A semi-parametric hourly space–time weather generator

Pidoto, Ross; Haberlandt, Uwe

doi:https://doi.org/10.5194/hess-27-3957-2023

Articles | Volume 27, issue 21

https://doi.org/10.5194/hess-27-3957-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-3957-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 21

Research article

|

08 Nov 2023

Research article |

| 08 Nov 2023

A semi-parametric hourly space–time weather generator

Ross Pidoto and Uwe Haberlandt

Data sets

Hydrometeorological raster dataset for Germany, v4.0 DWD Climate Data Center https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/

Historical hourly station observations for Germany, v21.3 DWD Climate Data Center https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/

Short summary

Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.