Abstract
We present a procedure for geometric, spectral, and absolute radiometric characterization of the weatherproof RGB camera HaloCam(RAw) and demonstrate its application in a case study. This characterization procedure can be generalized to other RGB camera systems with similar field of view. HaloCam(RAW) is part of the automated halo observation system HaloCam and designed for the quantitative analysis of halo displays. The geometric calibration was performed using a chessboard pattern to estimate camera matrix and distortion coefficients. For the radiometric characterization of HaloCam(RAW), the dark signal and vignetting effect were determined to correct the measured signal. Furthermore, the spectral response of the RGB sensor and the linearity of its radiometric response were characterized. The absolute radiometric response was estimated by cross calibrating HaloCam(RAW) against the completely characterized spectrometer of the Munich Aerosol Cloud Scanner (specMACS). For a typical measurement signal the relative (absolute) radiometric uncertainty amounts to 2.8 % (5.0 %), 2.4 % (5.8 %), and 3.3 % (11.8 %) for the red, green, and blue channel, respectively. The absolute radiometric uncertainty estimate is larger mainly due to the inhomogeneity of the scene used for cross calibration and the absolute radiometric uncertainty of specMACS. Geometric and radiometric characterization of HaloCam(RAw) were applied to a scene with a 22 degrees halo observed on 21 April 2016. The observed radiance distribution and 22 degrees halo ratio compared well with radiative transfer simulations assuming a range of ice crystal habits and surface roughness values. This application demonstrates the potential of developing a retrieval method for ice crystal properties, such as ice crystal size, shape, and surface roughness using calibrated HaloCam(RAW) observations together with radiative transfer simulations.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Physik |
Themengebiete: | 500 Naturwissenschaften und Mathematik > 530 Physik |
URN: | urn:nbn:de:bvb:19-epub-89634-5 |
ISSN: | 1867-1381 |
Sprache: | Englisch |
Dokumenten ID: | 89634 |
Datum der Veröffentlichung auf Open Access LMU: | 25. Jan. 2022, 09:32 |
Letzte Änderungen: | 22. Nov. 2022, 10:29 |