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Nikolaos S. Bartsotas, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, Stavros Solomos, and George Kallos


Flash floods develop over small spatiotemporal scales, an attribute that makes their predictability a particularly challenging task. The serious threat they pose for human lives, along with damage estimates that can exceed one billion U.S. dollars in some cases, urge toward more accurate forecasting. Recent advances in computational science combined with state-of-the-art atmospheric models allow atmospheric simulations at very fine (i.e., subkilometer) grid scales, an element that is deemed important for capturing the initiation and evolution of flash flood–triggering storms. This work provides some evidence on the relative gain that can be expected from the adoption of such subkilometer model grids. A necessary insight into the complex processes of these severe incidents is provided through the simulation of three flood-inducing heavy precipitation events in the Alps for a range of model grid scales (0.25, 1, and 4 km) with the Regional Atmospheric Modeling System–Integrated Community Limited Area Modeling System (RAMS–ICLAMS) atmospheric model. A distributed hydrologic model [Kinematic Local Excess Model (KLEM)] is forced with the various atmospheric simulation outputs to further evaluate the relative impact of atmospheric model resolution on the hydrologic prediction. The use of a finer grid is beneficial in most cases, yet there are events where the improvement is marginal. This underlines why the use of finer scales is a step in the right direction but not a solitary component of a successful flash flood–forecasting recipe.

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Xinxuan Zhang, Emmanouil N. Anagnostou, Maria Frediani, Stavros Solomos, and George Kallos


In this study, the authors investigate the use of high-resolution simulations from the Weather Research and Forecasting Model (WRF) for evaluating satellite rainfall biases of flood-inducing storms in mountainous areas. A probability matching approach is applied to evaluate a power-law relationship between satellite-retrieved and WRF-simulated rain rates over the storm domain. Satellite rainfall in this study is from the NOAA Climate Prediction Center morphing technique (CMORPH). Results are presented based on analyses of five heavy precipitation events that induced flash floods in northern Italy and southern France complex terrain basins. The WRF-based adjusted CMORPH rain rates exhibited improved error statistics against independent radar rainfall estimates. The authors show that the adjustment procedure reduces the underestimation of high rain rates, thus moderating the magnitude dependence of CMORPH rainfall bias. The Heidke skill score for the WRF-based adjusted CMORPH was consistently higher for a range of rain rate thresholds. This is an indication that the adjustment procedure ameliorates the satellite rain rates to provide a better estimation. Results also indicate that the low rain detection of CMORPH technique is also identifiable in the WRF–CMORPH comparison; however, the adjustment procedure herein does not incorporate this effect on the satellite rainfall bias adjustment.

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