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Semi-autonomous Sounding Selection for Oco-2 : Volume 6, Issue 3 (26/06/2013)

By Mandrake, L.

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Book Id: WPLBN0003999908
Format Type: PDF Article :
File Size: Pages 42
Reproduction Date: 2015

Title: Semi-autonomous Sounding Selection for Oco-2 : Volume 6, Issue 3 (26/06/2013)  
Author: Mandrake, L.
Volume: Vol. 6, Issue 3
Language: English
Subject: Science, Atmospheric, Measurement
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Frankenberg, C., O'dell, C. W., Wunch, D., Osterman, G., Mandrake, L., & Wennberg, P. (2013). Semi-autonomous Sounding Selection for Oco-2 : Volume 6, Issue 3 (26/06/2013). Retrieved from http://ebook.worldlibrary.net/


Description
Description: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. Many modern instruments generate more data than may be fully processed in a timely manner. For some atmospheric sounders, much of the raw data cannot be processed into meaningful observations due to suboptimal viewing conditions, such as the presence of clouds. Conventional solutions are quick, empirical-threshold filters hand-created by domain experts to weed out unlikely or unreasonable observations, coupled with randomized down sampling when the data volume is still too high. In this paper, we describe a method for the construction of a sub-sampling and ordering solution that maximizes the likelihood that a requested data subset will be usefully processed. The method can be used for any metadata-rich source and implicitly discerns informative vs. non-informative data features while still permitting user feedback into the final features selected for filter implementation. We demonstrate the method by creating a selector for the spectra of the Japanese GOSAT satellite designed to measure column averaged mixing ratios of greenhouse gases including carbon dioxide (CO2). This is done within the Atmospheric CO2 Measurements from Space (ACOS) NASA project with the intention of eventual use during the early Orbiting Carbon Observatory-2 (OCO-2) mission. OCO-2 will have a 1.5 orders of magnitude larger data volume than ACOS, requiring intelligent pre-filtration.

Summary
Semi-autonomous sounding selection for OCO-2

Excerpt
Boesch, H., Backer, D., Connor, B., Crips, D., and Miller, C.: Global Characterization of CO2 Column Retrievals from Shortwave-Infrared Satellite Observations of thet Orbiting Carbon Observatory-2 Mission, Remote Sens., 3, 270–304, 2011.; Crisp, D., Atlas, R. M., Breon, F.-M., Brown, L. R., Burrows, J. P., Ciais, P., Connor, B. J., Doney, S. C., Fung, I. Y., Jacob, D. J., Miller, C. E., O'Brien, D., Pawson, S., Randerson, J. T., Rayner, P., Salawitch, R. J., Sander, S. P., Sen, B., Stephens, G. L., Tans, P. P., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Yung, Y. L., Kuang, Z., Chudasama, B., Sprague, G., Weiss, B., Pollock, R., Kenyon, D., and Schroll, S.: The Orbiting Carbon Observatory (OCO) mission, Adv. Space Res., 34, 700–709, 2004.; Crisp, D., Fisher, B. M., O'Dell, C., Frankenberg, C., Basilio, R., Bösch, H., Brown, L. R., Castano, R., Connor, B., Deutscher, N. M., Eldering, A., Griffith, D., Gunson, M., Kuze, A., Mandrake, L., McDuffie, J., Messerschmidt, J., Miller, C. E., Morino, I., Natraj, V., Notholt, J., O'Brien, D. M., Oyafuso, F., Polonsky, I., Robinson, J., Salawitch, R., Sherlock, V., Smyth, M., Suto, H., Taylor, T. E., Thompson, D. R., Wennberg, P. O., Wunch, D., and Yung, Y. L.: The ACOS CO2 retrieval algorithm – Part II: Global XCO2 data characterization, Atmos. Meas. Tech., 5, 687–707, doi:10.5194/amt-5-687-2012, 2012.; Frankenberg, C., Platt, U., and Wagner, T.: Iterative maximum a posteriori (IMAP)-DOAS for retrieval of strongly absorbing trace gases: Model studies for CH4 and CO2 retrieval from near infrared spectra of SCIAMACHY onboard ENVISAT, Atmos. Chem. Phys., 5, 9–22, doi:10.5194/acp-5-9-2005, 2005.; Guyon, I. and Elisseeff, A.: An Introduction to Variable and Feature Selection, J. Mach. Learn. Res., 3, 1157–1182, 2003.; Jin, Y. and Sendhoff, B.: Pareto-based multi-objective machine learning: An overview and case studies, IEEE Sys. Man Cybern., 38, 397–415, 2008.; O'Brien, D. M., Pollock, R., Polonsky, I., and Rogers, M.: Identification and Correction of Residual Image in the O2 A-Band of the Orbiting Carbon Observatory, IEEE T. Geosci. Remote, 49, 2426–2437, 2011.; O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C., Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J., Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval algorithm – Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5, 99–121, doi:10.5194/amt-5-99-2012, 2012.; Periaux, J. and Galan, M.: Genetic Algorithms in Engineering and Computer Science, John Wiley & Son Ltd, 1995.; Reuter, M., Bovensmann, H., Buchwitz, M., Burrows, J. P., Connor, B. J., Deutscher, N. M., Griffith, D. W. T., Heymann, J., Keppel-Aleks, G., Messerschmidt, J., Notholt, J., Petri, C., Robinson, J., Schneising, O., Sherlock, V., Velazco, V., Warneke, T., Wennberg, P. O., and Wunch, D.: Retrieval of atmospheric CO2 with enhanced accuracy and precision from SCIA- MACHY: Validation with FTS measurements and comparison with model results, J. Geophys. Res., 116, D04301, doi:10.1029/2010JD015047, 2011.; Taylor, T. E., O'Dell, C. W., O'Brien, D. M., Kikuchi, N., Yokota, T., Nakajima, T. Y., Ishida, H., Crisp, D., and Nakajima, T.: Comparison of Cloud-Screening Methods Applied to GOSAT Near-Infrared Spectra, IEEE T. Geosci. Remote, 50, 295–309, 2012.; Yokota, T., Oguma, H., Morino, I., and Inoue, G.: A nadir looking SWIR FTS to monitor CO2 column

 

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