 |
| Titel |
Reduction of radiation biases by incorporating the missing cloud variability by means of downscaling techniques: a study using the 3-D MoCaRT model |
| VerfasserIn |
S. Gimeno García, T. Trautmann, V. Venema |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1867-1381
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 5, no. 9 ; Nr. 5, no. 9 (2012-09-20), S.2261-2276 |
| Datensatznummer |
250003090
|
| Publikation (Nr.) |
 copernicus.org/amt-5-2261-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
Handling complexity to the smallest detail in atmospheric radiative transfer
models is unfeasible in practice. On the one hand, the properties of the
interacting medium, i.e., the atmosphere and the surface, are only available
at a limited spatial resolution. On the other hand, the computational cost of
accurate radiation models accounting for three-dimensional heterogeneous
media are prohibitive for some applications, especially for climate modelling and
operational remote-sensing algorithms. Hence, it is still common practice to
use simplified models for atmospheric radiation applications.
Three-dimensional radiation models can deal with complex scenarios
providing an accurate solution to the radiative
transfer. In contrast, one-dimensional models are computationally more efficient, but introduce biases to the radiation
results.
With the help of stochastic models that consider the multi-fractal nature of
clouds, it is possible to scale cloud properties given at a coarse spatial
resolution down to a higher resolution. Performing the radiative transfer
within the cloud fields at higher spatial resolution noticeably helps to improve
the radiation results.
We present a new Monte Carlo model, MoCaRT, that computes the radiative transfer in three-dimensional
inhomogeneous atmospheres. The MoCaRT model is validated by comparison with the consensus results of the Intercomparison of
Three-Dimensional Radiation Codes (I3RC) project.
In the framework of this paper, we aim at characterising cloud heterogeneity
effects on radiances and broadband fluxes, namely: the errors due to
unresolved variability (the so-called plane parallel homogeneous, PPH, bias)
and the errors due to the neglect of transversal photon displacements
(independent pixel approximation, IPA, bias). First, we study the effect of
the missing cloud variability on reflectivities. We will show that the
generation of subscale variability by means of stochastic methods greatly
reduce or nearly eliminate the reflectivity biases. Secondly,
three-dimensional broadband fluxes in the presence of realistic
inhomogeneous cloud fields sampled at high spatial resolutions are calculated
and compared to their one-dimensional counterparts at coarser resolutions.
We found that one-dimensional calculations at coarsely resolved cloudy atmospheres systematically
overestimate broadband reflected and absorbed fluxes and underestimate transmitted ones. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|