EGU22-7485, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

The Spatial Weather Generator SPAGETTA: Hard Times of its Adolescence

Martin Dubrovsky1,2, Ondrej Lhotka1,2, Jiri Miksovsky2,3, Petr Stepanek2, and Jan Meitner2
Martin Dubrovsky et al.
  • 1Institute of Atmospheric Physics, Praha, Czechia (
  • 2Czech Academy of Sciences, Global Change Research Institute, Brno, Czechia
  • 3Charles University, Faculty of Mathematics and Physics, Prague, Czechia

Stochastic weather generators (WGs) are tools for producing weather series, mimicking statistical properties of their real-world counterparts. They are often used in climate change impact experiments as a source of the data representing the present and/or future climates (alternative to RCMs and GCMs). Development of our SPAGETTA generator started in 2016 (Dubrovsky et al 2020; The presentation will focus on (A) Basic details. (B) Functionalities of the generator. (C) Results obtained with the generator by now. (D) Most critical problems, which were met (and not yet satisfactorily solved) while making the generator fully operational.

A. SPAGETTA is a multivariate multisite parametric generator, which is based on autoregressive modeling (following the D. Wilks’ papers). It is designed mainly (but not solely) for use in agricultural and hydrological modeling. It may produce time series of up to 8 variables for as many as (approx.) 200 stations or grid points. Typically, it produces time series of temperature, precipitation, solar radiation, humidity and wind speed. It usually runs with a daily time step.

B. The main functionalities include: (1) It may produce arbitrarily long time series representing the climate defined by the data used for calibrating the generator (might be observational data or, for example, RCM outputs). (2) Having modified the WG parameters by the climate change scenario (typically derived from GCM or RCM simulations), SPAGETTA may produce weather series representing the future climate. In this case, one may study sensitivity of selected climatic indices to changes in various statistics (e.g. means and standard deviations of weather variables, and characteristics of temporal and spatial structure of the time series). (3) SPAGETTA may be interpolated so that it can produce weather series for sites with no observational data. (4) It can be linked with the circulation generator so that WG may better represent larger-scale (both in space and time) weather variability.

C. The results obtained with the generator by now include: (a) Validation of the generator in terms of WG parameters, various climatic indices, and outputs of hydrological model fed by the synthetic series produced by SPAGETTA. (b) Impacts of the forthcoming climate change on various climatic characteristics (RCM-based climate change scenarios were used here). Focus was put on spatial temperature-precipitation compound characteristics. (c) Validation of the interpolated generator. (d) Validation of the generator driven by the larger scale circulation generator.

D. Problems to be solved: (i) Under some circumstances (especially when a large number of the stations is used, or while interpolating the generator), matrices of the AR model imply unstable AR process which diverges to unrealistic values of weather variables. (ii) The generator underestimates the low frequency variability. Development of the larger scale circulation generator, which would eliminate this drawback, is still under development.

Only examples of the previous points will be shown in the presentation.

How to cite: Dubrovsky, M., Lhotka, O., Miksovsky, J., Stepanek, P., and Meitner, J.: The Spatial Weather Generator SPAGETTA: Hard Times of its Adolescence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7485,, 2022.


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