30 research outputs found
Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator
Spatio-temporal precipitation is modelled for urban application at 1 h
temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as
input to the WG are obtained from a network of 60 tipping-bucket rain gauges
irregularly placed in a 40 km  ×  60 km model domain. The WG simulates
precipitation time series that are comparable to the observations with
respect to extreme precipitation statistics. The WG is used for downscaling
climate change signals from regional climate models (RCMs) with spatial
resolutions of 25 and 8 km, respectively. Six different RCM simulation
pairs are used to perturb the WG with climate change signals resulting in
six very different perturbation schemes. All perturbed WGs result in more
extreme precipitation at the sub-daily to multi-daily level and these
extremes exhibit a much more realistic spatial pattern than what is observed
in RCM precipitation output. The WG seems to correlate increased extreme
intensities with an increased spatial extent of the extremes meaning that
the climate-change-perturbed extremes have a larger spatial extent than
those of the present climate. Overall, the WG produces robust results and is
seen as a reliable procedure for downscaling RCM precipitation output for
use in urban hydrology
Integrated Climate and Hydrology Modelling - Catchment Scale Coupling of the HIRHAM Regional Climate Model and the MIKE SHE Hydrological Model:Extended abstract
The Day after Tomorrow - uniformitaristernes mareridt?
Dmi.dk har smugkigget på The Day after Tomorrow. Filmen er sprængfyldt med vilde, visuelle vejrfænomener; som vi i denne artikel sætter under meteorologisk lup. Hvad er fup, og hvad er fakta
On the importance of observational data properties when assessing regional climate model performance of extreme precipitation
In recent years, there has been an increase in the number of climate studies
addressing changes in extreme precipitation. A common step in these studies
involves the assessment of the climate model performance. This is often
measured by comparing climate model output with observational data. In the
majority of such studies the characteristics and uncertainties of the
observational data are neglected.
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This study addresses the influence of using different observational data sets
to assess the climate model performance. Four different data sets covering
Denmark using different gauge systems and comprising both networks of point
measurements and gridded data sets are considered. Additionally, the
influence of using different performance indices and metrics is addressed. A
set of indices ranging from mean to extreme precipitation properties is
calculated for all the data sets. For each of the observational data sets, the
regional climate models (RCMs) are ranked according to their performance
using two different metrics. These are based on the error in representing
the indices and the spatial pattern.
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In comparison to the mean, extreme precipitation indices are highly
dependent on the spatial resolution of the observations. The spatial pattern
also shows differences between the observational data sets. These differences
have a clear impact on the ranking of the climate models, which is highly
dependent on the observational data set, the index and the metric used. The
results highlight the need to be aware of the properties of observational
data chosen in order to avoid overconfident and misleading conclusions with
respect to climate model performance