IOCCG WG on Atmospheric correction over turbid waters C. Jamet IOCCG meeting Santa Monica, USA March, 1, 2015

Summary Goal: Inter-comparison and evaluation of existing AC algorithms over turbid waters Challenge: to understand the advantages and limitations of each algorithm and their performance under certain atmospheric and oceanic conditions Only focus on AC algorithms that deal with a non-zero NIR water-leaving radiances.  Guidances on the use of AC over turbid waters  Recommendations for improving and selecting the optimal AC

Scope of the WG This WG: only on nLw(NIR) ≠ 0 Other issues not adressed One dedicated chapter –Adjacency effects –Other issues

Actual members Participants: –Sean Bailey, NASA, USA –Julien Brajard, LOCEAN, France –Xiangqiang He, SIO, China –Cédric Jamet (Chairman), LOG ULCO/CNRS, France –Els Knaeps, VITO, Belgium –Kevin Ruddick, MUMM, Belgium –Palanasamy Shanmugam, ITT, India –Thomas Schroeder, CSIRO, Australia –Knut Stamnes, Stevens Institute of Technology, USA –Menghua Wang, NOAA, USA

Which sensors SeaWiFS MODIS-AQUA for starting MERIS VIIRS GOCI ????

Which sensors MODIS-AQUA MERIS (NO as ESA founded a similar project on extreme case-2 waters)  Member of the Science Support Team

many approaches exist, here are a few examples: assign aerosols (  ) and/or water contributions (Rrs(NIR)) e.g., Hu et al. 2000, Ruddick et al use shortwave infrared bands e.g., Wang & Shi 2007 correct/model the non-negligible R rs (NIR) Lavender et al. 2005MERIS Bailey et al. 2010used in SeaWiFS Reprocessing 2010 Shanmugam, 2012 any sensor Wang et al. 2012GOCI use a coupled ocean-atmosphere optimization e.g., Moore et al., 1999; Chomko & Gordon 2001, Stamnes et al. 2003, Jamet et al., 2005, Brajard et al., 2006a, b, 2008, 2012; Ahn and Shanmugam, 2007; Kuchinke et al. 2009; Steinmetz et al., 2010; Other e.g., Chen et al., 2014; Doerrfer et al., 2007; He et al., 2013; Mao et al., 2013, 2014; Schroeder et al. 2007; Shanmugam and Tholkapiyan, 2014; Singh and Shanmugam, 2014 Which algorithms?

many approaches exist, here are a few examples: assign aerosols (  ) and/or water contributions (Rrs(NIR)) e.g., Ruddick et al use shortwave infrared bands e.g., Wang & Shi 2007 correct/model the non-negligible R rs (NIR) Bailey et al. 2010used in SeaWiFS Reprocessing 2010 Shanmugam, 2012 any sensor Wang et al GOCI/MODIS Jiang et al., 2014 use a coupled ocean-atmosphere optimization e.g., Moore et al., 1999; Stamnes et al. 2003; Brajard et al., 2012; Steimetz et al., 2012; Other e.g., Chen et al., 2014; He et al., 2013; Q. He et al., 2014; Schroeder et al., 2007; Singh and Shanmugam, 2014 Which algorithms?

 Classic match-up analysis  Simulated dataset for sensitivities studies  Inspection of satellite images over contrasted coastal regions (Rrs and aerosol optical properties) How to evaluate for providing guidances and recommendations?

 Send datasets to developers and send their results back?  YES  Every algorithm (when possible) implemented in SeaDAS so the entire processing chain is similar (differences in results only from AC)  Is it possible/reasonable?  Not easy and not always possible (might be implemented in a local version of SeaDAS) In practice

Work in teleconf (+numerous exchanges) Focused on the generation of the simulated dataset (and the in-situ datasets)

Schedule May 2015: Generation of the synthetic datasets  Dec. 2015

Schedule May 2015: Generation of the synthetic datasets  Dec Coupled ocean-atmosphere RTE: AccuRT (Jin and Stamnes, 1994; Thomas and Stamnes, 1999) –Aerosol models: NASA (Ahmad et al., 2010) –Bio-optical model: IOPs from COASTCOLOUR round robin –SeaWiFS, MODIS, MERIS, VIIRS completed –OLCI/SLSTR in progress

Schedule May 2015: Generation of the synthetic datasets  Dec cases: –5,000 cases for the ocean –For each ocean case, 4 aerosol models randomly selected among 80 possible –Geometry angles randomly selected: SZA: [0-70°] VZA: [0-70°] RAA: [0-180°]

Schedule May 2015: Generation of the synthetic datasets  Dec June-July 2015: Distribution of the synthetic datasets to the AC providers  January 2016

Schedule May 2015: Generation of the synthetic datasets  Dec June-July 2015: Distribution of the synthetic datasets to the AC providers  January 2016 Sept.-Oct. 2015: Compilation of the in-situ datasets owned by participants of the WG  In progress

In-situ datasets: AERONET-OC: moderately turbid waters LOG/MUMM TriOS: moderately to very turbid waters ASD measurements for VITO NOMAD, SeaBASS from NASA DATA from CSIRO DATA from X. He DATA from P. Shanmugam DATA from K. Ruddick

Schedule May 2015: Generation of the synthetic datasets  Dec June-July 2015: Distribution of the synthetic datasets to the AC providers  January 2016 Sept.-Oct. 2015: Compilation of the in-situ datasets owned by participants of the WG  In progress Nov.-Dec 2015: Distribution of the in-situ datasets to the AC providers  Spring 2016

Schedule Jan.-June 2016: Analysis of the results on the synthetical and in-situ datasets  In progress for the simulated dataset

Statistical Parameters for the green algorithm Vs simulated data

Statistical Parameters for the purple algorithm Vs simulated data

-- Purple algorithm -- Green algorithm -- Purple algorithm -- Green algorithm

Schedule Jan.-June 2016: Analysis of the results on the synthetical and in-situ datasets March 2016: Distribution of the satellite images to the AC providers  Compilation of the satellite dataset in progress French Guiana, Eastern English Channel/North Sea, Vietnam, Yangtze coasts, Amazon river, Arabian Sea

Schedule Jan.-June 2016: Analysis of the results on the synthetical and in-situ datasets March 2016: Distribution of the satellite images to the AC providers  Compilation of the satellite dataset in progress French Guiana, Eastern English Channel/North Sea, Vietnam, Yangtze coasts, Amazon river, Arabian Sea

Schedule Jan.-June 2016: Analysis of the results on the synthetical and in-situ datasets March 2016: Distribution of the satellite images to the AC providers June-July 2016: Analysis of the satellite images Fall 2016: Redaction of the report  Winter 2016

ANNEX

Evaluation of AC Can round robin lead to improvements? –What can we learn? Range of validity and advantages Limitations (water type?) –Sensitivity studies Fixed aerosols  Variation/change of the bio-optical model Fixed bio-optical model  Variation/change of the aerosol models –Uncertainties propagation and budget on the hypothesis Ruddick et al. (2000) Bayseian statistics for NN (Aires et al., 2004a, 2004b, 2004c) –Uncertainties on the NN parameters (weights) –Uncertainties on the outputs Others ?

Evaluation of AC Can round robin lead to improvements? –What can we learn? Drawbacks and advantages Limitations –Sensitivity studies Fixed aerosols  Variation/change of the bio-optical model Fixed bio-optical model  Variation/change of the aerosol models –Uncertainties propagation and budget on the hypothesis Ruddick et al. (2000) Neukermans et al. (2012) Bayseian statistics for NN (Aires et al., 2004a, 2004b, 2004c) –Uncertainties on the NN parameters (weights) –Uncertainties on the outputs