Log errors are defined :

1. Werdell, P.J. and S.W. Bailey, 2005: An improved bio-optical data set for ocean color algorithm development and satellite data product validation. Remote Sensing of Environment , 98(1), 122-140. Siegel, D., S. Maritorena, N. Nelson, M.Behrenfeld, and C. McClain, 2005: Colored dissolved organic matter and its influence on the satellite-based characterization of the cean biosphere. Geophysical Research Letters, 32(20), L20605, 10.1029/2005GL024310. Chlorophyll, mg m-3 The Chlorophyll Algorithm Revisited: Results of the OCBAM Workshop J. Campbell1, D. Aurin, S. Bailey, P. Bontempi, M. Dowell, R. Frouin, H. Feng, P. Lyon, C. McClain, S. Maritorena, T. Moore, R. Morrison, J. O’Reilly, H. Sosik, C. Trees, J. Werdell Abstract (OS24R-15) Adjusting for Differences in the Distribution of NOMAD Data vs. Global Climatology The Ocean Color Bio-optical Algorithm Mini Workshop (OCBAM) The Ocean Color Bio-optical Algorithm Mini Workshop (OCBAM) was held at the University of New Hampshire on September 27-29, 2005. The purpose of the workshop was to evaluate chlorophyll algorithms in light of a newly published bio-optical database (NOMAD) and to consider whether improved accuracy can be achieved by accounting for other optically active constituents. It was concluded that any significant improvement to the chlorophyll algorithm can only be achieved by accounting for the effects of other constituents. The time has come to begin pushing the envelope toward algorithms that yield information about dissolved and particulate materials as well as the chlorophyll concentration. Such information might be derived inherent optical properties rather than the materials themselves. Model-based algorithms that relate the apparent optical properties of radiance and reflectance to the inherent optical properties are likely to be the solution. In this poster, we describe a process whereby new model-based algorithms will be evaluated and potentially selected to replace the current empirical algorithms used by NASA for SeaWiFS and MODIS data processing. Goals Error statistics were calculated within bins of log C (right). The mean and mean square errors were weighted by the frequency in the global SeaWiFS climatology (red). Evaluate ocean color algorithms that produce chlorophyll retrievals. Algorithms tested may also retrieve other constituents and / or related inherent optical properties. Compare new algorithms to the operational empirical algorithms used for SeaWiFS (OC4.v4) and MODIS (OC3M). Can accuracy be improved by accounting for other optically active constituents? Quantifying the Uncertainty in a Chlorophyll Algorithm Motivation Log errors are defined: where is the algorithm CHL and C is the in situ CHL.These are the errors minimized by the 4th-order polynomial algorithms (right). • NOMAD. We have a new data set to use in evaluating algorithms. Global climatological CHL distribution di For the two operational algorithms, log errors are approximately normally distributed: • A recent paper (Siegel et al. 2005) argues for the importance of accounting for the effects of colored dissolved organic matter. The performance measures derived from NOMAD or any database are influenced by the distribution of the stations in the database. Often there’s an over abundance of high chlorophyll stations. The figure below compares the distribution of chlorophyll in the SeaWiFS climatology (1997-2005) (red) with that in NOMAD (blue). The GSM01 algorithm (Maritorena et al. 2000) was applied to global SeaWiFS data, and the derived chlorophyll distributions were compared with maps derived by the standard algorithm (OC4). Differences are quite significant – and related to the Colored Dissolved Organic Matter (CDOM) absorption as derived by the GSM01 algorithm. Log error (d) Relative errors are defined: Conclusions • The log error = is the basic measure of uncertainty. Relative error statistics can be derived assuming normal log errors. • Uncertainty in OC4 and OC3M algorithms has been quantified in log CHL bins for single retrievals. This enables the creation of uncertainty maps for level-2 and level-3 products. • The operational chlorophyll algorithms used by SeaWiFS (OC4) and MODIS (OC3M) are not “accurate to within 35%.” Globally, the uncertainty is ~ ± 50% about the median. • OCBAM participants concluded that little improvement can be achieved without accounting for other optically active constituents. • The Ocean Biology Data Processing Group at NASA Goddard is prepared to run algorithm codes and produce metrics for anyone wishing to have an algorithm considered for the next generation of ocean color algorithms. Attendees The red curves are the global chlorophyll distribution based on the SeaWiFS climatology (1997-2005). This relationship between the log error and relative error holds only for individual points. Statistics of relative errors can be derived assuming normal distribution of log errors. Dirk Aurin University of Connecticut Sean Bailey NASA Goddard Space Flight Center Shane Bradt University of New Hampshire Paula Bontempi NASA Headquarters Janet Campbell Ocean Process Analysis Lab, UNH Mark Dowell Joint Research Center, Ispra, Italy Robert Frouin University of California - San Diego Carol Fairfield NMFS Visiting Scientist, UNH Hui Feng Ocean Process Analysis Lab, UNH Paul Lyons ORBIMAGE, Inc. Haymarket, VA Chuck McClain NASA Goddard Space Flight Center Stephane Maritorena University of California - Santa Barbara Timothy Moore Ocean Process Analysis Lab, UNH Ru Morrison Ocean Process Analysis Lab, UNH Jay O'Reilly NMFS Narragansett Laboratory Heidi Sosik Woods Hole Oceanographic Institution Chuck Trees San Diego State University Jeremy Werdell NASA Goddard Space Flight Center Log errors (di) for OC4.v4 1Ocean Process Analysis Laboratory, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824. Email: janet.campbell@unh.edu. This work was supported by a NASA MODIS Science Team award to J. Campbell (NNG04HZ37C).