Improvements in Cloud Photogrammetry Using Airborne, Side-Looking, Time-Lapse Cameras

Biter, Cleon J.; Cannon, Theodore W.; Crow, Edwin L.; Knight, Charles; Roskowski, Philip M.

doi:10.1175/1520-0450(1983)022<1047:iicpua>2.0.co;2

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…The theoretical background on photogrammetry is given in Hartley and Zisserman (2004), while Hu et al (2009) applied these techniques for cloud geometrical reconstruction. The mathematics for the geometry retrieval, as it is used in this study, is based mainly on the method described by Biter et al (1983). They deployed a side-looking camera onboard of an aircraft to detect the position of cloud features, similar to the setup presented in this work.…”

Section: Cloud Geometry Retrievalmentioning

confidence: 99%

“…5a. The geometric problem comprises three coordinate systems: for the camera, the aircraft, and the geographic coordinate system (longitude, latitude, and altitude) for the observed point C (Biter et al, 1983). Coordinate transformations are required to relate the different coordinate systems.…”

Section: Cloud Geometry Retrievalmentioning

confidence: 99%

“…After coordinate transformation, trigonometric methods (Biter et al, 1983) are applied to calculate the distance between the camera positions P1 and P2 to the observed point C. Repeating this procedure for a number of points yields a relationship between elevation angle and cloud height. Note that the elevation angle represents the elevation angle of the selected tie point of the camera image after correction based on the aircraft attitude data.…”

Section: Cloud Geometry Retrievalmentioning

confidence: 99%

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Vertical distribution of the particle phase in tropical deep convective clouds as derived from cloud-side reflected solar radiation measurements

et al. 2017

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Abstract. Vertical profiles of cloud particle phase in tropical deep convective clouds (DCCs) were investigated using airborne solar spectral radiation data collected by the German High Altitude and Long Range Research Aircraft (HALO) during the ACRIDICON-CHUVA campaign, which was conducted over the Brazilian rainforest in September 2014. A phase discrimination retrieval based on imaging spectroradiometer measurements of DCC side spectral reflectivity was applied to clouds formed in different aerosol conditions. From the retrieval results the height of the mixedphase layer of the DCCs was determined. The retrieved profiles were compared with in situ measurements and satellite observations. It was found that the depth and vertical position of the mixed-phase layer can vary up to 900 m for one single cloud scene. This variability is attributed to the different stages of cloud development in a scene. Clouds of mature or decaying stage are affected by falling ice particles resulting in lower levels of fully glaciated cloud layers compared to growing clouds. Comparing polluted and moderate aerosol conditions revealed a shift of the lower boundary of the mixed-phase layer from 5.6 ± 0.2 km (269 K; moderate) to 6.2 ± 0.3 km (267 K; polluted), and of the upper boundary from 6.8 ± 0.2 km (263 K; moderate) to 7.4 ± 0.4 km (259 K; polluted), as would be expected from theory.

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Section: Cloud Geometry Retrievalmentioning

confidence: 99%

Section: Cloud Geometry Retrievalmentioning

confidence: 99%

Section: Cloud Geometry Retrievalmentioning

confidence: 99%

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Vertical distribution of the particle phase in tropical deep convective clouds as derived from cloud-side reflected solar radiation measurements

et al. 2017

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“…The density of water vapor is re-calculated using the relative humidity, temperature, and pressure measurements. Since Rayleigh scattering is calculated from the density profile according to Bodhaine et al (1999), the LOW-TRAN (Low Resolution Transmission Model) parametrization by Pierluissi and Peng (1985), as adapted from SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer; Ricchiazzi and Gautier, 1998) is used for gas absorption. The optical properties of clouds are derived from profiles of effective radius (r eff ) and liquid (ice) water contents (LWC, IWC) using Mie calculations for water clouds, while for ice clouds the parameterizations by Baum et al (2005Baum et al ( , 2007 are used.…”

Section: Radiative Transfer Modelmentioning

confidence: 99%