Comparison of five gridded precipitation products at climatological scales over West Africa

Akinsanola, A. A.; Ogunjobi, K. O.; Ajayi, Vincent O.; Adefisan, Elijah A.; Omotosho, J. Bayo; Sanogo, Souleymane

doi:10.1007/s00703-016-0493-6

Cited by 56 publications

(21 citation statements)

References 64 publications

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“…The monthly rainfall data used in this study consist of datasets from the Climate Research Unit (CRU) Time Series (TS) (CRU TS 4.01) [36], Global Precipitation Climatology Centre (GPCC) version 7 [37], and University of Delaware (UDEL) version 3.01, all at 0.5 • × 0.5 • . These products are widely used over West Africa owing to their high temporal and spatial resolution, and they have been highlighted as one of the best substitutes for rain-gauge rainfall data over West Africa [12]. Also the monthly pressure level vertical velocity, specific humidity, divergence, and zonal and meridional wind dataset used in this study was obtained from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis dataset [38]…”

Section: Datamentioning

confidence: 99%

“…Also observed is that the regions of highest rainfall coincide with regions of high topography (i.e., Fouta Djallon highland, Jos Plateau, and the Cameroonian mountains), and the highest variability as depicted by the coefficient of variation in Figure 2b,d,f is evident within the central Guinea coast subregion (i.e., Cote d'Ivoire, Ghana, Togo, Benin, and western Nigeria). In contrast, the Sahel (11)(12)(13)(14)(15)(16) • N, 20 • W-20 • E) subregion receives comparatively little rainfall (~5 mm day -1 ). Significant agreement can be found among the three gridded rainfall observations, although with noticeable differences.…”

Section: Spatial and Temporal Distributions Of Summer Rainfall Over Wmentioning

confidence: 99%

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Understanding the Variability of West African Summer Monsoon Rainfall: Contrasting Tropospheric Features and Monsoon Index

Akinsanola

Zhou

2020

Atmosphere

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West African Summer Monsoon (WASM) rainfall exhibits large variability at interannual and decadal timescales, causing droughts and floods in many years. Therefore it is important to investigate the major tropospheric features controlling the WASM rainfall and explore its potential to develop an objective monsoon index. In this study, monthly mean reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and monthly rainfall data from three gridded observations during the 65-year period of 1950–2014 were employed. Dry and wet rainfall years were identified using a standardized precipitation index. In a composite analysis of wet and dry years, the dynamical features controlling the WASM exhibit an obvious contrast between these years, and a weaker (stronger) African Easterly Jet (Tropical Easterly Jet) is observed during the wet years. Also, a well-developed and deep low-level westerly flow at about 850 hPa is evident in wet years while an obvious reversal is observed in dry years. Considering this, the main regions of the two easterly jet streams and low-level westerly wind are proposed for objectively defining an effective WASM index (WASMI). The results indicate that the WASMI defined herein can reflect variations in June–September rainfall over West Africa. The index exhibits most of the variabilities observed in the rainfall series, with high (low) index values occurring in the 1950–1960s (1970–1980s), suggesting that the WASMI is skilled in capturing the respective wet and dry rainfall episodes over the region. Also, the WASMI is significantly correlated (r = 0.8) with summer monsoon rainfall, which further affirms that it can indicate not only variability but also the intensity of WASM rainfall.

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Section: Datamentioning

confidence: 99%

Section: Spatial and Temporal Distributions Of Summer Rainfall Over Wmentioning

confidence: 99%

Understanding the Variability of West African Summer Monsoon Rainfall: Contrasting Tropospheric Features and Monsoon Index

Akinsanola

Zhou

2020

Atmosphere

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“…The issue is related to the rainfall gauge network in Africa having one of the lowest densities among all the continents, except for Antarctica [33]. In West Africa, the gauge network distribution is sparse and thus challenging due to the complex and non-uniform topography [34]. As suggested by Maidment et al [35], rainfall estimates derived from satellite imagery, reanalysis, and the numerical weather prediction (NWP) model outputs are of greater importance in such areas.…”

Section: Observation Datamentioning

confidence: 99%

Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics

Gnitou

Tan

et al. 2019

Atmosphere

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Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of assessment, but mostly to serve as a basis for data quality enhancement activities. In this study, we assessed the Rossby Centre Regional Climate Model (RCA4) over West Africa without targeting any application-specific feature, while jointly evaluating its boundary conditions and accounting for observational uncertainties. Results from this study revealed that the RCA4 signal highly modifies the boundary conditions (global climate models (GCMs) and reanalysis data), resulting in a significant reduction of their biases in the dynamically downscaled outputs. The results, with respect to the observational ensemble members, are in line with the differences between the observation datasets. Among the RCA4 simulations, the ensemble mean outperformed all individual simulations regardless of the statistical metric and the reference data used. This indicates that the RCA4 adds value to GCMs over West Africa, with no influence of observational uncertainty, and its ensemble mean reduces model-related uncertainties. climate is measured or estimated, GCM outputs are provided with past, present, and future climate variables. This feature thus allows a variety of applications that were infeasible with past and present climate data repositories. Although useful for general climate information purposes, GCMs suffer from uncertainty in process representation, error propagation, uncertainty in observational data, and sensitivity in resolution [1]. This final drawback is particularly common when GCMs are used to resolve regional-scale features due to being originally designed to serve global needs in terms of providing likely accurate climate information. A common and widely adopted solution within the climate research community is the use of high-resolution regional climate models (RCMs) driven by GCMs or reanalysis data as boundary conditions of the domain area being modeled [2].Like GCMs, RCMs result from the representation of climate processes, but with a particular emphasis on resolving regional-scale climate features. As improved and regionally resolved versions of GCMs, the availability of RCMs for past, present, and future climate data has provided new possibilities not only for understanding regional-scale historical climate phenomena, but also for inferring their likelihood in the future. RCMs are thus important tools for local governments, institutions, researchers, and local communities [1,3].The improvements in resolution provided by RCMs are not error-free. Being derived from GCM data means RCMs might be subjected to GCM-inherited errors [4]. The various uses of regional climate data suggest that different assumptions and considerations are required for different features and metrics with respect to the domain of application and highlight the...

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“…Although it is difficult to directly compare these studies because of varying methodologies, reference datasets, and region of interests, there is some general consensus on its performance. Some of these studies report on seasonal to decadal differences [16,19,20], an underestimation of heavy rainfall [19,21], and a lower bias in gauge-calibrated than in non-calibrated satellite-based datasets [16,18]. Furthermore, Dembélé and Zwart [19] provided specific application-related recommendations of the datasets based, among others on their tendency to under-and over-estimate rainfall events, which and desired for drought and flood monitoring, respectively.…”

Section: Introductionmentioning

confidence: 99%

Validation of Satellite and Merged Rainfall Data over Ghana, West Africa

et al. 2020

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In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r > 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region.

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Comparison of five gridded precipitation products at climatological scales over West Africa

Cited by 56 publications

References 64 publications

Understanding the Variability of West African Summer Monsoon Rainfall: Contrasting Tropospheric Features and Monsoon Index

Understanding the Variability of West African Summer Monsoon Rainfall: Contrasting Tropospheric Features and Monsoon Index

Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics

Validation of Satellite and Merged Rainfall Data over Ghana, West Africa

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