Abstract. The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) and the LIdar-Radiometer Inversion Code (LIRIC) provide the opportunity to study the aerosol vertical distribution by combining ground-based lidar and sun-photometric measurements. Here, we utilize the capabilities of both algorithms for the characterization of Saharan dust and marine particles, along with their mixtures, in the south-eastern Mediterranean during the CHARacterization of Aerosol mixtures of Dust and Marine origin Experiment (CHARADMExp). Three case studies are presented, focusing on dust-dominated, marinedominated and dust-marine mixing conditions. GARRLiC and LIRIC achieve a satisfactory characterization for the dust-dominated case in terms of particle microphysical properties and concentration profiles. The marine-dominated and the mixture cases are more challenging for both algorithms, although GARRLiC manages to provide more detailed microphysical retrievals compared to AERONET, while LIRIC effectively discriminates dust and marine particles in its concentration profile retrievals. The results are also compared with modelled dust and marine concentration profiles and surface in situ measurements.

The estimation of heavy precipitation events is a particularly difficult task, especially over high mountainous terrain typically associated with scant availability of in situ observations. Therefore, quantification of precipitation variability in such data‐limited regions relies on remote sensing estimates, due to their global coverage and near real‐time availability. However, strong underestimation of precipitation associated with low‐level orographic enhancement often limits the quantitative use of these data in applications. This study utilizes state‐of‐the‐art numerical weather prediction simulations, toward the reduction of quantitative errors in satellite precipitation estimates and an insight on the nature of detection limitations. Satellite precipitation products based on different retrieval algorithms (Climate Prediction Center morphing method, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks‐Cloud Classification System, and Global Satellite Mapping of Precipitation) are evaluated for their performance in a number of storm events over mountainous areas with distinct storm characteristics: Upper Blue Nile in Ethiopia and Alto Adige in NE Italy. High‐resolution (1 and 2 km) simulations from the Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System are used to derive adjustments to the magnitude of satellite estimates. Finally, a microphysical investigation is presented for occurrences of erroneous precipitation detection from the satellite instruments. Statistical indexes showcase improvement in numerical weather prediction‐adjusted satellite products and microphysical commodities among cases of no detection are discussed.

No abstract

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.