Abstract
The algorithm for and results of a newly developed multivariate non-parametric model, the Euclidean distance model (EDM), for the hourly disaggregation of daily climate data are presented here. The EDM is a resampling method based on the assumption that the day to be disaggregated has already occurred once in the past. The Euclidean distance (ED) serves as a measure of similarity to select the most similar day from historical records. EDM is designed to disaggregate daily means/sums of several climate elements at once, here temperature (T), precipitation (P), sunshine duration (SD), relative humidity (rH), and wind speed (WS), while conserving physical consistency over all disaggregated elements. Since weather conditions and hence the diurnal cycles of climate elements depend on the weather pattern, a selection approach including objective weather patterns (OWP) was developed. The OWP serve as an additional criterion to filter the most similar day. For a case study, EDM was applied to the daily climate data of the stations Dresden and Fichtelberg (Saxony, Germany). The EDM results agree well with the observed data, maintaining their statistics. Hourly results fit better for climate elements with homogenous diurnal cycles, e.g., T with very high correlations of up to 0.99. In contrast, the hourly results of the SD and the WS provide correlations up to 0.79. EDM tends to overestimate heavy precipitation rates, e.g., by up to 15% for Dresden and 26% for Fichtelberg, potentially due to, e.g., the smaller data pool for such events, and the equal-weighted impact of P in the ED calculation. The OWPs lead to somewhat improved results for all climate elements in terms of similar climate conditions of the basic stations. Finally, the performance of EDM is compared with the disaggregation tool MELODIST (Förster et al. 2015). Both tools deliver comparable and well corresponding results. All analyses of the generated hourly data show that EDM is a very robust and flexible model that can be applied to any climate station. Since EDM can disaggregate daily data of climate projections, future research should address whether the model is capable to respect and (re)produce future climate trends. Further, possible improvements by including the flow direction and future OWPs should be investigated, also with regard to reduce the overestimation of heavy rainfall rates.
Highlights
Climate data with high temporal resolution are needed in a multitude of hydrological models (e.g., WaSim-ETH, MIKE FLOOD) or ecological models (e.g., SWAT-CN, GASFLUX) and for climate research and analysis
Since the relative humidity is characterized by a more homogenous diurnal cycle, these results reveal higher correlations of 0.88 and 0.91 and lower root mean square difference (RMSDn) values of 0.48 and 0.42
These improvements are greater when the other basic stations are characterized by different climate conditions, as is the case for the mountain station Fichtelberg
Summary
Climate data with high temporal resolution are needed in a multitude of hydrological models (e.g., WaSim-ETH, MIKE FLOOD) or ecological models (e.g., SWAT-CN, GASFLUX) and for climate research and analysis. Hourly time series are often not available and do not have appropriate lengths or contain gaps due to failures of the measuring equipment. To adapt to this lack of data, there are several methods to generate, complete, or extend the hourly time series of different climate elements. A few models have been developed for the disaggregation of more than one climate element, such as in Debele et al (2007), Mezghani and Hingray (2009), or Kim et al (2016) These disaggregate daily temperature data among others.
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