Meridionally tilted ice cloud structures in the tropical upper troposphere as seen by CloudSat

Gong, Jie; Wu, Dong L.; Limpasuvan, Varavut

doi:10.5194/acp-15-6271-2015

Cited by 3 publications

(2 citation statements)

References 29 publications

(35 reference statements)

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“…The simulated 3-D cloud variables are first interpolated to a vertical grid with 250 m spacing, to match the horizontal resolution (1 km) in a 1:4 ratio. For the 45 • slant path calculation, we used the staggered shift method that was employed before in Gong et al [60] and Várnai and Davies [61], to select the cloud profile indices and pick every 4th vertical index for each horizontal index shift. These staggered shift profiles, starting from the model top (~25 km) to the surface, are input to CRTM to create the cloud images at 45 • view.…”

Section: Stereo Height Vs Ir Heightmentioning

confidence: 99%

MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds

Carr¹,

Kelly

et al. 2018

Remote Sensing

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Global wind observations are fundamental for studying weather and climate dynamics and for operational forecasting. Most wind measurements come from atmospheric motion vectors (AMVs) by tracking the displacement of cloud or water vapor features. These AMVs generally rely on thermal infrared (IR) techniques for their height assignments, which are subject to large uncertainties in the presence of weak or reversed vertical temperature gradients near the planetary boundary layer (PBL) and tropopause folds. Stereo imaging can overcome the height assignment problem using geometric parallax for feature height determination. In this study we develop a stereo 3D-Wind algorithm to simultaneously retrieve AMV and height from geostationary (GEO) and low Earth orbit (LEO) satellite imagery and apply it to collocated Geostationary Operational Environmental Satellite (GOES) and Multi-angle Imaging SpectroRadiometer (MISR) imagery. The new algorithm improves AMV and height relative to products from GOES or MISR alone, with an estimated accuracy of <0.5 m/s in AMV and <200 m in height with 2.2 km sampling. The algorithm can be generalized to other LEO-GEO or LEO-LEO combinations for greater spatiotemporal coverage. The technique demonstrated with MISR and GOES has important implications for future high-quality AMV observations, for which a low-cost constellation of CubeSats can play a vital role.

show abstract

Section: Stereo Height Vs Ir Heightmentioning

confidence: 99%

MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds

Carr¹,

Kelly

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The simulated 3-D cloud variables are first interpolated to a vertical grid with 250 m spacing, to match the horizontal resolution (1 km) in a 1:4 ratio. For the 45 slant path calculation, we use the staggered shift method, as described in Gong et al [57], to select the cloud profile indices and pick every 4th vertical index for each horizontal index shift. These staggered shift profiles, starting from the model top (~ 25 km) to the surface, are input to CRTM to create the cloud images at 45 view.…”

Section: Stereo Height Vs Ir Heightmentioning

confidence: 99%

MISR-GOES 3D Winds: Implications for Future LEO-GEO and LEO-LEO Winds

Carr¹,

Wu²,

Kelly³

et al. 2018

Preprint

View full text Add to dashboard Cite

Global wind observations are fundamental for studying weather and climate dynamics. Most wind measurements come from atmospheric motion vectors (AMVs) by tracking the displacement of cloud or water vapor features. These AMVs generally rely on thermal infrared (IR) techniques for their height assignments, which are subject to large uncertainties in the presence of weak or reversed vertical temperature gradients around the planetary boundary layer (PBL) and with tropopause folding. Stereo imaging can overcome the height assignment problem using geometric parallax for feature height determination. In this study we develop a stereo 3D-Wind algorithm to simultaneously retrieve AMV and height from geostationary (GEO) and low Earth orbit (LEO) satellite imagery and apply it to collocated Geostationary Operational Environmental Satellite (GOES) and Multi-angle Imaging SpectroRadiometer (MISR) imagery. The new algorithm improves AMV and height relative to products from GOES or MISR alone, with an estimated accuracy of <0.5 m/s in AMV and <200 m in height with 2.2 km sampling. The algorithm can be generalized to other LEO-GEO or GEO-GEO combinations for greater spatiotemporal coverage. The technique demonstrated with MISR and GOES has important implications for future high-quality AMV observations, for which a low-cost constellation of CubeSats can play a vital role.

show abstract