Use of a Kalman filtering technique for near-surface temperature analysis

Cantarello, Luca (2017) Use of a Kalman filtering technique for near-surface temperature analysis. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica del sistema terra [LM-DM270]
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A statistical post-processing of the hourly 2-meter temperature fields from the Nordic convective-scale operational Numerical Weather Prediction model Arome MetCoOp 2.5 Km has been developed and tested at the Norwegian Meteorological Institute (MET Norway). The objective of the work is to improve the representation of the temperature close to the surface combining model data and in-situ observations for climatological and hydrological applications. In particular, a statistical scheme based on a bias-aware Local Ensemble Transform Kalman Filter has been adapted to the spatial interpolation of surface temperature. This scheme starts from an ensemble of 2-meter temperature fields derived from Arome MetCoOp 2.5 Km and, taking into account the observations provided by the MET Norway network, produces an ensemble of analysis fields characterised by a grid spacing of 1 km. The model best estimate employed in the interpolation procedure is given by the latest avilable forecast, subsequently corrected for the model bias. The scheme has been applied off-line and the final analysis is performed independently at each grid point. The final analysis ensemble has been evaluated and its mean value has been proved to improve significantly the best estimate of Arome MetCoOp 2.5 km in representing the 2-meter temperature fields, in terms of both accuracy and precision, with a reduction in the root mean squared values as well as in the bias and an improvement in reproducing the cold extremes during wintertime. More generally, the analysis ensemble displays better forecast verification scores, with an overall reduction in the Brier Score and its reliability component and an increase in the resolution term for the zero degrees threshold. However, the final ensemble spread remains too narrow, though not as narrow as the model output.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Cantarello, Luca
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Numerical Weather Prediction,Statistical post-processing,Post-processing,Kalman Filter,Ensemble Kalman Filter,Local Ensemble Transform Kalman Filter,Arome MetCoOp 2.5 km,NWP,2-meter temperature,surface temperature,Kalman filtering,temperature analysis,Spatial interpolation,Bias correction,Grid fields,2-meter temperature fields
Data di discussione della Tesi
30 Marzo 2017

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