There is an increasing number of rainfall products available over Africa and globally. Rainfall has considerable socio-economic impacts in sub-Saharan Africa, and the sparse gauge and radar networks make such estimates particularly valuable. They are used in many important applications such as drought/flood forecasting, water management or climate monitoring. The choice of which one to use has a significant influence on the output and performance of such applications. The large number of available rainfall products makes it difficult to select the “best” one for one’s need. Among the rainfall products, there is an increasing number of satellite-based estimates with ever finer resolution. They are particularly valuable in Africa where the gauge network is not dense enough to represent the high variability of the rainfall during the monsoon season. However, there are substantial differences between them. Rainfall events are moving systems which can be described by their positions and timings beside of their intensity. A position or timing error will also lead to mismatches in the rainfall occurrence or intensity. This is especially true for localized rainfall events such as the convective rainstorms occurring during the rainy season in sub-Saharan Africa. However, rainfall is mainly evaluated with respect to its intensity or occurrence, while position and timing errors are rarely studied. @en

Many satellite-based estimates use gauge information for bias correction. In general, bias-correction methods are focused on the intensity error and do not explicitly correct possible position or timing errors. However, position and timing errors in rainfall estimates can also lead to errors in the rainfall occurrence or the intensity. This is especially true for localized rainfall events such as the convective rainstorms occurring during the rainy season in sub-Saharan Africa. We investigated the use of warping to correct such errors. The goal was to gauge-adjust satellite-based estimates with respect to the position and the timing of the rain event, instead of its intensity. Warping is a field-deformation method that transforms an image into another one. We compared two methods, spatial warping focusing on the position errors and time warping for the timing errors. They were evaluated on two case studies: a synthetic rainfall event represented by an ellipse and a rain event in southern Ghana during the monsoon season. In both cases, the two warping methods reduced significantly the respective targeted (position or timing) errors. In the southern Ghana case, the average position error was decreased by about 45 km by the spatial warping and the average timing error was decreased from more than 1 h to 0.2 h by the time warping. Both warping methods also improved the continuous statistics on the intensity: the correlation went from 0.18 to at least 0.62 after warping in the southern Ghana case. The spatial warping seems more interesting because of its positive impact on both position and timing errors.@en

Rainfall estimates based on satellite data are subject to errors in the position of the rainfall events in addition to errors in their intensity. This is especially true for localized rainfall events such as the convective rainstorms that occur during the monsoon season in sub-Saharan Africa. Many satellite-based estimates use gauge information for bias correction. However, bias adjustment methods do not correct the position errors explicitly. We propose to gauge-adjust satellite-based estimates with respect to the position using a morphing method. Image morphing transforms an image, in our case a rainfall field, into another one, by applying a spatial transformation. A benefit of this approach is that it can take both the position and the intensity of a rain event into account. Its potential is investigated with two case studies. In the first case, the rain events are synthetic, represented by elliptic shapes, while the second case uses real data from a rainfall event occurring during the monsoon season in southern Ghana. In the second case, the satellite-based estimate IMERG-Late (Integrated Multi-Satellite Retrievals for GPM ) is adjusted to gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) network. The results show that the position errors can be corrected, while preserving the higher spatial variability of the satellite-based estimate.

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An ever-increasing number of rainfall estimates is available. They are used in many important applications such as flood/drought monitoring, water management, or climate monitoring. Such data are especially valuable in sub-Saharan Africa, where rainfall has considerable socioeconomic impacts and the gauge and radar networks are sparse. The choice of a rainfall product can significantly influence the performance of such applications. This study reviews previous works, evaluating or comparing rainfall products over different parts of sub-Saharan Africa. Three types of rainfall products are considered: The gauge-only, the satellite-based, and the reanalysis ones. In addition to the global rainfall products, we included three regional ones specifically developed for Africa: The African Rainfall Climatology version 2 (ARC2), the Rainfall Estimate version 2 (RFE2), and the Tropical Applications ofMeteorology Using Satellite Data and Ground-Based Observations (TAMSAT) AfricanRainfall Climatology and Time Series (TARCAT). The gauge density, the orography, and the rainfall regime, which vary with the climate and the season, influence the performance of the rainfall products. This review does not focus on comparing results, as many other publications doing so are already available. Instead, we propose this review as a guide through the different rainfall products available over Africa, and the factors influencing their performances. With this review, the reader can make informed decisions about which products serve their specific purpose best.

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Many precipitation estimates are available over Africa. They can be based on observations, from gauges or satellite, and/or on data from numerical model or reanalysis. They differ by the type of data they use, but also by their algorithm and their spatial and temporal resolutions. It may be difficult to choose from the various rainfall products which one is the most suitable for a specific region of interest and application. It is difficult to compare rainfall products. Their performance varies according to the domain, the resolution and the rainfall regime. Moreover, quantitative evaluations depend on the reference dataset and the statistics used. Several evaluation studies of rainfall products have been done over various parts of Africa. We use them to assess the qualitative and relative performance of the rainfall products over Africa. Since the performance of a rainfall product is linked to its algorithm, we also look at the different algorithms. The algorithms allow to understand how reliable a product is and to what are its limits. Using the evaluation studies and the algorithm’s description, we identify their strengths, their limitations and the factors influencing their performances. @en

The impact of the self-attraction and loading effect (SAL) in a regional 2D barotropic tidal model has been assessed, a term with acknowledged and well-understood importance for global models but omitted for boundary-forced, regional models, for which the implementation of SAL is non-trivial due to its non-local nature. In order to understand the impact of the lack of SAL effects in a regional scale, we have forced a regional model of the Northwest European Continental Shelf and the North Sea (continental shelf model (CSM)) with the SAL potential field derived from a global model (GTSM), in the form of a pressure field. Impacts have been studied in an uncalibrated setup and with only tidal forcing activated, in order to isolate effects. Additionally, the usually adopted simple SAL parameterization, in which the SAL contribution to the total tide is parameterized as a percentage of the barotropic pressure gradient (typically chosen 10%), is also implemented and compared to the results obtained with a full SAL computation. A significant impact on M2 representation is observed in the English Channel, Irish Sea and the west (UK East coast) and south (Belgian and Dutch Coast) of the North Sea, with an impact of up to 20 cm in vector difference terms. The impact of SAL translates into a consistent M2 amplitude and propagation speeds reduction throughout the domain. Results using the beta approximation, with an optimal domain-wide constant value of 1.5%, show a somewhat comparable impact in phase but opposite direction of the impact in amplitude, increasing amplitudes everywhere. In relative terms, both implementations lead to a reduction of the tidal representation error in comparison with the reference run without SAL, with the full SAL approach showing further impacted, improved results. Although the overprediction of tidal amplitudes and propagation speeds in the reference run might have additional sources like the lack of additional dissipative processes and non-considered bottom friction settings, results show an overall significant impact, most remarkable in tidal phases. After showing evidence of the SAL impact in regional models, the question of how to include this physical process in them in an efficient way arises, since SAL is a non-local effect and depends on the instantaneous water levels over the whole ocean, which is non-trivial to implement.

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There are many rainfall products available, some of them are global and others have been developed specifically for Africa or another region. They are based on different methods and use different sources of data. All these products have different advantages and limitations and they are suitable for different purposes, like climate or drought monitoring. They can be divided in three categories: the gauge-only products, the satellite-based products and the reanalyses products. These different types of products are compared through literature study in order to find their strengths and weaknesses. The performance of each product varies from region to region, the behaviour of a product can be very different over two separate regions. The gauge-only products are dependent on the gauge coverage which is poor in many regions of Africa. The performance of the satellite-based products are influenced by several factors such as the orography, the rainfall regime and the gauge density (for the ones using them). The reanalyses products are outperformed by the other products but can be improved through downscaling. @en