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Dimensionality Reduction in Bayesian Estimation Algorithms : Volume 6, Issue 9 (04/09/2013)

By Petty, G. W.

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Book Id: WPLBN0003999166
Format Type: PDF Article :
File Size: Pages 10
Reproduction Date: 2015

Title: Dimensionality Reduction in Bayesian Estimation Algorithms : Volume 6, Issue 9 (04/09/2013)  
Author: Petty, G. W.
Volume: Vol. 6, Issue 9
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

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Petty, G. W. (2013). Dimensionality Reduction in Bayesian Estimation Algorithms : Volume 6, Issue 9 (04/09/2013). Retrieved from http://hawaiilibrary.net/


Description
Description: Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA. An idealized synthetic database loosely resembling 3-channel passive microwave observations of precipitation against a variable background is employed to examine the performance of a conventional Bayesian retrieval algorithm. For this dataset, algorithm performance is found to be poor owing to an irreconcilable conflict between the need to find matches in the dependent database versus the need to exclude inappropriate matches. It is argued that the likelihood of such conflicts increases sharply with the dimensionality of the observation space of real satellite sensors, which may utilize 9 to 13 channels to retrieve precipitation, for example.

An objective method is described for distilling the relevant information content from N real channels into a much smaller number (M) of pseudochannels while also regularizing the background (geophysical plus instrument) noise component. The pseudochannels are linear combinations of the original N channels obtained via a two-stage principal component analysis of the dependent dataset. Bayesian retrievals based on a single pseudochannel applied to the independent dataset yield striking improvements in overall performance.

The differences between the conventional Bayesian retrieval and reduced-dimensional Bayesian retrieval suggest that a major potential problem with conventional multichannel retrievals – whether Bayesian or not – lies in the common but often inappropriate assumption of diagonal error covariance. The dimensional reduction technique described herein avoids this problem by, in effect, recasting the retrieval problem in a coordinate system in which the desired covariance is lower-dimensional, diagonal, and unit magnitude.


Summary
Dimensionality reduction in Bayesian estimation algorithms

Excerpt
Di Michele, S., Tassa, A., Mugnai, A., Marzano, F., Bauer, P., and Baptista, J.: Bayesian algorithm for microwave-based precipitation retrieval: Description and application to TMI measurements over ocean, IEEE T. Geosci. Remote, 43, 778–791, 2005.; Grecu, M. and Olson, W.: Bayesian estimation of precipitation from satellite passive microwave observations using combined radar-radiometer retrievals, J. Appl. Meteorol. Clim., 45, 416–433, 2006.; Bauer, P., Amayenc, P., Kummerow, C., and Smith, E.: Over-ocean rainfall retrieval from multisensor data of the Tropical Rainfall Measuring Mission. Part II: Algorithm implementation, J. Atmos. Ocean Tech., 18, 1838–1855, 2001.; Bayes, T. and Price, R.: An Essay towards Solving a Problem in the Doctrine of Chances. By the Late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S., Philosophical Transactions, 53, 370–418, 1763.; Bellman, R.: Adaptive Control Processes: A Guided Tour, Princeton University Press, 1961.; Chiu, J.-Y. and Petty, G.: Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations, J. Appl. Meteorol. Clim., 45, 1073–1095, 2006.; Evans, K., Turk, J., Wong, J., and Stephens, T.: A Bayesian approach to microwave precipitation profile retrieval, J. Appl. Meteorol., 34, 260–279, 1995.; Haddad, Z., Smith, E., Kummerow, C., Iguchi, T., Farrar, M., Durden, S., Alves, M., and Olson, W.: The TRMM day-1 radar/radiometer combined rain-profiling algorithm, J. Meteorol. Soc. Jpn., 75, 799–809, 1997.; Kummerow, C., Olson, W., and Giglio, L.: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors, IEEE T. Geosci. Remote, 34, 1213–1232, 1996.; Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson, J.: The Tropical Rainfall Measuring Mission (TRMM) sensor package, J. Atmos. Ocean Tech., 15, 809–817, 1998.; Kummerow, C., Hong, Y., Olson, W., Yang, S., Adler, R., McCollum, J., Ferraro, R., Petty, G., Shin, D., and Wilheit, T.: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors, J. Appl. Meteorol., 40, 1801–1820, 2001.; Kummerow, C. D., Ringerud, S., Crook, J., Randel, D., and Berg, W.: An Observationally Generated A Priori Database for Microwave Rainfall Retrievals, J. Atmos. Ocean Technol., 28, 113–130, 2011.; L'Ecuyer, T. and Stephens, G.: An uncertainty model for Bayesian Monte Carlo retrieval algorithms: Application to the TRMM observing system, Q. J. Roy. Meteorol. Soc., 128, 1713–1737, 2002.; Marzano, F., Mugnai, A., Panegrossi, G., Pierdicca, N., Smith, E., and Turk, J.: Bayesian estimation of precipitating cloud parameters from combined measurements of spaceborne microwave radiometer and radar, IEEE T. Geosci. Remote, 37, 596–613, 1999.; Olson, W., Kummerow, C., Heymsfield, G., and Giglio, L.: A method for combined passive-active microwave retrievals of cloud and precipitation profiles, J. Appl. Meteorol., 35, 1763–1789, 1996.; Olson, W., Kummerow, C., Yang, S., Petty, G., Tao, W., Bell, T., Braun, S., Wang, Y., Lang, S., Johnson, D., and Chiu, C.: Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: Improved method and uncertainties, J. Appl. Meteorol. Clim., 45, 702–720, 2006.; Panegrossi, G., Dietrich, S., Marzano, F., Mugnai, A., Smith, E., Xiang, X., Tripoli, G., Wang, P., and Baptista, J.: Use of cloud model microphysics for passive microwave-based precipitation retrieval: Significance of consistency between model and measurement manifolds, J. Atmos. Sci., 55, 1644–1673, 1998.; Petty, G. and Li, K.: Improved passive microwave precipitation retrievals over land and ocean. 1. Algorithm description., J. Atmos. Ocean. Technol., online first, doi:10.1175/JTECH-D-12-00144.1, 2013.; Seo, E.-K., Sohn, B.-J., Liu, G., Ryu, G.-H., and Han, H.-J.: Improvement of microwave

 

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