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| Titel |
Inverse modeling of cloud-aerosol interactions – Part 1: Detailed response surface analysis |
| VerfasserIn |
D. G. Partridge, J. A. Vrugt, P. Tunved, A. M. L. Ekman, D. Gorea, A. Sorooshian |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 11, no. 14 ; Nr. 11, no. 14 (2011-07-25), S.7269-7287 |
| Datensatznummer |
250009945
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| Publikation (Nr.) |
 copernicus.org/acp-11-7269-2011.pdf |
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| Zusammenfassung |
New methodologies are required to probe the sensitivity of parameters
describing cloud droplet activation. This paper presents an inverse modeling-based
method for exploring cloud-aerosol interactions via response surfaces. The
objective function, containing the difference between the measured and model
predicted cloud droplet size distribution is studied in a two-dimensional
framework, and presented for pseudo-adiabatic cloud parcel model parameters that are
pair-wise selected. From this response surface analysis it is shown that the
susceptibility of cloud droplet size distribution to variations in different
aerosol physiochemical parameters is highly dependent on the aerosol
environment and meteorological conditions. In general the cloud droplet
size distribution is most susceptible to changes in the updraft velocity.
A shift towards an increase in the importance of chemistry for the cloud nucleating
ability of particles is shown to exist somewhere between marine average and rural
continental aerosol regimes.
We also use these response surfaces to explore the feasibility of inverse
modeling to determine cloud-aerosol interactions. It is shown that the "cloud-aerosol"
inverse problem is particularly difficult to solve due to significant parameter
interaction, presence of multiple regions of attraction, numerous local optima,
and considerable parameter insensitivity.
The identifiability of the model parameters will be dependent on the choice
of the objective function. Sensitivity analysis is performed to investigate
the location of the information content within the calibration
data to confirm that our choice of objective function maximizes information
retrieval from the cloud droplet size distribution.
Cloud parcel models that employ a moving-centre based calculation of the
cloud droplet size distribution pose additional difficulties when applying
automatic search algorithms for studying cloud-aerosol interactions. To aid
future studies, an increased resolution of the region of the size spectrum
associated with droplet activation within cloud parcel models, or further
development of fixed-sectional cloud models would be beneficial. Despite
these improvements, it is demonstrated that powerful search algorithms
remain necessary to efficiently explore the parameter space and successfully
solve the cloud-aerosol inverse problem. |
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