Appropriateness of correlated first order auto-regressive processes for modeling daily temperature records
Radhakrishnan Nagarajan, R. B. Govindan
Abstract
The present study investigates linear and volatile (nonlinear) correlations of first-order autoregressive process with uncorrelated AR (1) and long-range correlated CAR (1) Gaussian innovations as a function of the process parameter (θ). In the light of recent findings jano, we discuss the choice of CAR (1) in modeling daily temperature records. We demonstrate that while CAR (1) is able to capture linear correlations it is unable to capture nonlinear (volatile) correlations in daily temperature records.
Create a lesson
Related papers
Bridging short- and medium-range weather forecasting with machine learning
Timothy A. Smith, Mariah Pope, Sergey Frolov et al.
Detectability of Forced ENSO Changes under Global Warming: Insights from the Recharge Oscillator
Sooman Han, Jérôme Vialard, Alexey V. Fedorov et al.
When Does Forecast-Error Energy Grow Logistically in Geophysical Turbulence?
Malaquias Peña
A place for stabilization alongside tipping cascades: the AMOC-cryosphere system
Sacha Sinet
Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?
Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun et al.
AICON: An operational global machine learning weather forecasting model
Tobias Goecke, Marek Jacob, Florian Prill et al.