Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval

Abstract

SUMMARYWe propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar-orbiting platforms. In comparison with the established cloud-screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false-alarm rate by one quarter. The rate of occurrence of cloud-screening-related errors of >1 K in area-averaged SSTs is reduced by 83%.

APOLLO_NG – a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels

“…Klüser and Holzer-Popp (2010) reported that the lack of cloud effective radius retrieval is very disadvantageous in aerosol-cloud-interaction studies. Merchant et al (2005) point out the advantage of a more flexible probabilistic cloud detection scheme. The same argument is brought by HolzerPopp et al (2013) with a different focus: in comparing cloud masks from the aerosol_cci and the cloud_cci projects they found that about 20 % of all observations were used neither for aerosol retrieval nor for cloud retrieval.…”

“…It has been developed for cloud detection from Advanced Very High Resolution Radiometer (AVHRR) observations (Saunders and Kriebel, 1988). It also has been expanded (e.g., Gesell et al, 1989) and updated (Kriebel et al, 2003) on several occasions. The AVHRR cloud detection with APOLLO has been evaluated a couple of times (Kriebel et al, 2003;Meerkötter et al, 2004).…”

“…It also has been expanded (e.g., Gesell et al, 1989) and updated (Kriebel et al, 2003) on several occasions. The AVHRR cloud detection with APOLLO has been evaluated a couple of times (Kriebel et al, 2003;Meerkötter et al, 2004). Also in a wide range of other applications APOLLO has been used in adaptations to different satellite sensors.…”

“…Different versions of the APOLLO algorithm have evolved over time (e.g., Kriebel et al, 2003;Holzer-Popp et al, 2008;Klüser and Schepanski, 2009;Klüser and HolzerPopp, 2010). The evolution of varying versions for different purposes and sensors made it desirable to harmonize the sensor dependent versions again.…”

“…The evolution of varying versions for different purposes and sensors made it desirable to harmonize the sensor dependent versions again. Necessary requirements like the introduction of a cloud droplet effective radius retrieval along with the optical depth estimation (Nakajima and King, 1990), the use of modern representations of ice cloud optical properties (Baum et al, 2014) and the requirement for more flexible cloud detection (Merchant et al, 2005;Holzer-Popp et al, 2013) consequently motivated the development of a new cloud detection and cloud retrieval scheme based on the physical principles of APOLLO.…”