Conclusions
It is apparent that the evolution of the concentration fields, and thus, their predictability using a numerical model, is strongly influenced by the uncertainty in boundary concentration fields and the emission inventories. In this work, we applied an automatic differentiation tool (ADIFOR) to investigate the sensitivity of model outputs with respect to perturbations in the boundary conditions of 25 species and emission rates of 10 species. Then, we computed the relative importance of the most important perturbations in the system and in O3 fields. The inserted perturbations had a magnitude similar to observational uncertainties.
The tangent linear model solutions, which describe the evolution of perturbations along trajectories of a time-dependent non-linear base state, represent well the corresponding non-linear perturbation fields accurately for the modelled period, which included the 98%tile O3 concentration. This type of sensitivity analysis identifies, for the same change in each variable, which variable may include a larger forecast error at some specific time. Although this sensitivity information does not include non-linear effects, it does provide the fundamental characteristics of the changes in solution behaviour and forecast error. All simulations demonstrate that the highest sensitivity in an atmospheric system arises from perturbations at the boundary conditions of O3.
Keywords
- Forecast Error
- Atmospheric Environment
- Automatic Differentiation
- Monthly Weather Review
- Photochemical Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Bischof C., P. Khademi, A. Mauer and A. Carle (1996): ADIFOR 2.0-automatic differentiation of Fortran 77 programs, IEEE Computational Science & Engineering, 3, 18–32
Bischof C. (1994): Automatic differentiation, tangent linear models and pseudo-adjoints, High-Performance Computing in the Geosciences, Kluwer Academic Publishers, 462, 59–80.
Bischof C., A. Carle, G. Corliss, A. Griewank and P. Hovland (1992): ADIFOR-Generating derivative codes from Fortran programs, Scientific Programming, 1, 11–29.
Bischof C., A. Carle, P. Hovland, P. Khademi and A. Mauer (1998): ADIFOR 2.0 User’s Guide, Mathematics and Computer Science Division, Argonne National Laboratory, Technical Memorandum 192.
Carmichael G.R., A. Sandu and F.A. Potra (1997): Sensitivity analysis for atmospheric chemistry models via automatic differentiation, Atmospheric Environment, 31, 475–489.
Derwent R.G. (1993): United Kingdom Photochemical Oxidants Review Group, Ozone in the United Kingdom, prepared at the request of the Air Quality Division, Department of the Environment, London.
Environ (1998): User’s Guide for Comprehensive Air Quality Model With Extensions (CAMx) 2.0, 123p.
Errico R., T. Vukicevic and K. Raeder (1993): Examination of the accuracy of a tangent linear model, Tellus, 45A, 462–477 and, 45A, 539–557.
Griewank A. (1989): On Automatic Differentiation, Mathematical Programming: Recent Developments and Applications, Kluwer Academic Press, 83–108.
Hanna S., J. Chang and M. Fernau (1998): Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (UAM-IV) due to uncertainties in input variables, Atmospheric Environment, 32, 21, 3619–3628.
Horwedel J., R. Raridon and R. Wright (1992): Automated Sensitivity Analysis of an Atmospheric Dispersion Model, Atmospheric Environment, 26A, 1643–1649.
Hwang D., D.W. Byun and M.T. Odman (1997): An automatic differentiation technique for sensitivity analysis of numerical advection schemes in air quality models, Atm. Envir., 31, 879–888.
Kioutsioukis I., A. (2001): Sensitivity Analysis of 3D Air Quality Models, PhD thesis, University of Thessaloniki, Greece.
Park S.K. and K.K. Droegemeier (1999): Sensitivity analysis of a moist 1D Eulerian cloud model using automatic differentiation, Monthly Weather Review, 127, 2180–2196.
Park S.K. and K.K. Droegemeier (2000): Sensitivity analysis of a 3D convective storm-Implications for variational data assimilation and forecast error, Monthy Weather Review, 128, 140–159.
Park S.K. and K.K. Droemegeier (1997): Validity of the tangent linear approximation in a moist convective cloud model, Monthly Weather Review, 125, 3320–3340.
Peters L., C. Berkowitz, G. Carmichael, R. Easter, G. Fairweather, S. Ghan, J. Hales, R. Leung, W. Pennell, F. Potra, R. Saylor and T. Tsang (1995): The current state and future direction of direction models in simulating the tropospheric chemistry and transport of trace species-A review, Atmospheric Environment, 29, 2, 189–222.
Pielke R. (1998): The need to assess uncertainty in Air Quality Evaluations, Atmospheric Environment, 32, 1467–1468.
Rall L. (1981): Automatic Differentiation-Techniques and Applications, Lectrure Notes in Computer Science, Vol. 120, Springer-Verlag, Berlin.
Saltelli A., K. Chan and E. Scott (2000): Sensitivity Analysis, John Wiley & Sons, 475p.
Silibello C., G. Calori, G. Brusasca, G. Catenacci and G. Finzi (1998): Application of a photochemical grid model to Milan metropolitan area, Atmospheric Environment, 32, 2025–2038.
Skouloudis A.N. (2001): The Auto-Oil II Programme-Air Quality Report, Version 7.2, 31 March, EUR Report 19725 EN.
Vautard R. (2000): Real-Time validation of a forecasting chemistry transport model, Proc. of the EUMENET Workshop on Ground-Level Ozone Forecasting, Langen, Germany.
Volta M. and G. Finzi (1997): Photochemical pollution in Northern Italy. Emission inventory analysis and processing. Environmental Research Forum, 7–8, 416–419.
Vukicevic T. (1991): Nonlinear and linear evolution of initial forecast errors, Monthly Weather Review, 119, 1602–1611.
Vukicevic T. and R.M. Errico (1993): Linearization and adjoint of parameterized moist adiabatic processes, Tellus, 45A, 493–510.
Winner D.M., G.R. Cass and R.A. Harley (1995): Effect of alternative boundary conditions on predicted ozone control strategy performance: A case study in Los Angeles area, Atmospheric Environment 29, 3451–3464.
Ziomas I. (1998): The Mediterranean Campaign of Photochemical Tracers-Transport and Chemical Evolution (MEDCAPHOT-TRACE): An Outline, Atmospheric Environment, 32, 2045–2053.
Ziomas I., P. Tzoumaka, D. Balis, D. Melas, C. Zerefos and O. Klemm (1998): Ozone episodes in Athens, Greece. A modelling approach using data from the MEDCAPHOT-TRACE, Atmospheric Environment, 32, 2313–2321.
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Kioutsioukis, I.A., Skouloudis, A.N. (2004). Sensitivity Analysis of Nested Photochemical Simulations. In: Borrego, C., Schayes, G. (eds) Air Pollution Modeling and Its Application XV. Springer, Boston, MA. https://doi.org/10.1007/0-306-47813-7_34
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