 |
| Titel |
Estimating bias in the OCO-2 retrieval algorithm caused by 3-D radiation scattering from unresolved boundary layer clouds |
| VerfasserIn |
A. Merrelli, R. Bennartz, C. W. O'Dell, T. E. Taylor |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1867-1381
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 4 ; Nr. 8, no. 4 (2015-04-01), S.1641-1656 |
| Datensatznummer |
250116291
|
| Publikation (Nr.) |
 copernicus.org/amt-8-1641-2015.pdf |
|
|
|
|
|
| Zusammenfassung |
| Due to the complexity of the multiple scattering problem for
shortwave radiative transfer in Earth's atmosphere, operational
physical retrieval algorithms commonly use a plane parallel
radiative transfer model (RTM). This so-called one-dimensional (1-D)
assumption allows practical retrieval algorithms to be
implemented. In order to understand the impacts of this assumption
for low altitude, unresolved clouds observed by OCO-2, the
three-dimensional (3-D) radiative transfer model SHDOM is used to
generate synthetic observations which are then processed by the
operational retrieval algorithm based on a 1-D RTM. Simulations are
performed over three realistic surface spectral albedos,
corresponding to snow, vegetation, and bare soil. The results show
that the existing cloud screening algorithm has difficulty
identifying sub-field of view (FOV), unresolved clouds that fill
less than half of the FOV. The unresolved clouds introduce a bias in
the retrieved CO2 concentration, as quantified by the dry
air mole fraction (XCO2). The biases are relatively small
(less than 1 ppm) when the albedo at 2.1 μm is
high, which is common over bare land surfaces. For cases with low
2.1 μm albedo, such as snow, the bias becomes much
larger, up to 5 ppm. These results indicate that the
XCO2 retrieval appears robust to 3-D scattering effects
from unresolved low level clouds when the short wave infrared
surface albedo is large, but for darker surfaces these clouds can
introduce significant biases. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|