Abstract
Characterizing 3-D structure of clouds is needed for a more complete understanding of the Earth’s radiative and latent heat fluxes. Here we develop and explore a ray casting algorithm applied to data from the Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite, in order to reconstruct 3-D cloud volumes of observed clouds. The ray casting algorithm is first applied to geometrically simple synthetic clouds to show that, under the assumption of perfect, clear-conservative cloud masks, the reconstruction method yields overestimation in the volume whose magnitude depends on the cloud geometry and the resolution of the reconstruction grid relative to the image pixel resolution. The method is then applied to two hand-picked MISR scenes, fully accounting for MISR’s viewing geometry for reconstructions over the Earth’s ellipsoidal surface. The MISR Radiometric Camera-by-camera Cloud Mask (RCCM) at 1.1-km resolution and the custom cloud mask at 275-m resolution independently derived from MISR’s red, green, and blue channels are used as input cloud masks. A wind correction method, termed cloud spreading, is applied to the cloud masks to offset potential cloud movements over short time intervals between the camera views of a scene. The MISR cloud-top height product is used as a constraint to reduce the overestimation at the cloud top. The results for the two selected scenes show that the wind correction using the cloud spreading method increases the reconstructed volume up to 4.7 times greater than without the wind correction, and that the reconstructed volume generated from the RCCM is up to 3.5 times greater than that from the higher-resolution custom cloud mask. Recommendations for improving the presented cloud volume reconstructions, as well as possible future passive remote sensing satellite missions, are discussed.
Highlights
Characterizing 3-D cloud structures, such as shape and volume, is critical in understanding the distribution of radiative and latent heat fluxes on Earth (e.g., References [1,2])
The results presented here come from hand-picked scenes that show a good vertical development of clouds favorable for multi-angle volume reconstruction from Multi-angle Imaging SpectroRadiometer (MISR) data
This paper explores 3-D cloud volume reconstruction from MISR using a ray casting method
Summary
Characterizing 3-D cloud structures, such as shape and volume, is critical in understanding the distribution of radiative and latent heat fluxes on Earth (e.g., References [1,2]). Compared to traditional 1-D or 2-D treatments, 3-D cloud structures have a distinguishing impact on the forward calculations of the fluxes (e.g., References [3,4,5,6]), and the inverse problem of retrieving cloud optical and microphysical properties from radiation measurements (e.g., References [7,8,9,10]) Their impact is evident in the spectral, textural, and angular distributions of the outgoing radiation field observed by satellites (e.g., References [11,12]). Bearing in mind the current status of the research area, we present a method of reconstructing 3-D cloud volumes using data from the Multi-angle Imaging SpectroRadiometer (MISR) [19].
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