AI-derived 3D cloud tomography from geostationary 2D satellite data

Brüning, Sarah; Niebler, Stefan; Tost, H.

doi:10.5194/egusphere-2023-1834

Cited by 1 publication

(1 citation statement)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Airborne multi-angle platforms like RSP have been used to study multi-layer clouds (Sinclair et al, 2017). The estimation of vertical cloud profiles in geostationary data using machine learning has been studied (Brüning et al, 2023). This concurrent work employs a similar strategy to ours, but on geostationary, single-view imagery.…”

Section: Introductionmentioning

confidence: 99%

3-D Cloud Masking Across a Broad Swath using Multi-angle Polarimetry and Deep Learning

Foley,

Knobelspiesse,

Sayer

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques that must make limiting assumptions. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, open-source dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA's Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.

show abstract

Section: Introductionmentioning

confidence: 99%