TY - JOUR

T1 - A semi-parametric hourly space-time weather generator

AU - Pidoto, Ross

AU - Haberlandt, Uwe

N1 - Funding Information: This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. FOR 2416).The publication of this article was funded by the open-access fund of Leibniz Universität Hannover. Funding Information: The authors gratefully acknowledge the financial support of the German Research Foundation (Deutsche Forschungsgemeinschaft, regarding the research group “Space-Time Dynamics of Extreme Floods (SPATE)”, and the German Weather Service (Deutscher Wetterdienst, DWD) for providing the observed meteorological datasets. The author (Ross Pidoto) also thanks Hannes Müller-Thomy for productive and thoughtful discussions regarding the development of the spatial consistency optimisation approach for large station networks. Lastly, the authors thank the two reviewers, Simon Papalexiou and Dongkyun Kim, for their constructive comments and feedback, which greatly contributed to the final version of this paper.

PY - 2023/11/8

Y1 - 2023/11/8

N2 - Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. However, observed time series are generally too short, too sparse in space or incomplete, especially at the sub-daily timestep. Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space-time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables. The point-based rainfall model is extended into space by the resampling of simulated rainfall events via a simulated annealing optimisation approach. This approach enforces observed spatial dependency as described by three bivariate spatial rainfall criteria. A new non-sequential branched shuffling approach is introduced which allows the modelling of large station networks (N>50) with no significant loss in the spatial dependence structure. Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric k-nearest neighbour (k-NN) resampling approach, coupled to the space-time rainfall model via the daily catchment rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final deterministic disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution. The proposed weather generator was tested on 400 catchments of varying size (50-20000km2) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size and a generally good reproduction of observed climate and rainfall statistics.

AB - Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. However, observed time series are generally too short, too sparse in space or incomplete, especially at the sub-daily timestep. Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space-time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables. The point-based rainfall model is extended into space by the resampling of simulated rainfall events via a simulated annealing optimisation approach. This approach enforces observed spatial dependency as described by three bivariate spatial rainfall criteria. A new non-sequential branched shuffling approach is introduced which allows the modelling of large station networks (N>50) with no significant loss in the spatial dependence structure. Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric k-nearest neighbour (k-NN) resampling approach, coupled to the space-time rainfall model via the daily catchment rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final deterministic disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution. The proposed weather generator was tested on 400 catchments of varying size (50-20000km2) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size and a generally good reproduction of observed climate and rainfall statistics.

UR - http://www.scopus.com/inward/record.url?scp=85177779989&partnerID=8YFLogxK

U2 - 10.5194/hess-27-3957-2023

DO - 10.5194/hess-27-3957-2023

M3 - Article

AN - SCOPUS:85177779989

VL - 27

SP - 3957

EP - 3975

JO - Hydrology and Earth System Sciences

JF - Hydrology and Earth System Sciences

SN - 1027-5606

IS - 21

ER -