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Thierry Lebel and Abou Amani

Abstract

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 1° × 1° 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 1° × 1° 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 2.5° × 2.5° or 5° × 5°. Further work is thus needed to extend the results presented here to larger spatial scales.

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Christopher M. Taylor and Thierry Lebel

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This paper examines observational evidence of a positive feedback between the land surface and rainfall in semiarid conditions. The novelty of the work lies in the length scale of study, investigating interactions between soil moisture patterns and deep convection at scales of less than 20 km. The feedback mechanism was proposed in a previous study to explain the development of an anomalous rainfall gradient in the West African Sahel. The aim here is to assess whether such rainfall persistence occurs elsewhere in the region.

Convective-scale rainfall patterns are examined using two years of observations from a dense rain gauge network in southwest Niger. Rainfall differences are analyzed between neighboring gauges separated by 7.5–15 km. Under certain surface conditions, a positive correlation between daily and antecedent rainfall differences is established. These circumstances arise when previous storm patterns have modified local evaporation rates. Rainfall gradients in subsequent events tend to persist, reinforcing soil moisture patterns. The effect appears to be most pronounced in mature, large-scale storms. The widespread occurrence of persistence in the dataset provides strong observational evidence of a surface feedback mechanism, with surface-induced low-level humidity anomalies locally enhancing convection in passing storms. Several rainfall patterns that persist for a month are identified. These patterns are linked to surface processes and the frequency of storm passage.

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Abdou Ali, Thierry Lebel, and Abou Amani

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The occurrence of rainfall in the semiarid regions is notoriously unreliable and characterized by great spatial variability over a large spectrum of timescales. Based on analytical considerations, an integrated approach is presented here in order to describe the spatial structure of rain fields for timescales used in climatological studies, that is from the daily to the seasonal scales and beyond to the interannual scale. At the scale of the rain event, two factors determine the spatial structure of rain fields. One is the spatial variability of the conditional rainfall H* (H > 0), represented by its variogram γ*e. The other is the intermittency, its spatial structure being described by the indicator variogram γ 1. It is shown that the spatial structure of rain fields for time steps larger than the event may be analytically derived from γ*e and γ 1, taking into account the anisotropy and nonstationarity that may affect either of these two functions, which are thus two timescale invariants of the rainfall process. The upscaling factor used to obtain the structure at large timescales is the number of rain events recorded over the period under consideration. An application using a large dataset of 450 Sahelian rain events observed with the Estimation des Précipitations par Satellite (EPSAT)–Niger monitoring network is presented. The theoretical model provides a good representation of the spatial variability observed in the data. The validation of the model confirms that knowledge of the average event rain field structure and the number of events N is sufficient to determine the structure of the N-event rain fields.

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Luc Le Barbé, Thierry Lebel, and Dominique Tapsoba

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The study presented here makes use of about 300 daily rain gauges covering a 1 700 000 km2 area in order to characterize the rainfall regimes of West Africa at hydrological scales. The rainfall regime is analyzed as a combination of two variables, the average number of events over a given period of time (n T) and the average cumulative rainfall per event (h). These two parameters are a measure of the occurrence rate and magnitude of the convective storms that generate most of the rainfall in this region. They define the average water input to the hydrological systems and the average time available for this water to be redistributed into the continental hydrological cycle before a new input occurs. By analyzing for a period of 40 yr (1951–90), the space and time variations of these two parameters, it is possible to better understand how the intraseasonal to decadal rainfall variability may impact on the hydrological cycle. The analysis is carried out in two steps. First, the annual cycle and migrations of the weather zones characterizing the climate of West Africa are considered. This leads to evidence of a sudden and synchronous rain onset between 9° and 13°N, which does not follow the classic scheme of a progressive migration of the rain zones, north and south with the sun. Second, the differences in the rainfall regimes between the two succeeding subperiods of 20 yr are obtained, the subperiod P1 (1951–70) being wet and the subperiod P2 (1971–90) being dry. The difference—averaged over the 16° by 12° study region—of the mean interannual rainfall between the wet and the dry periods is 180 mm yr−1. This difference is relatively evenly distributed in space, with no clear meridional gradient. Between these two periods, the parameter n T displays a systematic decrease, which appears well correlated to the decrease of the mean interannual rainfall. The variations of h are, by contrast, smaller in amplitude and more erratically distributed in space. When looking at the intraseasonal scale, it appears that the rainfall deficit of the dry period is primarily linked to a deficit of the number of events occurring during the core of the rainy season over the Sahel, and during the first rainy season for the region extending south to 9°–10°N. It is also shown that, in the south, the dry period is characterized by a shift in time of the second rainy season. All these characteristics have strong implications in term of agricultural and water resources management. They also raise questions about the traditional scheme used to characterize the dynamics of the West African monsoon.

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Christopher M. Taylor, Frédérique Saïd, and Thierry Lebel

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The Hydrological Atmospheric Pilot Experiment in the Sahel (HAPEX-Sahel) was designed to investigate land–atmosphere interactions in the semiarid conditions of southwest Niger. During the intensive observation period (IOP) in 1992, a pronounced mesoscale rainfall gradient developed over the Southern Super Site (SSS). Measurements from a high-resolution rain gauge network indicate that over a distance of 9 km, cumulative rainfall in the final 7 weeks of the wet season (31 July–18 September) ranged from 224 mm in the south to 508 mm in the north. The extreme rainfall gradient is not apparent in other years and evolves through persistent local intensification of convection in passing large-scale storms. This paper assesses the influence of the rainfall variability on the surface and atmosphere, and explores the possibility of a land surface feedback on rainfall at this scale.

Soil moisture estimates across the SSS illustrate the importance of rainfall on the water balance and indicate that gradients of soil moisture deficit are likely throughout the IOP. Observations from the three dominant vegetation types reveal the sensitivity of available energy and evaporative fraction to antecedent rainfall. This arises from the high coverage of bare soil and the growth response of Sahelian vegetation to soil moisture. A broad range of evaporation rates are found, while sensible heat fluxes are generally less sensitive to antecedent rainfall. Surface and airborne measurements of temperature and humidity show that rainfall-induced surface variability across the SSS leads to mesoscale gradients in properties of the planetary boundary layer (PBL). On a day with light winds, a thermally induced area of PBL convergence associated with antecedent rainfall conditions is observed.

A surface feedback mechanism has been proposed to explain the organization of rainfall on scales of about 10 km. Typical Sahelian surface conditions generate large anomalies of low-level moist static energy following mesoscale rainfall events. This variability influences the development of individual convective cells within subsequent larger-scale disturbances. The anomalous rainfall pattern at the SSS is linked to typical spatial scales of a convective cell and the preferred direction of travel of Sahelian squall lines. This hypothesis is supported by the temporal variability of the rainfall anomalies. Differences in precipitation across the SSS show a pronounced diurnal cycle in phase with PBL anomalies and are largest during periods when surface variability is high. A case study is also presented from an isolated convective storm over the SSS. This highlights the sensitivity of deep convective instabilities to PBL anomalies of the magnitude that were measured throughout the experiment.

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Vincent Mathon, Henri Laurent, and Thierry Lebel

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Based on a full-resolution Meteosat dataset, an extensive climatological study of the mesoscale convective systems (MCSs) observed by satellite over the Sahel leads to the definition of a subpopulation of MCSs—called organized convective systems (OCSs)—that represents only 12% of the total number of MCSs observed during 9 yr over the central Sahel while accounting for almost 80% of the total convective cloud cover defined at the 233-K threshold. Using a high-resolution rainfall dataset, it is shown that these OCSs are also the main source of rain in this region, accounting for about 90% of the seasonal rainfall, with a mean areal rainfall of 14.7 mm per system. All of the OCSs are associated with a rain event, and more than 90% of the major rain events are associated with an OCS. These figures are compared with those obtained for mesoscale convective complexes (MCCs). Each MCC produces more rainfall on average (19 mm per system) but there are only a few of them (1.2% of the total number of MCSs), and they consequently produce only 19% of the seasonal rainfall. The interannual rainfall variability is first determined by the year-to-year fluctuation of the number of events defined from satellite rather than by the fluctuations of their mean rain efficiency. In fact, the total rain yield of an OCS appears to be linked primarily to its duration (which itself is largely determined by its spatial extension) rather than to its average rain rate. The diurnal cycle over the region is also studied, and it is shown that it is largely conditioned by the propagative nature of the OCSs associated with orography-driven generations located a few hundred kilometers to the east of the validation area.

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Abdou Ali, Thierry Lebel, and Abou Amani

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Rainfall 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.

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Abdou Ali, Abou Amani, Arona Diedhiou, and Thierry Lebel

Abstract

This 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.

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Catherine Wilcox, Claire Aly, Théo Vischel, Gérémy Panthou, Juliette Blanchet, Guillaume Quantin, and Thierry Lebel

Abstract

Stochastic rainfall generators aim to reproduce the main statistical features of rainfall at small spatial and temporal scales. The simulated synthetic rainfall series are recognized as suitable for use with impact analysis in water, agricultural, and ecological management. Convection-driven precipitation, dominant in certain regions of the world such as the intertropical belt regions, presents properties that require specific consideration when modeling: (i) strong rainfall intermittency, (ii) high variability of intensities within storms, (iii) strong spatiotemporal correlation of intensities, and (iv) marked seasonality of storm properties. In this article, improvements for an existing stochastic generator of rainfall fields that models convective storms are presented. Notable novelties include (i) the ability to model precipitation event timing, (ii) an improved temporal disaggregation scheme representing the rainfall distribution at subevent scales, and (iii) using covariates to reflect seasonal changes in precipitation occurrence and marginal distribution parameters. Extreme values are explicitly considered in the distribution of storm event intensities. The simulator is calibrated and validated using 28 years of 5-min precipitation data from the 30-rain-gauge AMMA-CATCH network in the Sahelian region of southwest Niger. Both large propagative systems and smaller local convective precipitation are generated. Results show that simulator improvements coherently represent the local climatology. The simulator can generate scenarios for impact studies with accurate representation of convective precipitation characteristics.

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Jean-Luc Redelsperger, Chris D. Thorncroft, Arona Diedhiou, Thierry Lebel, Douglas J. Parker, and Jan Polcher

African Monsoon Multidisciplinary Analysis (AMMA) is an international project to improve our knowledge and understanding of the West African monsoon (WAM) and its variability with an emphasis on daily-to-interannual time scales. AMMA is motivated by an interest in fundamental scientific issues and by the societal need for improved prediction of the WAM and its impacts on West African nations. Recognizing the societal need to develop strategies that reduce the socioeconomic impacts of the variability of the WAM, AMMA will facilitate the multidisciplinary research required to provide improved predictions of the WAM and its impacts. This will be achieved and coordinated through the following five international working groups: i) West African monsoon and global climate, ii) water cycle, iii) surface-atmosphere feedbacks, iv) prediction of climate impacts, and v) high-impact weather prediction and predictability.

AMMA promotes the international coordination of ongoing activities, basic research, and a multiyear field campaign over West Africa and the tropical Atlantic. AMMA is developing close partnerships between those involved in basic research of the WAM, operational forecasting, and decision making, and is establishing blended training and education activities for Africans.

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