Abstract: Snow and ice surfaces play a key role in climate change, as increased melting over the past decades significantly contributes to global sea level rise. Melting dynamics are controlled by the amount of solar radiation absorbed by the surface, which increases when snow and ice are darkened, typically due to the accumulation of small light-absorbing particles (LAP), such as dust or algae. The detection of these particles is essential for predicting melt rates on ice sheets and glaciers. A new generation of orbital VSWIR imaging spectrometers provides the prerequisites needed to achieve this objective by featuring narrow spectral channels that are able to resolve subtle LAP absorption features. NASA’s Earth Surface Mineral Dust Source Investigation (EMIT), launched in July 2022 and installed on the International Space Station (ISS), aims to improve our understanding of the Earth’s mineral dust sources and their climate impacts. These impacts include dust deposition on snow and ice surfaces in mountainous regions, such as the Western US and the Andes in South America. Recent work has demonstrated that a simultaneous inversion of atmosphere and surface state using optimal estimation (OE) shows promising potential to quantify LAP from spaceborne imaging spectroscopy observations. In this talk, I present a modification of the algorithm by using a coupled snow-atmosphere radiative transfer model. It includes the effects of topography and anisotropy, whose consideration is particularly important for strongly forward scattering surfaces such as snow and ice. The new approach leads to a dimensionality reduction of the state vector by several orders of magnitude, and facilitates a more comprehensive and surface specific forward model parameterization. It is applied to selected EMIT images from the Patagonian Ice Sheet, resulting in a detailed outline of retrieval sensitivity to topography and anisotropy. The results show an increased independence of the algorithm, which provides more directly attributable uncertainties to key physical properties. In particular, my talk highlights significantly increased retrieval errors of dust concentration when topographic characteristics are not accounted for. These findings will be essential for the development of global retrieval algorithms for upcoming spaceborne imaging spectroscopy missions, including NASA’s Surface Biology and Geology (SBG).
About the Speaker: I am maintaining and improving open-source atmospheric correction codebases for air- and spaceborne imaging spectroscopy data as a postdoc in JPL’s Imaging Spectroscopy group. Furthermore, I’m working on snow and ice spectroscopy by developing new retrieval methods for surface parameters focusing on the Greenland Ice Sheet and mountainous regions in North and South America. I received a Ph.D. in Meteorology from the Free University of Berlin, Germany, in 2022, and spent five years at the GFZ German Research Centre for Geosciences in Potsdam, Germany, as a research associate being part of the EnMAP project, before joining JPL in Summer 2022.
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