Research Article Olaniran et al., J Climatol Weather Forecasting 2017, 5:2 DOI: 10.4172/2332-2594.1000210 Research Article Journal of Climatology & Weather Forecasting J o u r n a l o f C l i m a t o l o g y & W e a t h e r F o r e c a s t i n g ISSN: 2332-2594 Volume 5 • Issue 2 • 1000210 J Climatol Weather Forecasting, an open access journal ISSN: 2332-2594 Open Access Abstract This study assessed the uncertainty in estimating long-term (1971-2010) mean precipitation, its inter-annual variability, and linear trend of three network observation datasets over West Africa. A reference data, defned as a multi-dataset ensemble of precipitation observations of the Climate Research Unit (CRU) of the University of East Anglia, the Global Precipitation Climatology Centre (GPCC) and the University of Delaware (UDEL), all at horizontal resolutions of 0.5 ° by 0.5 ° were obtained and used in this study. Uncertainties in these climatological parameters of precipitation at both annual and seasonal time scales were examined in terms of inter-dataset variability using signal- to-noise ratio (SNR), correlation, root-mean-square errors and the normalised standard deviation. Results showed that the mean, inter-annual variability and trends climatology varied for different datasets. The three datasets had good agreement (SNR>5) in terms of the annual mean precipitation and its inter-annual variability in most parts of West Africa. However, the agreement between the datasets was poor in the very dry Sahel parts of northern Niger, Mali, and Mauritania (SNR ≤ 1) due to very little precipitation and possibility of relatively low station density in these regions of complex terrain. In terms of correlation (0.89 ≤ r ≤ 0.98), and normalised standard deviation, NSD (0.8 ≤ NSD ≤ 1.7), the uncertainties in the spatial variations in linear trend were larger than mean precipitation and their inter-annual variability for both annual and seasonal scales. The long-term annual precipitation trend in the region is highly uncertain except in a few small areas. *Corresponding author: Matthew OJ, Institute of Ecology and Environmental Studies, Obafemi Awolowo University, Ile-Ife, Nigeria, Tel: +234 (0)703 3312 735; E-mail: abefematt@yahoo.com Received August 04, 2017; Accepted August 28, 2017; Published August 31, 2017 Citation: Matthew OJ, Abiye OE, Sunmonu LA, Ayoola MA, Oluyede OT (2017) Uncertainties in the Estimation of Global Observational Network Datasets of Precipitation over West Africa. J Climatol Weather Forecasting 5: 210. doi:10.4172/2332-2594.1000210 Copyright: © 2017 Matthew OJ, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Uncertainties in the Estimation of Global Observational Network Datasets of Precipitation over West Africa Matthew OJ * , Abiye OE 2 , Sunmonu LA 3 , Ayoola MA 3 and Oluyede OT 1 1 Institute of Ecology and Environmental Studies, Obafemi Awolowo University, Ile-Ife, Nigeria 2 Centre for Energy Research and Development (CERD), Obafemi Awolowo University, Ile-Ife, Nigeria 3 Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife, Nigeria observational errors are much more problematic, because their efects become relatively more pronounced as greater numbers of observations are aggregated. In this case, the author believed that averaging observations together from many diferent instruments/sources would tend to reduce the contribution of systematic observational errors to the uncertainty of the average. A number of researchers and institutions have developed observation-based gridded analysis datasets of global or regional coverage with fne spatial resolutions [8-14]. Tese network of observation datasets provide precipitation and/or surface air temperatures over extended periods of multiple decades at spatial resolutions of 0.5 ° or fner. Tis is, of course, a substantial improvement over previous generation data sets that are typically at much coarser (e.g. 2.5 ° ) horizontal resolutions [15]. Tese recent fne-scale datasets allow us to better examine the regional precipitation and temperature climatology and to perform more reliable evaluations of today’s high- resolution climate simulations, especially over the regions of complex terrain, that are important for climate-change impact assessments and climate model evaluations [16]. Keywords: Global network datasets; Uncertainties; Reference data; SNR; West Africa Introduction All measurements have some degree of uncertainty that may come from a variety of sources. Te term “uncertainty” is used to refer to a possible value that an error in a measured dataset may have [1,2]. In science, true measurement value doesn’t exist; what we usually have is an estimate. An error in the estimate is the amount of inaccuracy in the estimate compared with a known standard value [3-5]. Sources of uncertainty in observational measurements are ofen broadly categorized as (statistical) random or systematic errors [6,7]. Random observational errors are associated with the observed frequency distributions in the primary data. Tey occur for many reasons ranging from misreading of the pointer instrument, rounding errors, the difculty of reading the instrument to a precision higher than the smallest marked gradation, incorrectly recorded values, errors in transcription from written to digital sources, and sensor noise among others. Te contribution of random independent errors to the uncertainty on the average/ensemble of a number of datasets is much smaller than the contribution of random error to the uncertainty on a single observation, even in the most sparsely observed years [6]. Nonetheless, where observations are few, random observational errors can be an important component of the total uncertainty and can, in principle, be made arbitrarily small by repeated measurement or large enough sample size [7]. Uncertainties due to systematic efects include calibration error, poorly-sited instrument, and background uncertainties and almost everything else that might bias a measurement, which are caused by a lack of knowledge or uncertainty in the measurement model, such as the reading error of an instrument as well as uncertainties in the interpretation of a measurement which cannot be made arbitrarily small by simply getting more data. Kennedy [6] submitted that systematic