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Satellite‐based estimation of cloud‐base heights using constrained spectral radiance matching
Author(s) -
Sun X. J.,
Li H. R.,
Barker H. W.,
Zhang R. W.,
Zhou Y. B.,
Liu L.
Publication year - 2015
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.2647
Subject(s) - radiance , remote sensing , cloud computing , satellite , environmental science , computer science , cloud fraction , cloud top , matching (statistics) , satellite imagery , meteorology , algorithm , cloud cover , geology , mathematics , physics , statistics , astronomy , operating system
A method for estimating cloud‐base heights (CBHs) across wide swaths of passive satellite imagery is introduced. The constrained spectral radiance matching (CSRM) algorithm assigns donor columns observed by CloudSat/CALIPSO to recipient pixels across MODIS imagery. The column meeting the constraint is selected as a donor via spectral radiance matching (SRM). Results are compared using eight cloud characteristics, retrieved from passive imagery, as constraints and distinct values for a matching controlling factor α . Cloud‐top pressure and α = 0.3 are used in the final algorithm. Estimates are made of CBH and layer‐cloud fraction profile made by SRM, CSRM, cloud‐type matching (CTM), and retrieved data matching (RDM) using a data‐exclusion procedure. Results show that the CSRM is superior at estimating lowest CBH and layer‐cloud fraction. It is also shown that, when cloud types are provided by merged CloudSat and CALIPSO data, the CTM provides the best estimates of uppermost CBH, with the CSRM taking second. Both the CSRM method and the straight SRM method construct layer‐cloud fraction profiles very well for clouds between 2 and ∼15 km. A preliminary 3D rendering of tropical storm Soulik is presented.