Scientific Basis and Initial Evaluation of the CLAVR-1 Global Clear/Cloud Classification Algorithm for the Advanced Very High Resolution Radiometer

Stowe, Larry L.; Davis, Paul A.; McClain, E. Paul

doi:10.1175/1520-0426(1999)016<0656:sbaieo>2.0.co;2

Cited by 237 publications

(165 citation statements)

References 43 publications

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“…The Clouds from AVHRR Phase 1 (CLAVR-1) algorithm developed by Stowe et al (1999) is implemented to detect clouds. It is a hierarchical decision tree algorithm that uses thresholds based on spectral and spatial signatures of clouds and the underlying surface.…”

Section: Data Processing Cloud Detection and Typingmentioning

confidence: 99%

“…Only cloudy pixels are used for the analysis. Further details on the physical basis of the different tests, thresholds used and validation can be seen in Stowe et al (1999). The algorithm developed by Pavolonis et al (2005) is implemented to decide if the cloudy pixel represents the opaque ice cloud or any other type.…”

Section: Data Processing Cloud Detection and Typingmentioning

confidence: 99%

“…The footprints of such active and break spells of monsoon can be seen in the way convective cloud classes are distributed during such periods and help in understanding cloud-precipitation interactions during such contradicting conditions. So far, there are few studies that investigate the climatology of deep convective clouds from the satellite sensors, however they use only few years of satellite sensor data and/or have very coarse spatio-temporal resolution (Stubenrauch et al, 1999;Wylie and Menzel, 1999;Gettelman et al, 2002;Jiang et al, 2004;Liu and Zipser, 2005;Stubenrauch et al, 2006;Tang and Chen, 2006;Rossow and Pearl, 2007;Hong et al, 2008), and more importantly, do not explicitly examine the distribution of various convective cloud classes during individual monsoon months over India.…”

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confidence: 99%

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A daytime climatological distribution of high opaque ice cloud classes over the Indian summer monsoon region observed from 25-year AVHRR data

Devasthale

Graßl

2009

Atmos. Chem. Phys.

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Abstract.A daytime climatological spatio-temporal distribution of high opaque ice cloud (HOIC) classes over the Indian subcontinent (0-40 • N, 60 • E-100 • E) is presented using 25-year data from the Advanced Very High Resolution Radiometers (AVHRRs) for the summer monsoon months. The HOICs are important for regional radiative balance, precipitation and troposphere-stratosphere exchange. In this study, HOICs are sub-divided into three classes based on their cloud top brightness temperatures (BT ). Class I represents very deep convection (BT <220 K). Class II represents deep convection (220 K<=BT <233 K) and Class III background convection (233 K<=BT <253 K). Apart from presenting finest spatial resolution (0.1×0.1 degrees) and long-term climatology of such cloud classes from AVHRRs to date, this study for the first time illustrates on (1) how these three cloud classes are climatologically distributed during monsoon months, and (2) how their distribution changes during active and break monsoon conditions. It is also investigated that how many deep convective clouds reach the tropopause layer during individual monsoon months. It is seen that Class I and Class II clouds dominate the Indian subcontinent during monsoon. The movement of monsoon over continent is very well reflected in these cloud classes. During monsoon breaks strong suppression of convective activity is observed over the Arabian Sea and the western coast of India. On the other hand, the presence of such convective activity is crucial for active monsoon conditions and all-IndiaCorrespondence to: A. Devasthale (abhay.devasthale@smhi.se) rainfall. It is found that a significant fraction of HOICs (3-5%) reach the tropopause layer over the Bay of Bengal during June and over the north and northeast India during July and August. Many cases are observed when clouds penetrate the tropopause layer and reach the lower stratosphere. Such cases mostly occur during June compared to the other months.

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Section: Data Processing Cloud Detection and Typingmentioning

confidence: 99%

Section: Data Processing Cloud Detection and Typingmentioning

confidence: 99%

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confidence: 99%

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A daytime climatological distribution of high opaque ice cloud classes over the Indian summer monsoon region observed from 25-year AVHRR data

Devasthale

Graßl

2009

Atmos. Chem. Phys.

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“…The refractive index is wavelength independent and includes some aerosol absorption (n=1.5-0.003i) (Stowe et al, 1999) Modified ISCCP cloud detection scheme as described in Mishchenko et al (1999) Threshold of 360 nm reflectance and TOMS Aerosol Index information.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000

et al. 2005

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Abstract. Monthly mean aerosol optical depth (AOD) over ocean is compared from a total of 9 aerosol retrievals during a 40 months period. Comparisons of AOD have been made both for the entire period and sub periods. We identify regions where there is large disagreement and good agreement between the aerosol satellite retrievals. Significant differences in AOD have been identified in most of the oceanic regions. Several analyses are performed including spatial correlation between the retrievals as well as comparison with AERONET data. During the 40 months period studied there have been several major aerosol field campaigns as well as events of high aerosol content. It is studied how the aerosol retrievals compare during such circumstances. The differences found in this study are larger than found in a previous study where 5 aerosol retrievals over an 8 months period were compared. Part of the differences can be explained by limitations and deficiencies in some of the aerosol retrievals. In particular, results in coastal regions are promising especially for aerosol retrievals from satellite instruments particularly suited for aerosol research. In depth analyses explaining the differences between AOD obtained in different retrievals are clearly needed. We limit this study to identifying differences and similarities and indicating possible sources that affect the quality of the retrievals. This is a necessary first step towards understanding the differences and improving the retrievals.

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“…Hence, most atmospheric measurements are pre-filtered for the presence of cloud via one of a plethora of empirical techniques (e.g. Ackerman et al, 1998;Stowe et al, 1999;Pavolonis and Heidinger, 2004;Curier et al, 2009). This constrains the retrieval to observations believed to be appropriate to the forward model used.…”

Section: Impact Of Samplingmentioning

confidence: 99%

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

Povey

Grainger

2015

Atmos. Meas. Tech.

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Abstract. This paper discusses a best-practice representation of uncertainty in satellite remote sensing data. An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and in auxiliary data -known, quantified "unknowns". The underconstrained nature of most satellite remote sensing observations requires the use of various approximations and assumptions that produce non-linear systematic errors that are not readily assessed -known, unquantifiable "unknowns". Additional errors result from the inability to resolve all scales of variation in the measured quantity -unknown "unknowns". The latter two categories of error are dominant in underconstrained remote sensing retrievals, and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. This paper proposes the use of ensemble techniques to present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties. These are generated using various systems (different algorithms or forward models) believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more complete representation of the uncertainty as understood by the data producer and greater freedom to consider different realisations of the data.

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Scientific Basis and Initial Evaluation of the CLAVR-1 Global Clear/Cloud Classification Algorithm for the Advanced Very High Resolution Radiometer

Cited by 237 publications

References 43 publications

A daytime climatological distribution of high opaque ice cloud classes over the Indian summer monsoon region observed from 25-year AVHRR data

A daytime climatological distribution of high opaque ice cloud classes over the Indian summer monsoon region observed from 25-year AVHRR data

Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

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