The MODIS cloud products: algorithms and examples from Terra

By Steven Platnick, Michael D. King, Steven A. Ackerman, W. Paul Menzel, Bryan A. Baum, J. Riedi, R. Frey. IEEE Transactions on Geoscience and Remote Sensing, 2003

1,840 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/0bb320c5c0219d2cbf43d03916d555046091a398

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Citations in the corpus

Aerosol indirect effects in a multi-scale aerosol-climate model PNNL-MMF (2011, methodology): “Aerosol effects in the clear-sky (assuming entirely clear grid boxes) in Quaas et al. (2009) range from −0.17 to −1.76 W m−2, with a mean value of −0.68 W m−2. Our estimated aerosol effect in the clear-sky (assuming entirely clear grid boxes) is−0.54 W m−2, and is in the range of the AeroCom model e”
The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua (2017, methodology): “night) and cloud optical and microphysical properties [cloud optical thickness (COT), effective particle radius (CER), and derived water path (CWP) for both liquid water and ice cloud thermodynamic phases during daytime only] [6].”
Aerosol-cloud interaction inferred from MODIS satellite data and global aerosol models (2006, background): “Analyses of MODIS data on aerosols and clouds are a back bone in the present investigation.”
Observational evidence of aerosol enhancement of lightning activity and convective invigoration (2011, methodology): “We use daily Level 2 quality controlled retrievals [ Platnick et al., 2003 ].”
Aerosol properties over bright-reflecting source regions (2004, background): “Examples of the application of the cloud mask to scenes composed of desert, coastal, and water ecosystems can be found in [23] and [31].”
The Distribution of Cloud Horizontal Sizes (2011, methodology): “We use the cloud mask product (Ackerman et al. 1998) reported in the MOD06 level 2 (collection 5) cloud product (Platnick et al. 2003).”
MODIS observations of enhanced clear sky reflectance near clouds (2009, methodology): “…nearest cloud as the distance to the nearest pixel where clouds were not only detected but were even suitable for optical thickness retrievals in the operational MODIS cloud algorithm [Platnick et al., 2003] (http://modis-atmos.gsfc.nasa.gov/ C005_Changes/C005_CloudOpticalProperties_ver311.pdf).”
Observed Characteristics of the MJO Relative to Maximum Rainfall (2007, methodology): “Although the accuracy of CTP for multiple cloud layers is uncertain, CTP values for nonoverlapping clouds above 700 hPa are accurate to within 50 hPa [see Platnick et al. (2003) and King et al. (2003) for reviews].”
Predicting species distributions across the Amazonian and Andean regions using remote sensing data (2008, neutral): “Even though the MODIS algorithm is equipped with improved cloud masking (Platnick et al., 2003), effects from subpixel cloudiness on LAI estimates over areas with persistent cloud cover, such as within the Amazon basin and along the Amazonian–Andean elevational gradients, were occasionally observed.”
Underestimation of deep convective cloud tops by thermal imagery (2004, background): “Much work has been done on addressing these issues for thin or broken clouds using multiple wavelengths [e.g., Smith and Platt, 1978; Platnick et al., 2003; Minnis et al., 1998].”
CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms (2011, methodology): “The MAST algorithms, which have been used to generate the standard MAST products, i.e., MOD06/MYD06 and MOD35/MYD35 products from MODIS data [13], would be unable to yield cloud properties consistent with the standard MAST results when applied to the five-channel VIRS data.”
Land surface skin temperature climatology: benefitting from the strengths of satellite observations (2010, methodology): “In addition, water vapor and cloud observations from MODIS (Gao et al 2003, King et al 2003, Platnick et al 2003) are also used to examine the connections between surface temperature and atmospheric conditions.”
Bulk Scattering Properties for the Remote Sensing of Ice Clouds. Part II: Narrowband Models (2005, methodology): “The RT calculations account for viewing geometry, solar illumination, surface temperature, surface emissivity, surface albedo, and bidirectional reflectance characteristics in addition to cloud macrophysical, microphysical, and optical properties (King et al. 1992; Platnick et al. 2003).”