Chapter 6 6.2 Statistical methods time series analysis methods (1) Graphical depiction: trends/cycles Different functⅰonS near exp Inverse po Assessment of Climate/Change Impacts
Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • time series analysis methods: (1) Graphical depiction: trends/cycles Different functions: linear exp ln inverse poly-
Chapter 6 6.2 Statistical methods time series analysis methods (2)Autocorrelation: temporal autocorrelation e.g. today's wind speed is similar to yesterdays wind speed this is the persistence or inertia of the climate system Diurnal/annual cycles should be removed before estimating autocorrelation X-average (X)/ fitting and removing an analytical function such as a series of sinusoids or polynomials The most natural way to visualize autocorrelation in a time series is by plotting the autocorrelation as a function of lag time. that is the autocorrelation function Assessment of Climate/Change Impacts
Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • time series analysis methods (2) Autocorrelation: temporal autocorrelation • e.g. today’s wind speed is similar to yesterday’s wind speed this is the persistence or inertia of the climate system • Diurnal/annual cycles should be removed before estimating autocorrelation • X-average(X)/ fitting and removing an analytical function, such as a series of sinusoids or polynomials • The most natural way to visualize autocorrelation in a time series is by plotting the autocorrelation as a function of lag time, that is the autocorrelation function
Chapter 6 6.2 Statistical methods time series analysis models Component decomposition (1)Alinear trend an annual cycle (3)diurnal cycle (4)autocorrelation (5)a random component Assessment of Climate/Change Impacts
Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • time series analysis models Component decomposition (1)A linear trend (2) an annual cycle (3) a diurnal cycle (4) autocorrelation (5) a random component
Chapter 6 6.2 Statistical methods time series analysis models Spectrum analysis (1) Discrete Fourier Transform (2) The power spectrum (3)Cross spectrum analysis (4) Filtering (5) Wavelets Assessment of Climate/Change Impacts
Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • time series analysis models Spectrum analysis (1) Discrete Fourier Transform (2) The power spectrum (3) Cross spectrum analysis (4) Filtering (5) Wavelets
Chapter 6 6.2 Statistical methods ° spatial analysis Grid Interpolation Relationships between related fields Geostatistics: Kriging Assessment of Climate/Change Impacts
Chapter 6 Assessment of Climate/Change Impacts 6.2 Statistical Methods • spatial analysis Grid Interpolation Relationships between related fields Geostatistics: Kriging