Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS

King, Michael D.; Menzel, W. Paul; Kaufman, Yoram J.; Tanré, D.; Gao, Bo‐Cai; Platnick, S.; Ackerman, S. A.; Remer, L. A.; Pincus, Robert; Hubanks, Paul A.

doi:10.1109/tgrs.2002.808226

Cited by 905 publications

(630 citation statements)

References 26 publications

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“…Their relationships to uncertainty are further complicated by differences in retrieval approaches, namely interpolating two independent uncertain observations in a LUT (bispectral) or curve fitting through numerous observations that are each independently uncertain (polarimetric). Targeted uncertainties for cloud and aerosol remote sensing are δ DOLP = 0.5 % in degree of linear polarization and δI = 3 % in total reflectance (Knobelspiesse et al, 2012). A simple propagation of uncertainty analysis yields a polarized reflectance uncertainty of δQ = 2.5 % (in the principal plane).…”

Section: Sensitivity To Measurement Uncertaintymentioning

confidence: 99%

“…One such retrieval method is called the bispectral total reflectance technique, hereafter referred to as the "bispectral technique", which simultaneously retrieves cloud optical thickness (τ ) and r e from a pair of cloud reflectances, typically one in the visible to near infrared (VNIR) and the other in the shortwave infrared (SWIR) or midwave infrared (MWIR) spectral range (Nakajima and King, 1990b). This retrieval technique has been implemented for numerous satellite and airborne instruments, such as the Moderate Resolution Imaging Spectroradiometer (MODIS; King et al, 2003;, the Spinning Enhanced Visible and Infrared Imager (SEVIRI; Roebeling et al, 2006), and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi NPP VIIRS; Rosenfeld et al, 2014).…”

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confidence: 99%

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Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

et al. 2018

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Abstract. Many passive remote-sensing techniques have been developed to retrieve cloud microphysical properties from satellite-based sensors, with the most common approaches being the bispectral and polarimetric techniques. These two vastly different retrieval techniques have been implemented for a variety of polar-orbiting and geostationary satellite platforms, providing global climatological data sets. Prior instrument comparison studies have shown that there are systematic differences between the droplet size retrieval products (effective radius) of bispectral (e.g., MODIS, Moderate Resolution Imaging Spectroradiometer) and polarimetric (e.g., POLDER, Polarization and Directionality of Earth's Reflectances) instruments. However, intercomparisons of airborne bispectral and polarimetric instruments have yielded results that do not appear to be systematically biased relative to one another. Diagnosing this discrepancy is complicated, because it is often difficult for instrument intercomparison studies to isolate differences between retrieval technique sensitivities and specific instrumental differences such as calibration and atmospheric correction. In addition to these technical differences the polarimetric retrieval is also sensitive to the dispersion of the droplet size distribution (effective variance), which could influence the interpretation of the droplet size retrieval. To avoid these instrument-dependent complications, this study makes use of a cloud remote-sensing retrieval simulator. Created by coupling a large-eddy simulation (LES) cloud model with a 1-D radiative transfer model, the simulator serves as a test bed for understanding differences between bispectral and polarimetric retrievals. With the help of this simulator we can not only compare the two techniques to one another (retrieval intercomparison) but also validate retrievals directly against the LES cloud properties. Using the satellite retrieval simulator, we are able to verify that at high spatial resolution (50 m) the bispectral and polarimetric retrievals are highly correlated with one another within expected observational uncertainties. The relatively small systematic biases at high spatial resolution can be attributed to different sensitivity limitations of the two retrievals. In contrast, a systematic difference between the two retrievals emerges at coarser resolution. This bias largely stems from differences related to sensitivity of the two retrievals to unresolved inhomogeneities in effective variance and optical thickness. The influence of coarse angular resolution is found to increase uncertainty in the polarimetric retrieval but generally maintains a constant mean value.

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Section: Sensitivity To Measurement Uncertaintymentioning

confidence: 99%

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confidence: 99%

Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

et al. 2018

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“…We use IWV from the version 5 release which is retrieved from the near-infrared (NIR) and infrared (IR) channels (King et al, 2003). The former is produced during daytime, over bright surfaces (ocean and land) and clouds, with 1 km resolution.…”

Section: Description Of the Datasetmentioning

confidence: 99%

Water vapour variability induced by urban/rural surface heterogeneities during convective conditions

Champollion

Drobinski

Haeffelin

et al. 2009

Quart J Royal Meteoro Soc

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Scientific interest in urban meteorology has increased because highly populated areas experience high vulnerability to pollution or heavy rain. However, compared to urban air quality or urban heat island (UHI) processes, the urban water vapour cycle is poorly understood because it has been investigated less due to the lack of upper-air measurements and the high sensitivity of surface measurements to local heterogeneities. In this paper, surface measurements of wind, temperature, pressure and humidity, as well as integrated water vapour (IWV) from GPS and MODIS and numerical simulations, have been used to investigate the urban cycle of water vapour in May and June 2004 during the VAPIC field experiment in the Paris area.The surface data show the typical characteristics of an urban area with the absence of water vapour sources and a UHI of about 6 • C at night. The urban IWV distribution differs completely, with an urban IWV excess on average between 1600 and 0600 UTC (with a maximum of about 1.5 kg m −2 at 0600 and 1700 UTC). No IWV difference between the urban and rural areas is found in the middle of the day. The numerical simulations reproduce accurately the urban IWV anomaly.Shallow surface wind convergence associated with the UHI during nighttime provides moisture from the rural areas. Urban areas are therefore under wind convergence for most of the time. The rural water vapour sources and the depth of the convergence control the amplitude of the urban IWV excess. At about 1200 UTC, entrainment at the top of the urban boundary layer is the key process that inhibits the urban IWV excess observed at night.

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“…The SSMI column water vapor and cloud liquid water are derived from satellite microwave radiometers as described in Wentz and Spencer (1998). The MODIS total column water vapor product was obtained from near-IR and IR algorithms,while cloud liquid water path is derived, along with a number of physical and radiative cloud properties, using IR and visible algorithms (King et al 2003). We use the ISCCP D2 gridded cloud product data for total cloud.…”

Section: Introductionmentioning

confidence: 99%

Simulation of the Global Hydrological Cycle in the CCSM Community Atmosphere Model Version 3 (CAM3): Mean Features

et al. 2006

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The seasonal and annual climatological behavior of selected components of the hydrological cycle are presented from coupled and uncoupled configurations of the atmospheric component of the Community Climate System Model (CCSM), the Community Atmosphere Model Version 3 (CAM3). The formulations of processes that play a role in the hydrological cycle are significantly more complex when compared with earlier versions of the atmospheric model.Major features of the simulated hydrological cycle will be compared against available observational data, and the strengths and weaknesses will be discussed in the context of the fully-coupled model simulation.

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Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS

Abstract: In this paper we describe each of these atmospheric data products, including characteristics of each of these products such as file size, spatial resolution used in producing the product, and data availability.

Cited by 905 publications

References 26 publications

Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

Comparisons of bispectral and polarimetric retrievals of marine boundary layer cloud microphysics: case studies using a LES–satellite retrieval simulator

Water vapour variability induced by urban/rural surface heterogeneities during convective conditions

Simulation of the Global Hydrological Cycle in the CCSM Community Atmosphere Model Version 3 (CAM3): Mean Features

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