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| Titel |
Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008 |
| VerfasserIn |
S. Federico |
| 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 ; 11, no. 2 ; Nr. 11, no. 2 (2011-02-16), S.487-500 |
| Datensatznummer |
250009161
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| Publikation (Nr.) |
 copernicus.org/nhess-11-487-2011.pdf |
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| Zusammenfassung |
Since 2005, one-hour temperature forecasts for the Calabria region (southern
Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have
been issued by CRATI/ISAC-CNR (Consortium for Research and Application of
Innovative Technologies/Institute for Atmospheric and Climate Sciences of
the National Research Council) and are available online at
http://meteo.crati.it/previsioni.html (every six hours). Beginning in June 2008,
the horizontal resolution was enhanced to 2.5 km. In the present paper,
forecast skill and accuracy are evaluated out to four days for the 2008
summer season (from 6 June to 30 September, 112 runs). For this purpose,
gridded high horizontal resolution forecasts of minimum, mean, and maximum
temperatures are evaluated against gridded analyses at the same horizontal
resolution (2.5 km).
Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS
first-day temperature forecast as the background field. Observations from
87 thermometers are used in the analysis system. The analysis error is
introduced to quantify the effect of using the RAMS first-day forecast as
the background field in the OI analyses and to define the forecast error
unambiguously, while spatial interpolation (SI) analysis is considered to
quantify the statistics' sensitivity to the verifying analysis and to show
the quality of the OI analyses for different background fields.
Two case studies, the first one with a low (less than the 10th
percentile) root mean square error (RMSE) in the OI analysis, the second
with the largest RMSE of the whole period in the OI analysis, are discussed
to show the forecast performance under two different conditions. Cumulative
statistics are used to quantify forecast errors out to four days. Results
show that maximum temperature has the largest RMSE, while minimum and mean
temperature errors are similar. For the period considered, the OI analysis
RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and
2.0 °C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 °C, respectively, for the fourth-day forecast.
Cumulative statistics are computed using both SI and OI analysis as
reference. Although SI statistics likely overestimate the forecast error
because they ignore the observational error, the study shows that the
difference between OI and SI statistics is less than the analysis error.
The forecast skill is compared with that of the persistence forecast. The
Anomaly Correlation Coefficient (ACC) shows that the model forecast is
useful for all days and parameters considered here, and it is able to
capture day-to-day weather variability. The model forecast issued for the
fourth day is still better than the first-day forecast of a 24-h
persistence forecast, at least for mean and maximum temperature. The impact
of using the RAMS first-day forecast as the background field in the OI
analysis is quantified by comparing statistics computed with OI and SI
analyses. Minimum temperature is more sensitive to the change in the
analysis dataset as a consequence of its larger representative error. |
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