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Free Content Towards a quality control of precipitation data

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Abstract:

The priority program 'Quantitative Precipitation Forecast' funded by the German Research Foundation (DFG) has been implemented in April 2004 for a period of six years. Within this program observations from almost 1000 rain gauges, which are not routinely used, have been collecting since 2007. Therefore a quality control of this observed raw data is needed. First of all a conditional probabilistic model for precipitation was developed. Based on the large scale atmospheric circulation or on small scale precipitation observations, the probability of precipitation exceeding a threshold is estimated. The model is applied to daily precipitation sums (1986–1999) of 231 rain gauge stations from the German Weather Service (DWD) network in Nordrhein-Westfalen (NRW) and NCEP reanalysis data. The procedure is based on a generalized linear model using logistic regression (GLM). Relative vorticity, vertical velocity (both in 850 hPa), and relative humidity (in 700 hPa and 850 hPa) are used as predictor variables. Alternatively precipitation observations are also used as predictor variables. The statistical model forecasts are validated by observations in a cross validation modus using the Brier Skill Score (BSS) and relative operating characteristics (ROC) curves. It is shown that the model represents the observations very well.

German
Während des DFG-Schwerpunktprogramms SPP 1167 'Quantitative Niederschlagsvorhersage' werden u.a. Niederschlagsbeobachtungen von nahezu 1000 Niederschlagsstationen gesammelt. Diese Stationen sind nicht im operationellen Gebrauch. Deshalb ist eine Qualitätskontrolle nötig. Dazu wurde ein bedingtes Wahrscheinlichkeitsmodell für Niederschlag entwickelt. Ausgehend von der bekannten großskaligen atmosphärischen Zirkulation oder von benachbarten Niederschlagsbeobachtungen wird die Wahrscheinlichkeit geschätzt, dass der Niederschlag einen Schwellwert überschreitet. Das Modell basiert auf einem Generalisierten Linearen Modell (GLM) und benutzt eine logistische Regression. Es wird anhand von Niederschlags-Tagessummen von über 230 Stationen des Deutschen Wetterdienstes (DWD) in Nordrhein-Westfalen (NRW) und NCEP-Reanalysedaten trainiert. Relative Vorticity, relative Feuchtigkeit und Vertikalgeschwindigkeit sowie die (jeweils benachbarten) Niederschlagsbeobachtungen stellen die Prediktorvariablen dar. Die statistischen Modellvorhersagen werden mittels einer Kreuzvalidation an den Beobachtungen validiert. Dabei werden der Brier Skill Score (BSS) und Relative Operating Characteristics (ROC) Kurven benutzt. Das Modell repräsentiert die Beobachtungen ausgezeichnet.

Document Type: Research Article

DOI: https://doi.org/10.1127/0941-2948/2008/0347

Publication date: 2008-12-01

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  • Meteorologische Zeitschrift (originally founded in 1866) is the joint periodical of the meteorological societies of Austria, Germany and Switzerland. It accepts high-quality peer-reviewed manuscripts on all aspects of observational, theoretical and computational research out of the entire field of meteorology, including climatology. Meteorologische Zeitschrift represents a natural forum for the meteorological community of Central Europe and worldwide.
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