An improved method for detecting clear sky and cloudy radiances from AVHRR data

Abstract

To obtain accurate estimates of surface and cloud parameters from satellite radiance data a scheme has to be devised which identifies cloud-free and cloud-filled pixels (i.e. fields of view). Such a scheme has been developed for application to high resolution (1'1 km pixel) images recorded over Western Europe and the North Atlantic by the AYHRR on the TIROS-NjNOAA polar orbiters. The scheme consists of five daytime or five night-time tests applied to each individual pixel to determine whether that pixel is cloud-free, partly cloudy or cloud-filled.The pixelis only identifiedas cloud-free or cloud-filledifit passes all the tests to identify that condition; otherwise it is assumed to be partly cloudy. Surface parameters (e.g. skin temperature, reflectance, vegetation index, snow cover) can then be inferred from the cloud-free radiances, and cloud parameters (e.g.cloud top temperature, optical depth and liquid water content) from the cloud-filled radiances. Only fractional cloud cover is derived from the partly cloudy pixels which, together with the number of cloud-filled pixels, gives total cloud cover over a given area. The scheme has been successfully applied to data for all seasons, including images with unusually cold or warm surface temperatures. To assess the method both daytime and night-time NOAA-9 passes over the U.K. were obtained for a week in April 1985 and some results from this data set are presented here.

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

“…He explained the differences between ground-based observations and satellite imagery with geometric, synoptic and orographic factors. With increasing availability of satellite data, scientists started deriving cloud properties (Ackerman et al, 1998;Christodoulou et al, 2003;Ebert, 1987;Gao and Wiscombe, 1994;Garand, 1988;Parikh, 1977;Porcú and Levizzani, 1992;Romano et al, 2007;Saunders and Kriebel, 1988;Schröder et al, 2002;Welch et al, 1992). The cloud detection threshold test by Derrien et al (1993) is a real-time processing scheme that is applied to the different channels of irradiances from the NOAA-11 satellite.…”

Lee region of Gran Canaria

“…The raw data were subsequently geolocated using the satellite orbit elements and adjusted using the island coastlines to reach a final ground location accuracy of •-2 km. Sea surface temperatures were then estimated from the five-channel records using the algorithm of McClain et al [1985] to correct for atmospheric water vapor to an absolute precision better than i"C. Clouds were detected and flagged using a combination of tests, including a textural test on the visible and infrared channels to detect small cumulus clouds and a differential test on the infrared channels to detect fbg and low stratus clouds [Saunders and Kriebel, 1988]. The final cloud-masked SST images were remapped to a common Mercator grid, to eliminate geometric distortions due to earth rotation and curvature.…”

Reef Temp Next Generation: A new operational system for monitoring reef thermal stress

“…33 Secondly, the cloud quality control methods differ between the IMOS and CSIRO products. Griffin et al 31 states that approximately 40% of thin cloud at night is not detected by the Saunders and Kribel cloud detection algorithm 34 , which was used in the CSIRO dataset. This would result in a cooler SST for the CSIRO SST climatology although the effect could be, at least partially, offset by using the 65 th percentile median SST.…”