Application of Partial Regression Methods to Long Range Forecasts

• Autorzy: Konca-Kędzierska K.
• Języki publikacji: EN

Abstrakty

EN

The article presents the construction of a regression model for the long-range forecast of tercile categories of the monthly mean temperature. Two methods from the group of the partial least squares (PLS) and sparse partial least squares (SPLS) methods were used. The selected methods combine the properties of principal component analysis (PCA) with features of multiple regression methods, and apply the creation of latent layers. These methods also have no restrictions related to the independence of predictors and no constraints on the model dimension. The predictors are percentiles (10%, 50% and 90%) for selected fields of the NCEP/NCAR Reanalysis dataset. The model uses a time series of predictors for periods from 5 to 30 years. The obtained set of forecasts is subjected to the evaluation process based on indicators for the dependent period. This allows for the selection of a reliable ensemble of forecasts. The presented model was tested between January 2014 and December 2016.

Słowa kluczowe

EN

long range forecasts; regression model; partial least squares

• Wydawca: Polskie Towarzystwa Geofizyczne ; Komitet Geofizyki PAN
• Czasopismo: Przegląd Geofizyczny
• Rocznik: 2018
• Tom: Z. 4
• Strony: 353--362
• Opis fizyczny: Bibliogr. 9 poz., rys., tab., wykr.

Twórcy

autor

Konca-Kędzierska K.

• Institute of Meteorology and Water Management, National Research Institute

Bibliografia

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