Accuracy of the independent pixel approximation for satellite estimates of oceanic boundary layer cloud optical depth

Abstract

. The random error decreases as the averaging scale increases, but error due to inhomogeneity remains. At the 60 km scale the average •rror is about 6% for high Sun, 2% for low Sun. Individual scenes, however, have retrieved optical depth errors as high as 45% due to horizontal radiative transport. The ability to retrieve higher statistical moments of the frequency distribution of optical depth is also assessed. Sigma, (a), the standard deviation of 7-, is retrieved quite well up to a point, then is underestimated due to the smoothing effect of horizontal radiative transport. The gamma function parameter another measure of the width of the 7-frequency distribution, is retrieved quite well over a wide range but with a systematic bias which varies with solar zenith angle, again due to horizontal radiative transport. A method is sought to reduce the optical depth retrieval error using a simple correction based on remotely sensed cloud properties. Of those considered, cloud physical aspect ratio (computed here from one possible relation which depends on properties obtainable from remote sensing) is tbund to be the most effective correction parameter. The aspect ratio correction reduces the retrieved optical depth bias error by 50 to 100% and the RMS error by 20 to 50%. Correction coefficients are presented at three solar zenith angles. This work is limited by its consideration of only single-level marine boundary layer clouds, assumptions of conservative scattering, constant cloud droplet size, no gas absorption or surface reflectance, and restriction to two-dimensional radiative transport. Future work will attempt to remove some of these limitations. The Landsat data used are also limited due to radiative smoothing.

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

“…Errors due to cloud heterogeneity have been analyzed mainly in the context of satellite remote sensing. Varnai (1998) and Chambers et al (1997) observed that the cloud spatial reflectance variation is smoother than variations in τ c . They hypothesized that optically thicker clouds would scatter more light to their thinner neighboring clouds, causing the thinner clouds to appear brighter and thicker (looking from space), while the thinner clouds would scatter less light to the thicker clouds, making them appear darker and thinner than expected for a homogeneous cloud scene.…”

Marine boundary layer cloud property retrievals from high-resolution ASTER observations: case studies and comparison with Terra MODIS

“…For observations with a high spatial resolution, such as the Thematic Mapper onboard the Landsat satellites, cloud heterogeneities at scales larger than the sensor spatial resolution yield a breakdown of IPA (Barker and Liu, 1995;Chambers et al, 1997). In contrast, satellite observations with a lower spatial resolution cannot resolve heterogeneous cloud structures within a pixel, introducing significant biases in retrieved τ and r eff (Cahalan et al, 1994a;Marshak et al, 2006;Zhang and Platnick, 2011;Zhang et al, 2012).…”

Measurements of the HONO photodissociation constant

“…During conditions of broken cloud cover, however, this approximation is especially limited. In an attempt to investigate the 3-D effects of clouds, several recent model studies (Stephens et al, 1990;Chambers et al, 1997) have mainly examined the change in radiance and actinic flux values at fixed locations due to inhomogeneities. Although the 3-D effect of clouds on the actinic flux is solvable, the major challenge that one is faced with is the specification of cloud input (Kylling et al, 2005).…”