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Jean-François Rysman, Yvon Lemaître, and Emmanuel Moreau


This study describes the main patterns of rainfall distribution in the Alps–Mediterranean “Euroregion” using a ground radar and characterizes the associated processes using model output. The radar dataset spans 2009–12 with fine spatial (1 km) and temporal (5 min) resolutions. The most significant rain accumulations were observed in 2009 and 2010, and the most intense extreme events occurred in 2010. Conversely, 2012 was a dry year. Model output revealed that the wind shear, the pressure, and the meridional wind at low level were the three main factors explaining the rainfall variability between 2009 and 2012. At the monthly scale, the maximum of rain accumulation was observed in November along the coast. Results also showed that the most intense rain rates were observed during early summer and autumn in the “Pre-Alps.” The monthly variability was characterized by a displacement of extreme rain events from land to sea from late spring to winter. Correlation analyses showed that this displacement was essentially controlled by the convective available potential energy (CAPE). Rainfall showed a diurnal variability from April to August for the land areas of the Alps–Mediterranean Euroregion. The diurnal variability was significant during the spring and summer months, with maximal rain intensity between 1600 and 1800 UTC. The correlation of the rainfall with CAPE showed that this cycle was related to atmospheric instability. A secondary peak in average rain rate was observed during the early morning and was likely triggered by land breezes. The results highlighted that rainfall characteristics are extremely diverse in terms of intensity and distribution in this relatively small region.

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Lisa Milani, Mark S. Kulie, Daniele Casella, Pierre E. Kirstetter, Giulia Panegrossi, Veljko Petkovic, Sarah E. Ringerud, Jean-François Rysman, Paolo Sanò, Nai-Yu Wang, Yalei You, and Gail Skofronick-Jackson


This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.

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