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Deep convection is a threat to many human activities, with a great impact on aviation safety. On 7 July 2017, a widespread torrential precipitation event (associated with a cut-off low at mid-levels) was registered in the vicinity of Madrid, causing serious flight disruptions. During this type of episode, accurate short-term forecasts are key to minimizing risks to aviation. The aim of this research is to improve early warning systems by obtaining the best WRF model setup. In this paper, the aforementioned event was simulated. Various model configurations were produced using four different physics parameterizations, 3-km and 1-km domain resolutions, and 0.25° and 1° initial condition resolutions. Simulations were validated using data from 17 rain gauge stations. Two validation indices are proposed, accounting for the temporal behaviour of the model. Results show significant differences between microphysics parameterizations. Validation of domain resolution shows that improvement from 3 to 1 km is negligible. Interestingly, the 0.25° resolution for initial conditions produced poor results compared with 1°. This may be linked to a timing error, because precipitation was simulated further east than observed. The use of ensembles generated by combining different WRF model configurations produced reliable precipitation estimates. [ABSTRACT FROM AUTHOR]

Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite rainfall products are exploited to study the spatial and temporal variability of East Africa (EA, 5S–20N, 28–52E) rainfall between 1983 and 2015. Time series of selected rainfall indices from the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices are computed at yearly and seasonal scales. Rainfall climatology and spatial patterns of variability are extracted via the analysis of the total rainfall amount (PRCPTOT), the simple daily intensity (SDII), the number of precipitating days (R1), the number of consecutive dry and wet days (CDD and CWD), and the number of very heavy precipitating days (R20). Our results show that the spatial patterns of such trends depend on the selected rainfall product, as much as on the geographic areas characterized by statistically significant trends for a specific rainfall index. Nevertheless, indications of rainfall trends were extracted especially at the seasonal scale. Increasing trends were identified for the October–November–December PRCPTOT, R1, and SDII indices over eastern EA, with the exception of Kenya. In March–April–May, rainfall is decreasing over a large part of EA, as demonstrated by negative trends of PRCPTOT, R1, CWD, and R20, even if a complete convergence of all satellite products is not achieved. [ABSTRACT FROM AUTHOR]