Abstract
The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.
Highlights
One of the visible impacts of climate change and climate variability is extreme weather events that occur from time to time in several parts of the globe
With Nigeria being sandwiched between the moist Atlantic Ocean and the dry Sahara Desert in the tropics, Table 3 shows the huge climatic disparity in Nigeria with some parts of Northern Nigeria having only 23 wet days in a year while some areas in the south have as much as 244 wet days out of 365 days in a year
We can conclude that all indices are moderately correlated, indicating their suitability to characterize the different features of the precipitation regimes in Nigeria [6]
Summary
One of the visible impacts of climate change and climate variability is extreme weather events that occur from time to time in several parts of the globe. Climate extremes can result in changes in the frequency, intensity, spatial extent, duration, and timing of climatic phenomena. There is a growing global concern that anthropogenic activities are a major cause of the variability in the intensity and frequency of weather and climate extremes [2,3,4]. Climate extremes, including the ones related to precipitation, can be analyzed using several approaches [6,7,8]. The use of indices to characterize the frequency, intensity, and duration of precipitation extremes is one of the ways to assess them [9,10,11,12].
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