Daytime Cloud Overlap Detection from AVHRR and VIIRS

Pavolonis, Michael J.; Heidinger, Andrew K.

doi:10.1175/2099.1

Cited by 116 publications

(85 citation statements)

References 31 publications

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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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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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“…If T 5 ≤ 233.15 K and T bg ≤ 233.15 K (homogeneous freezing threshold, Pavolonis and Heidinger, 2004) it can be assumed that the target is a synoptic scale convective system, when also R 1 ≥ 0.4. Then P IGT is set to 0.95 without evaluating Eq.…”

Section: Infrared Gross Temperature (Igt)mentioning

confidence: 99%

“…If no cirrus cloud is detected, i.e., if the topmost cloud layer is assumed to be opaque, the liquid phase fraction is once more updated with the likelihood of glaciated cloud droplet expressed as a function of temperature. Therefore it is assumed that below the homogeneous freezing temperature of 233.15 K all droplets at cloud top are frozen and that above the pure water freezing point of 273.15 K all cloud droplets are liquid (Pavolonis and Heidinger, 2004). Thus applying Eq.…”

Section: Cloud Top Phase Cloud Top Temperature and Cloud Water Pathmentioning

confidence: 99%

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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“…Calibration and atmospheric correction processing July 2012are carried out to the satellite data to obtain the channel reflectance and the brightness temperature. The cloud mask is built by the cloud detection algorithm of the multispectral threshold (Pavolonis and Heidinger 2004). The ENVI commercial software solution is adopted for processing and analyzing geospatial imagery (http://www.ittvis.com).…”

Section: Latent Heat Flux Lementioning

confidence: 99%

“…Also, the ground-based observed underlying surface is the key component of the corresponding satellite pixel. Since the Sichuan Basin is mostly cloudy with few fine days, only 8 samples were selected through the cloud detection algorithm (Pavolonis and Heidinger 2004) Table 1). …”

Section: Analysis Of Satellite Remotely-sensed Estimationmentioning

confidence: 99%

Comparative Analysis of Satellite Remotely-Sensed Surface Energy Flux and Ground-Based Observation in the Sichuan Basin

Wang

et al. 2012

Journal of the Meteorological Society of Japan

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Integrating the clear-sky NOAA/AVHRR satellite data with station meteorological observations, we conduct a remote sensing estimation of surface energy fluxes of the Sichuan Basin based on surface energy balance. The correlation between the estimation of satellite pixel and the ground-based observed value of Wenjiang Atmospheric Boundary Layer Observation Station is analyzed to verify the satellite remotely-sensed surface energy flux. Result indicates that the distributions of satellite remotely-sensed surface energy fluxes are consistent with those of the underlying surface. There exist remarkable positive correlations among the satellite-estimated net radiation flux, soil heat flux, sensible heat flux, and the ground-based observed values of key components. All correlation coe‰cients significantly exceed 0.9. Hence, the remotely-sensed estimations of net radiation flux, soil heat flux, and sensible heat flux are reliable to a certain extent. Since the surface energy balance at Wenjiang test site is usually unclosed, the correlation coe‰cient between the satellite pixel latent heat flux estimated by surface energy balance method and the value measured by eddy covariance method of the key components is only 0.717. If the unclosed value of surface energy balance is taken into consideration, the correlation coe‰-cient increases to 0.945, meaning that the deviation between the two decreases obviously. It is thus unadvisable to use the residual of surface energy balance equation to estimate the latent heat flux in the Sichuan Basin, because the unclosed energy can cause large errors. Closure of surface energy is required when the residual method is applied.

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Daytime Cloud Overlap Detection from AVHRR and VIIRS

Cited by 116 publications

References 31 publications

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

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

Comparative Analysis of Satellite Remotely-Sensed Surface Energy Flux and Ground-Based Observation in the Sichuan Basin

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