Optimization of the numerical algorithms of the ADREA-I mesoscale prognostic meteorological model for real-time applications

Authors:
Ivan V. Kovalets;Spyros Andronopoulos;Alexander G. Venetsanos;John G. Bartzis
Affiliations:
Institute of Mathematical Machines and Systems Problems, National Academy of Sciences of Ukraine, Department of Environmental Modelling, Prospect Glushkova-42, 03187 Kiev, Ukraine;National Centre for Scientific Research (NCSR) "Demokritos", Institute of Nuclear Technology and Radiation Protection, Environmental Research Laboratory, 15310 Aghia Paraskevi Attikis, Greece;National Centre for Scientific Research (NCSR) "Demokritos", Institute of Nuclear Technology and Radiation Protection, Environmental Research Laboratory, 15310 Aghia Paraskevi Attikis, Greece;University of Western Macedonia, Department of Engineering and Management of Energy Resources, Bakola and Sialvera Str., 50100 Kozani, Greece
Venue:
Environmental Modelling & Software
Year:
2008

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Abstract

The real-time applicability of the ADREA-I prognostic mesoscale meteorological model was enhanced by applying the preconditioned BiCGSTAB method for the numerical solution of the pressure equation in combination with increasing the magnitude of the time steps up to the values allowed by the Courant number. The ILU, MILU, ILUT and ILUM preconditioning methods with different ordering strategies were used. The implementation was developed for arbitrarily complex geometries. The application of MILU(1) preconditioning and ILUT preconditioning with red-black ordering of the unknowns (RB+ILUT) has resulted in up to six times shorter overall computational time in comparison to the previously implemented line relaxation (LR) method. The feasibility of increasing the time steps has been proved by comparing the results of the 24-h ADREA-I forecasts with the observations during a real sea breeze case in Attiki, Greece: decreasing the time steps by a factor of 10 in comparison with the values allowed by the Courant number leads to decrease of the statistical error indicators by only 1-5%.