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| Titel |
Averaging kernel prediction from atmospheric and surface state parameters based on multiple regression for nadir-viewing satellite measurements of carbon monoxide and ozone |
| VerfasserIn |
H. M. Worden, D. P. Edwards, M. N. Deeter, D. Fu, S. S. Kulawik, J. R. Worden, A. Arellano |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 6, no. 7 ; Nr. 6, no. 7 (2013-07-11), S.1633-1646 |
| Datensatznummer |
250017917
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| Publikation (Nr.) |
 copernicus.org/amt-6-1633-2013.pdf |
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| Zusammenfassung |
| A current obstacle to the observation system simulation experiments (OSSEs)
used to quantify the potential performance of future atmospheric composition
remote sensing systems is a computationally efficient method to define the
scene-dependent vertical sensitivity of measurements as expressed by the
retrieval averaging kernels (AKs). We present a method for the efficient
prediction of AKs for multispectral retrievals of carbon monoxide (CO) and
ozone (O3) based on actual retrievals from MOPITT (Measurements Of
Pollution In The Troposphere) on the Earth Observing System (EOS)-Terra
satellite and TES (Tropospheric Emission Spectrometer) and OMI (Ozone
Monitoring Instrument) on EOS-Aura, respectively. This employs a multiple
regression approach for deriving scene-dependent AKs using predictors based
on state parameters such as the thermal contrast between the surface and
lower atmospheric layers, trace gas volume mixing ratios (VMRs), solar zenith
angle, water vapor amount, etc. We first compute the singular value
decomposition (SVD) for individual cloud-free AKs and retain the first three
ranked singular vectors in order to fit the most significant orthogonal
components of the AK in the subsequent multiple regression on a training set
of retrieval cases. The resulting fit coefficients are applied to the
predictors from a different test set of test retrievals cased to reconstruct
predicted AKs, which can then be evaluated against the true retrieval AKs
from the test set. By comparing the VMR profile adjustment resulting from
the use of the predicted vs. true AKs, we quantify the CO and O3 VMR
profile errors associated with the use of the predicted AKs compared to the
true AKs that might be obtained from a computationally expensive full
retrieval calculation as part of an OSSE. Similarly, we estimate the errors
in CO and O3 VMRs from using a single regional average AK to represent
all retrievals, which has been a common approximation in chemical OSSEs
performed to date. For both CO and O3 in the lower troposphere, we find
a significant reduction in error when using the predicted AKs as compared to
a single average AK. This study examined data from the continental United
States (CONUS) for 2006, but the approach could be applied to other regions
and times. |
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