 |
| Titel |
Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models |
| VerfasserIn |
E. Eccel, L. Ghielmi, P. Granitto, R. Barbiero, F. Grazzini, D. Cesari |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1023-5809
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 14, no. 3 ; Nr. 14, no. 3 (2007-05-25), S.211-222 |
| Datensatznummer |
250012195
|
| Publikation (Nr.) |
 copernicus.org/npg-14-211-2007.pdf |
|
|
|
|
|
| Zusammenfassung |
| Model Output Statistics (MOS) refers to a method of post-processing the
direct outputs of numerical weather prediction (NWP) models in order to
reduce the biases introduced by a coarse horizontal resolution. This
technique is especially useful in orographically complex regions, where large
differences can be found between the NWP elevation model and the true
orography. This study carries out a comparison of linear and non-linear MOS
methods, aimed at the prediction of minimum temperatures in a fruit-growing
region of the Italian Alps, based on the output of two different NWPs (ECMWF
T511–L60 and LAMI-3). Temperature, of course, is a particularly important
NWP output; among other roles it drives the local frost forecast, which is of
great interest to agriculture. The mechanisms of cold air drainage, a
distinctive aspect of mountain environments, are often unsatisfactorily
captured by global circulation models. The simplest post-processing technique
applied in this work was a correction for the mean bias, assessed at
individual model grid points. We also implemented a multivariate linear
regression on the output at the grid points surrounding the target area, and
two non-linear models based on machine learning techniques: Neural Networks
and Random Forest. We compare the performance of all these techniques on four
different NWP data sets. Downscaling the temperatures clearly improved the
temperature forecasts with respect to the raw NWP output, and also with
respect to the basic mean bias correction. Multivariate methods generally
yielded better results, but the advantage of using non-linear algorithms was
small if not negligible. RF, the best performing method, was implemented on
ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the
target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC
were approximately 1.2°C, close to the natural variability
inside the area itself. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|