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Accepted, under review or submitted

K. Fackeldey, P. Koltai, P. Névir, H. W. Rust, A. Schild, M. Weber: From Metastable to Coherent Sets – time-discretization schemes. Accepted for publication in Chaos

A. Pasternack, J. Grieger, H. W. Rust, U. Ulbrich: Recalibrating Decadal Climate Predictions – What is an adequate model for the drift?, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-191, in review, 2020.

Published in peer-reviewed journal


O. E. Jurado, J. Ulrich, M. Scheibel, H. W. Rust: Evaluating the Performance of a Max-Stable Process for Estimating Intensity-Duration-Frequency Curves. Water 2020, 12, 3314.

J. Ulrich, O. E. Jurado, M. Peter, M. Scheibel, H. W. Rust: Estimating IDF Curves Consistently over Durations with Spatial Covariates. Water 2020, 12, 3119.

E. Rousi, H.W. Rust, U. Ulbrich, C. Anagnostopoulou: Winter NAO flavors and their effects on European climate. Climate, 2020


E. Meredith, H. W. Rust, U. Ulbrich: The diurnal nature of future extreme precipitation intensification. Geophy. Res. Lett., 2019.

Y. Liu, M. Donat, H. W. Rust, l. Alexander, M. England: Decadal predictability of temperature and precipitation means and extremes in a perfect-model experiment. Clim. Dyn, 2019.

H. Feldmann, J. G. Pinto, N. Laube, M. Uhlig, J. Moemken, A. Pasternack, B. Früh, H. Pohlmann, C. Kottmeier: Characterization of Skill and Added Value of the MiKlip Regional Decadal Prediction System for Temperature over Europe. Tellus A, 2019.


M. Fischer, H. W. Rust, U. Ulbrich: A spatial and seasonal climatology of extreme precipitation return-levels: A
case study
. Spatial Statistics, 2018

S.Kremser, J. S. Tradowsky, H. W. Rust, G. E. Bodeker: Is it feasible to estimate radiosonde biases from interlaced measurements? Atmos. Measure. Tech., 11: 3021 - 3029, 2018

S. Liersch, J. Teckenburg, H. W. Rust, A. Dobler, M. Fischer, T. Kruschke, H. Koch, F. Hattermann: Are we using the right fuel to drive hydrological models? A climate impact study in the Upper Blue Nile, Hydrol. Earth Syst. Sci.,2018

E. P. Meredith, H. W. Rust, U. Ulbrich: A classification algorithm for selective dynamical downscaling of precipitation ex-
Hydrol. Earth. Syst. Sci., 22,4183 - 4200, 2018

N. Otero, J. Silmann, K. A. Mar, H. W. Rust, et al.: A multi-model comparison of meteorological drivers of surface ozone over Europe. Atmos. Chem. Phys.,2018.

A. Pasternack, H. W. Rust, J. Bhend, M. Liniger, W. A. Müller: Parametric Decadal Climate Forecast Recalibration, Geosci. Model Develop., 2018

A. Paxian, M. Ziese, F. Kreienkamp, K. Pankatz, S. Brand, A. Pasternack, H. Pohlmann, K. Modali, B. Früh: User-oriented global predictions of the GPCC drought index for the next decade, Meteorol. Z.,2018.


M. Fischer, H. W. Rust, U. Ulbrich: Seasonal Cycle in German Daily Precipitation Extremes, Meteorol. Z. 2018, DOI: 10.1127/metz/2017/0845

C. Ritschel, H.W. Rust, U. Ulbrich: Precipitation extremes on multiple time scales -- Bartlett-Lewis Rectangular Pulse Model and Intensity-Duration-Frequency curves, Hydrol. Earth Syst. Sci.

M. Walz, T. Kruschke, H. W. Rust, G. Leckebusch, U. Ulbrich: Quantifying the extremity of windstorms for regions featuring infrequent events, Atmos. Sci. Lett., 2017, DOI: 10.1002/asl.758


S. Babian, H. W. Rust, K. Prömmel, J. Grieger, U. Cubasch: Representation of Antarctic Oscillation and related precipitation in the MPI Earth System Model, Meteorol. Z., 2016, 767-774, DOI: 10.1127/metz/2016/0661

F. Kaspar, H. W. Rust, U. Ulbrich and P. Becker: Verification and process oriented validation of the MiKlip decadal prediction system. Meteorol. Z., 629 - 630, 2016, DOI: 10.1127/metz/2016/0831

J. Marotzke, W. A. Müller, F. S. E. Vamborg, P. Becker, U. Cubasch, H. Feldmann, F. Kaspar, C. Kottmeier, C. Marini, I. Polkova, K. Prömmel, H. W. Rust, D. Stammer, U. Ulbrich, C. Kadow, A. Köhl, J. Kröger, T. Kruschke, J. G. Pinto, H. Pohlmann, M. Reyers, M. Schröder, F. Sienz, C. Timmreck, M. Ziese: MiKlip - a National Research Project on Decadal Climate Prediction, Bulletin of the American Meteorological Society, 2016, DOI: 10.1175/BAMS-D-15-00184.1.

Otero Felipe, N., Sillmann, J., Schnell, J. L., Rust, H. W., Butler, T. M.: Synoptic and meteorological drivers of extreme ozone concentrations over Europe. - Environ. Res. Lett., 11, 2, 024005, 2016. DOI: 10.1088/1748-9326/11/2/024005

Vor 2016

Ulrich, J.; C. Detring; C. Ritschel und H. Rust: R-Paket zur Schätzung physikalisch konsistenter IDF-Kurven, DACH 2019, Garmisch-Partenkirchen, Germany

Trojand, A.; N. Becker und H.Rust: Windstauvorhersage in der Deutschen Bucht - ProbabilistischeVorhersage und Verifikation, DACH 2019, Garmisch-Partenkirchen, Germany

Detring, C.; A. Müller; P. Névir und H. Rust: Ein Markov-Modell für High-over-Low und Omega-Blockierungen, DACH 2019, Garmisch-Partenkirchen, Germany

IDF: Estimation and Plotting of IDF Curves

Autoren: Christoph Ritschel, Carola Detring, Sarah Joedicke

Beschreibung: Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package 'IDF' provides a function to read precipitation data from German weather service (DWD) 'webwerdis' <http://www.dwd.de/EN/ourservices/webwerdis/webwerdis.html> files and Berlin station data from 'Stadtmessnetz' <http://www.geo.fu-berlin.de/en/met/service/stadtmessnetz/index.html> files, and additionally IDF parameters can be estimated also from a given data.frame containing a precipitation time series. The data is aggregated to given levels yearly intensity maxima are calculated either for the whole year or given months. From these intensity maxima IDF parameters are estimated on the basis of a duration-dependent generalised extreme value distribution [Koutsoyannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>]. IDF curves based on these estimated parameters can be plotted.

Download: IDF_1.1.tar.gz

Download über die CRAN-Webseite: https://cran.r-project.org/package=IDF

BLRPM: Stochastic Rainfall Generator Bartlett-Lewis Rectangular Pulse Model

Autor: Christoph Ritschel

Beschreibung: Due to a limited availability of observed high-resolution precipitation records with adequate length, simulations with stochastic precipitation models are used to generate series for subsequent studies [e.g. Khaliq and Cunmae, 1996, <doi:10.1016/0022-1694(95)02894-3>, Vandenberghe et al., 2011, <doi:10.1029/2009WR008388>]. This package contains an R implementation of the original Bartlett-Lewis rectangular pulse model (BLRPM), developed by Rodriguez-Iturbe et al. (1987) <doi:10.1098/rspa.1987.0039>. It contains a function for simulating a precipitation time series based on storms and cells generated by the model with given or estimated model parameters. Additionally BLRPM parameters can be estimated from a given or simulated precipitation time series. The model simulations can be plotted in a three-layer plot including an overview of generated storms and cells by the model (which can also be plotted individually), a continuous step-function and a discrete precipitation time series at a chosen aggregation level.

Download: BLRPM_1.0.tar.gz

Download über die CRAN- Webseite: https://cran.r-project.org/package=BLRPM