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| Titel |
The forecaster's added value in QPF |
| VerfasserIn |
M. Turco, M. Milelli |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: Precipitation: Measurement, Climatology, Remote Sensing, and Modeling (EGU Session 2009) ; Nr. 25 (2010-03-09), S.29-36 |
| Datensatznummer |
250015733
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| Publikation (Nr.) |
 copernicus.org/adgeo-25-29-2010.pdf |
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| Zusammenfassung |
To the authors' knowledge there are relatively few studies that try to answer
this question: "Are humans able to add value to computer-generated forecasts
and warnings?". Moreover, the answers are not always positive. In particular
some postprocessing method is competitive or superior to human forecast.
Within the alert system of ARPA Piemonte it is possible to study in an
objective manner if the human forecaster is able to add value with respect to
computer-generated forecasts. Every day the meteorology group of the Centro
Funzionale of Regione Piemonte produces the HQPF (Human Quantitative
Precipitation Forecast) in terms of an areal average and maximum value for
each of the 13 warning areas, which have been created according to
meteo-hydrological criteria. This allows the decision makers to produce an
evaluation of the expected effects by comparing these HQPFs with predefined
rainfall thresholds. Another important ingredient in this study is the very
dense non-GTS (Global Telecommunication System) network of rain gauges
available that makes possible a high resolution verification. In this work we
compare the performances of the latest three years of QPF derived from the
meteorological models COSMO-I7 (the Italian version of the COSMO Model, a
mesoscale model developed in the framework of the COSMO Consortium) and IFS
(the ECMWF global model) with the HQPF. In this analysis it is possible to
introduce the hypothesis test developed by Hamill (1999), in which a confidence
interval is calculated with the bootstrap method in order to establish the
real difference between the skill scores of two competitive forecasts. It is
important to underline that the conclusions refer to the analysis of the
Piemonte operational alert system, so they cannot be directly taken as
universally true. But we think that some of the main lessons that can be
derived from this study could be useful for the meteorological community. In
details, the main conclusions are the following:
– despite the overall improvement in global scale and the fact that the resolution
of the limited area models has increased considerably over recent years, the QPF produced
by the meteorological models involved in this study has not improved enough to allow its
direct use: the subjective HQPF continues to offer the best performance for the period
+24 h/+48 h (i.e. the warning period in the Piemonte system);
– in the forecast process, the step where humans have the largest added value with
respect to mathematical models, is the communication. In fact the human characterization
and communication of the forecast uncertainty to end users cannot be replaced by any
computer code;
– eventually, although there is no novelty in this study, we would like to show that
the correct application of appropriated statistical techniques permits a better
definition and quantification of the errors and, mostly important, allows a correct
(unbiased) communication between forecasters and decision makers. |
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