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International audienceThis study investigates the accuracy of various precipitation products for the Sahel. A first set of products is made of three ground-based precipitation estimates elaborated regionally from the gauge data collected by Centre Regional Agrometeorologie–Hydrologie–Meteorologie (AGRHYMET). The second set is made of four global products elaborated by various international data centers. The comparison between these two sets covers the period of 1986–2000. The evaluation of the entire operational network of the Sahelian countries indicates that on average the monthly estimation error for the July–September period is around 12% at a spatial scale of 2.5° × 2.5°. The estimation error increases from south to north and remains below 10% for the area south of 15°N and west of 11°E (representing 42% of the region studied). In the southern Sahel (south of 15°N), the rain gauge density needs to be at least 10 gauges per 2.5° × 2.5° grid cell for a monthly error of less than 10%. In the northern Sahel, this density increases to more than 20 gauges because of the large intermittency of rainfall. In contrast, for other continental regions outside Africa, some authors have found that only five gauges per 2.5° × 2.5° grid cell are needed to give a monthly error of less than 10%. The global products considered in this comparison are the Climate Prediction Center (CPC) merged analysis of precipitation (CMAP), Global Precipitation Climatology Project (GPCP), Global Precipitation Climatology Center (GPCC), and Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI). Several methods (scatterplots, distribution comparisons, root-mean-square error, bias, Nash index, significance test for the mean, variance, and distribution function, and the standard deviation approach for the kriging interval) are first used for the intercomparison. All of these methods lead to the same conclusion that CMAP is slightly the better product overall, followed by GPCC, GPCP, and GPI, with large errors for GPI. However, based on the root-mean-square error, it is found that the regional rainfall product obtained from the synoptic network is better than the four global products. Based on the error function developed in a companion paper, an approach is proposed to take into account the uncertainty resulting from the fact that the reference values are not the real ground truth. This method was applied to the most densely sampled region in the Sahel and led to a significant decrease of the raw evaluation errors. The reevaluated error is independent of the gauge references

International audienceRainfall estimation in semiarid regions remains a challenging issue because it displays great spatial and temporal variability and networks available for monitoring are often of low density. This is especially the case in the Sahel, a region of 3 million km2 where the life of populations is still heavily dependent on rain for agriculture. Whatever the data and sensors available for rainfall estimation—including satellite IR and microwave data and possibly weather radar systems—it is necessary to define objective error functions to be used in comparing various rainfall products. This first of two papers presents a theoretical framework for the development of such an error function and the optimization of its parameters for the Sahel. A range of time scales—from rain event to annual—are considered, using two datasets covering two different spatial scales. The mesoscale [Estimation des Pluies par Satellite (EPSAT)-Niger (E-N)] is documented over a period of 13 yr (1990–2002) on an area of 16 000 km2 covered by 30 recording rain gauges; the regional scale is documented by the Centre Regional Agrometeorologie–Hydrologie–Meteorologie (AGRHYMET) (CRA) dataset, with an annual average of between 600 and 650 rain gauges available over a period of 8 yr. The data analysis showed that the spatial structure of the Sahelian rain fields is markedly anisotropic, nonstationary, and dominated by the nesting of two elementary structures. A cross-validation procedure on point rainfall values leads to the identification of an optimal interpolation algorithm. Using the error variances computed from this algorithm on 1° × 1° and 2.5° × 2.5° cells, an error function is derived, allowing the calculation of standard errors of estimation for the region. Typical standard errors for monthly rainfall estimation are 11% (10%) for a 10-station network on a 2.5° × 2.5° (1° × 1°) grid, and 40% (30%) for a single station on a 2.5° × 2.5° (1° × 1°) grid. In a companion paper, this error function is used to investigate the differences between satellite rainfall products and how they compare with ground-based estimates

Areal rainfall estimation from ground sensors is essential as a direct input to various hydrometeorological models or as a validation of remote sensing estimates. More critical than the estimation itself is the assessment of the uncertainty associated with it. In tropical regions knowledge on this topic is especially scarce due to a lack of appropriate data. It is proposed here to assess standard estimation errors of the areal rainfall in the Sahel, a tropical region of notoriously unreliable rainfall, and to validate those errors using the data of the EPSAT-Niger experiment. A geostatistical framework is considered to compute theoretical variances of estimation errors for the event-cumulative rainfall, and rain gauge networks of decreasing density are used for the validation. As a result of this procedure, charts giving the standard estimation error as a function of the network density, the area, and the rainfall depth are proposed for the Sahelian region. An extension is proposed for larger timescales (decade, month, and season). The seasonal error is estimated as a product of the error at the event scale by a reduction coefficient, which is a function of the number K of recorded events and the probability distribution function of the point storm rain depth. For a typical network of 10 stations regularly dispatched over a 1Њ ϫ 1Њ square, the relative estimation error decreases from 14% for an average storm rain depth of 16 mm to 5% for an average August rainfall of 160 mm. For a density comparable to that of the operational rain gauge network of southern Niger and similar Sahelian regions, the standard errors are, respectively, 26% at the event scale and 10%-15% at the monthly scale, depending on the number of events recorded during the month. The areas considered here are 1Њ ϫ 1Њ and smaller, which makes a comparison with results obtained in previous studies for other regions of the world difficult since the reference area most often used in these studies is either 2.5Њ ϫ 2.5Њ or 5Њ ϫ 5Њ. Further work is thus needed to extend the results presented here to larger spatial scales.

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