Maxima of Station-based Rainfall Data over Different Accumulation Durations and Large Scale Covariates

Description

These data were used in the study "Non-Stationary Large-Scale Statistics of Precipitation Extremes in Central Europe" (Fauer et al., 2022, https://doi.org/10.21203/rs.3.rs-2542862/v1). Rainfall data were collected from stations by the German Meteorological Service (DWD) and Wupperverband (corrected data). Raw time series data from the German Meteorological Service is publicly available under https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/. Only the annual and monthly precipitation maxima over different durations are published here. For more detailed information on our work and the modeling of extreme rainfall data, see also Fauer et al. (2021, https://doi.org/10.5194/hess-25-6479-2021).

Files

precipMax_and_covariates.csv: This file contains aggregated rainfall data over different durations and for different stations ("xdat"). Also, it contains covariates for the variables "blocking", "NAO", surface air temperature ("tas"), humidty and their polynomials up the the fourth order.

precip_meta.csv: This file contains additional information of the different stations such as longitude, latitude, altitude, temporal resolution (m=minutely, h=hourly, d=daily), group. The same group is assigned to stations which have a distance of less than 250 meters and can be treated as one station. The value in "group" corresponds to the value "station" in precipMax_and_covariates.csv.

Abstract of the according study

Extreme precipitation shows non-stationary behavior over time, but also with respect to other large-scale variables. While this effect is often neglected, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. The models are selected with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large scale information. Also, the new model enables insights into the effect of large scale variables on extreme precipitation. Results suggest that the probability of extreme precipitation increases with time since 1950 in all seasons. High probabilities of extremes are positively correlated with blocking situations in summer and with temperature in winter. However, they are negatively correlated with blocking situations in winter and temperature in summer.

Acknowledgements

We would like to thank the German Weather Service (DWD), especially Thomas Junghänel, and the Wupperverband, especially Marc Scheibel, for maintaining the station-based rainfall gauge and providing us with data.

Funding

This study is part of the ClimXtreme project (Grant number 01LP1902H) and is sponsored by the Federal Ministry of Education and Research in Germany.

Changelog

added humidity as large-scale covariate

large-scale covariates are normalized to mean=0 and standard deviation=1