CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms

By Patrick Minnis, S. Sun-Mack, D. Young, P. Heck, D. Garber, Yan Chen, D. Spangenberg, R. Arduini, Q. Trepte, W. Smith and 9 more. IEEE Transactions on Geoscience and Remote Sensing, 2011

500 citations recorded by the source corpus.

The National Aeronautics and Space Administration's Clouds and the Earth's Radiant Energy System (CERES) Project was designed to improve our understanding of the relationship between clouds and solar and longwave radiation. This is achieved using satellite broad-band instruments to map the top-of-atmosphere radiation fields with coincident data from satellite narrow-band imagers employed to retrieve the properties of clouds associated with those fields. This paper documents the CERES Edition-2 cloud property retrieval system used to analyze data from the Tropical Rainfall Measuring Mission Visible and Infrared Scanner and by the MODerate-resolution Imaging Spectrometer instruments on board the Terra and Aqua satellites covering the period 1998 through 2007. Two daytime retrieval methods are explained: the Visible Infrared Shortwave-infrared Split-window Technique for snow-free surfaces and the Shortwave-infrared Infrared Near-infrared Technique for snow or ice-covered surfaces. The Shortwave-infrared Infrared Split-window Technique is used for all surfaces at night. These methods, along with the ancillary data and empirical parameterizations of cloud thickness, are used to derive cloud boundaries, phase, optical depth, effective particle size, and condensed/frozen water path at both pixel and CERES footprint levels. Additional information is presented, detailing the potential effects of satellite calibration differences, highlighting methods to compensate for spectral differences and correct for atmospheric absorption and emissivity, and discussing known errors in the code. Because a consistent set of algorithms, auxiliary input, and calibrations across platforms are used, instrument and algorithm-induced changes in the data record are minimized. This facilitates the use of the CERES data products for studying climate-scale trends.

Subfields: Artificial Intelligence, Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e9a3550dda1ff00d5ad32a0d15da345609212c27

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Citations in the corpus

• Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites (2013, methodology): “There are many similarities as well as substantial differences between these results and those derived independently by the Clouds and the Earth’s Radiant Energy System (CERES) that makes use of the same MODIS instrument but a restricted set of spectral bands [28], [29].”
• Advances in Understanding Top-of-Atmosphere Radiation Variability from Satellite Observations (2012, methodology): “Cloud retrievals are based upon Minnis et al. (2011), while aerosol data are from Remer et al. (2005).”
• Observational Evidence That Enhanced Subsidence Reduces Subtropical Marine Boundary Layer Cloudiness (2013, methodology): “Changes in cloud structure occurring in the Sc-to-Cu transition are not readily perceived by the passive remote sensing of ISCCP, PATMOS-x, and MODIS.”
• Next-generation angular distribution models for top-of-atmosphere radiative flux calculation from CERES instruments: validation (2015, methodology): “The scene identification used to develop these ADMs is based upon the cloud algorithms described in Minnis et al. (2011).”
• The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua (2017, background): “ing [37], [38]; comparisons between many CERES Edition-2”
• Illuminating the Capabilities of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (2013, methodology): “The center two panels compare nighttime cloud optical depth retrievals for the same region based on an adaptation of the DCOMP to accommodate DNB lunar reflectance measurements (Figure 12b; [61]) and the experimental Shortwave-infrared Infrared Split-window Technique (SIST) GOES-15 (Figure 12c; [62]”
• CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux (2015, methodology): “The CERES single satellite footprint (SSF), edition 2, cloud algorithm (Minnis et al. 2011a,b) derives cloud properties (e.g., fraction, optical depth, top height, particle size, and phase) from narrowband radiances measured by MODIS twice a day fromMarch 2000 through August 2002 (Terra only) and 4…”
• Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel (2013, methodology): “CWP is derived from COD and CRE.”