Lecture notes

Part I: Statistical Modeling

Chapter1: Basic concepts and notions

Chapter2: Regression analysis and an example of coreelation test

Chapter3: Linear multivariate statistical analysis and modeling and supplementary, plus CCA test

Chapter4: Nonlinear modeling - Neural Network Regression

Part II: Numerical Modeling and Data Assimilation

Chapter5-6: An Introduction to the numerical solution of differential equation: Discretization , supplementary and codes

Chapter7: Solving Ordinary Differential Equations with Runge-Kutta Methods and ppt and practice programming

Chapter8: A simple model of the unpredictability of weather: The Lorenz Equations

Chapter9: Ensemble-based Data assimilation methods