Linear and nonlinear post-processing of numerically forecasted surface temperature

M. Casaioli (1) , R. Mantovani (2) , F. Proietti Scorzoni (2) , S. Puca (2) , A. Speranza (3) , B. Tirozzi (2)

1 ISAC - CNR Institute of Atmospheric Sciences and Climate
2 Physics Department
3 Mathematics and Informatics Department

In this paper we test different approaches to the statistical post-processing of gridded numerical surface air temperatures (provided by the European Centre for Medium-Range Weather Forecasts) onto the temperature measured at surface weather stations located in the Italian region of Puglia. We consider simple post-processing techniques, like correction for altitude, linear regression from different input parameters and Kalman filtering, as well as a neural network training procedure, stabilised (i.e. driven into the absolute minimum of the error function over the learning set) by means of a Simulated Annealing method. A comparative analysis of the results shows that the performance with neural networks is the best. It is encouraging for systematic use in meteorological forecast-analysis service operations.

Domaines: Cosmologie et astrophysique extra-galactique [astro-ph.CO]; Astrophysique [astro-ph]; Sciences de la Terre

M. Casaioli, R. Mantovani, F. Proietti Scorzoni, S. Puca, A. Speranza, et al.. Linear and nonlinear post-processing of numerically forecasted surface temperature. Nonlinear Processes in Geophysics, 2003, 10 (4/5), pp.373-383. ⟨hal-00302229⟩