Classification of daily precipitation patterns on the basis of radar-derived precipitation rates for Saxony, Germany

Kronenberg, Rico; Franke, Johannes; Bernhofer, Christian

doi:10.1127/0941-2948/2012/0343

Cited by 8 publications

(16 citation statements)

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“…The temporal classification of daily precipitation fields effectively resulted in 10 classes (Kronenberg et al , ) for each approach (CA and ANN) and the considered domain and data. These classes were used to calculate mixture distributions.…”

Section: Resultsmentioning

confidence: 99%

“…The temporal classification resulted in classes that not only differ by their statistical properties but also by their meteorological properties. Kronenberg et al () showed that significant differences could be found in the dominant wind direction, the underlying pressure fields and the moisture content of the atmosphere. Each class represents a subpopulation of the precipitation spectrum, which is why the derived classes (subpopulations) were combined with frequency distributions.…”

Section: Methodsmentioning

confidence: 99%

“…A detailed description of these products can be found in Bartels et al (). A discussion of potential errors due to temporal aggregation for the considered product in Kronenberg et al () is also recommended. The radar database is aggregated from hourly products (0650 UTC) to daily precipitation grids at a 1 × 1 km 2 resolution for the study region with an extent of 252 × 236 pixels (Kronenberg et al , ).…”

Section: Case Study Region and Datamentioning

confidence: 99%

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Comparison of different approaches to fit log-normal mixtures on radar-derived precipitation data

2013

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Mixture models of precipitation frequency distributions describe the precipitation spectrum more adequately than simple probability distributions. The estimation of mixture distribution parameters by the maximum likelihood approach is commonly known. The reliability of these models and their parameters derived by the maximum likelihood method and by temporal classifications over Saxony was investigated using a radar-based precipitation product on a daily basis. Therefore, two different 'unsupervised' classification approaches were applied, a cluster analysis and a neural network, to derive subpopulation estimates. The temporal classification summarized high-resolution spatial grids of daily precipitation fields into 10 precipitation classes. The mixture model evaluation consists of two parts. First, an information criterion was calculated to describe the model complexity and the significance of the number of parameters. Second, the qualities of the fitted models were compared with the database through ordinary Kolmogorov-Smirnov tests and Kolmogorov-Smirnov tests in the context of field significance. Under the assumption of log-normal distributed precipitation, the neural network delivered the best mixture models for a spatial resolution of 2 and 3 km while the cluster analysis delivered the better initial parameters for an expectation-maximization algorithm on mixture distributions over the considered domain.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Case Study Region and Datamentioning

confidence: 99%

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Comparison of different approaches to fit log-normal mixtures on radar-derived precipitation data

2013

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“…24 h composites) were considered for further processing. The data sets were already applied by Kronenberg et al (2012, 2013).…”

Section: Case Study Region and Data Setsmentioning

confidence: 99%

A method to adapt radar-derived precipitation fields for climatological applications

Kronenberg

Bernhofer

2015

Met. Apps

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ABSTRACT:In the present study, a rapid and simple approach is presented to rearrange long-term precipitation fields (i.e. periods ≥ 1 year) from a multi-site radar product, which were originally derived on an hourly basis as quantitative precipitation estimates (QPEs) for flood risk forecasting and management. The method consists of interquartile outlier detection and linear transformations to achieve a better representation of elevation above sea level (asl). The presented approach was deployed in the Free State of Saxony, Germany, for the period from April 2004 through December 2009. The results are compared with two independently derived and highly resolved gridded precipitation data sets (i.e. 1 km resolution). An algorithm based on the golden section search and parabolic interpolation was deployed to determine an optimal set of parameters. The results show a significant increase in quality in the long-term precipitation information in comparison to the original QPEs. Hence, a more reasonable order of the correlation and of the empirical histogram could be obtained after processing. The scorings within the means of the Taylor diagram show that the patterns of the compared data sets are very similar. It can be inferred that the results support the application of the introduced procedure for the adjustment of QPEs for periods of 1 year and greater because a more realistic appearance of the climatological precipitation field can be achieved; in addition, it is an approach that is simple and reliable. The results can be used for climatological analysis and should help in the application of radar products for improving station-based precipitation climatology.

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“…Thus the assumption was made that the locally recorded precipitation events can be coupled to synoptic scale properties of the atmosphere, which in turn yield information about the formation mechanisms of precipitation, i.e. convective or stratiform (Kronenberg et al, 2012). After the classification process and the construction of composite maps of predictor fields, these circulation pattern classes in turn can be recognised from the synoptic scale output of GCMs and from reanalysis data.…”

Section: Circulation Pattern Datamentioning

confidence: 99%

Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

Lisniak

Franke

Bernhofer

2013

Hydrol. Earth Syst. Sci.

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Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969-1979 and 1989-1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989-1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice [1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999], as compared to the simple MRC. Despite similar results of both MRCs in the validation period of 1969-1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.

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Classification of daily precipitation patterns on the basis of radar-derived precipitation rates for Saxony, Germany

Cited by 8 publications

References 0 publications

Comparison of different approaches to fit log-normal mixtures on radar-derived precipitation data

Comparison of different approaches to fit log-normal mixtures on radar-derived precipitation data

A method to adapt radar-derived precipitation fields for climatological applications

Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

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