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| Titel |
Combining 2-m temperature nowcasting and short range ensemble forecasting |
| VerfasserIn |
A. Kann, T. Haiden, C. Wittmann |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 18, no. 6 ; Nr. 18, no. 6 (2011-12-02), S.903-910 |
| Datensatznummer |
250014004
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| Publikation (Nr.) |
 copernicus.org/npg-18-903-2011.pdf |
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| Zusammenfassung |
| During recent years, numerical ensemble prediction systems have become an
important tool for estimating the uncertainties of dynamical and physical
processes as represented in numerical weather models. The latest generation
of limited area ensemble prediction systems (LAM-EPSs) allows for
probabilistic forecasts at high resolution in both space and time. However,
these systems still suffer from systematic deficiencies. Especially for
nowcasting (0–6 h) applications the ensemble spread is smaller than the
actual forecast error. This paper tries to generate probabilistic short
range 2-m temperature forecasts by combining a state-of-the-art nowcasting
method and a limited area ensemble system, and compares the results with
statistical methods. The Integrated Nowcasting Through Comprehensive
Analysis (INCA) system, which has been in operation at the Central Institute
for Meteorology and Geodynamics (ZAMG) since 2006 (Haiden et al., 2011),
provides short range deterministic forecasts at high temporal (15 min–60 min)
and spatial (1 km) resolution. An INCA Ensemble (INCA-EPS) of 2-m
temperature forecasts is constructed by applying a dynamical approach, a
statistical approach, and a combined dynamic-statistical method. The
dynamical method takes uncertainty information (i.e. ensemble variance) from
the operational limited area ensemble system ALADIN-LAEF (Aire Limitée
Adaptation Dynamique Développement InterNational Limited Area Ensemble
Forecasting) which is running operationally at ZAMG (Wang et al., 2011). The
purely statistical method assumes a well-calibrated spread-skill relation
and applies ensemble spread according to the skill of the INCA forecast of
the most recent past. The combined dynamic-statistical approach adapts the
ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian
regression (NGR) which yields a statistical \mbox{correction} of the first and
second moment (mean bias and dispersion) for Gaussian distributed continuous
variables. Validation results indicate that all three methods produce sharp
and reliable probabilistic 2-m temperature forecasts. However, the
statistical and combined dynamic-statistical methods slightly outperform the
pure dynamical approach, mainly due to the under-dispersive behavior of
ALADIN-LAEF outside the nowcasting range. The training length does not have
a pronounced impact on forecast skill, but a spread re-scaling improves the
forecast skill substantially. Refinements of the statistical methods yield a
slight further improvement. |
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