This study investigates the importance of convective systems for extreme rainfall along the northern coast of the Gulf of Guinea (GG) and their relationship with atmospheric and oceanic conditions. Convective system data (MCSs), daily precipitation, sea surface temperature (SST) and moisture flux anomalies from June to September 2007–2016 are used. The results show that 2/3 of MCSs crossing Abidjan are produced in June, which is the core of the major rainy season. Likewise, 2/3 of MCSs originate from continental areas, while 1/3 come from the ocean. Oceanic MCSs are mostly initiated close to the coast, which also corresponds to the Marine Heat Waves region. Continental MCSs are mostly initiated inland. The results also highlight the moisture flux contribution of three zones which have an impact on the onset and the sustaining of MCSs: (i) the seasonal migration of the intertropical convergence zone (ITCZ), (ii) the GG across the northern coastline, and finally (iii) the continent. These contributions of moisture fluxes coincide with oceanic warming off Northeast Brazil and the northern coast of the GG both two days before and the day of extreme rainfall events. The ocean contributes to moisten the atmosphere, and therefore to supply and sustain the MCSs during their lifecycle.

In the District of Abidjan, flooding typically occurs suddenly during intense rainfall events. The individual rainfall event provides the basic element for the study. Its analysis is required to develop solutions for managing the impact of extreme rainfall occurrences. Our study proposes to identify individual rainfall events that occurred in the District of Abidjan from a densified network and analyze some of their characteristics related to the amount of rainfall they provided, their duration, and their level of intensity. A total of 1240 individual rainfall events were identified between 2018 and 2021 using a network of 21 rain gauges. Rainfall events were identified based on criteria such as a minimum inter-event time without rainfall of 30 min, a detection threshold of 0.02 mm/5 min, a minimum duration of 30 min applicable to the average hyetograph, and a minimum of 1 mm of rainfall in at least one rain gauge. The analysis of characteristics related to accumulation, intensity, and duration showed that the rainfall events were essentially convective, with an average duration of more than 2 h and a rainfall of 11.30 mm/event. For 70% of the rainfall events of a mixed nature, the convective episodes last up to 33.33% of the total duration of the event and produce an average of 80% of the cumulative rainfall. The 30-min peak intensities generally occur in the first half of the event. Less than 13.5% of the events have peaks greater than 50 mm/h. The probability of observing more than two, three, or four events per day is high in June and October, the core of the two rainy seasons.

In radar quantitative precipitation estimates (QPE), the progressive evolution of rainfall algorithms has been guided by attempts to reduce the uncertainties in rainfall retrieval. However, because most of the algorithms are based on the linear dependence between radar and rain variables and designed for rain rates ranging from light to moderate rainfall, they result in misleading estimations of intense or strong rainfall rates. In this paper, based on extensive data gathered during the AMMA and Megha-Tropiques data campaigns, we provided a way to improve the estimation of intense rainfall rates from radar measurements. To this end, we designed a formulation of the QPE algorithm that accounts for the co-dependency between radar observables and rainfall rate using copula simulation synthetic datasets and using the quantile regression features for a more complete picture of covariate effects. The results show a clear improvement in heavy rainfall retrieval from radar data using copula-based R(K_DP) algorithms derived from a realistic simulated dataset. For a better performance, Gaussian copula-derived algorithms require a 0.8 percentile distribution to be considered. Conversely, lower percentiles are better for Student’s, Gumbel and HRT copula estimators when retrieving heavy rainfall rates (R > 30). This highlights the need to investigate the entire conditional distribution to determine the performance of radar rainfall estimators.

Commercial microwave links (CML) from cellular phone networks have become a source of opportunistic information on rainfall. In this study we use a dense network of 144 CMLs over the city of Niamey, in Niger, to estimate rainfall with a 15-minute resolution. The validation of these estimates with rain gauges shows that for links located within 2 km of a gauge the correlation of 15-minute time series is around 0.85, and the overall bias is low (0 to 10 % depending which link length and frequencies are considered). The CMLs are well suited to detect and quantify the intense rainfall rates associated with the mesoscale convective systems which bring most of the rain – and often floods – in the region. These good results are obtained with a simple and easily transferable processing chain. The study confirms the interest of CMLs in otherwise data scarce areas.