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

Merchant, Christopher J.; Harris, Anthony M.; Maturi, Eileen; MacCallum, Stuart N

doi:10.1256/qj.05.15

Cited by 142 publications

(156 citation statements)

References 25 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Klüser¹,

Killius²,

Gesell³

2015

Atmos. Meas. Tech.

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Abstract. The cloud processing scheme APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) has been in use for cloud detection and cloud property retrieval since the late 1980s. The physics of the APOLLO scheme still build the backbone of a range of cloud detection algorithms for AVHRR (Advanced Very High Resolution Radiometer) heritage instruments. The APOLLO_NG (APOLLO_NextGeneration) cloud processing scheme is a probabilistic interpretation of the original APOLLO method. It builds upon the physical principles that have served well in the original APOLLO scheme. Nevertheless, a couple of additional variables have been introduced in APOLLO_NG. Cloud detection is no longer performed as a binary yes/no decision based on these physical principles. It is rather expressed as cloud probability for each satellite pixel. Consequently, the outcome of the algorithm can be tuned from being sure to reliably identify clear pixels to conditions of reliably identifying definitely cloudy pixels, depending on the purpose. The probabilistic approach allows retrieving not only the cloud properties (optical depth, effective radius, cloud top temperature and cloud water path) but also their uncertainties. APOLLO_NG is designed as a standalone cloud retrieval method robust enough for operational near-realtime use and for application to large amounts of historical satellite data. The radiative transfer solution is approximated by the same two-stream approach which also had been used for the original APOLLO. This allows the algorithm to be applied to a wide range of sensors without the necessity of sensorspecific tuning. Moreover it allows for online calculation of the radiative transfer (i.e., within the retrieval algorithm) giving rise to a detailed probabilistic treatment of cloud variables. This study presents the algorithm for cloud detection and cloud property retrieval together with the physical principles from the APOLLO legacy it is based on. Furthermore a couple of example results from NOAA-18 are presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Klüser¹,

Killius²,

Gesell³

2015

Atmos. Meas. Tech.

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Probabilistic detection of volcanic ash using a Bayesian approach

Mackie

Watson

2014

JGR Atmospheres

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Airborne volcanic ash can pose a hazard to aviation, agriculture, and both human and animal health. It is therefore important that ash clouds are monitored both day and night, even when they travel far from their source. Infrared satellite data provide perhaps the only means of doing this, and since the hugely expensive ash crisis that followed the 2010 Eyjafjalljökull eruption, much research has been carried out into techniques for discriminating ash in such data and for deriving key properties. Such techniques are generally specific to data from particular sensors, and most approaches result in a binary classification of pixels into “ash” and “ash free” classes with no indication of the classification certainty for individual pixels. Furthermore, almost all operational methods rely on expert-set thresholds to determine what constitutes “ash” and can therefore be criticized for being subjective and dependent on expertise that may not remain with an institution. Very few existing methods exploit available contemporaneous atmospheric data to inform the detection, despite the sensitivity of most techniques to atmospheric parameters. The Bayesian method proposed here does exploit such data and gives a probabilistic, physically based classification. We provide an example of the method's implementation for a scene containing both land and sea observations, and a large area of desert dust (often misidentified as ash by other methods). The technique has already been successfully applied to other detection problems in remote sensing, and this work shows that it will be a useful and effective tool for ash detection.Key PointsPresentation of a probabilistic volcanic ash detection schemeMethod for calculation of probability density function for ash observationsDemonstration of a remote sensing technique for monitoring volcanic ash hazards

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An objective regional cloud mask algorithm for GOES infrared imager radiance assimilation

Zou

2014

JGR Atmospheres

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A local, regime-dependent cloud mask (CM) algorithm is developed for isolating cloud-free pixels from cloudy pixels for Geostationary Operational Environmental Satellite (GOES) imager radiance assimilation using mesoscale forecast models. In this CM algorithm, thresholds for six different CM tests are determined by a one-dimensional optimization approach based on probability distribution functions of the nearby cloudy and clear-sky pixels within a 10°× 10°box centered at a target pixel. It is shown that the optimizal thresholds over land are, in general, larger and display more spatial variations than over ocean. The performance of the proposed CM algorithm is compared with Moderate Resolution Imaging Spectroradiometer (MODIS) CM for a 1 week period from 19 to 23 May 2008. Based on MODIS CM results, the average Probability of Correct Typing reaches 92.94% and 91.50% over land and ocean, respectively.

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

Cited by 142 publications

References 25 publications

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

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

Probabilistic detection of volcanic ash using a Bayesian approach

An objective regional cloud mask algorithm for GOES infrared imager radiance assimilation

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