A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Forecasting climate variables using a mixed‐effect state‐space model
This paper demonstrates the potential advantage of using a linear, mixed‐effect state‐space model for statistical downscaling of climate variables compared to the frequently used approach of linear regression. This comparison leads to the development of a method for estimation of model parameters using the EM algorithm approach. The model is applied to the prediction of temperature and rainfall statistics at both a sub‐tropical and temperate location in Australia. The results indicate that for lead times of 1–10 years this state‐space approach is able to predict observed seasonal temperature and rainfall means with substantially greater precision than climatology, multivariate linear regression (MLR) or a standard linear state‐space (LSS) approach. The model is seen as a first step in the development of a short‐term climate change projection system that will utilise both historical climate data as well as dynamically derived mean climate change projection information obtained from global climate models (GCMs). Copyright © 2010 John Wiley & Sons, Ltd.
Forecasting climate variables using a mixed‐effect state‐space model
This paper demonstrates the potential advantage of using a linear, mixed‐effect state‐space model for statistical downscaling of climate variables compared to the frequently used approach of linear regression. This comparison leads to the development of a method for estimation of model parameters using the EM algorithm approach. The model is applied to the prediction of temperature and rainfall statistics at both a sub‐tropical and temperate location in Australia. The results indicate that for lead times of 1–10 years this state‐space approach is able to predict observed seasonal temperature and rainfall means with substantially greater precision than climatology, multivariate linear regression (MLR) or a standard linear state‐space (LSS) approach. The model is seen as a first step in the development of a short‐term climate change projection system that will utilise both historical climate data as well as dynamically derived mean climate change projection information obtained from global climate models (GCMs). Copyright © 2010 John Wiley & Sons, Ltd.
Forecasting climate variables using a mixed‐effect state‐space model
Kokic, Philip (author) / Crimp, Steve (author) / Howden, Mark (author)
Environmetrics ; 22 ; 409-419
2011-05-01
11 pages
Article (Journal)
Electronic Resource
English
Forecasting climate variables using a mixed‐effect state‐space model
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