Satellite‐based estimation of cloud‐base heights using constrained spectral radiance matching

Abstract

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.

Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data

“…However, when distances are 50 km, it has the worst performance and imparts a negative effect on results. A stricter constraint results in better estimates, but too strict a constraint worsens estimation of CBH in some cases [25]. The aim of this research is to achieve CBH estimation with large coverage.…”

“…The current algorithm is an extension of Maximum Donors that includes an additional CTP and CWP simultaneous constraint, namely, Constrained Maximum Donors. Sun et al [25] suggest that cloud parameters retrievable by passive radiometers may help express cloud geometric information and develop a spectral matching method based on CTP constraint for estimating CBH. Similarly, Li and Sun [24] select the most appropriate donor to a recipient for estimating CBH based on CWP and CTP.…”

“…Sun et al [25] consider the performance of CBH estimation for different values for , finding that = 0.3 provides the most suitable value for his algorithm. The choices of for Constrained Maximum Donors method will be discussed later.…”

“…While passive remote sensing (e.g., scanning imaging radiometers) provides cloud top information over a relatively large spatial swath, the nature of passive observations presents inherent challenges for retrieving CLT and deriving CBH [20][21][22]. The National Aeronautics and Space Administration (NASA) A-Train satellite constellation has Aqua/MODIS, CloudSat/CPR, and CALIPSO/ CALIOP remote sensing instruments, providing the convenience of observing CBH with a combination of active and passive sensors [23][24][25].…”

“…Barker et al [26] estimate CVS using a radiationsimilarity approach based on thermal infrared and visible channels to relate donor pixels (from the active sensor) to recipient pixels in the surrounding region. Sun et al [25] proposed a spectral matching method based on the cloud top pressure constraint for further accurate estimation of CBH. Miller et al [27] propose a cloud-type-dependent decorrelation-length and distance-weighting method to reduce CVS estimation error due to dramatic changes in CVS based on regional clouds of a similar type having similar morphological properties.…”