the significant progress of computing technology over these years, the NWP models of convective systems could be used for nowcasting (Sokol et al 2016), but they still have some limitations. For example, NWP models are sensitive to the initial conditions and also limited by the horizontal resolution (Bli z nák et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Nowcasting Multicell Short-Term Intense Precipitation Using Graph Models and Random Forests

Wang

Hou

et al. 2020

Monthly Weather Review

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Short-term intense precipitation (SIP; i.e., convective precipitation exceeding 20 mm per hour) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with graph model and random forest for the nowcasting of SIP. Different from the pixel by pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multi-cell convective systems. In particular, the following question could be addressed: will a multi-cell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multi-cell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves the probability of detection (POD) 79.2% and critical success index (CSI) 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms.

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