Abstract
In this paper, the weather generator (WG) used by the empirical statistical downscaling method, weather situation-based regionalization method (in German: WETTerlagen-basierte REGionalisierungsmethode, WETTREG), is described. It belongs to the class of multi-site parametric models that aim at the representation of the spatial dependence among weather variables with conditioning on exogenous atmospheric predictors. The development of the WETTREG WG was motivated by (i) the requirement of climate impact modelers to obtain input data sets that are consistent and can be produced in a relatively economic way and (ii) the well-sustained hypothesis that large scale atmospheric features are well reproduced by climate models and can be used as a link to regional climate. The WG operates at daily temporal resolution. The conditioning factor is the temporal development of the frequency distribution of circulation patterns. Following a brief description of the strategy of classifying circulation patterns that have a strong link to regional climate, the bulk of this paper is devoted to a description of the WG itself. This includes aspects, such as the utilized building blocks, seasonality or the methodology with which a signature of climate change is imprinted onto the generated time series. Further attention is given to particularities of the WG’s conditioning processes, as well as to extremes, areal representativity and the interface of WGs and user requirements.
Highlights
Weather generators (WG) are parametric stochastic models emulating weather data [1]
We have given a description of a weather generator (WG), which is the heart of the statistical downscaling method WETTREG
It combines random sampling of episodes of measured climate from the present with a steering algorithm that governs the selection process according to the modelled changes in frequency of circulation patterns—according to 20C and scenario runs of a climate model
Summary
Weather generators (WG) are parametric stochastic models (i.e., mathematical formulations that explicitly include elements of randomness) emulating weather data [1] In other words, they are algorithms that are devised with the intention to synthesize time series that are conditioned by external factors—of (in principle) unlimited length and number. Some WG-specific background is covered in [27] It belongs to the class of multi-site parametric models, which, in addition, aim at the representation of the spatial dependence among weather variables with conditioning on exogenous atmospheric predictors [30] operating at daily temporal resolution. Since the WG is a component of an ESD method, a further transfer step (signified by box E in Figure 1) takes place It encompasses a fanning out of the aggregated, i.e., areally averaged information, that emerged from the pre-processing towards the locations of the network of stations used.
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