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| Titel |
Intercomparison of two meteorological limited area models for quantitative precipitation forecast verification |
| VerfasserIn |
E. Oberto, M. Milelli, F. Pasi, B. Gozzini |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 12, no. 3 ; Nr. 12, no. 3 (2012-03-08), S.591-606 |
| Datensatznummer |
250010604
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| Publikation (Nr.) |
 copernicus.org/nhess-12-591-2012.pdf |
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| Zusammenfassung |
| The demand for verification of numerical models is still very high,
especially for what concerns the operational Quantitative Precipitation
Forecast (QPF) used, among others, for evaluating the issuing of
warnings to the population. In this study, a comparative verification of the QPF,
predicted by two operational Limited Area Models (LAMs) for the Italian
territory is presented: COSMO-I7 (developed in the framework of the COSMO
Consortium) and WRF-NMM (developed at NOAA-NCEP). The observational dataset
is the precipitation recorded by the high-resolution non-GTS rain gauges
network of the National Civil Protection Department (NCPD) over two years
(2007–2008). Observed and forecasted precipitation have been treated as
areal quantity (areal average of the values accumulated in 6 and 24 h
periods) over the 102 "warning areas", defined by the NCPD both for
administrative and hydrological purposes. Statistics are presented through a
series of conventional indices (BIAS, POD and POFD) and, in addition, the
Extreme Dependency Score (EDS) and the Base Rate (BS or 1-BS) have been used
for keeping into account the vanishing of the indices as the events become
rare. Results for long-period verification (the whole 2 yr) with
increasing thresholds, seasonal trend (3 months period), diurnal error cycle
and error maps, are presented. Results indicate that WRF has a general
tendency of QPF overestimation for low thresholds and underestimation for
higher ones, while COSMO-I7 tends to overestimate for all thresholds. Both
models show a seasonal trend, with a bigger overestimation during summer and
spring, while during autumn and winter the models tend to be more accurate. |
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