J
Jan O. Haerter
Researcher at University of Copenhagen
Publications - 78
Citations - 5167
Jan O. Haerter is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Precipitation & Climate model. The author has an hindex of 25, co-authored 72 publications receiving 4180 citations. Previous affiliations of Jan O. Haerter include Niels Bohr Institute & Max Planck Society.
Papers
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Journal ArticleDOI
Statistical bias correction for daily precipitation in regional climate models over Europe.
TL;DR: In this paper, a methodology for correcting climate model output to produce internally consistent fields that have the same statistical intensity distribution as the observations was proposed, referred to as a statistical bias correction.
Journal ArticleDOI
Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models
C. Piani,Graham P. Weedon,Martin Best,S. M. Gomes,Pedro Viterbo,Stefan Hagemann,Jan O. Haerter +6 more
TL;DR: In this paper, a statistical bias correction methodology for global climate simulations is developed and applied to daily land precipitation and mean, minimum and maximum daily land temperatures, based on a fitted histogram equalization function.
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Strong increase in convective precipitation in response to higher temperatures
TL;DR: In this paper, a combination of radar and rain gauge measurements over Germany with synoptic observations and temperature records reveals that convective precipitation, for example from thunderstorms, dominates events of extreme precipitation.
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Impact of a Statistical Bias Correction on the Projected Hydrological Changes Obtained from Three GCMs and Two Hydrology Models
TL;DR: In this article, a methodology of a statistical bias correction has been developed for correcting climate model output to produce long-term time series with a statistical intensity distribution close to that of the observations.
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Climate model bias correction and the role of timescales
TL;DR: The effects of mixing fluctuations on different timescales are examined and an alternative statistical methodology is suggested, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.