![Hier klicken, um den Treffer aus der Auswahl zu entfernen](images/unchecked.gif) |
Titel |
Albedo over rough snow and ice surfaces |
VerfasserIn |
Stef Lhermitte, Jakob Abermann, Christophe Kinnard |
Konferenz |
EGU General Assembly 2014
|
Medientyp |
Artikel
|
Sprache |
Englisch
|
Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 16 (2014) |
Datensatznummer |
250096058
|
Publikation (Nr.) |
EGU/EGU2014-11542.pdf |
|
|
|
Zusammenfassung |
Surface albedo determines the shortwave radiation balance, arguably the largest energy
balance component of snow and ice surfaces. Consequently, incorporation of the
spatio-temporal variability of albedo is essential when assessing the surface energy balance of
snow and ice surfaces. This can be done by using ground-based measurements or albedo data
derived from remote sensing, or by modelling albedo based on radiative transfer models or
empirically based parameterizations.
One decisive factor when incorporating albedo data is the representativeness of surface
albedo, certainly over rough surfaces where albedo measurements at a specific location (i.e.,
apparent albedo) can differ strongly from the material albedo or the true albedo (i.e., effective
albedo) depending on the position of the sun/sensor and the surface roughness. This stresses
the need for a comprehensive understanding of the effect of surface roughness on albedo and
its impact when using albedo data for validation of remote sensing imagery, interpretation of
automated weather station (AWS) radiation data or incorporation in energy balance
models.
To assess the effect of surface roughness on albedo an intra-surface radiative transfer
(ISRT) model was combined with albedo measurements on a penitente field on
Glaciar Tapado in the semi-arid Andes of Northern Chile. The ISRT model shows
albedo reductions between 0.06 and 0.35 relative to flat surfaces with a uniform
material albedo. The magnitude of these reductions primarily depends on the penitente
geometry, but the shape and spatial variability of the material albedo also play a major
role.
Secondly, the ISRT model was used to reveal the effect of using apparent albedo to infer
the effective albedo over a rough surface. This effect is especially strong for narrow
penitentes, resulting in sampling biases up to ±0.05. The sampling biases are more
pronounced when the sensor is low above the surface, but remain relatively constant
throughout the day. Consequently, the only beneficial approach to minimize the sampling bias
of surface albedo over rough surfaces is to use a large number of samples at various
places.
Thirdly, the temporal evolution of broadband albedo over a penitente-covered
surface was analyzed to place the experiments and their uncertainty into a larger
temporal context. Time series of albedo measurements at an automated weather
station over two seasons reveal albedo decreases early in the ablation season. These
decreases stabilize from February onwards with variations being caused by fresh
snow-fall events. The 2009/2010 and 2011/2012 seasons differ notably, where the
latter shows lower albedo caused by larger penitentes. Finally, a comparison of the
ground-based albedo observations with Landsat and MODIS-derived albedo showed that
both satellite derived albedo products capture the albedo evolution with root mean
square errors of 0.08 and 0.15, respectively, but also illustrate their shortcomings
related to temporal resolution and spatial heterogeneity over mountain glaciers. |
|
|
|
|
|