The FLASH Project: using lightning data to better understand and predict flash floods

Price, Colin; Yair, Yoav; Mugnai, A.; Lagouvardos, K.; Llasat, María Carmen; Michaelides, Silas; Dayan, Uri; Dietrich, Stefano; Galanti, Eli; Garrote, Luís; Harats, N.; Katsanos, Dimitrios; Kohn, M.; Kotroni, Vassiliki; Llasat-Botija, Montserrat; Lynn, Barry; Mediero, Luis; Morin, Efrat; Nicolaides, K.; Rozalis, S.; Savvidou, K.; Ziv, Baruch

doi:10.1016/j.envsci.2011.03.004

Cited by 36 publications

(21 citation statements)

References 29 publications

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“…The present work analyses four flash flood events with great social impact that were studied within the framework of the FLASH project (Price et al, 2011). The most relevant rainfall amounts for these cases are detailed in Table 1.…”

Section: Case Studies and Datamentioning

confidence: 99%

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

Atencia

Mediero

Llasat

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. The performance of a hydrologic model depends on the rainfall input data, both spatially and temporally. As the spatial distribution of rainfall exerts a great influence on both runoff volumes and peak flows, the use of a distributed hydrologic model can improve the results in the case of convective rainfall in a basin where the storm area is smaller than the basin area. The aim of this study was to perform a sensitivity analysis of the rainfall time resolution on the results of a distributed hydrologic model in a flash-flood prone basin. Within such a catchment, floods are produced by heavy rainfall events with a large convective component. A second objective of the current paper is the proposal of a methodology that improves the radar rainfall estimation at a higher spatial and temporal resolution. Composite radar data from a network of three C-band radars with 6-min temporal and 2 × 2 km 2 spatial resolution were used to feed the RIBS distributed hydrological model. A modification of the Window Probability Matching Method (gaugeadjustment method) was applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation by computing new Z/R relationships for both convective and stratiform reflectivities. An advection correction technique based on the cross-correlation between two consecutive images was introduced to obtain several time resolutions from 1 min to 30 min. The RIBS hydrologic model was calibrated using a probabilistic approach based on a multiobjective methodology for each time resolution. A sensitivity analysis of rainfall time resolution was conducted to find the resolution that best represents the hydrological basin behaviour.

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Section: Case Studies and Datamentioning

confidence: 99%

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

Atencia

Mediero

Llasat

et al. 2011

Hydrol. Earth Syst. Sci.

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“…The data are especially interesting for monitoring of severe storms in regions where no or insufficient other high-resolution data are available for observation of deep convection, for example over the Mediterranean Sea (Price et al, 2011;Kohn et al, 2010), in data-sparse areas of the Alps (Bertram and Mayr, 2004), and for tropical cyclones over oceans (Demetriades and Holle, 2006). Because of low detection efficiencies in the areas of interest, these studies are restricted to persistent, long-lived storm types with strong electrical discharges.…”

Section: K Meyer Et Al: Automated Thunderstorm Trackingmentioning

confidence: 99%

Automated thunderstorm tracking: utilization of three-dimensional lightning and radar data

2013

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This paper presents a new hybrid method for automated thunderstorm observation by tracking and monitoring of electrically charged cells (ec-TRAM). The developed algorithm combines information about intense ground precipitation derived from low-level radar-reflectivity scans with three-dimensionally resolved lightning data, which are provided by the European VLF/LF lightning detection network LINET. Based on the already existing automated radar tracker rad-TRAM (Kober and Tafferner, 2009), the new method li-TRAM identifies and tracks electrically active regions in thunderclouds using lightning data only. The algorithm ec-TRAM uses the output of the two autonomously operating routines rad-TRAM and li-TRAM in order to assess, track, and monitor a more comprehensive picture of thunderstorms. The main motivation of this work is to assess the benefit of three-dimensionally resolved total lightning (TL) information for thunderstorm tracking and monitoring. The focus is laid on the temporal development whereby TL is characterized by an effective in-cloud (IC) and cloud-to-ground (CG) event discrimination. It is found that the algorithms li-TRAM and ec-TRAM are both feasible methods for thunderstorm monitoring with potential for nowcasting. The tracking performance of li-TRAM turns out to be comparable to that of rad-TRAM, a result that strongly encourages utilization of lightning data as independent data source for thunderstorm tracking. It is found that lightning data allow an accurate and close monitoring of storm regions with intense internal dynamics as soon as convection induces electrical activity. A case study shows that the current short-term storm dynamics are clearly reflected in the amount of strokes, change of stroke rates and IC/CG ratio. The hybrid method ec-TRAM outperforms rad-TRAM and li-TRAM regarding reliability and continuous assessment of storm tracks especially in more complexly developing storms, where the use of discharge information contributes to more detailed information about storm stage and storm evolution

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“…However, this potential has not been fully explored. In Europe, where there is a less dense lightning network than in the United States, the ''FLASH'' project (Price et al 2011) exists, where lightning is utilized to identify and locate convective precipitation, although not as a precipitation estimator. Likewise, in southern Arizona, a region where radar coverage is strongly affected by topography, the National Weather Service Office in Tucson, Arizona, uses lightning strike location as a subjective way to locate thunderstorms, but not for precipitation estimation (J. Brost, National Weather Service, 2010, personal communication).…”

Section: Introductionmentioning

confidence: 99%

An Improved QPE over Complex Terrain Employing Cloud-to-Ground Lightning Occurrences

Minjarez-Sosa

Castro

Cummins

et al. 2017

Journal of Applied Meteorology and Climatology

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A lightning-precipitation relationship (LPR) is studied at high temporal and spatial resolution (5 min and 5 km). As a proof of concept of these methods, precipitation data are retrieved from the National Severe Storms Laboratory (NSSL) NMQ product for southern Arizona and western Texas while lightning data are provided by the National Lightning Detection Network (NLDN). A spatial-and time-invariant (STI) linear model that considers spatial neighbors and time lags is proposed. A data denial analysis is performed over Midland, Texas (a region with good sensor coverage), with this STI model. The LPR is unchanged and essentially equal, regardless of the domain (denial or complete) used to obtain the STI model coefficients. It is argued that precipitation can be estimated over regions with poor sensor coverage (i.e., southern Arizona) by calibrating the LPR over well-covered domains that are experiencing similar storm conditions. To obtain a lightning-estimated precipitation that dynamically updates the model coefficients in time, a Kalman filter is applied to the STI model. The correlation between the observed and estimated precipitation is statistically significant for both models, but the Kalman filter provides a better precipitation estimation. The best demonstration of this application is a heavy-precipitation, high-frequency lightning event in southern Arizona over a region with poor radar and rain gauge coverage. By calibrating the Kalman filter over a data-covered domain, the lightning-estimated precipitation is considerably greater than that estimated by radar alone. Therefore, for regions where both rain gauge and radar data are compromised, lightning provides a viable alternative for improving QPE.

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The FLASH Project: using lightning data to better understand and predict flash floods

Cited by 36 publications

References 29 publications

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

Automated thunderstorm tracking: utilization of three-dimensional lightning and radar data

An Improved QPE over Complex Terrain Employing Cloud-to-Ground Lightning Occurrences

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