The role of Optical Water Type classification in the context of GIOP Timothy S. Moore University of New Hampshire, Durham NH Mark D. Dowell Joint Research Centre, Ispra Italy September 25, 2010

Rationale There is necessity to describe a considerable amount of variability in Inherent Optical Property (IOP) subcomponent models. This is particularly true, if inversion algorithms are to be applicable at global scale yet remain quantitatively accurate in coastal & shelf seas. This is unlikely to be achieved in the foreseeable future, with a single representation of IOP subcomponents. – BEAM – Case2R, GIOP The proposed approach is an algorithm framework more than a specific algorithm.

Practical uses of a classification approach based on Optical Water Types (OWT) Describe variance and co-variance of optically active constituents Parameterizing IOP subcomponent models (or fit coefficients for empirical relationships) Selecting different inversions methods for different optical waters Avenue to spatial uncertainty estimates for remotely-sensed products Value-added products (directing new Cal/Val field work, data collection)

Advantages of fuzzy logic defined provinces They allow for spatial and temporal dynamics both seasonal and inter-annual in the optical properties of a given region. They address the issue of transitions at the boundaries of provinces (through the fuzzy membership function of each class) thus resulting finally in the seamless reconstruction of a single geophysical product. Our OWT method uses a fuzzy logic approach for optical classification of in situ and satellite data based on remote sensing reflectance.

In-situ Database (NOMAD) Rrs( ) IOPs Sgd, aph*,……. Station data sorted by class Class based relationships 8 classes Class Mi,  i Satellite Measurements Individual class derived products Merged Product Calculate membership Rrs( ) Conceptual Framework for class-based algorithms Cluster Analysis IOP model parameterization IOP model/algorithm selection

2407 data points (NOMAD v2) 8 clusters ‘optimal’ representations of different optical water types (OWT) mean and covariance matrix form the basis of the fuzzy membership function. Base OWT Definition

OWT 1OWT 2OWT 3OWT 4 OWT 5OWT 6OWT 7OWT 8 Mapping of the OWTs in ocean color data - example

a( ) = a w ( ) + Ac( )[Chl]Bc( ) + [a cdm (440)] exp(-Sdg( -440)) Possible coefficients to parameterize on an OWT-basis in a standard semi-analytic algorithm configuration. b b ( ) = b bw ( ) +[b bp (555)] [555/ ] Y Red - variables Yellow - parameters that need to be set (possible OWT dependency a( ) = a w ( ) + a ph (  Chl) + a d (  TSS) + a cdom (  CDOM) b b ( ) = b bw ( ) + b bp (  Chl,TSS)

Class–based GIOPClass–based QAA S gd, S g, S d a ph *( ) slope of b bp S gd variable based on class a t (443) versus r rs (443)/r rs (555) class based a t (555) versus a t (443) class based a ph (443) versus Chl class based a ph *(443) One could imagine applying a tuning algorithm (e.g. simulated annealing) to each class to determine optimal class based model coefficients.

What follows is a look at the distribution and relationships of optical properties in the context of semi (quasi)-analytic algorithms from an OWT perspective based on the NOMAD v2 and IOCCG simulated data set.

OWTNOMAD* w/ IOPs IOCCG Sim. Global Avg <1 Distribution of OWTs in Data sets vs. Ocean Obsverations (numbers are in percent)

Sg v. ag443 OWTSg Avg Points are color coded by degree of membership to the OWT (based on Rrs).

IOCCG OWT 1 NOMAD ag slope ag 443

ag440 -NOMAD Sg (Bricaud et al, 2009)

a ph * log10 Chl a ph OWT 1 OWT 2 OWT 4OWT 8 OWT 7 OWT 6 OWT 5 OWT 3 OWT µ=7.87 µ=7.39 µ=3.22 µ=3.04 µ=0.148 µ=0.086 µ=0.331 µ=1.01 aph

IOCCG NOMAD log ag443 OWT 1 log aph443 ag443/at443bbp slope

log ag443bbp slopelog aph443 OWT 2 IOCCG NOMAD ag443/at443

OWT 3 log ag443 bbp slope log aph443 IOCCG ag443/at443 NOMAD

OWT 4 log ag443 bbp slope log aph443 IOCCG ag443/at443 NOMAD

OWT 5 log ag443 bbp slope log aph443 IOCCG ag443/at443 NOMAD

OWT 6 log ag443 bbp slope log aph443 IOCCG ag443/at443 NOMAD

OWT 7 log ag443 bbp slope log aph443 IOCCG ag443/at443 NOMAD

For what its worth…

Sgbbp yaph 443ag 443 OWTNINININI ~ Avg Averages

Chl a ph * Bricaud aph* function Miscellaneous bio-optical empirical functions OWT rrs443/rrs555 Y (QAA) OWT LAS Kd function K d 443 rrs443/rrs555 rho QAA a555

“Blue Hole” Frequency of ‘low membership’ areas 100 %

Summary There are some inconsistencies in the OWT-based distributions of IOPs between NOMAD and the IOCCG simulated data set. Both data sets are skewed towards coastal/case 2 waters. If a new simulated data set is being considered, the generation of IOPs and IOP pairs could be further constrained by the variance and co-variance as seen in NOMAD within different OWTs. In addition, the representation of data points could be guided by the global distribution of naturally occurring OWTs.

Summary (continued) OWT code is currently in Seadas, but has yet to receive the final green light for public usage (we see no problem here). Preliminary OWT-based IOP parameters now exist and can be used in the GIOP framework. Potential for further use in parameterizing empirical models within GIOP is being explored. OWTs themselves may change over time, which could effect some of the OWT-based parameters (don’t think this to be major). Sensitivity and performance analysis remains to be assessed for GIOP- related products.

log ag443 OWT 1 OWT 2 OWT 3 OWT 4 OWT 5 OWT 6 OWT 7 OWT 8 NOMAD ag443 OWT distributions ag411 OWT

ag440 -NOMAD Sg (Bricaud et al, 2009)

Sd v. ad443 OWTSd Avg.0.011

Sdg v. adg443 OWTSdg Avg.0.014

OWT 5 a ph *a ph There are some issues with data quality that might be revealed. Effects of aph to aph* conversion

OWT 1 OWT 4 OWT 3 OWT 2 OWT 7 OWT 6 OWT 5 b bp OWTY Avg1.03 b bp slope estimation Y

Y OWTY Avg.*1.03 b bp slope estimation * Negative values excluded OWT 1 OWT 4 OWT 3 OWT 2 OWT 5 OWT 6 OWT 7 All