A new daily weather generator to preserve extremes and low-frequency variability
This paper addresses deficiencies of stochastic Weather Generators (WGs) in terms of reproduction of low-frequency variability and extremes, as well as the unanticipated effects of changes to precipitation occurrence under climate change scenarios on secondary variables. A new weather generator (nam...
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| Veröffentlicht in: | Climatic change 2013-08, Vol.119 (3-4), p.631-645 |
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| creator | Khazaei, Mohammad Reza Ahmadi, Shahin Saghafian, Bahram Zahabiyoun, Bagher |
| description | This paper addresses deficiencies of stochastic Weather Generators (WGs) in terms of reproduction of low-frequency variability and extremes, as well as the unanticipated effects of changes to precipitation occurrence under climate change scenarios on secondary variables. A new weather generator (named IWG) is developed in order to resolve such deficiencies and improve WGs performance. The proposed WG is composed of three major components, including a stochastic rainfall model able to reproduce realistic rainfall series containing extremes and inter-annual monthly variability, a multivariate daily temperature model conditioned to the rainfall occurrence, and a suitable multi-variate monthly generator to fit the low-frequency variability of daily maximum and minimum temperature series. The performance of IWG was tested by comparing statistical characteristics of the simulated and observed weather data, and by comparing statistical characteristics of the simulated runoff outputs by a daily rainfall-runoff model fed by the generated and observed weather data. Furthermore, IWG outputs are compared with those of the well-known LARS-WG weather generator. The tested characteristics are a variety of different daily statistics, low-frequency variability, and distribution of extremes. It is concluded that the performance of the IWG is acceptable, better than LARS-WG in the majority of tests, especially in reproduction of extremes and low-frequency variability of weather and runoff series. |
| doi_str_mv | 10.1007/s10584-013-0740-5 |
| format | Article |
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A new weather generator (named IWG) is developed in order to resolve such deficiencies and improve WGs performance. The proposed WG is composed of three major components, including a stochastic rainfall model able to reproduce realistic rainfall series containing extremes and inter-annual monthly variability, a multivariate daily temperature model conditioned to the rainfall occurrence, and a suitable multi-variate monthly generator to fit the low-frequency variability of daily maximum and minimum temperature series. The performance of IWG was tested by comparing statistical characteristics of the simulated and observed weather data, and by comparing statistical characteristics of the simulated runoff outputs by a daily rainfall-runoff model fed by the generated and observed weather data. Furthermore, IWG outputs are compared with those of the well-known LARS-WG weather generator. The tested characteristics are a variety of different daily statistics, low-frequency variability, and distribution of extremes. It is concluded that the performance of the IWG is acceptable, better than LARS-WG in the majority of tests, especially in reproduction of extremes and low-frequency variability of weather and runoff series.</description><identifier>ISSN: 0165-0009</identifier><identifier>EISSN: 1573-1480</identifier><identifier>DOI: 10.1007/s10584-013-0740-5</identifier><identifier>CODEN: CLCHDX</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Atmospheric Sciences ; Bias ; Civil engineering ; Climate change ; Climate Change/Climate Change Impacts ; Climatology ; Climatology. Bioclimatology. Climate change ; Computer simulation ; Earth and Environmental Science ; Earth Sciences ; Earth, ocean, space ; Exact sciences and technology ; External geophysics ; Frequencies ; Generators ; Geophysics. 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A new weather generator (named IWG) is developed in order to resolve such deficiencies and improve WGs performance. The proposed WG is composed of three major components, including a stochastic rainfall model able to reproduce realistic rainfall series containing extremes and inter-annual monthly variability, a multivariate daily temperature model conditioned to the rainfall occurrence, and a suitable multi-variate monthly generator to fit the low-frequency variability of daily maximum and minimum temperature series. The performance of IWG was tested by comparing statistical characteristics of the simulated and observed weather data, and by comparing statistical characteristics of the simulated runoff outputs by a daily rainfall-runoff model fed by the generated and observed weather data. Furthermore, IWG outputs are compared with those of the well-known LARS-WG weather generator. 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| subjects | Atmospheric Sciences Bias Civil engineering Climate change Climate Change/Climate Change Impacts Climatology Climatology. Bioclimatology. Climate change Computer simulation Earth and Environmental Science Earth Sciences Earth, ocean, space Exact sciences and technology External geophysics Frequencies Generators Geophysics. Techniques, methods, instrumentation and models Hydrology Mathematical models Meteorology Precipitation Radiation Rain Rainfall Rainfall-runoff relationships Reproduction Runoff Stochastic models Time series Variability Variables Weather |
| title | A new daily weather generator to preserve extremes and low-frequency variability |
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