Statistical Modeling for Climate Data

Statistical modeling plays an important role in prediction and forecasting. Various types of modeling are available in the literature; some of the important models and methods, like simple and multiple linear regression, nonparametric regression, artificial neural networks, and time series models are discussed in this chapter. These models are very useful in climate data modeling for weather forecasting and prediction. Models are described with application to a real data set, which is available on the Climate Knowledge Portal of World Bank. R – Software is used for fitting models incorporating various packages like Tidyverse, Rfit, and zoo.