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CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms
Abstract
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.
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Cited by
The National Solar Radiation Data Base (NSRDB)
Sengupta, M., Xie, Y., Lopez, A., Habte, A., Maclaurin, G., & Shelby, J. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.03.003 · Cited by 1,443
Self-citationGeostationary Enhanced Temporal Interpolation for CERES Flux Products
Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D., Nguyen, C., Wielicki, B. A., Young, D. F., & Sun, M. (2013) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-12-00136.1 · Cited by 498
Self-citationKato, S., Loeb, N. G., Rose, F. G., Doelling, D. R., Rutan, D. A., Caldwell, T. E., Yu, L., & Weller, R. A. (2013) · Journal of Climate · doi:10.1175/jcli-d-12-00436.1 · Cited by 437
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., & Patrick Hofmann, R. J. (2012) · Journal of Climate · doi:10.1175/jcli-d-11-00267.1 · Cited by 342
Wood, R., Mechoso, C. R., Bretherton, C. S., Weller, R. A., Huebert, B., Straneo, F., Albrecht, B. A., Coe, H., Allen, G., Vaughan, G., et al. (2011) · Atmospheric Chemistry and Physics · doi:10.5194/acp-11-627-2011 · Cited by 304
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Metadata
- Authors
- Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers, J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y., Liou, K.-N., Xie, Y., & Yang, P.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2011-11
- Volume
- 49
- Issue
- 11
- Pages
- 4374-4400
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2140592783
- Cited by
- 553
- References
- 0
- FWCI
- 31.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers, J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y., Liou, K.-N., Xie, Y., & Yang, P. (2011). CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2011.2144601
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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