Abstract
Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local weather variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorological records used to characterize the large-scale atmospheric configuration of the generation day. To overcome these limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analogue-based weather generators.
Highlights
Increasing the resilience of socio-economic systems to natural hazards and identifying the required adaptations is one of today’s challenges
The stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature
For the sake of consistency between the outputs, we compare the 30 scenarios of 111 years obtained from ANALOGUE and SCAMP to 300 scenarios of 100 years from SCAMP+
Summary
Increasing the resilience of socio-economic systems to natural hazards and identifying the required adaptations is one of today’s challenges. Among the large panel of existing weather generators, stochastic ones are used to construct, via a stochastic generation process, single or multi-site time series of predictands (e.g. precipitation and temperature) based on the distributional properties of observed data. Stochastic weather generators are able to produce large ensembles of weather time series presenting a wide diversity of multi-scale weather events For all these reasons, they have been used for a long time to enlighten the sensitivity and possible vulnerabilities of socio-ecosystems to the climate variability (Orlowsky and Seneviratne, 2010) and to weather extremes. These two steps (random atmospheric trajectories and random daily precipitation and temperature values) improve the reproduction of extreme values, overcoming previous limitations of analogue-based weather generators, usually known to underestimate observed precipitation extremes These developments are carried out for the exploration of hydrological extremes (extreme floods) of the Aare River basin in Switzerland (Andres et al, 2019a, b).
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