Abstract
Chemical transport modeling, 3-D Eulerian grid modeling, started around 1975. Its development, based on work by the authors’ Ph.D.-students, is followed over the last 40 years. An attempt is made to analyse model improvement.
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Questioner Name: Silvia Trini Castelli
Q: A colleague of us stated 20 years ago that models are affected by an intrinsic uncertainty, or error, around 30%. Do you think that we have a chance to go over it? Do you agree?
A: I agree that models will always have an inherent uncertainty, and that 30% is in fact not so bad. And as I stated, there is a limit in further model improvement. We should also not forget that observations have an inherent uncertainty also, not forgetting to include their spatial representativeness.
Questioner Name: Heinke Schluenzen
Q: Do you think that the bias of O3 is also the same as 40 year ago? And as a remark: We might have to consider non-Gaussian error measures for very high resolution model result evaluation.
A: I did not look in other statistical measures, which I should have done. I think that the bias might has improved, but I don’t have looked into these data. Using other statistical measures is a very good idea.
Questioner name: Bertrand Carissimo
Q: I was interested in your point that model improvement does not show in the statistical comparison with observations because I encounter this problem in my work on small scale modeling. So my suggestion is do we need also to improve the statistics? This is a serious remark as for example, with current statistics, a refined model that reproduces a realistic small feature at slightly the wrong place and wrong time will do worse than a larger scale model that does not reproduce the feature.
A: My first answer is that the focus should be on model improvement, not on improvement in statistics because that does not change the model results. I do agree that we should look into other statistical measures in addition to the conventional ones. See also the remark by Heinke Schluenzen.
Questioner name: Steven Hanna
Q: You say that the correlations between model predictions and observations of ozone have remained at about 0.7 for the past 40 years. But isn’t it easy for an ozone model to have a good correlation because observed concentrations are usually high in the day and low at night? Even a bad model should be able to reproduce that behaviour.
A: I agree, any simple, but still basically correct model is able to model ozone right. My point is that I think that, due to inherent uncertainties, it will be impossible to have a model for ozone with a correlation of 0.9.
Questioner Name: B. Armand
Q: You say that observations do not lead to knowledge. I am not sure that everyone would agree with you. What do you think of Big Data?
A: My opinion is that just observations without further data-analysis or modeling do not lead to knowledge, this just lead to data. In case Big Data are seen as just many data, which are subsequently used to create algorithms without further thinking, this should in my opinion be avoided. It might even be dangerous and lead to misleading algorithms and correlations that give the wrong information.
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Builtjes, P. (2021). Improvements of Chemical Transport Modeling Over the Last 40 Years—A Personal Journey. In: Mensink, C., Matthias, V. (eds) Air Pollution Modeling and its Application XXVII. ITM 2019. Springer Proceedings in Complexity. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-63760-9_9
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