Rainfall timing: variation, characteristics, coherence, and interrelationships in Nigeria

Obarein, Omon A.; Amanambu, Amobichukwu Chukwudi

doi:10.1007/s00704-018-2731-y

Cited by 16 publications

(10 citation statements)

References 31 publications

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“…Coastal zone has also the highest interannual variability in terms of seasonal rainfall. This variability ranging from 19 to 67% (Table 2) for all indices is high with reference to the study of Obarein and Amanambu (2019). These results are broadly in agreement with more recent studies on rainfall patterns over this area with large-scale dataset (Baidu et al 2017;Atiah et al 2019).…”

Section: Trend Variability and Predictabilitysupporting

confidence: 90%

“…This method is proven to be robust for trend analyses of time series (Partal and Kahya 2006;Obot et al 2010;Manzanas et al 2014a). The coefficient of variation (Cv) was used as a measure interannual variability as suggested by Obarein and Amanambu (2019). The t test was used to determine both the (i) significance of correlation relationships between the dynamical forecasts System 4 and GMet derived agro-meteorological indices and the (ii) significance of correlation relationships between statistical forecasts driven by SST and GMet-derived agrometeorological indices.…”

Section: Statistical Trend Variability and Significance Analysesmentioning

confidence: 99%

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Rainfall and dry spell occurrence in Ghana: trends and seasonal predictions with a dynamical and a statistical model

Gbangou

Ludwig

Slobbe

et al. 2020

Theor Appl Climatol

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Improved information on the distribution of seasonal rainfall is important for crop production in Ghana. The predictability of key agro-meteorological indices, namely, seasonal rainfall, maximum dry spell length (MDSL) and dry spell frequency (DSF) was investigated across Ghana (with an interest on the coastal savannah agro-ecological zone). These three variables are relevant for local agricultural water management. A dynamical model (i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 seasonal forecasts) and a statistical model (i.e. response to sea surface temperatures (SSTs)) were used and analysed using correlation and other discrimination skill metrics. ECMWF-System 4 was bias-corrected and verified with 14 local stations' observations. Results show that differences in variability and skills of the agro-meteorological indices are small between agro-ecological zones as compared to the differences between stations. The dynamic model System 4 explains up to 31% of the variability of the MDSL and seasonal rainfall indices. Coastal savannah exhibits the highest level of discrimination skills. However, these skills are generally higher for the below and above normal MDSL and seasonal rainfall categories at lead time 0. Similarity in skills for the agro-meteorological indices over the same zones and stations is found both for the dynamical and statistical models. Although System 4 performs slightly better than the statistical model, especially, for dry spell length and seasonal rainfall. For dry spell frequency and longer lead time dry spell length, the statistical model tends to perform better. These results suggest that the agro-meteorological indices derived from System 4′ updated versions, corrected with local observations, together with the response to SST information, can potentially support decision-making of local smallholder farmers in Ghana.

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Section: Trend Variability and Predictabilitysupporting

confidence: 90%

Section: Statistical Trend Variability and Significance Analysesmentioning

confidence: 99%

Rainfall and dry spell occurrence in Ghana: trends and seasonal predictions with a dynamical and a statistical model

Gbangou

Ludwig

Slobbe

et al. 2020

Theor Appl Climatol

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“…Agricultural production depends on precipitation requirements of specific crops. Therefore, using fixed thresholds for the amount of rainfall (Nichols et al ., ; Lau and Yang, ; Marengo et al ., ; Li and Fu, ; Nieto‐Ferreira and Rickenbach, ; Obarein and Amanambu, ) or even outgoing longwave radiation (OLR; Kousky, ) to define the onset and end dates of the rainy season has its value. The threshold is usually selected based on statistics of precipitation (or OLR) over the region of interest.…”

Section: Defining Onset and End Datesmentioning

confidence: 99%

Detection, variability, and predictability of monsoon onset and withdrawal dates: A review

Bombardi

Moron

Goodnight

2019

Intl Journal of Climatology

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This article presents a review of the scientific literature on detection, sources of variability, and predictability of the timing of monsoons. The timing of monsoons is characterized by the beginning (commonly referred to as onset) and end (commonly referred to as demise, cessation, retreat, or withdrawal) dates of the summer monsoons. The main methods used to detect the timing of monsoons are divided into two categories: local‐scale methods and regional to large‐scale methods. The sources of variability of the timing of monsoons are also separated into two categories: local‐scale and large‐scale sources. Finally, the article presents a summary of the literature on the predictability of the timing of monsoons using both dynamical and statistical approaches. We show that all methods are parameterized in some way. A comparison between two different methods shows that while there might be large differences in the definition of onset and demise dates at the local level, spatial aggregation usually reduces the noise and enhances the regional monsoonal signal, which may be predictable.

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“…Coastal zone has also the highest interannual variability in terms of seasonal rainfall. This variability ranging from 19-67% (Table 3.2) for all indices is high with reference to the study of Obarein and Amanambu (2019). These results are broadly in agreement with more recent studies on rainfall patterns over this area with large-scale dataset (Baidu et al, 2017, Atiah et al, 2019.…”

Section: Trend Variability and Predictabilitysupporting

confidence: 91%

Designing agrometeorological information services in Ghana by combining local and scientific forecasting knowledge

Gbangou¹

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Rainfall timing: variation, characteristics, coherence, and interrelationships in Nigeria

Cited by 16 publications

References 31 publications

Rainfall and dry spell occurrence in Ghana: trends and seasonal predictions with a dynamical and a statistical model

Rainfall and dry spell occurrence in Ghana: trends and seasonal predictions with a dynamical and a statistical model

Detection, variability, and predictability of monsoon onset and withdrawal dates: A review

Designing agrometeorological information services in Ghana by combining local and scientific forecasting knowledge

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