SPAGETTA, a Gridded Weather Generator: Calibration, Validation and its Use for Future Climate

0474172 - ÚFA 2018 DE eng A - Abstrakt
Dubrovský, Martin - Rotach, M. - Huth, Radan
SPAGETTA, a Gridded Weather Generator: Calibration, Validation and its Use for Future Climate.
Geophysical Research Abstracts. Göttingen: European Geosciences Union, 2017. EGU2017-8550. ISSN 1607-7962.
[EGU General Assembly 2017. 23.04.2017-28.04.2017, Vienna]
Institucionální podpora: RVO:68378289
Klíčová slova: stochastic weather generator * climate change scenarios
Kód oboru RIV: DG - Vědy o atmosféře, meteorologie
http://meetingorganizer.copernicus.org/EGU2017/EGU2017-8550.pdf

Spagetta is a new (started in 2016) stochastic multi-site multi-variate weather generator (WG). It can produce
realistic synthetic daily (or monthly, or annual) weather series representing both present and future climate
conditions at multiple sites (grids or stations irregularly distributed in space). The generator, whose model is based
on the Wilks’ (1999) multi-site extension of the parametric (Richardson’s type) single site M&Rfi generator, may
be run in two modes:
In the first mode, it is run as a classical generator, which is calibrated in the first step using weather data from
multiple sites, and only then it may produce arbitrarily long synthetic time series mimicking the spatial and
temporal structure of the calibration weather data. To generate the weather series representing the future climate,
the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM
simulations. In the second mode, the user provides only basic information (not necessarily to be realistic) on the
temporal and spatial auto-correlation structure of the surface weather variables and their mean annual cycle; the
generator itself derives the parameters of the underlying autoregressive model, which produces the multi-site
weather series. In the latter mode of operation, the user is allowed to prescribe the spatially varying trend, which
is superimposed to the values produced by the generatorr; this feature has been implemented for use in developing
the methodology for assessing significance of trends in multi-site weather series (for more details see another
EGU-2017 contribution: Huth and Dubrovsky, 2017, Evaluating collective significance of climatic trends: A
comparison of methods on synthetic data; EGU2017-4993).
In this experiment, the generator is calibrated
using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are
derived from the selected RCM simulation (taken from the CORDEX database).
Trvalý link: http://hdl.handle.net/11104/0271281