NASA Technical Memorandum 104566, Vol. 26

SeaWiFS Technical Report Series

Stanford B. Hooker and Elaine R. Firestone, Editors

Volume 26, Results of the SeaWiFS Data

Analysis Round-Robin, July 1994 (DARR-94)

David A. Siegel, Margaret C. O’Brien, Jens C. Sorensen, Daniel A. Konnoff,

Eric A. Brody, James L. Mueller, Curtiss O. Davis, W. Joseph Rhea, and

Stanford B. Hooker

May 1995
NASA Technical Memorandum 104566, Vol. 26

SeaWiFS Technical Report Series

Stanford B. Hooker, Editor

NASA Goddard Space Flight Center, Greenbelt, Maryland

Elaine R. Firestone, Technical Editor

General Sciences Corporation, Laurel, Maryland

Volume 26, Results of the SeaWiFS Data

Analysis Round-Robin, July 1994 (DARR-94)

David A. Siegel, Margaret C. O’Brien, Jens C. Sorensen, Daniel A. Konnoff, and Eric A. Brody

University of California, Santa Barbara, Santa Barbara, California

James L. Mueller

San Diego State University, San Diego, California

Curtiss O. Davis and W. Joseph Rhea

Naval Research Laboratory, Washington, DC

Stanford B. Hooker

NASA Goddard Space Flight Center, Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

1995
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Preface

One of the primary objectives of the SeaWiFS Project, as stated in Volume 1 of this technical memorandum

series (Hooker et al. 1992), is “to achieve radiometric accuracy of 5% absolute and 1% relative, waterleaving radiances to within 5% absolute, and chlorophyll a concentration to within 35% over the range of 0.05–

50.0 mg m−3.” This objective presents a challenge to the ocean color community, and the SeaWiFS Calibration

and Validation Team (CVT) has initiated a number of activities directed towards attaining these accuracy goals

which include

1) The SeaWiFS Intercalibration Round-Robin Experiments (SIRREXs),

2) The development of in situ measurement protocols (Mueller and Austin 1992 and 1995),

3) The construction of the SeaWiFS transfer radiometer (SXR),

4) Support for the Marine Optical Buoy (MOBY), and

5) Support for the collection of high quality bio-optical data sets.

These activities address the issues of instrument calibration, data collection techniques, and bio-optical data set

diversity.

An additional aspect to the problem is the methodology by which vertical profiling radiometric observations are

extrapolated to the surface so field observations can be compared with the satellite derived products or used

as inputs for satellite bio-optical algorithms. The first SeaWiFS Data Analysis Round-Robin (DARR-94) was

convened for the purpose of comparing the various techniques being used by the ocean color community, to

clarify the sources of error, and work toward establishing standard methods for deriving the relevant products,

e.g., water-leaving radiance and diffuse attenuation. This technical memorandum summarizes the results of

DARR-94 and establishes that careful data analysis is necessary and that errors in the analysis can lead to very

large errors in the derived fields. As a result of DARR-94, it is clear that continued refinement of radiometric

data analysis techniques is necessary. Therefore, as with the instrument round-robins, the CVT will promote

additional analysis round-robins in a continuing effort to attain the SeaWiFS mission objectives.

Greenbelt, Maryland — C. R. McClain

February 1995

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Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Table of Contents

Prologue ................................................................................................. 1

1. The First SeaWiFS Ocean Optics DARR ............................................................. 4

1.1 Introduction ......................................................................................... 4

1.2 Data Analysis Methods .............................................................................. 6

1.3 Data and Results .................................................................................... 7

1.4 Discussion .......................................................................................... 35

1.5 Conclusions ........................................................................................ 36

2. The BBOP Data Processing System ..................................................................37

2.1 Introduction ........................................................................................ 37

2.2 Philosophy ......................................................................................... 38

2.3 LCD File Format .................................................................................... 38

2.4 BBOP Filters ........................................................................................39

2.5 Data Processing Steps ............................................................................... 41

2.6 Conclusions ..........................................................................................43

3. Integral Method for Analyzing Irradiance and Radiance Attenuation Profiles ..........................44

3.1 Introduction ........................................................................................ 44

3.2 Deck Cell Smoothing ............................................................................... 45

3.3 Bin-Averaged Profiles ................................................................................46

3.4 Optical Depth Profile Fit ............................................................................ 46

4. Automated and Interactive Bio-Optical Processing Package ...........................................49

4.1 Introduction ........................................................................................ 49

4.2 Instrumentation .................................................................................... 49

4.3 Level-1 Processing ...................................................................................50

4.4 Level-2 Processing ...................................................................................50

4.5 Calculation of K .....................................................................................52

4.6 Data File Formats ................................................................................... 52

4.7 Log Files ............................................................................................ 52

5. The SIO Method .................................................................................... 53

5.1 Introduction ........................................................................................ 53

5.2 Data Processing .................................................................................... 53

Appendix ...............................................................................................54

Glossary ...............................................................................................54

Symbols ................................................................................................ 55

References ............................................................................................ 55

The SeaWiFS Technical Report Series ............................................................ 57

iii
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Abstract

The accurate determination of upper ocean apparent optical properties (AOPs) is essential for the vicarious

calibration of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument and the validation of the derived

data products. To evaluate the role that data analysis methods have upon values of derived AOPs, the first

Data Analysis Round-Robin (DARR-94) workshop was sponsored by the SeaWiFS Project during 21–23 July,

1994. The focus of this intercomparison study was the estimation of the downwelling irradiance spectrum just

beneath the sea surface, Ed(0−, λ); the upwelling nadir radiance just beneath the sea surface, Lu(0−, λ); and the

vertical profile of the diffuse attenuation coefficient spectrum, Kd(z, λ). In the results reported here, different

methodologies from four research groups were applied to an identical set of 10 spectroradiometry casts in order

to evaluate the degree to which data analysis methods influence AOP estimation, and whether any general

improvements can be made. The overall results of DARR-94 are presented in Chapter 1 and the individual

methods of the four groups are presented in Chapters 2–5. The DARR-94 results do not show a clear winner

among data analysis methods evaluated. It is apparent, however, that some degree of outlier rejection is required

in order to accurately estimate Lu(0−, λ) or Ed(0−, λ). Furthermore, the calculation, evaluation and exploitation

of confidence intervals for the AOP determinations needs to be explored. That is, the SeaWiFS calibration and

validation problem should be recast in statistical terms where the in situ AOP values are statistical estimates

with known confidence intervals.

PROLOGUE

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS)

Project is tasked with executing a program to acquire the

global SeaWiFS data set, validate and monitor its accuracy

and quality, process the radiometric data into geophysical

units using a set of atmospheric and bio-optical algorithms,

and distribute the final products to the scientific community through the Goddard Space Flight Center (GSFC)

Distributed Active Archive Center (DAAC). The SeaWiFS

data products are prominent components of major scientific programs studying global climate change, including

the Joint Global Ocean Flux Study (JGOFS), the World

Ocean Circulation Experiment (WOCE), and the Global

Ocean Ecosystems dynamics (GLOBEC) programs.

The accurate determination of upper ocean apparent

optical properties (AOPs) is essential for the vicarious calibration of the SeaWiFS instrument and the validation of

the derived data products. The only economically feasible approach for minimizing spatial biases is to maximize

the acquisition of global in situ measurements by soliciting contributions of data from the oceanographic community at large, and to combine them with data collected

from SeaWiFS sponsored activities into a single database.

The SeaWiFS Calibration and Validation Team (CVT)

have responded to this need by implementing the SeaWiFS

Bio-Optical Archive and Storage System (SeaBASS). Data

from a variety of sources are expected to go into this database including

The Bermuda Bio-Optics Project (BBOP),

The Bermuda Atlantic Time-series Study (BATS),

The California Cooperative Fisheries Institute (CalCoFI),

The JGOFS Equatorial Pacific (EqPac) process

study,

The Gulf of Mexico Experiment (GOMEX), and

The Tropical Ocean Global Atmosphere (TOGA)

Coupled Ocean-Atmosphere Response Experiment

(COARE).

The accuracy of any AOP determination is a function

of the quality of the measurement, and the differences in

the data analysis method employed. In order to minimize

observational errors, the SeaWiFS Project has sponsored a

variety of multidisciplinary workshops to outline the observations and sampling protocols required for bio-optical algorithm development (Mueller and Austin 1992 and 1995).

One of the consequences of the workshops was the establishment of a series of SeaWiFS Intercalibration RoundRobin Experiment (SIRREX) activities to establish and

advance the state of the art for calibrating the instruments

used in field activities, e.g., the Marine Environmental Radiometers (MERs).

Although the SeaBASS architecture allows for some

quality control (Hooker et al. 1994), it is based primarily on resolving obvious clerical errors in the reporting of

where and when data acquisiton activities took place—it

does not attempt to quantify differences in the data analysis methods employed. The latter is, in part, a function

of how the individual software packages deal with data despiking, binning and smoothing, and removing possible artifacts from changes in surface illumination, ship shadow,

or reflections and wave focusing.

The focus of this intercomparison study is the estimation of the downwelling irradiance spectrum just beneath

the sea surface, Ed(0−, λ), the upwelling nadir radiance

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Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

just beneath the sea surface, Lu(0−, λ), and the vertical profile of the diffuse attenuation coefficient spectrum,

Kd(z, λ). In the results reported here, different methodologies from four research groups are applied to the aforementioned spectroradiometry profiles in order to evaluate

the degree to which data analysis methods influence AOP

estimation, and whether any general improvements can be

made. The four groups involved were as follows:

1. The University of California at Santa Barbara

(UCSB) Institute for Computational Earth System Science (ICESS),

2. San Diego State University (SDSU) Center for

Hydro-Optics and Remote Sensing (CHORS),

3. The Naval Research Laboratory (NRL), and

4. Scripps Institution of Oceanography (SIO).

The overall results of the first Data Analysis Round-Robin

(DARRone) are presented in Chapter 1 and the individual

methods of the four groups are presented in Chapters 2–5,

respectively. The attendees to the workshop are given in

Appendix A. A summary of the material presented in each

Chapter is given below.

1. The First SeaWiFS Ocean Optics DARR

This study shows that with good data (near-constant

incident irradiance), the different analyses produce estimates of Lu(0−, λ) and Ed(0−, λ) that are within 3–4%

of the aggregate mean value. The statistical uncertainties

in determining Ed(0−, λ) are considerably larger (approximately 7%) than those for Lu(0−, λ) (about 2%). These

differences can be attributed to geophysical noise sources in

the raw data streams which appear to be primarily due to

the aliasing of surface wave induced glinting in the vertical

radiometric profiles. Furthermore, the profile of the diffuse

attenuation coefficient spectrum, Kd(z, λ), can be replicated to better than 5% with the different analyses. These

differences account for much of the total 5% uncertainty

that is tolerated for SeaWiFS calibration and validation

purposes (Mueller and Austin 1995). For bad data, large

deviations among methods occur. The DARR-94 results

do not show a clear winner among data analysis methods evaluated. It is apparent, however, that some degree

of outlier rejection is required in order to accurately estimate Lu(0−, λ) or Ed(0−, λ). Possible solutions include

manual or automated data disqualification, robust curve

fitting routines, or extrapolation using the incident flux

(not fully evaluated in this study). Furthermore, the calculation, evaluation and exploitation of confidence intervals

for the AOP determinations needs to be explored; that is,

the SeaWiFS calibration and validation problem should be

recast in statistical terms where the in situ AOP values are

statistical estimates with known confidence intervals. This

is critical for the long-term assessment of ocean color imagery and its calibration and validation using in situ data

sets. Finally, there is no substitute for good in situ data

for accurately determining AOPs. Good at-sea procedures

and clear skies are essential and cannot be over emphasized.

2. The BBOP Data Processing System

The BBOP group has developed a data analysis method

based on the philosophy that they will take more casts then

they need. For example, the BBOP collects over 1,000 profiles each year in order to link time-series observations of

primary production rates to bio-optical parameters. This

means suspect data will be flagged and eliminated from

future consideration rather than corrected. A computer

data processing system capable of efficiently calibrating,

processing, reducing, analyzing, and interpreting the data

in a timely manner is presented. The processing system is

comprised of a suite of American National Standards Institute (ANSI) C++ programs that read and operate on a

specified file format, the least common denominator (LCD)

data file. The LCD file contains all relevant data and

metadata, which include calibration information and atsea comments, in a single easy-to-read file that conforms

to American Standard Code for Information Interchange

(ASCII) standards. UNIX shell scripts are used in the

control of data flow as well as error and log handling. The

final product is a binned spectroradiometer data set with

relevant derived parameters included [Kd(z,λ), Rrs(z,λ),

Ed(0−, λ), etc.] that may be disseminated to other groups

or databases.

3. Integral Method for Analyzing Irradiance and

Radiance Attenuation Profiles

The CHORS group has taken a very different approach

and has developed an integral method for determining the

slope of the log-transformed irradiance profile, Kd(z, λ),

and its intercepts, Lu(0−, λ) and Ed(0−, λ). Their analysis goals are driven by the fact that they must make good

estimates of AOPs from every cast in order to meet their

own as well as their colleagues’scientific objectives. The

CHORS method determines the profile of K for a vertical

profile of irradiance or radiance through a least-squares fit

to the optical depth profile, expressed as the integral of K

from the surface to each depth z. The measured optical

depth at each z is calculated as the natural logarithm of

the surface-to-depth ratio of measured irradiances (or radiances). The K profile is represented analytically by Hermitian cubic polynomials connecting nodes at several discrete

depths, with unknown values of K and its vertical derivative at each node as coefficients. These polynomials are

integrated analytically to each z, which allows each measured optical depth to be set equal to a polynomial with

node values of K and its derivative at the node depths.

This results in an overdetermined set of equations corresponding to all measured depths in the irradiance (or radiance) profile, which is solved using classical least-squares

methods. Prior to solution, irradiance data are normalized to minimize effects of surface irradiance variations,

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Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

and segments near major anomalies (resulting from strong

cloud shadows or ship shadows) are eliminated from the fit.

In contrast to the classical derivative solutions for K, the

integral approach ensures a correct representation of total

attenuation through missing data intervals. The CHORS

method utilizes human-selected data rejection and node

setting, and is developed with a sophisticated graphical

user interface (GUI). It should be noted that the determination of the Lu(0−, λ) and Ed(0−, λ) values strongly affect

the fit to the entire profile.

4. Automated and Interactive Bio-Optical

Processing Package

The NRL method has been developed with the same

analysis goal as the CHORS method. Namely, they need

to make accurate determinations of AOPs from every cast

during a cruise to achieve their science goals. NRL has

developed a multiple level, prediction-correction software

package called Automated and Interactive Bio-Optical Processing (AIBOP). The data is processed in two parts: first

the is despiked and binned using an automated routine,

and second, the effects of changes in surface illumination,

ship shadow, or reflection and near surface effects (such

as wave focusing) are removed using interactive spectral

processing. The second level method is not used for the

DARR-94 comparisons as many of the data sets used were

missing required data types. Once the data is processed,

routines are provided for calculating extinction coefficients,

surface radiance and irradiance values, and remote sensing

reflectance.

5. The SIO Method

For DARR-94, the SIO group employed a bulk method

for deriving AOPs from spectroradiometry profiles. The

bulk approach has been long employed in the past and

much of what is known about AOPs has come from using this approach. The bulk method has been used in

the past by the SIO group, although, they are not doing

so presently. The bulk approach fits a straight line to the

log-transformed irradiance and radiance profiles within the

oceanic mixed layer. The fluxes just below the sea surface

are estimated from the exponentiated intercept and the

slope gives a mixed layer averaged diffuse attenuation coefficient, Kd(z0, λ). This method provides only a single

estimate of Kd(z0, λ) and cannot be used to determine the

depth dependence of Kd(z, λ). One distinct advantage of

the bulk method is that it can easily be applied using any

spreadsheet program on a personal computer (PC).

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Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Chapter 1

The First SeaWiFS Ocean Optics DARR

David A. Siegel,Margaret C. O’Brien,and Jens C. Sorensen

University of California, Santa Barbara, Santa Barbara, California

Abstract

The focus of this intercomparison study is the estimation of the downwelling irradiance spectrum just beneath

the sea surface, Ed(0−, λ), the upwelling nadir radiance just beneath the sea surface, Lu(0−, λ), and the vertical

profile of the diffuse attenuation coefficient spectrum, Kd(z, λ). This study shows that with good data (nearconstant incident irradiance), the different analyses produce estimates of Lu(0−, λ) and Ed(0−, λ) that are within

3–4% of the aggregate mean value. The statistical uncertainties in determinations of Ed(0−, λ) are considerably

larger (approximately 7%) than those for Lu(0−, λ) (about 2%), as found using the BBOP analyzed results.

These differences can be attributed to geophysical noise sources in the raw data streams which appear to be

primarily due to the aliasing of surface wave induced glinting in the vertical radiometric profiles. Furthermore,

the profile of the diffuse attenuation coefficient spectrum, Kd(z, λ), can be replicated to better than 5% with

the different analyses. These differences account for much of the total 5% uncertainty that is tolerated for

SeaWiFS calibration and validation purposes (Mueller and Austin 1995). For bad data, large deviations among

methods occur. The DARR-94 results do not show a clear winner among data analysis methods evaluated. It is

apparent, however, that some degree of outlier rejection is required in order to accurately estimate Lu(0−, λ) or

Ed(0−, λ). Possible solutions include manual or automated data disqualification, robust curve fitting routines, or

extrapolation using the incident flux (not fully evaluated in this study). Furthermore, the calculation, evaluation

and exploitation of confidence intervals for the AOP determinations needs to be explored. That is, the SeaWiFS

calibration and validation problem should be recast in statistical terms where the in situ AOP values are

statistical estimates with known confidence intervals. This is critical for the long-term assessment of ocean color

imagery and its calibration and validation using in situ data sets. Last, there is no substitute for good in situ

data for accurately determining AOPs. Good at-sea procedures and clear skies are essential and cannot be over

emphasized.

1.1 INTRODUCTION

Accurate determinations of upper ocean AOPs are essential for the vicarious calibration and validation of the

SeaWiFS instrument and its derived data products (McClain et al. 1992 and Mueller and Austin 1995). To date,

much of the effort in the SeaWiFS CVT has been spent

on the radiometric calibration of at-sea optical instrumentation and on the development of protocols for deploying

them (e.g., Mueller et al. 1994 and Mueller and Austin

1995). These efforts have produced significant and important improvements in the SeaWiFS research community’s

ability to provide accurate AOP estimates for SeaWiFS

calibration and validation, as well as algorithm development requirements. The correct deployment of accurate

spectroradiometric instrumentation, however, is only the

first step in making accurate determinations of AOPs.

In order to calculate AOPs using a vertically profiling spectroradiometer, a number of critical assumptions

must be made concerning how the data is handled. For

example, to determine the water-leaving radiance from a

near-surface profile of upwelled spectral radiance, Lu(z, λ),

one must numerically extrapolate the profile from depth

to just beneath the sea surface (where z=0−). Difficulties arise due to superposition of the time course signals

from surface gravity waves and random variations in package orientation, as well as ship shadows, upon the time

mean upwelled radiance profile. These factors can lead

to noise in spectroradiometry data. Hence, least-squares

estimation methods are generally used to objectively extrapolate Lu(z, λ) determinations to the sea surface to estimate Lu(0−, λ), the upwelling radiance spectrum just beneath the sea surface. This procedure has been applied by

many investigators (e.g., Smith and Baker 1984, Smith and

Baker 1986, Siegel and Dickey 1987, Sorensen et al. 1994,

and Siegel et al. 1995a) and is recommended by the SeaWiFS Ocean Optics Protocols (Mueller and Austin 1995).

However, a variety of procedural questions remain which

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Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

may impact the derived value of Lu(0−, λ):

1. Are Lu(z, λ) data vertically smoothed before extrapolation to the sea surface?

2. Is the data binned vertically before calculating

Lu(0−, λ)?

3. How are ship shadows excluded?

4. How is a surface sensor used if one is available?

5. What effect(s) do these decisions have on an estimate of Lu(0−, λ)?

6. How about other important AOPs?

To evaluate the role that data analysis methods have

upon values of derived AOPs, the DARR-94 workshop was

sponsored by the SeaWiFS Project Office during July 21–

23, 1994 at ICESS (UCSB). Four research groups used

their standard spectroradiometer data analysis routines

(Table 1) on an identical data set of 10 spectroradiometry casts. The goal of DARR-94 was to assess the degree

to which data analysis methods affect AOP estimation and

to make general recommendations where applicable. This

assessment is critical as fully processed spectroradiometry

data will be used to calibrate and validate the SeaWiFS

satellite data stream. Hence, the direct comparisons of

the results of the four data analysis methods will put some

bounds on the degree of certainty for AOP estimates and

their effects upon the calibration and validation of the upcoming SeaWiFS ocean color mission.

Table 1. A listing of data contributors for DARR-94

along with their methods and primary study sites.

Contact Method Primary Site

D. Siegel Differential† JGOFS BATS

TOGA COARE

J. Mueller Integral Gulf of Mexico

Gulf of California

C. Davis Differential‡ JGOFS EqPac

Arabian Sea

G. Mitchell Bulk CalCoFI

† With confidence intervals and quality flags.

‡ With sea surface and surface irradiance adjustments.

This study evaluates and documents the magnitude of

uncertainty in derived estimates of ocean color relevant

AOPs due to differences in data analysis methods. In

particular, estimates of the spectrum of upwelling radiance and downwelling irradiance just beneath the sea surface, Lu(0−, λ) and Ed(0−, λ), respectively, and the vertical profile of the diffuse attenuation coefficient spectrum,

Kd(z, λ), are considered in detail. The approach taken for

the DARR-94 is rather simple. Each of the four research

groups (Table 1) provided up to three spectroradiometry casts (discussed below) and each group analyzes the

data to relevant AOPs. The resulting values of Lu(0−, λ)

and Ed(0−, λ) and vertical profiles of Kd(z, λ) are compared and relevant conclusions presented. All raw data

and analyzed products are available using the anonymous

file transfer protocol (ftp):

ftp.icess.ucsb.edu

cd pub/bbop/DARR-94/data

or from the SeaWiFS Project Office data system (Hooker

et al. 1994).

Since the scope of this first data analysis intercomparison study (DARR-94) has been purposely limited, the

present results will obviously have several inherent limitations:

1. The methods are compared for a limited amount of

data (9 casts) over a small range of environmental

conditions. These data are for the mostly Case1 waters (Table 2) and will not cover all possible

observational scenarios.

2. It is not known which method (if any) is providing

the right answer as these are field data sets. Clearly,

if all four methods coincide, the differences amongst

the methods applied can be assumed unimportant;

however if their results diverge, it remains unclear

which method is right and which is wrong.

3. A variety of correction procedures, which may be

important for accurately determining AOPs, were

not evaluated. These include corrections for Raman emissions, instrument self-shading, gimballing

(or not) of surface irradiance sensors, tilt corrections for underwater spectroradiometer signals, and

propagating the Lu(0−, λ) and Ed(0−, λ) through

the air-sea interface. All of these corrections may

be important although they are not assessed here

in an effort to simplify the goals of the DARR. It is

likely that these corrections will be examined, along

with a more extensive data set, in future DARR

workshops. The use of synthetic data sets may also

prove to be useful.

It should be stressed that the goal of the DARR-94

workshop was not to codify a single recipe by which data

must be processed for SeaWiFS calibration and validation

needs. This recipe is unknown and there are many factors,

involving instrumentation, data acquisition, at-sea deployment procedures, and the analysis goals of the investigation, which make it difficult to provide a simple recipe for

all investigators and all applications. That task is clearly

beyond a three-day workshop. Nonetheless, an attempt

is made to make generalities concerning what should and

should not be done in analyzing spectroradiometry profiles.

Furthermore, the conclusion is reached that the determination of AOPs should be cast as a stochastic problem where

statistical estimates are made with assessible confidence

limits. This approach is critical for achieving SeaWiFS

calibration and validation goals.

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Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

1.2 DATA ANALYSIS METHODS

The present objective is to assess the differences among

four different data analysis methodologies when applied

to identical spectroradiometry data. All of the methods

are presently used in the field and will be utilized by the

CVT (McClain et al. 1992). Complete descriptions for each

data analysis method are presented in subsequent chapters. Here, only a brief introduction of each method is

presented. It should be mentioned that code (both source

and executable) for most of the data analysis methods are

available via anonymous ftp (see the authors of the individual chapters involved).

The four methods have been developed with very different design objectives which are primarily specified by the

science goals and logistical limitations of each group’s atsea sampling programs (Table 1). For example, the method

employed by the BBOP (Chapter 2) has been designed to

deal with large volumes of data (as many as 70 casts from a

single day and over 2,000 casts a year). The design philosophy is that bad data are flagged and discarded rather than

corrected. On the other hand, investigators at CHORS

have developed an integral method for determining AOPs

with the ability to hand select spurious data points. This

design goal is predicated by inherent wire-time limitations

due to large multidisciplinary cruises in which the CHORS

group participates. These design goals reflect the at-sea

sampling opportunities available to each group. The data

analysis methods are each group’s solution to these problems while optimizing their science objectives.

The BBOP group has developed a data analysis method

with the data analysis philosophy that they will take more

casts than they need (Chapter 2, Sorensen et al. 1994,

Siegel et al. 1995a, and Siegel et al. 1995b). This means

suspect data will be eliminated from future consideration

rather than corrected. Thus, the BBOP data processing

system produces quality flags and is able to routinely handle large volumes of data, which requires that nearly all

of the processing steps be completed with little human intervention. The suspect data condition is determined by

evaluating a variety of predetermined quality flags (stability of incident irradiance levels, tilt and roll limitations,

etc.) as well as the calculation of statistical confidence

limits for most derived products. Relevant to the present

intercomparison study, the BBOP data processing system

estimates the values of Lu(0−, λ) and Ed(0−, λ) by fitting

a straight line to the upper 20 meters of a profile (10 m

for λ > 600 nm). The vertical profile of Kd(z, λ) is determined using a differential method where least-square fits

are applied over 10 m vertical intervals. Robust curve fitting algorithms are used to reduce the effects of spurious

data points or outliers (Press et al. 1992); also, confidence

intervals for estimates of Lu(0−, λ) and Ed(0−, λ) are determined. The BBOP processing system runs on UNIX

workstation computers and requires a GNU C++ compiler

and a GUI package, such as Matlab or Interactive Data

Language (IDL). For more information concerning acquiring the BBOP system see Chapter 2 or Siegel et al. (1995b).

The CHORS group has taken a very different approach

and has developed an integral method for determining the

slope of the log-transformed irradiance profile, Kd(z, λ),

and its intercepts, Lu(0−, λ) and Ed(0−, λ) (Chapter 3).

Their analysis goals are driven by the fact that they must

make good estimates of AOPs from every cast in order to

meet their own, as well as their colleagues’, scientific objectives. The CHORS method determines the profile of

Kd(z, λ) and values of Lu(0−, λ) and Ed(0−, λ) simultaneously using an integral method by fitting the radiation

profile using low-order polynomials between user-selected

node locations. The node locations may be determined initially by examining a companion chlorophyll fluorescence

profile, or if one is not present, by examination of curvature

in the radiometric profile. The number and depths of nodes

are then iteratively adjusted to minimize systematic departures between the modeled and measured profile curves.

Values of Kd(z, λ) are determined analytically from the

fitted polynomials. The CHORS method utilizes humanselected data rejection and node setting, and is developed

with a sophisticated GUI. It should be noted that the determination of the Lu(0−, λ) and Ed(0−, λ) values strongly

affect the fit to the entire profile. The CHORS method

presently runs on Silicon Graphics Inc. (SGI) workstations

under IRIX 4.0 and higher.

The NRL method (Chapter 4) has been developed with

the same analysis goal as the CHORS method; namely, the

need to make accurate determinations of AOPs from every

cast during a cruise to achieve the science goals. NRL has

employed a multiple level, prediction-correction procedure

for estimating AOPs. The first level utilizes a differential approach to make first order estimates of AOPs and is

fully automated. The second level employs a series of correction procedures and modeling to affect a robust surface

normalization evaluating the z = 0− and z = 0+ difference

in an interactive mode through a sophisticated GUI. This

surface normalization is done in order to eliminate the effects of changing solar illumination due to clouds, as well

as a correction procedure for the potential effects of ship

shadowing and hull reflection. The second level method

is not used for the DARR-94 comparisons as many of the

data sets used were missing required data types. The NRL

method works on UNIX workstations using the IDL software package.

The SIO group has employed a bulk method for deriving AOPs from spectroradiometry profiles (Chapter 5).

The bulk approach has been long employed in the past and

much of what is known about AOPs has come from using

this approach (e.g., Strickland 1958, Tyler and Smith 1970,

Smith and Baker 1978a and 1978b, and Morel 1988). The

bulk method has been used in the past by the SIO group

(e.g., Mitchell and Holm-Hansen 1991), although, they are

not doing so presently. The bulk approach fits a straight

6
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Table 2. Geolocation and sky conditions for data files compared in DARR-94.

Group File Priority Location Station Longitude Latitude Sky

BBOP a010992b.dn 1 Sargasso BATS 64◦44.20 W 31◦23.61 N Haze

a061792f.dn 1 Sargasso BATS 64 18.24 31 30.40 8/8 Stratus

a082092c.dn 1 Sargasso BATS 64 7.84 31 50.99 2/8 Cumulus

CHORS 04111945.dn 2 GOMEX 611 89 59.88 28 30.12 Clear

04111700.dn 2 GOMEX 510 90 0.00 28 45.12 Clear

04141407.dn 1 GOMEX 1625 89 27.48 28 30.48 Patchy Clouds

NRL e920328a.dn 1 EqPac 5 139 57.90 0 1.80 Overcast

e921016a.dn 1 EqPac 5 140 2.09 0 1.69 Clear

SIO c0403d.up 1 CalCoFI 122 40.40 31 5.30

c0411g.up 2 CalCoFI 121 10.30 34 7.30

Table 3. Temporal and in-water parameters for files compared in DARR-94. The maximum depth of the profile

is given by zmax and the mixed layer depth by zmld.

Group File Priority Date Local Time zmax zmld Notes

BBOP a010992b.dn 1 9 Jan 92 1600 200 m 100 m Late afternoon (sunset).

a061792f.dn 1 17 Jun 92 1000 200 20

a082092c.dn 1 20 Aug 92 1100 200 30 Clear overhead.

CHORS 04111945.dn 2 11 Apr 93 1345 80 Surface and bottom nephels.

04111700.dn 2 11 Apr 93 1100 30 Dark at 22 m (K ≈ 1.4 m−1).

04141407.dn 1 14 Apr 93 0800 200 Deep water, clouds.

NRL e920328a.dn 1 16 Oct 92 1000 180 90 Variable overcast.

e921016a.dn 1 28 Mar 92 1000 200 70 Ship shadow?

SIO c0403d.up 1 3 Apr 93 0928 200 60 Ship shadow, no surface.

c0411g.up 2 11 Apr 93 1325 200 10 Good data to 30 m.

line to the log-transformed irradiance and radiance profiles within the oceanic mixed layer. The fluxes just below

the sea surface are estimated from the exponentiated intercept and the slope gives a mixed layer averaged diffuse

attenuation coefficient, Kd(z0, λ). Obviously, only a single

estimate of Kd(z0, λ) is provided by this method; hence,

it cannot be used to determine the depth dependence of

Kd(z, λ). The bulk method is included in the DARR-94 as

an experimental control providing a single analysis from

which one can interpret differences among the other methods. One distinct advantage of the bulk method is that it

can easily be applied using any spreadsheet program on a

PC.

There are significant differences among the four data

analysis methods due to differing scientific objectives and

at-sea logistics of the individual research groups. For the

most part, differences among the data analysis methods

are due to the way each group collects data at sea. The

sampling philosophy for BBOP is based upon the understanding that not every cast will produce good AOPs. The

CHORS and NRL groups must be able to interact with

other researchers on synoptic survey cruises and, hence,

must determine AOPs for every daytime cast made. Thus,

the CHORS and NRL groups have developed extensive surface normalization and correction procedures. The BBOP

method assumes bad data can never be made into good

data and, therefore, rejects AOP determinations based

upon quality flags and the size of confidence intervals. The

bulk method (SIO) completes the methods evaluated and

is presented in a historical context from which an evaluation of the degree of improvement made by the other more

sophisticated data analysis methods can be made.

1.3 DATA AND RESULTS

1.3.1 The DARR-94 Data Set

A summary of the spectroradiometry data files and

basic environmental conditions for these files used in the

DARR-94 is shown in Tables 2 and 3. The files are labeled with a priority number of 1 or 2 based upon whether

these profiles provide adequate light data deep in the sampled water column. In some sense, priority 1 casts correspond to open ocean conditions in that the good data are

found throughout the vertical profile and no anomalous

features are observed, i.e., extremely high Kd(z, λ) values,

nepheloid layers, etc. Using this criteria, there are seven

priority 1 profiles from which comparisons of data analysis methods are made. The priority 2 data appear to be

mostly Case-2 water casts; hence, the primary comparisons

made using the DARR-94 data set will be valid primarily

for open ocean conditions using the priority 1 data set.

7
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Unprocessed vertical profiles of Ed(z, 488), Lu(z, 488),

Ed(0+, 488),temperature, and in situ chlorophyll fluorescence used in DARR-94 are shown in Figs. 1–10. Note

that not all of parameters are sampled for each cast. In

particular, there are no temperature or chlorophyll fluorescence data for the CHORS casts (CHORS 04111945,

04111700, and 04141407) nor are there surface radiation

measurements made with the SIO data set (c0403d and

c0411g; the CHORS analysis was applied assuming the incident irradiance was constant). It must be stressed that

the data provided here are not the best data collected by

the contributing research group, but rather the data represent typical data collected from the field. Indeed, some

casts were selected to demonstrate how data analysis methods deal with bad data. Obviously, bad casts are not useful

for making AOP determinations for SeaWiFS calibration

and validation purposes.

It is apparent that there is a wide range of profile shapes

in the aggregate DARR-94 data set. Water columns with

deep chlorophyll maxima are found (BBOP a061792f and

a082092c, NRL e921016a, and SIO c0403d) where the

depth of chlorophyll fluorescence maximum varies from approximately 50 m (NRL e921016a) to more than 125 m

(BBOP a082092c). Casts with high chlorophyll values in

the near surface layer are also found (BBOP a010992b,

NRL e920328a, and SIO c0411g). It is obviously difficult to make this determination from the CHORS data as

there are no chlorophyll fluorescence data. Also, mixed

layer depths of the DARR-94 data files vary from 10 to

more than 100 m (Table 3). For the most part, the profiles are taken under excellent incident illumination conditions. The obvious exceptions are the low light conditions

of BBOP a010992b, which was taken at sunset, and the extreme incident irradiance variability found in the CHORS

04141407 cast. Again, these distinctions cannot be made

using the SIO casts, since incident irradiance is not included in the data; hence, a wide range of conditions are

represented by the DARR-94 data set, although they are

by no means inclusive.

The upper 30 m of the downwelling irradiance, Ed(z, λ),

and upwelling radiance, Lu(z, λ) profiles are of particular

relevance for assessing AOPs for SeaWiFS calibration and

validation purposes (Figs. 1–10). Within this depth region, the intercept values, Lu(0−, λ) and Ed(0−, λ), are determined using a statistical method (such as least-squares

estimation). If stable values of Lu(0−, λ) and Ed(0−, λ) are

to be retrieved, the log-transformed Ed(z, λ) and Lu(z, λ)

profiles must be found along a straight line. The raw

Ed(z, λ) and Lu(z, λ) profiles show a significant degree of

variation off this assumed straight line (Figs. 1–10). Furthermore, the variance about a straight line appears to

increase as the sea surface is approached and is greater for

Ed(z, λ) than for Lu(z, λ). There are a variety of causes

for these variations, including, ship shadow and hull reflection, surface gravity wave-induced glint and focusing,

random tilting of the profiling spectroradiometer off zenith,

as well as cloud-induced fluctuations of the incident irradiance (which may affect a profile of light at any depth).

Large near-surface radiation variations in radiometric

profiles may also result from strong, vertically stratified

region of increased attenuation by particles and dissolved

materials in the water column. In the CHORS 04111945

cast, for example, both the Ed(z, λ) and Lu(z, λ) profiles

show a strongly nonlinear decrease with depth over the

top 5 m. This profile was taken near the Mississippi River

outflow, and the highly turbid surface lay responsible for

large values of Kd(z, λ) for both Ed(z, λ) and Lu(z, λ) are

of Mississippi River origin. However for most of the other

casts, the high degree of near-surface Ed(z, λ) and Lu(z, λ)

variations is not due to changes in optical water masses,

but rather to random sources of irradiance noise within the

profile. Ship-induced sources of noise (i.e., ship shadows)

are apparent in some of the profiles (cf., NRL e921016a

and SIO c0403d). Note that for the BBOP data, the ship

shadow signal has been shown to be smaller than other

inherent sources of noise in the estimation of AOPs (Weir

et al. 1994).

1.3.2 Near-Surface Data Comparisons

Central to the agreed upon goals of DARR-94 was the

comparison of estimates of the downwelling irradiance and

the upwelling radiance spectra just beneath the sea surface

(z = 0−), Ed(0−, λ) and Lu(0−, λ) respectively. Estimates

of Ed(0−, λ) and Lu(0−, λ) for the four individual analyses of the priority 1 data set and their aggregate statistics

are presented in Figs. 11–17. A casual examination of the

Ed(0−, λ) and Lu(0−, λ) estimates illustrates several generalities concerning the abilities to make accurate determinations of Ed(0−, λ) and Lu(0−, λ) from spectroradiometry

profiles:

1. The variations among the various methods is

greater for Ed(0−, λ) than for Lu(0−, λ).

2. This difference is accentuated in the red wavelengths.

3. The spread among the individual Ed(0−, λ) estimates is mostly smaller than the size of the

95% confidence interval for the BBOP estimate;

although, at times, there are exceptions (BBOP

a010992b).

4. The simple least-squares methods employed by

the SIO group appears to be the onle one of the

four methods that consistently produces outlier

estimates of Ed(0−, λ) and Lu(0−, λ). This is

particularly apparent in the red wavelengths.

5. The spread among the differing Ed(0−, λ) and

Lu(0−, λ) estimates does not appear to be related to the clarity of the water as inferred by

the estimated color, Lu(0−, λ).

8
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

 a) b)

c) d)

Fig. 1. Vertical profiles of the raw data used in the DARR-94 comparison for BBOP a010992b. The four

panels for each vertical distribution are assembled: a) downwelling irradiance at 488 nm and upwelling radiance

at 488 nm, Ed(z, 488) (in units of mW cm−2 nm−1) and Lu(z, 488) (mW cm−2 nm−1 sr−1), respectively; b) the

incident irradiance at 488 nm during the time of the cast, Ed(0+, 488); c) in situ temperature (◦C); and d)

chlorophyll fluorescence (in volts).

9
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

 a) b)

c) d)

Fig. 2. Vertical profiles of the raw data used in the DARR-94 comparison for BBOP a061792f. The four

panels for each vertical distribution are assembled: a) downwelling irradiance at 488 nm and upwelling radiance

at 488 nm, Ed(z, 488) (in units of mW cm−2 nm−1) and Lu(z, 488) (mW cm−2 nm−1 sr−1), respectively; b) the

incident irradiance at 488 nm during the time of the cast, Ed(0+, 488); c) in situ temperature (◦C); and d)

chlorophyll fluorescence (in volts).

10
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

 a) b)

c) d)

Fig. 3. Vertical profiles of the raw data used in the DARR-94 comparison for BBOP a082092c. The four

panels for each vertical distribution are assembled: a) downwelling irradiance at 488 nm and upwelling radiance

at 488 nm, Ed(z, 488) (in units of mW cm−2 nm−1) and Lu(z, 488) (mW cm−2 nm−1 sr−1), respectively; b) the

incident irradiance at 488 nm during the time of the cast, Ed(0+, 488); c) in situ temperature (◦C); and d)

chlorophyll fluorescence (in volts).

11
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

 a) b)

c) d)

Fig. 4. Vertical profiles of the raw data used in the DARR-94 comparison for NRL e920328a. The four panels

for each vertical distribution are assembled: a) downwelling irradiance at 488 nm and upwelling radiance at

488 nm, Ed(z, 488) (in units of mW cm−2 nm−1) and Lu(z, 488) (mW cm−2 nm−1 sr−1), respectively; b) the

incident irradiance at 488 nm during the time of the cast, Ed(0+, 488); c) in situ temperature (◦C); and d)

chlorophyll fluorescence (in volts).

12
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

 a) b)

c) d)

Fig. 5. Vertical profiles of the raw data used in the DARR-94 comparison for NRL e921016a. The four panels

for each vertical distribution are assembled: a) downwelling irradiance at 488 nm and upwelling radiance at

488 nm, Ed(z, 488) (in units of mW cm−2 nm−1) and Lu(z, 488) (mW cm−2 nm−1 sr−1), respectively; b) the

incident irradiance at 488 nm during the time of the cast, Ed(0+, 488); c) in situ temperature (◦C); and d)

chlorophyll fluorescence (in volts).

13
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

 a) b)

c)

Fig. 6. Vertical profiles of the raw data used in the DARR-94 comparison for SIO c0403d. The three panels

for each vertical distribution are assembled: a) downwelling irradiance at 488 nm and upwelling radiance at

488 nm, Ed(z, 488) (in units of mW cm−2 nm−1) and Lu(z, 488) (mW cm−2 nm−1 sr−1), respectively; b) in situ

temperature (◦C); and c) chlorophyll fluorescence (in volts).

14
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

 a) b)

c)

Fig. 7. Vertical profiles of the raw data used in the DARR-94 comparison for SIO c0411g. The three panels

for each vertical distribution are assembled: a) downwelling irradiance at 488 nm and upwelling radiance at

488 nm, Ed(z, 488) (in units of mW cm−2 nm−1) and Lu(z, 488) (mW cm−2 nm−1 sr−1), respectively; b) in situ

temperature (◦C); and c) chlorophyll fluorescence (in volts).

15
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

 a) b)

Fig. 8. Vertical profiles of the raw data used in the DARR-94 comparison for CHORS 04111700. The two

panels for each vertical distribution are assembled: a) downwelling irradiance at 486 nm and upwelling radiance

at 486 nm, Ed(z, 486) (in units of mW cm−2 nm−1) and Lu(z, 486) (mW cm−2 nm−1 sr−1), respectively; b) the

incident irradiance at 487 nm during the time of the cast, Es(0+, 487).

 a) b)

Fig. 9. Vertical profiles of the raw data used in the DARR-94 comparison for CHORS 04111945. The two

panels for each vertical distribution are assembled: a) downwelling irradiance at 486 nm and upwelling radiance

at 486 nm, Ed(z, 486) (in units of mW cm−2 nm−1) and Lu(z, 486) (mW cm−2 nm−1 sr−1), respectively; b) the

incident irradiance at 487 nm during the time of the cast, Es(0+, 487).

16
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

 a) b)

Fig. 10. Vertical profiles of the raw data used in the DARR-94 comparison for CHORS 04141407. The two

panels for each vertical distribution are assembled: a) downwelling irradiance at 486 nm and upwelling radiance

at 486 nm, Ed(z, 486) (in units of mW cm−2 nm−1) and Lu(z, 486)(mW cm−2 nm−1 sr−1), respectively; b) the

incident irradiance at 487 nm during the time of the cast, Es(0+, 487).

A quantitative evaluation of the differing Ed(0−, λ) and

Lu(0−, λ) estimates may be made by examining the coefficient of variation (c.v.) among the four analyses for each

priority 1 cast. Values of c.v. (in percentages) for the priority 1 DARR-94 casts are shown in Table 4. Only overlapping wavebands are shown in Table 4 (where Lu(0−, 530) is

substituted for Lu(0−, 520) in the CHORS data). Typical

c.v. values range from about 1–8%. When all of the priority 1 data are evaluated, mean c.v. values are slightly lower

for Ed(0−, λ) estimates than they are for Lu(0−, λ). Of the

individual casts, the CHORS data file 04141407 is by far

the worst of the priority 1 casts, especially for Lu(0−, λ).

This is expected as this cast had extensive cloud-induced

incident irradiance variations (Fig. 10). When this cast is

removed from consideration (the EC values in Table 4),

the DARR-94 mean c.v. values are around 3–4% with no

significant difference between the Lu(0−, λ) and Ed(0−, λ)

estimates; hence, the scatter of the results of the four analyses about the analysis aggregate mean for good casts is

less than 4% of the retrieved value.

The comparison of the relative size of the 95% confidence intervals for the BBOP determined Ed(0−, λ) and

Lu(0−, λ) provides another objective measure of the degree

to AOPs can be accurately determined. Here, the relative

uncertainty in the least-squares estimation of Ed(0−, λ)

and Lu(0−, λ) is presented in Table 5 as the ratio of the

confidence interval to the retrieved value. In general, the

relative uncertainty in Lu(0−, λ) is much smaller than it is

for Ed(0−, λ) (with the obvious exception of the CHORS

04141407 cast). Excluding this one bad cast, the relative uncertainty is approximately 2% for Lu(0−, λ) and

about 7% for Ed(0−, λ). Some casts (BBOP a061792f

and a082092c, and SIO c0403d) result in very large values (greater than or equal to 8%) of relative uncertainty for

Ed(0−, λ). This is due to the high degree of variability in

the near-surface Ed(z, λ) profile for these casts (Figs. 2, 3,

and 6). The Lu(0−, λ) estimates have significantly lower

relative uncertainties for these casts suggesting that the

source of error may be due to surface gravity wave-induced

irradiance fluctuations (see below).

Comparison of the Kd(z, λ) estimates are shown in

Figs. 18–26. In general, the comparison looks very good

for most of the casts where normalized root mean square

(rms) deviations among the four analyses are typically less

than 5%. If anything, the normalized rms deviations are

smaller for the higher wavelengths (λ > 550 nm), which

17
BBOP CHORS NRL SIO

Legend

0

0.5

Lu[442]Lu[456]Lu[489]Lu[531]Lu[550]Lu[590]

Lu[711]

Channel

0.5

1

0

0.25

0.75

0.75

0.25

04141407

10

20

30

40

50

60

70

04141407

Ed[452]Ed[440]Ed[486]

Ed[519]

Ed[530]Ed[548]Ed[589]Ed[632]Ed[655]Ed[670]Ed[696]

Channel

10

20

30

40

50

60

Ed (0-)[μW sr-1 cm-2 s-1]

Lu(0-)[μW sr-1 cm-2 s-1]

Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Fig. 11. Estimates of a) the downwelling irradiance just beneath the sea surface, Ed(0−, λ), and b) the

upwelling radiance just beneath the sea surface, Lu(0−, λ) for CHORS 04141407. In the upper panels, all four

of the individual analyses are shown. The error bars in the upper panels correspond to the 95% confidence

intervals for the BBOP extrapolations for Ed(0−, λ) and Lu(0−, λ) and are shown in each of the upper panels.

The lower panels show the mean of the four analyses and the error bars represent the standard deviation about

the mean for each waveband. Only priority 1 casts are shown. Confidence intervals are not calculated as the

sample size (N = 4) is very small.

18
5

10

15

20

Ed[410]Ed[441]Ed[465]Ed[488]Ed[520]Ed[565]Ed[589]Ed[665]

5

10

15

20

25

0

0.05

0.1

0.15

Lu[410]Lu[441]Lu[465]Lu[488]Lu[520]Lu[565]Lu[589]Lu[665]Lu[683]

0

0.05

0.1

0.15

0.2

0.25

a010992b.lcd.1

Channel

BBOP CHORS NRL SIO

Legend

Channel

a010992b.lcd.1

Ed (0-)[μW sr-1 cm-2 s-1]

Lu(0-)[μW sr-1 cm-2 s-1]

Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Fig. 12. Estimates of a) the downwelling irradiance just beneath the sea surface, Ed(0−, λ), and b) the

upwelling radiance just beneath the sea surface, Lu(0−, λ) for BBOP a010992b. In the upper panels, all four of

the individual analyses are shown; the lower panels show the mean of the four analyses. The error bars for the

upper and lower panels are the same as those indicated in Fig. 11. Only priority 1 casts are shown. Confidence

intervals are not calculated as the sample size (N = 4) is very small.

19
10

20

30

40

50

60

70

a0617 92f.l cd.1

10

20

30

40

50

60

Ed[410]

Ed[441]Ed[465]Ed[488]Ed[520]Ed[565]Ed[589]Ed[665]

0

0.25

0.5

0.75

Lu[410]Lu[441]Lu[465]Lu[488]Lu[520]Lu[565]Lu[589]Lu[665]Lu[683]

MEAN

0.25

0.5

0.75

1

1.25

Channel

a0617 92f.l cd.1

0

BBOP CHORS NRL SIO

Legend

Channel Channel

Lu(0-)[μW sr-1 cm-2 s-1]

Ed (0-)[μW sr-1 cm-2 s-1]

Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Fig. 13. Estimates of a) the downwelling irradiance just beneath the sea surface, Ed(0−, λ), and b) the

upwelling radiance just beneath the sea surface, Lu(0−, λ) for BBOP a061792f. In the upper panels, all four of

the individual analyses are shown; the lower panels show the mean of the four analyses. The error bars for the

upper and lower panels are the same as those indicated in Fig. 11. Only priority 1 casts are shown. Confidence

intervals are not calculated as the sample size (N = 4) is very small.

20
0

50

0

50

Ed[410]Ed[441]

Ed[465]

Ed[488]

Ed[520]Ed[565]Ed[589]Ed[665]

Channel

a08 209 2c.l cd.1

0

1

2

3

Lu[410]Lu[441]Lu[465]Lu[488]Lu[520]Lu[565]

Lu[589]

Lu[665]Lu[683]

1

2

3

4

Channel

a08 209 2c.l cd.1

Lu(0-)[μW sr-1 cm-2 s-1]

Channel

150

100

200

150

100

BBOP CHORS NRL SIO

Legend

Ed (0-)[μW sr-1 cm-2 s-1]

0

Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Fig. 14. Estimates of a) the downwelling irradiance just beneath the sea surface, Ed(0−, λ), and b) the

upwelling radiance just beneath the sea surface, Lu(0−, λ) for BBOP a082092c. In the upper panels, all four of

the individual analyses are shown; the lower panels show the mean of the four analyses. The error bars for the

upper and lower panels are the same as those indicated in Fig. 11. Only priority 1 casts are shown. Confidence

intervals are not calculated as the sample size (N = 4) is very small.

21
0

0.25

0.5

0.75

1

0

0.25

0.5

0.75

Lu[441]Lu[488]Lu[520]Lu[560]Lu[683]

c040 3d

10

20

30

40

50

60

10

20

30

40

50

Ed[410]Ed[441]Ed[488]Ed[520]

Ed[560]Ed[630]

Ed[683]

c040 3d

BBOP CHORS NRL SIO

Legend

Channel Channel

Lu(0-

)[μW sr-1 cm-2 s-1]

Ed (0-)[μW sr-1 cm-2 s-1]

Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Fig. 15. Estimates of a) the downwelling irradiance just beneath the sea surface, Ed(0−, λ), and b) the

upwelling radiance just beneath the sea surface, Lu(0−, λ) for SIO c0403d. In the upper panels, all four of the

individual analyses are shown; the lower panels show the mean of the four analyses. The error bars for the

upper and lower panels are the same as those indicated in Fig. 11. Only priority 1 casts are shown. Confidence

intervals are not calculated as the sample size (N = 4) is very small.

22
10

20

30

40

50

60

70

80

10

20

30

40

50

60

Ed[408]

Ed[439]

Ed[488]

Ed[518]Ed[550]

Ed[560]

Ed[589]

Ed[632]

Ed[655]

Ed[671]

Ed[683]

Ed[693]Ed[710]

e92 032 8a_dn

0

0.2

0.4

0.6

0.8

0

0.2

0.4

0.6

e92 032 8a_dn

Lu[410]Lu[446]

Lu[488]

Lu[519]Lu[551]

Lu[631]

Lu[655]Lu[682]

BBOP CHORS NRL SIO

Legend

Channel Channel

Lu(0-)[μW sr-1 cm-2 s-1]

Ed (0-)[μW sr-1 cm-2 s-1]

Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Fig. 16. Estimates of a) the downwelling irradiance just beneath the sea surface, Ed(0−, λ), and b) the

upwelling radiance just beneath the sea surface, Lu(0−, λ) for NRL e920328a. In the upper panels, all four of

the individual analyses are shown; the lower panels show the mean of the four analyses. The error bars for the

upper and lower panels are the same as those indicated in Fig. 11. Only priority 1 casts are shown. Confidence

intervals are not calculated as the sample size (N = 4) is very small.

23
0

50

100

150

200

0

50

100

150

Ed[408]Ed[439]Ed[488]Ed[518]Ed[550]Ed[560]Ed[589]Ed[632]Ed[655]Ed[671]Ed[683]Ed[693]Ed[710]

e92101 6a_dn

0

0.5

1

1.5

Lu[410]Lu[446]Lu[488]

Lu[519]

Lu[551]

Lu[631]Lu[655]Lu[682]

0.5

1

1.5

2

0

e92101 6a_dn

BBOP CHORS NRL SIO

Legend

Channel

Lu(0-)[μW sr-1 cm-2 s-1]

Ed (0-)[μW sr-1 cm-2 s-1]

Channel

Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Fig. 17. Estimates of a) the downwelling irradiance just beneath the sea surface, Ed(0−, λ), and b) the

upwelling radiance just beneath the sea surface, Lu(0−, λ) for NRL e921016a. In the upper panels, all four of

the individual analyses are shown; the lower panels show the mean of the four analyses. The error bars for the

upper and lower panels are the same as those indicated in Fig. 11. Only priority 1 casts are shown. Confidence

intervals are not calculated as the sample size (N = 4) is very small.

24
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Table 4. Coefficients of variation (in percentage) among the four analyses for estimates of Ed(0−, λ) and

Lu(0−, λ) from the priority 1 casts. Data are given only where there are overlapping wavebands. Data set mean

and standard deviation EC values in the last two lines exclude the spurious CHORS cast.

Group Cast Ed(0−, 441) Ed(0−, 490) Ed(0−, 520) Lu(0−, 441) Lu(0−, 490) Lu(0−, 520)

BBOP a010992b 7.02 6.20 7.17 8.84 8.91 8.24

a061792f 4.41 4.02 4.12 2.65 2.33 7.97

a082092c 3.85 3.47 3.32 3.98 3.83 2.57

CHORS 04141407 4.41 9.41 4.69 19.89 27.52 22.30

NRL e920328a 0.58 0.80 3.30 2.00 1.59 2.00

e921016a 2.09 2.63 1.99 1.75 1.78 2.05

SIO c0403d 1.43 1.59 1.18 2.20 1.54 1.49

Mean 3.40 4.02 3.68 5.90 6.79 6.66

Standard Deviation 2.19 2.95 1.95 6.64 9.51 7.47

EC Mean 3.23 3.12 3.51 3.57 3.33 4.05

EC Standard Deviation 2.35 1.92 2.08 2.70 2.86 3.16

Table 5. Ratio of the BBOP-analyzed 95% confidence estimates for Ed(0−, λ) and Lu(0−, λ) to the estimated

value (in percentage) for the priority 1 DARR-94 casts. EC values in the last two lines correspond to statistical

values excluding the CHORS data.

Group Cast Ed(0−, 441) Ed(0−, 490) Ed(0−, 520) Lu(0−, 441) Lu(0−, 490) Lu(0−, 520)

BBOP a010992b 2.16 2.11 2.27 2.16 2.03 2.56

a061792f 8.75 8.97 9.74 1.52 1.11 0.64

a082092c 16.60 17.40 16.70 1.62 1.84 3.10

CHORS 04141407 16.80 19.70 21.20 18.90 21.60 19.40

NRL e920328a 5.69 6.11 6.46 1.43 1.57 1.96

e921016a 0.99 0.99 1.16 0.98 1.43 3.94

SIO c0403d 8.18 8.28 8.64 3.42 3.42 3.65

Mean 8.45 9.08 9.45 4.29 4.71 5.04

Standard Deviation 6.31 7.14 7.31 6.49 7.48 6.43

EC Mean 7.06 7.31 7.50 1.86 1.90 2.64

EC Standard Deviation 5.62 5.90 5.65 0.86 0.81 1.22

is due to the fact that the values of Kd(z, λ) are much

larger for these wavebands. The tracking of the results

of the three depth-varying methods (CHORS, BBOP, and

NRL) is encouraging. Since the CHORS method is an integral method, its Kd(z, λ) profiles are smoother and for

many of the DARR-94 profiles it seems to do an excellent job. However, it may not reproduce all the real high

wavenumber variations in Kd(z, λ) as the other methods

do.

The largest deviations among the methods occur for

those casts that are affected by cloud-induced variations of

the incident flux (CHORS 04141407), obvious ship shadows (NRL e921016a and SIO c0403d) or what appear to

be surface wave induced variations (BBOP a082092c). Indications of the noise sources are apparent via the pattern of the Kd(z, λ) variations with depth. For example, the CHORS 04141407 cast has large random-looking

variations in the derived Kd(z, λ) values for the BBOP

and NRL (differential analyses). The CHORS analyzed

Kd(z, λ) profile seems to fall in the middle in the scatter

between the other two noisy Kd(z, λ) profiles. It should

be mentioned that this particular cast has an extensive

amount of incident irradiance noise and is flagged as unacceptable by the BBOP analysis. The NRL e921016a data

shows a single wiggle in the Kd(z, λ) profile at about 30 m

which is likely due to a ship shadow since there are little

significant short-time scale variations in the incident flux

during this profile (Fig. 5). The BBOP cast with large

rms deviations among analyses (BBOP a082092c) seems

to show a regular pattern with depth.

The bulk method for Kd(z, λ) (SIO) provides only a

single point, which is representative of the mixed layer.

In general, its retrieved value is consistent with the more

sophisticated analyses and there are no apparent biases in

the bulk methods Kd(z0, λ) value and the results of the

other analyses. It is obvious that the bulk method cannot

produce data appropriate for assessing the vertical profile

of Kd(z, λ).

25
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

0

50

100

150

200

0.00 0.04 0.08 0 5 10

Kd(411)

0

50

100

150

200

0.00 0.20 0.40 0 5 10

Kd(665)

0

50

100

150

200

0.00 0.04 0.08 0 5 10

Kd(565)

0

50

100

150

200

0.00 0.04 0.08 0 5 10

Kd(488)

bbop.010992b.dn

 a) b)

c) d)

Fig. 18. Vertical profiles of Kd(z, λ) for downwelling irradiance from BBOP 010992b, and the normalized rms

deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

26
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

bbop.061792f.dn

0

50

100

150

200

0.00 0.04 0.08

0

50

100

150

200

0.00 0.04 0.08

Kd(411) Kd(488)

0 5 10 0 5 10

0

50

100

150

200

0.00 0.04 0.08

0

50

100

150

200

0.00 0.20 0.40

Kd(565) Kd(665)

0 5 10 0 5 10

 a) b)

c) d)

Fig. 19. Vertical profiles of Kd(z, λ) for downwelling irradiance from BBOP 061792f, and the normalized rms

deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

27
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

0

50

100

150

200

0.00 0.04 0.08

Kd(411)

0

50

100

150

200

0.00 0.04 0.08

0

50

100

150

200

0.00 0.04 0.08

Kd(488)

Kd(565) Kd(665)

0

50

100

150

200

0.00 0.20 0.40

bbop.a082092c.dn

0 5 10 0 5 10

0 5 10 0 5 10

37%

24% 37%

11%

43%

13%

 a) b)

c) d)

Fig. 20. Vertical profiles of Kd(z, λ) for downwelling irradiance from BBOP a082092c, and the normalized rms

deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

28
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

0

50

100

150

200

0.00 0.10 0.20 0.30

0

50

100

150

200

0.00 0.20 0.40 0.60

0

50

100

150

200

0.00 0.10 0.20 0.30

Kd(440)

0

50

100

150

200

0.00 0.10 0.20 0.30

Kd(486)

0 5 10 0 5 10

0 5 10 0 5 10

Kd(548) Kd(670)

chors.04111945

 a) b)

c) d)

Fig. 21. Vertical profiles of Kd(z, λ) for downwelling irradiance from CHORS 04111945, and the normalized

rms deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

29
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

chors.04141407

0

50

100

150

200

0.00 0.05 0.10 0 25 50

0

50

100

150

200

0.00 0.05 0.10 0 25 50

0

50

100

150

200

0.00 0.05 0.10 0.15 0 25 50

0

50

100

150

200

0.00 0.20 0.40 0 5 10

K (440) d (486)

51%

62%

66%

63%

K d(486)

 a) b)

c) d)

K d K (670) d(548)

Fig. 22. Vertical profiles of Kd(z, λ) for downwelling irradiance from CHORS 04141407, and the normalized

rms deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

30
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

0

50

100

150

200

0.00 0.04 0.08

0

50

100

150

200

0.00 0.04 0.08

Kd(560)

Kd K (488) d(410)

0

50

100

150

200

0.00 0.04 0.08

0

50

100

150

200

0.00 0.20 0.40

0 5 10

Kd(670)

0 5 10

0 5 10 0 5 10

nrl.e920328a.dn

 a) b)

c) d)

Fig. 23. Vertical profiles of Kd(z, λ) for downwelling irradiance from NRL e920328a, and the normalized rms

deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

31
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

0

50

100

150

200

0.00 0.20 0.40

Kd(671)

0

50

100

150

200

0.00 0.04 0.08 0 5 10

Kd(560)

0

50

100

150

200

0.00 0.04 0.08 0 5 10

Kd(410)

26%

0

50

100

150

200

0.00 0.04 0.08 0 5 10

Kd(488)

12%

12%

nrl.e921016a.dn

0 5 10

 a) b)

c) d)

Fig. 24. Vertical profiles of Kd(z, λ) for downwelling irradiance from NRL e921016a, and the normalized rms

deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

32
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

0

50

100

150

200

0.00 0.04 0.08

0

50

100

150

200

0.00 0.05 0.10

0 5 10

0

50

100

150

200

0.00 0.04 0.08 0 5 10

0 5 10

sio.0403d.up

0

50

100

150

200

0.00 0.20 0.40

14%

16% 24%

0 5 10

Kd K (683) d(560)

Kd(410) Kd(488) a) b)

c) d)

Fig. 25. Vertical profiles of Kd(z, λ) for downwelling irradiance from SIO 0403d, and the normalized rms

deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

33
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

0

50

100

150

200

0.00 0.20 0.40

0

50

100

150

200

0.00 0.20 0.40

0

50

100

150

200

0.00 0.20 0.40

0

50

100

150

200

0.00 0.20 0.40

Kd(410)

Kd K (683) d(560)

Kd(488)

0 10 20 0 10 20 30

0 10 20 0 10 20

sio.c0411g.up

 a) b)

c) d)

Fig. 26. Vertical profiles of Kd(z, λ) for downwelling irradiance from SIO c0411g, and the normalized rms

deviation for the four DARR-94 analyses in the a) blue, b) blue-green, c) green-yellow, and d) red spectral

regions. Normalized rms deviations are calculated over 20 m intervals. The four individual analyses are shown

as plus signs (+) for BBOP, dots (•) for CHORS, diamonds () for NRL, and triangles () for SIO.

34
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

1.4 DISCUSSION

The possible cause of deviation among the differing

Lu(0−, λ), Ed(0−, λ) and Kd(z, λ) estimates can be better understood by considering how a profiling spectroradiometer samples a time-varying sea and how values of

Lu(0−, λ) and Ed(0−, λ) are determined. Specifically, a

profiling spectroradiometer samples both in time and depth

as it is lowered through the water column. If there are

sources of in situ radiation variability with time scales at,

or less than, the time required to sample the upper 20 or so

meters of the water column, these variations will be superimposed upon the time-mean signal whose properties are

being assessed. The different analysis methods presented

here all smooth the data in different manners and evaluate

Ed(0−, λ) and Lu(0−, λ) over different portions of the vertical profile and, therefore, will be evaluated over differing

sampling times. This results in different values of retrieved

AOPs as has been shown.

Cloud-induced variations of the incident flux are an obvious source of noise in an irradiance profile, e.g., CHORS

04141407. Fortunately, the presence or absence of cloud

variations can be evaluated by simultaneously sampling the

incident downwelling irradiance. Although time variations

at depth may not exactly correspond with incident ones,

the presence or absence of incident flux variations provides

a measure of whether cloud-induced variations are important (see Figs. 1–10). With the exception of the CHORS

04141407 cast, the priority 1 data set appears to be nearly

free from this source of noise variability. The effect of incident flux variations upon the Kd(z, λ) estimates is obvious,

even when attempts are made to normalize the underwater with the surface fluxes (the CHORS analysis of the

CHORS 04141407 cast in Fig. 22).

A major source of Ed(z, λ) variability near the sea surface is caused by surface gravity waves (e.g., Dera and Gordon 1968, Snyder and Dera 1970, Siegel and Dickey 1988,

and Stramski et al. 1992). The tilting of the sea surface due

to surface gravity wave motions results in quasi-random

fluctuations in the in situ irradiance with time scales on

the order of the wave period (cf., Gordon et al. 1971 and

Siegel and Dickey 1988). An estimate of the effects of surface gravity waves upon a vertical profile of downwelling

irradiance can be made by assuming a dominant swell period of 8 seconds and a package lowering rate of 0.5 m s−1.

In this example, the surface swell would produce a fluctuating component of irradiance with a vertical scale of

approximately 4 m. This signal must be eliminated if accurate estimates of AOPs are to be made by determining

the AOPs over some large vertical interval (on the order

of 10–20 m). Differences in how this is accomplished will

obviously result in differences in the retrieved AOPs. However, this fact points to AOP estimation methods which

eliminate (or de-emphasize) the effects of outliers off the

time-mean irradiance profiles, e.g., Press et al. 1992.

The observed differences between the uncertainties in

the Ed(0−, λ) and Lu(0−, λ) estimates can also be reconciled if one considers the underwater radiance distribution

near the sea surface, e.g., Smith 1974 and Voss 1989. Neglecting diffuse light for the moment, the downwelling photons will be restricted to within a Fresnel cone (within 41◦

of zenith). The area of sea surface sampled by the radiometer increases dramatically with the depth of measurement.

Near the sea surface, the extent of the sea surface intersecting the sampling Fresnel cone is small and the effects of

surface wave glint will be very important. As the depth of

sampling is increased, however, the area of sea surface increases to include many glinting facets. This will effectively

average out the effects of the randomly glinting sea surface

elements on the sampled irradiance signal. Hence, the effects of sea surface gravity waves will be most prominent on

Ed(z, λ) profiles near the sea surface which are being used

to estimate values of Ed(0−, λ). Profiles of Lu(z, λ) will be

smoother with respect to depth since upwelling radiance

is backscattered downwelling light and, hence, has entered

the sea over a considerably larger area than the Fresnel

sampling cone for that depth. Therefore, the surface gravity wave noise will affect Ed(z, λ) profiles to a much larger

degree than Lu(z, λ). This can be seen qualitatively in the

raw data distributions in Figs. 1–10 or in the relative uncertainty estimates of Table 5. This argument is similar to

the ship shadow avoidance distance discussion presented

in Mueller and Austin (1995).

Quasi-random variations in the orientation of the spectroradiometer package may also be an important factor in

creating noise in Ed(z, λ) and Lu(z, λ) profiles, particularly near the sea surface. In essence, random tilts can

push the collection of Ed(z, λ) into, and out of, the Fresnel cone. This affects the Lu(z, λ) profile determination to

a much lesser degree because of the more uniform angular distribution of the upwelling radiance (Smith 1974 and

Voss 1989).

Ship shadows may also be an important factor, which

will give rise to variations in the retrieved AOP values. In

some sense, a ship shadow provides a time-mean perturbation to in situ radiation signals with a depth scale on

the order of 10 m. This again will create differences in sea

surface extrapolated fluxes if the data are handled in different manners. There may also be a differential spectral

response. Unfortunately, this effect cannot be objectively

assessed using the present data set.

The results of the DARR-94 workshop do not point to

one analysis being better than another, but rather give

indications of the inherent uncertainty of AOP estimation

due to geophysical noise sources. The different analyses assess different parts of the profile and thus, different parts

of the space-time distribution of the in situ radiation signal. The different methods average, smooth, and perform

curve fits on the raw data profiles in different ways. This

strongly suggests the determination of AOPs from spectroradiometry profiles should be posed as a statistical problem. Statistically determined AOP estimates should have

35
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

associated confidence intervals. The size of the confidence

intervals provides an objective measure of the goodness of

the derived AOP estimate. Presently, only the BBOP data

analysis method provides a measure of confidence for the

estimate; although this is certainly not a criticism of the

other analysis methods.

This vision must be carried through in the analysis

and utilization of spectroradiometry profiles. Generally, a

statistical technique is employed to estimate AOP values,

which will naturally provide an uncertainty estimate. This

confidence estimate needs to be determined and used. In

addition, the means to exploit AOP uncertainties in further analyses and interpretations needs to be developed.

For example, a data point with a large uncertainty about

its estimated value should have a smaller contribution to

a global regression analysis than an estimate with a small

uncertainty, e.g., Press et al. 1992.

There are obvious extensions of this thinking to objective mapping of satellite imagery, e.g., Santoreli et al. 1991.

This use of confidence intervals may be most important

when in situ AOP estimates are compared with satellite

imagery, i.e., calibration and validation. When performing

calibration and validation, a retrieved satellite-sensed pixel

value, which has been corrected for the effects of the atmosphere, is compared with the estimated in situ AOP value.

Both values will have a quantifiable degree of uncertainty

associated with them; hence, the calibration and validation problem should be restated formally as a statistical

one. This uncertainty information is important and must

be considered in future calibration and validation analyses.

Researchers must now work to develop effective means to

use these important pieces of information.

1.5 CONCLUSIONS

The bottom line result of the DARR-94 study is that,

with good data, the spread among different data analysis

methods for determining the upwelling radiance and downwelling irradiance just beneath the sea surface, Lu(0−, λ)

and Ed(0−, λ), respectively, is about 3–4% of the analyses aggregate mean estimate. The statistical uncertainty

in determinations of Ed(0−, λ) are considerably larger (approximately 7%) than those for Lu(0−, λ) (approximately

2%), as found using the BBOP results. These differences

can be attributed to geophysical noise sources in the raw

data streams, which appear to be dominated by surface

wave glinting processes. Furthermore, the profile of the

diffuse attenuation coefficient spectrum, Kd(z, λ), can be

replicated to better than 5% with the differing analyses.

These differences account for much of the total 5% uncertainty that is tolerated for SeaWiFS calibration and validation purposes (Mueller and Austin 1995). For bad casts,

much larger deviations among methods can occur.

The DARR-94 results do not show a clear winner or

loser among the three sophisticated data analysis methods.

Some degree of outlier rejection is required to accurately

estimate Lu(0−, λ) or Ed(0−, λ). Whether this happens

automatically (i.e., BBOP) or manually (i.e., CHORS or

NRL) does not seem to matter. The bulk method, however, is simply not adequate. Possible solutions include

manual or automated data disqualification, robust curve

fitting routines, or extrapolation using the incident flux

(not fully evaluated here). Furthermore, the calculation,

evaluation, and exploitation of confidence intervals for the

AOP determinations need to be explored in more detail.

This is critical for the long-term assessment of ocean color

imagery and its calibration and validation using in situ

data sets.

Critical to the results presented here is the need for

good data. There are no substitutes. Good at-sea procedures are essential including the sampling of unaliased,

continuous data sets. The SeaWiFS Project has spent a

great deal of effort ensuring that this is the case (Mueller

and Austin 1995) and recommends strongly that these

guidelines be followed.

36
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Chapter 2

The BBOP Data Processing System

Jens C. Sorensen,Margaret C. O’Brien,Daniel A. Konnoff,and

David A. Siegel

University of California, Santa Barbara, Santa Barbara, California

Abstract

The shear volume of profiling spectroradiometer data is increasing dramatically as global change research programs are placing more emphasis on the evaluation of spatial and temporal structure of ocean biogeochemical

cycles. For example, BBOP collects over 1,000 profiles each year in order to link time-series observations of

primary production rates to bio-optical parameters. It is likely that these trends will continue as there are

several satellite ocean color sensors that are planned to be deployed between now and the end of the century.

However, these vast amounts of data must be calibrated, processed, reduced, analyzed, and interpreted in a

timely manner. Here, a computer data processing system for efficiently achieving this goal is presented in terms

of both computational and human resources. The processing system is comprised of a suite of ANSI C++ programs that read and operate on a specified file format, the LCD data file. The LCD file contains all relevant

data and metadata, which include calibration information and at-sea comments, in a single ASCII file. UNIX

shell scripts are used in the control of data flow, as well as error and log handling. The final product is a binned

spectroradiometer data set with relevant derived parameters included [Kd(z,λ), Rrs(z,λ), Ed(0−, λ), etc.] that

may be disseminated to other groups or databases. At UCSB, the BBOP system has been used for the past two

years and the system is available for use by other research groups.

2.1 INTRODUCTION

It is well recognized that ocean optics data sets have

a huge potential for addressing many aspects of ocean

biogeo-chemical cycles (see for example, Dickey and Siegel

1993). To be effectively used in interdisciplinary studies,

such as JGOFS, optical data sets must be made available in

a timely manner. For example, in order to effectively collaborate with other investigators in the JGOFS BATS, the

BBOP activity must provide and interpret ocean optical

data sets on the same time scale as the BATS collaborators to work up pigment, primary production and water

chemistry data. In addition, the calibration and validation needs of the upcoming SeaWiFS ocean color mission

requires processed ocean optics data in near-real time (McClain et al. 1992).

A major stumbling block in the dissemination and utilization of bio-optical data sets has been the lack of efficient

and straightforward data processing schema. Bio-optical

Editors’ Note: This Chapter has appeared as an article in an

SPIE publication (Sorensen et al. 1994) and is being included

in this volume with permission of the authors and SPIE. Minor

editorial changes have been made to reflect the style of the

SeaWiFS Technical Report Series.

data sets have several characteristics which make their

final products difficult to produce quickly. First, they tend

to be large (several Mbytes each) due to high data sampling rates and a diverse parameter range. Second, many

profiles (tens to thousands) are often made during a single

cruise due to experimental design and the at-sea ease of

making these profiles. Recalculation of measured parameters is another characteristic of profiling spectroradiometer

data sets that is often necessary, as radiometric calibrations are at times uncertain (Mueller and Austin 1995).

Each year, the BBOP sampling collects over 1,000 spectroradiometer profiles, and the instrument is recalibrated

at UCSB three times each year. In order to effectively meet

scientific, logistic, and collaboration goals, these data must

be efficiently processed and analyzed.

In anticipation of this large volume of data, as well as

the rapid turn-around requirements imposed by the calibration and validation of satellite data sets, a new data

processing system for large volumes of multispectral profile data has been developed. In what follows, the structure and data flow of the BBOP data processing system

is introduced. This manuscript is intended as a simplified

overview and not as a detailed users’guide (see Siegel et

al. 1995b for a users’guide).

37
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

2.2 PHILOSOPHY

As with any data processing system, there are several conceptual and philosophical positions that need to

be taken based on the scientific goals of the project and

the available resources. In no specific order, the goals of

the BBOP data processing system are to:

Utilize as much of the existing spectroradiometer

analysis methods as possible,

Rapidly produce final archival data sets,

Minimize human intervention steps,

Maximize the number of data quality assessment

and assurance checks,

Generate fully self-describing data sets,

Provide data files in ASCII which are easily read

and edited,

Make quick-look products easy to obtain at several

stages of the processing,

Include processing documentation in the data files

at each step,

Work on many present and future computer platforms, and

Allow much of the data reduction to be accomplished by an experienced undergraduate student.

As a limitation imposed by the number of casts generated and the imposed rapid turn-around requirements,

each profile cannot be manipulated individually. Therefore, in order to assure data quality, many efficient quality assessment and control steps must be implemented.

Known sensor problems must be corrected or flagged. Examples of known problems include low signal-to-noise ratios (SNRs) in irradiance and radiance channels near the

dark current values, and obvious spiking in data fields due

to acquisition errors. These problems require that the affected data values be replaced with trap flags (−9.9×1035)

so that no processing is performed on these spurious fields.

In addition, the quality of the data for some applications

is affected by events occurring during collection, including

excessive package tilts and variations in incident illumination during a cast due to changes in cloud cover. These

events require that entire data records be qualified. In

these cases, a new field is created containing a flag which

defines the quality of the data based on data values in one

or two fields. Data records containing either trap or quality flags can be later deleted or accepted by making simple

threshold comparisons.

Provisions also must be made for unknown and unexplained sources of error. These may be simple, the complete failure of a channel, or insidious, the slow change in

a calibration constant. To catch these errors, plots must

be made of several important quantities on a regular basis.

These may be spectra of calculated diffuse attenuation coefficients, reflection ratios, or simply profiles of processed

conductivity, temperature, and depth (CTD) data. The

BBOP data processing system streamlines the plotting of

important parameters by allowing quick output of selected

variables as simple ASCII files, which are easily read and

manipulated by plotting programs.

The documentation of data processing procedures is often neglected in many data processing schemes. For example, it is essential to trace changes in calibration constants.

Things as simple as the digital filtering method used are

also important characteristics imposed upon a data set.

As part of the BBOP data processing system, processing

documentation is added automatically to the data file so

that every data user knows explicitly what was done to the

data file.

2.3 LCD FILE FORMAT

At the heart of the BBOP processing system is the LCD

file and its structure. The LCD file is self-contained, with

all pertinent header, calibration, and processing history

included. This system is somewhat redundant since much

of the header and calibration data will be identical for all

casts in a cruise; however, the benefits of easy access to

calibration constants and processing history far outweigh

the cost of slightly larger data files. During the first 18

months of the BBOP activity, 20 different calibration files

have been required due to changes in spectroradiometer

calibration, ancillary instruments, and collection software.

Record keeping has been simplified by recording all calibration information within each data file. The LCD file

can be stored in a compressed format to save disk space.

The LCD file is organized into five major sections separated by keywords in brackets (<...>). The header portion, <cruise_info>, contains general cast information

gleaned from several sources. The <sampled_parameters>

and <derived_parameters> sections contain a list of the

contents of the data fields found in the <data> section.

The <sampled_parameters> section also contains the calibration constants. At the end of the LCD file is the

<filters_used> section which contains a record of all

BBOP programs applied to the file as well as any statistical output generated by the programs. Excerpts of an

LCD file after some processing are shown in Fig. 27.

The LCD file structure is required by all BBOP filters

and scripts. The choice of a conversion program depends

on the software used to collect the data. If the data have

been collected with Biospherical Instrument’s M24-PROF

software, the LCD file can be created with the BBOP program, mer2lcd. The LCD file is created from the card,

binary, and calibration files. The output is an LCD file

containing all the necessary features with an abbreviated

header, which can be augmented later or concurrently by

using the BBOP shell script smklcd (Section 2.4.1). Once

the LCD file is completed, it is ready for processing using

the BBOP filters.

38
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

<cruise info>

filename a111292f

date 11-12-1992

position 31 17.96 64 18.81

cruise b50

...

castid index1mer time 1depth

a111292f.dt1 2.5000000e+01 1.8150000e+01 1.3406400e+00

a111292f.db1 4.9100000e+02 1.3502500e+02 1.2275800e+02

a111292f.ub1 5.2000000e+02 1.4727500e+02 1.2317100e+02

a111292f.ut1 9.2000000e+02 2.4760000e+02 1.1174000e+00

<sampled parameters>

1ed410 0. 4.547500e-02 1.603000e-03

...

<derived parameters>

kc-1ed410

...

<data>

-9.900000e+35 3.457528e+01 3.790249e+01 4.242258e+01 ...

...

<filters used>

...

bscalc -fr 1ed410 1 20 hmdqa111292f.lcd.1 bhmdqa111292f.lcd.1

#stats ---> samples = 19 abdev = 1.663616e-01

int = 3.457528e+01 slope = 9.904044e-01

min = 9.872207e+00 max= 3.772964e+01

mean = 2.863836e+01 stdDev = 1.364106e+00 var = 1.101210e+00

confidence(95) = 1.381770e+00

bbopkc -fs 1ed410 10 bhmdqa111292f.lcd.1 kbhmdqa111292f.lcd.1

...

Fig. 27. Example of an LCD file.

2.4 BBOP FILTERS

The BBOP filters are a suite of UNIX programs designed to perform a variety of functions in the processing

of LCD data files:

mer2lcd creates the LCD file from the M24-PROF created card, binary, and calibration files.

bboprecal recalibrates sampled parameters based on

new scales and offsets.

bbopradq compares data to thresholds and replaces

them with trap flags if they are below the threshold.

bbopangq compares package angle data to a constant

and writes a quality flag.

bbopkq calculates incident irradiance changes over a

depth interval and writes a quality flag.

bbopdespike flags spikes based on thresholds and forward first difference or slope difference, replacing

data with a mean of windowed points.

bbopmovavg smooths data using a moving arithmetic

mean window (boxcar).

bboph2o calculates water properties using UNESCO

FORTRAN routines.

bbopbin breaks the cast into profiles, sorts the profiles

on depth, and averages data over depth intervals

creating new LCD files for each profile.

bscalc extrapolates data to a null depth (z=0−) over

a specified depth window.

bbopkc calculates the diffuse attenuation coefficients.

bboptrans performs transformation operations (log,

sqrt, sin, etc.) on data.

bbopmath performs simple math operations with data

by columns, or by one operand and a scalar.

bbopfutil extracts or removes fields from an LCD file

and writes the result as a new LCD file.

bbopdeflag removes or keeps lines of data based on

flag values and thresholds.

bbopstrip extracts LCD data columns and writes them

to a simple ASCII file.

The filters are designed as stand-alone programs that

can be applied to LCD files directly or embedded in shell

scripts. The BBOP Filters are built using g++ (gcc) version

2.4.0 and are stable on Sun Microsystems (SUN) Sparc,

Digital Equipment Corporation (DEC) Ultrix, and SGI

platforms. The BBOP filters will either replace data values within the <data> section of the LCD file, add new

39
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Raw Data

bbopradq

bbopangq

bbopkq

SRAKQ

bboprecal

SZCORR

STRANSCORR

SMKLCD

mer2lcd

SBT

bbopstrip

Matlab

bbopdespike

SDESPIKE

bbopmovavg

SMOVAVG

SH2O

bboph2o

bbopbin

SBIN Archive

Analysis

bscalc

SBSC

bbopkc

SKC

bbopmath

SMATH

Fig. 28. Schematic cartoon tracing some of the processing steps used in BBOP. The outer shell holds the shell

script(s) which in turn call the BBOP filter(s) within the box. The resulting files are binned, qualified LCD files

for archiving.

data fields, or do both. New data field names are placed in

the <derived_parameters> section and can be operated

on by most of the filters in the same way as those in the

<sampled_parameters> section.

All filters share certain fundamental features:

1. Filters read and operate on an external ASCII LCD

file and create an output file with a name specified by the user, with the exception of bbopbin,

which creates the output file name using the input

file name.

2. Filters require that LCD files contain all the keywords described above.

3. Filters append the contents of their command lines

to the <filters_used> section of the output file.

4. Filters will not operate on a field if the same filter

and arguments have been used before, or if the new

field name already exists.

The flow of data processing is controlled using UNIX

shell scripts written in the Bourne and Bash shells. These

scripts can create and extract header information, build

LCD files, and call graphics packages or BBOP filters. The

scripts are used to point to the appropriate directories,

automate processing, check for errors in execution, and

update log files. Figure 28 illustrates the overall structure

of the BBOP processing system. The scripts currently used

are as follows:

smklcd creates the LCD file from the card, binary and

calibration files, and cruise notes.

szcorr recalculates the pressure channel using a new

cruise offset.

stranscorr recalculates the transmissometer channel

using the new cruise air calibration and dark offset.

sbt determines tops and bottoms of profiles using an

interactive Matlab script and inserts the castid table into the LCD header section.

srakq qualifies data based on dark value thresholds,

instrument tilt, and constant surface illumination

intensity.

sdespike despikes conductivity, temperature, fluorometer, and transmissometer data twice (two passes).

smovavg smoothes despiked conductivity, temperature,

fluorometer, and transmissometer channels.

sh2o calculates salinity, σ, σt, potential temperature,

and σθ.

40
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

sbin breaks a cast into profiles, and sorts and averages

the data into 1 m bins.

sbsc extrapolates below surface irradiance and radiance to a null depth over a predefined window using

the robust algorithm.

skc calculates attenuation coefficients over a 10 m moving window.

smath calculates irradiance and radiance products and

ratios.

sdeflag removes data lines containing trap flags.

sloop is a simple looping program for passing file names

to other scripts.

scmds automates the entire BBOP processing system

(see example below).

The scripts calling individual BBOP filters can be run

singly, or more commonly, are combined in a global script

(scmds). By combining the scripts and utilizing the UNIX

environment, the entire data processing system can be automated. Figure 29 shows an example of the global script

scmds, processing a recalibrated LCD file through completion.

#!/local/gnu/bin/bash

lcd_file=‘basename $1‘

logfile=$PWD/log/${lcd_file}.log

{

echo -n "$lcd_file : "; date

srakq $1&&\

sdespike q${lcd_file}&&rm q${lcd_file}&&\

sfmovavg dq${lcd_file}&&rm dq${lcd_file}&&\

sh2o mdq${lcd_file}&&rm mdq${lcd_file}&&\

sbin hmdq${lcd_file}&&\

{ for filename in ‘ls hmdq${lcd_file}.*‘

do

sbsc $filename&&\

skc b${filename}&&rm bhmdq${lcd_file}.*&&\

smath kb${filename}

done }

date

} 2>&1 | tee -a $logfile

Fig. 29. An example of the global script scmds.

The && construct allows the following command to be

executed only if the command preceding it is successful (returns a zero exit value). This insures that if one step fails,

the running of the script on a particular file will stop. The

standard output of each script is sent to a log file for each

LCD. Each script called in scmds prefixes a unique letter to

the input file name. For example, hmdqa010193.lcd indicates that the scripts srakq, sdespike, smovavg, and sh2o

have been run on the LCD file a010193.lcd. In scmds,

most intermediate LCD files are removed with the exception of the files that precede and follow bbopbin, which

are later archived. Files are available for quick looks at

any step within the processing sequence.

2.5 DATA PROCESSING STEPS

2.5.1 Bottom-to-Top Indexing

Because the LCD file must be eventually split into individual profiles, each cast is examined to determine the

tops and bottoms of down- and up-casts. Initially, an automated method for marking these points was pursued,

but the great variety in the shape of the time versus depth

curve made this determination prohibitively complicated.

It was decided that the time versus depth curve must be

evaluated manually for each cast. There are any number of ways this could be done. Matlab, a commercial

software package, was chosen as a GUI to allow a trained

user to interactively select these points. A table comprised

of a header line and corresponding indices and depths is

inserted into the <cruise_info> section of the LCD file

(see the example LCD file). These indices and depths are

points marking the tops and bottoms of the profiles within

the cast. Once these data are inserted, the first line of the

table (beginning castid) functions as a list of keywords.

The data within this table are used by bbopkq in anticipation of the binning process and then by bbopbin.

2.5.2 Recalculation

Recalculation may be needed due to changes in instrument calibration constants, at-sea atmospheric pressure

offsets, or transmissometer offsets. The bboprecal filter

converts data from engineering units to voltages using scale

and offset values from the <sampled_parameters> section

and converts back to engineering units using the scale and

offset values from the command line. The new scale and

offset constants are inserted following the variable name

in the <sampled_parameters> section maintaining a calibration history within the LCD file. Recalibration is not

implemented for Sea-Bird (CTD) sampled parameters or

derived fields.

2.5.3 Data Qualification

Because the BBOP data processing approach minimizes

human intervention, the number of data quality assessment

and assurance checks that are made as part of the BBOP

data processing is maximized. The first data qualification

uses bbopradq to assess the ambient light levels for each

channel. When ambient light levels fall below a predefined level, the individual data fields are replaced by a trap

flag (−9.9 × 1035). At all later stages of BBOP processing, flagged fields are not operated upon. Lines containing

these flags can be later deleted using bbopdeflag or in an

application program.

The bbopangq filter qualifies lines of data based on the

orientation of the radiometer in the water, since spectral

data may be compromised by excessive tilting of the instrument. This filter creates a new field containing a quality

flag rather than replacing data in a field.

41
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

0.00

25.00

50.00

75.00

100.00

0 50 100 150 200 250 300

mer_time

2 Ed (665) and KQ Flags

Fig. 30. Incident downwelling irradiance (solid) and Kd quality (KQ) flags produced by bbopkq. Regions of

non-zero KQ flags (shaded) indicate periods where the calculation of Kd(z,λ) may be uncertain due to variable

incident irradiance. Lines of data can be later deleted or accepted using bbopdeflag and user specified flag

thresholds.

Accuracy of the diffuse attenuation coefficient calculation is strongly dependent on a constant intensity of incident radiation during the calculation time interval. The

bbopkq filter is used to identify the segments of the cast

over which surface illumination is steady enough for the

diffuse attenuation coefficient to be accurately determined.

The bbopkq filter uses the castid table information to

perform its qualification in anticipation of the binning and

calculation of attenuation coefficients (see bbopkc in Section 2.5.8). The bbopkq routine calculates the standard

deviation and the mean of the first difference for the group

of points centered at each data line within the user-selected

depth interval (in meters) over which the attenuation coefficients will be calculated. The user also specifies a threshold values for the standard deviation and maximum first

difference with which bbopkq compares its calculated values and writes a quality flag into a new field dependent

upon the results of the comparison. Later, these flag values

can be used to assess the quality of the calculated diffuse

attenuation coefficients (Fig. 30).

2.5.4 Despiking and Smoothing

Two types of digital low-pass filters are used to reduce

spurious data values (spikes) within the BBOP data processing system. The first filter (bbopdespike) uses two

criteria, either together or individually, to flag potential

data spikes. Flagged values are either replaced with arithmetic means over a user defined window or trap flags if no

valid (non-flagged) data exists within that window. The

criteria are based upon either a forward first difference or

a forward slope difference compared to specified threshold values. The second filter, bbopmovavg, uses a moving

arithmetic mean (boxcar) with the window size defined by

the user. Despiking and smoothing are carried out on temperature, conductivity, transmissometer, and fluorometer

fields only. Both the despiking and moving average filters

create new fields, leaving the input field untouched.

2.5.5 CTD Calculations

The bboph2o routine calculates water characteristics

using the standard UNESCO FORTRAN subroutines and

writes the calculated parameter as a new field in the LCD

file. The bboph2o routine must have despiked temperature

and conductivity data to produce a smooth salinity profile. Time constant differences between the conductivity

and temperature sensors are accounted for using a singlepole digital filter. The following parameters are calculated:

salinity, in situ density, σt, potential temperature, σθ, and

coefficients of thermal and saline expansion.

42
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

2.5.6 Binning

A cast may contain from one to several up or down profiles (yo-yos) and hence, may not be monotone in depth.

The binning filter, bbopbin, uses the information from

the castid table to break up the LCD file into individual daughter profiles that are sorted based on pressure

and compacted using arithmetic averages over a pressure

interval (bin±∆p). The resulting pressure field for each

binned profile is also binned to evaluate the true depth of

each binned data record. Each daughter LCD file retains

the complete header and processing history of the original

LCD file and contains two new <derived_parameters>

fields named bin_m, where m is the interval chosen by the

user, and bin_pts, containing the number of raw bandwidth data records that have been averaged into that bin.

If the file is missing pressure interval data near the surface, bbopbin fills in the lines for the missing bins with

trap flags such that the binned LCD file begins at its lowest bin interval (i.e., 1.0) and increases monotonically.

2.5.7 Null Depth Extrapolations

The determination of optical fluxes just beneath the sea

surface, or at null depth, are of obvious relevance for ocean

color remote sensing (Dickey and Siegel 1993 and McClain

et al. 1993). The direct determination of null depth optical signals is particularly difficult due to high levels of

surface wave glint noise and uncertain water depth determinations. In order to provide accurate data at a null

depth for the irradiance and radiance channels, profiling

data sets must extrapolate their signals to the sea surface.

The BBOP filter bscalc employs an algorithm which statistically extrapolates a depth profile to the surface over a

user-specified depth interval. The bscalc filter allows the

user to specify both the upper and lower bounds in the

extrapolation interval, and the log-transformed fields are

extrapolated to the sea surface using the Beer-Lambert relation (Smith and Baker 1986 and Siegel and Dickey 1987).

Two extrapolation algorithms are currently available, the

standard chi-square (χ2) algorithm and a robust algorithm

which eliminates points greater than two standard deviations (2σ) from the mean (Press et al. 1992). The bscalc

filter does not make any derived fields, but rather creates

a new line of data at the top of the <data> section with

0 in the bin_m field and trap flags in any field for which

the extrapolation was not calculated. Statistics for the extrapolation fits are included following the command line in

the <filters_used> section (see the example LCD file).

2.5.8 K Calculations

The BBOP filter bbopkc calculates the attenuation coefficient, Kd(z,λ), at each binned depth interval using the

Beer-Lambert relation by employing a sliding regression

window. This procedure is similar to that employed by

Smith and Baker (1984) and Siegel and Dickey (1987).

The user specifies the channel, algorithm, and depth window over which to perform the regressions using either

standard χ2 or robust algorithms (Press et al. 1992). It

was decided not to normalize the underwater irradiance

fields to simultaneous above-water irradiance data for the

Kd(z,λ) calculations. The approach is to select profiles

that are free from perturbations and to create composites

of profiles based on bbopkq KQ flags. It is unclear whether

the normalization would be relevant in a quickly changing

irradiance field due to the passing of small convective cloud

elements. Furthermore, it is uncertain whether a surface

irradiance normalization routine will mask any variations

associated with the incident radiance distribution. The

depth window used with bbopkc should be the same as

was used for bbopkq.

2.5.9 Simple Math and Transformations

A variety of utility BBOP filters are also available. For

example, bbopmath performs simple math operations on

LCD data fields (add, subtract, multiply, ratio), and adds

a new field to the data section. The bboptrans filter performs simple math transformations (log, exp, sin, asin,

sqrt, etc.) on the data and adds a new field to the data

section. Both these tools are used in the final stages of processing to produce spectral ratios and reflectance ratios, as

well as to calculate the beam attenuation coefficients for

the transmissometry field.

2.6 CONCLUSIONS

The BBOP data processing system meets the data processing goals. By making this system available to the community, other investigators will hopefully benefit from the

investments made at UCSB and will contribute incremental improvements to the existing system. It is intended

that investigators will customize or rewrite the filters and

scripts to suit their own needs and systems, as well as

contribute suggestions for improvement. This processing

system and source code are available to anyone via anonymous ftp from ftp.icess.ucsb.edu (/pub/bbop/soft).

The BBOP data processing system, however, is not intended as a software product, but the usual free software

licensing caveats apply.

Acknowledgments

The development of the BBOP data processing system was

supported by NSF (OCE 90–16372) and by NASA (NAGW–

3145). Support from Digital Equipment Corporation’s flagship

research project, Sequoia 2000, is also gratefully acknowledged.

43
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Chapter 3

Integral Method for Analyzing Irradiance and

Radiance Attenuation Profiles

James L. Mueller

San Diego State University, San Diego, California

Abstract

A method is presented for determining the profile of attenuation coefficient (K) for a vertical profile of irradiance

or radiance through a least-squares fit to the optical depth profile, expressed as the integral of K from the surface

to each depth z. The measured optical depth at each z is calculated as the natural logarithm of the surface-todepth ratio of measured irradiances (or radiances). The K profile is represented analytically by Hermitian cubic

polynomials connecting nodes at several discrete depths, with unknown values of K and its vertical derivative at

each node as coefficients. These polynomials are integrated analytically to each z, which allows each measured

optical depth to be set equal to a polynomial with node values of K and its derivative at the node depths.

This results in an overdetermined set of equations corresponding to all measured depths in the irradiance (or

radiance) profile, which is solved using classical least-squares methods. Prior to solution, irradiance data are

normalized to minimize effects of surface irradiance variations, and segments near major anomalies (resulting

from strong cloud shadows or ship shadows) are eliminated from the fit. In contrast to the classical derivative

solutions for K, the integral approach ensures a correct representation of total attenuation through missing data

intervals.

3.1 INTRODUCTION

Vertical profiles of downwelling irradiance Ed(z,λ) are

related to Ed(0−, λ), incident irradiance just below the sea

surface, by the equation

Ed(z,λ) = Ed(0−, λ)e

−

z

0

Kd(z,λ)dz

. (1)

The function Kd(z,λ) is the profile of irradiance attenuation coefficients throughout the water column. This same

equation relates upwelling irradiance Eu(z,λ) to Eu(0−, λ)

and Ku(z,λ) and relates upwelling radiance Lu(z,λ) to

Lu(0−, λ) and KL(z,λ).

It has become relatively common practice to measure

vertical profiles of Ed(z,λ), Eu(z,λ), and Lu(z,λ) using

underwater radiometers, with the usual objective being to

determine Kd(z,λ), Ku(z,λ), and KL(z,λ). The classical

approach to determining K(z,λ) from E(z,λ) is described

in detail by Smith and Baker (1984 and 1986). It is necessary to first normalize the E(z,λ) profile to surface irradiance Es(z,λ) profiles, measured on the deck of the ship

during a cast. K(z,λ) is then determined by estimating

the slope of lnE(z,λ)



at depth z; this is accomplished by

least-squares fitting a straight line through a data window

extending several meters above and below depth z. The

window is then slid down approximately 1 m and a leastsquares K is determined again, and so on over the entire

profile.

A typical window size ranges from 8 m (Smith and

Baker 1984 and 1986) to 20 m; the latter is used in the

present work for preliminary K estimates. This precludes

the fitted K profile from following rapid variations through

sharply defined layers of suspended particles, which are

commonly observed in beam attenuation profiles in coastal

and frontal water masses. Even with a 20 m window, moreover, the local slope-fitting procedure is sensitive to high

frequency fluctuations in incident irradiance which are not

in phase at the on-deck and underwater radiometers.

A new method is presented for determining K profiles

by minimizing the departure of the estimated optical depth

profile

τˆ(z,λ) = z

0

K(z, λ)dz (2)

from the measured optical depth profile

τ (z, λ) = ln

E(0−, λ)

E(z,λ)



. (3)

The new approach has the advantage of being able to

more closely follow variations in K through very turbid

44
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

layers, as indicated independently by beam attenuation.

At the same time, the integral approach is more successful

at smoothing through surface irradiance fluctuations than

is the local slope-fitting method.

3.2 DECK CELL SMOOTHING

The first step in this procedure is to fit the deck cell irradiance profiles Es(z,λ) directly to Hermitian cubic basis

functions,

γ01(ξ) = 1

4

(ξ − 1)2(ξ + 2),

γ02(ξ) = −1

4 (ξ + 1)2(ξ − 2),

γ11(ξ) = Li

8 (ξ − 1)2(ξ + 1), and

γ12(ξ) = Li

8 (ξ + 1)2(ξ − 1).

(4)

For z in the ith depth element zi−1 ≤ z<zi, ξ is a

local coordinate ranging from −1 at node zi−1 (the shallow

end point of the element) to +1 at node zi, that is,

ξ = 1

Li



2z − zi + zi−1

	

, (5)

with differential

dξ = 2

Li

dz, (6)

where Li is the length of ith finite element, i.e.,

Li = zi − zi−1. (7)

At any depth z corresponding to location ξ in element

i, the smoothed estimate of deck cell irradiance Eˆs(z,λ)

will be given by

Eˆs(z,λ) = γ01(ξ)Es,i−1(λ)

+ γ02(ξ)Es,i(λ)

+ γ11(ξ) ∂

∂z Es,i−1(λ)

+ γ12(ξ) ∂

∂z Es,i(λ),

(8)

where Es,i(λ) is the value of Es(z,λ), and ∂

∂z Es,i(λ) is its

derivative with respect to z, at node depth zi. Both the

function Eˆs(z,λ) and its first derivative are continuous at

nodes joining adjacent finite depth elements.

The profile of measured Es(z,λ) is displayed on the

computer’s screen. Nodes are placed at N depths zn (n =

0, . . . ,N − 1) selected to allow the cubic functions of (8)

to closely follow major excursions of incident irradiance,

while smoothing high frequency wiggles which are unlikely

to be well matched to incident irradiance fluctuations observed by the underwater radiometer. The selection of

which Es fluctuations follow closely is one subjective element in this procedure. The recommended practice is

to pick nodes which allow the fitted curve to closely follow fluctuations with wavelengths greater than or equal to

10 m, and to smooth out shorter scale fluctuations. In the

time domain, 10 m corresponds to approximately 20 s at a

typical winch speed of 0.5 m s−1.) The smoothed Es(z,λ)

and normalized Ed(z,λ) profiles should, in any event, be

examined jointly before proceeding further. If features in

the fitted Es curve are reflected by opposing features in

the Ed curve indicating overcompensation by the deck cell

normalization, then the data from that segment of both

profiles should be eliminated from the fitting procedure.

Once node depths zn are selected, the Es(zm,λ) vector,

measured at m = 1, . . . ,M depths zm, may be expressed

in matrix form as

Es(zm,λ) = H

Es(λ), (9)

where Es(λ) is the measured irradiance vector of length M

Es(λ) =





Es(z1,λ)

Es(z2,λ)

.

.

.

Es(zm,λ)

.

.

.

Es(zM,λ)





, (10)



Es(λ) is the unknown vector (of length 2N) of irradiances

and vertical gradients of irradiance at the N node depths

zn,



Es(λ) =





Es,0(λ)

Es,1(λ)

.

.

.

Es,n(λ)

.

.

.

Es,N−1(λ)

∂

∂z Es,0(λ)

∂

∂z Es,1(λ)

.

.

. ∂

∂z Es,n(λ)

.

.

. ∂

∂z Es,N−1(λ)





, (11)

and H is a matrix with 2N columns and M rows

H = hmj 

, (12)

with elements

hmj =







γ01, zm in element n and j = n − 1;

γ11, zm in element n and j = n − 1 + N;

γ02, zm in element n and j = n;

γ12, zm in element n and j = n + N;

0, otherwise.

(13)

45
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

To reduce (9) to normal form for least-squares determination of the vector Es, multiply both sides by HT , the

transpose of H, and rearrange to obtain



Es(λ) = HT H

−1

HT Es(λ), (14)

where HT H

−1 is the inverse matrix of 

HT H



.

The resulting components Es,n(λ) are the coefficients

needed, together with the array of node depths zn, to compute smoothed deck cell irradiance Es(λ) at any depth z

using either (8) or its matrix equivalent (9).

The smoothed Es profiles are now ready for use in obtaining smoothed fits to measured profiles of downwelling

irradiance Ed(z,λ), upwelled irradiance Eu(z,λ), and upwelled radiance Lu(z,λ). To provide guidance for node

placement in a least-squares fit to cubic Hermitian finite

elements, the profiles are first binned to 1 m intervals, and

preliminary attenuation coefficients K are estimated using

the classic practice of fitting a least-squares slope over a

window extending a few meters (in this case ±10 m) on

each side of each depth point, e.g., Smith and Baker (1984

and 1986).

3.3 BIN-AVERAGED PROFILES

Profiles of Ed(z,λ), Eu(z,λ), and Lu(z,λ) are averaged

into 1 m depth bins in preparation for preliminary leastsquares smoothing to obtain the vertical attenuation coefficients of each quantity. The preliminary smoothing algorithm outlined below is similar to that described by Smith

and Baker (1984):

K

d(z,λ) = −1

Ed(z,λ)

d

dz Ed(z,λ),

K

u(z,λ) = −1

Eu(z,λ)

d

dz Eu(z,λ), and

K

L(z,λ) = −1

Lu(z,λ)

d

dzLu(z,λ)

(15)

are determined by least-squares fits to the equations

ln

Ed(z,λ)Es(550)

Es(z,550) 

= Ad(z,λ) − K

d(z,λ)[z − z],

ln

Eu(z,λ)Es(550)

Es(z,550) 

= Au(z,λ) − K

u(z,λ)[z − z],

ln

Lu(z,λ)Es(550)

Es(z,550) 

= Al(z,λ) − K

L(z,λ)[z − z],

(16)

where z is the mid-depth of a 20 m window, Es(550) is the

smoothed 550 nm deck cell irradiance at depth z, Es(z,550)

is the 550 nm deck cell irradiance coincident with measurements at depth z, and the intercepts A correspond to the

natural logarithm of smoothed irradiance or radiance at

depth z, i.e.,

Ad(z,λ) = ln

E

d(z,λ)



,

Au(z,λ) = lnE

u(z,λ)



, and

Al(z,λ) = ln

L

u(z,λ)



.

(17)

The values of E

d(z,λ), E

u(z,λ), and Lu(z,λ) [as obtained from Ad(z,λ), Au(z,λ), and Al(z,λ) by the above

least-squares smoothing algorithm] are then each normalized to the surface by the ratio of deck cell irradiance

Es(0,550):Es(z,550) calculated using (14).

The resulting smoothed profiles of K and E are used

as a guide in selecting node depths for fitting improved K

and E profiles to the measured irradiance.

3.4 OPTICAL DEPTH PROFILE FIT

Measured optical depth τ (z) at depth z is calculated

as

τ (z) = ln

E(0−)

E(z)



, (18)

where E may be Ed, Eu or Lu, and E(0−) is irradiance

(or radiance) just beneath the sea surface. A first guess

estimate of E(0−) is obtained from the preliminary binaveraged, window-fit K and E profiles as

E(0−) ∼= E

(10)e10K

(10). (19)

[Because of the 20 m fitting window, K

(10) and E(10) at

10 m are the shallowest estimates in that profile.]

Optical depth is related to the irradiance (or radiance)

attenuation coefficient K(z) profile by the

τ (z) = z

0

K(z)dz. (20)

If once again the water column is divided into N − 1

finite elements, separated by N nodes at depths zn (n =

0, . . . ,N −1), K(z) may be estimated in the depth element

zn−1 ≤ z<zn as

Kˆ (z) = γ01(ξ)Kn−1 + γ02(ξ)Kn

+ γ11(ξ) ∂

∂z Kn−1 + γ12(ξ) ∂∂z Kn, (21)

where γij (ξ) and ξ are defined in (4) and (5), respectively.

The same form of cubic Hermitian polynomial expansion

is used in (8) and (21) to represent the variables Es(z) and

K(z), respectively.

46
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

If (21) is substituted into (20), the resulting equation

obtains for zn−1 ≤ z<zn:

τ (z) = i<n

i=1 

Ki−1

zi

zi−1

γ01(ξ)dz + Ki−1

zi

zi−1

γ02(ξ)dz

+

∂

∂z Ki−1

zi

zi−1

γ11(ξ)dz +

∂

∂z Ki

zi

zi−1

γ12(ξ)dz



+ Kn−1

z

zn−1

γ01(ξ)dz + Kn

z

zn−1

γ02(ξ)dz

+

∂

∂z Kn−1

z

zn−1

γ11(ξ)dz +

∂

∂z Kn

z

zn−1

γ12(ξ)dz.

(22)

By the definition of ξ and its differential in (5), variables may be changed to express the integral terms in (22)

in the form

z

zn−1

γ(ξ)dz = Li

2



ξ

−1

γ(ξ)dξ. (23)

Thus, (22) may be formally integrated from ξ = −1 to ξ,

to obtain new coefficients

g01(ξ) = 1

4



ξ4

4 − 3ξ22 + 2ξ +

13

4



,

g02(ξ) = −1

4



ξ4

4 − 3ξ22 − 2ξ − 34



,

g11(ξ,Li) = Li

8



ξ4

4 − ξ33 − ξ22 + ξ +

11

12

,

g12(ξ,Li) = Li

8



ξ4

4 + ξ33 − ξ22 − ξ − 512

,

(24)

with values at ξ = 1 (i.e., the integrals over a complete

finite element from −1 to 1):

g01(1) = 1,

g02(1) = 1,

g11(1,Li) = Li

6 , and

g12(1,Li) = −Li

6 .

(25)

With substitutions from (23) through (25) and some rearrangement of the terms involved, (22) may be expressed

for z = zm, zn−1 ≤ zm < zn, as

wmτ (zm) = wm

n

i=0



hm,iKi + hm,i+N

∂

∂z Ki



, (26)

where

hmi =







L1

2 g01(ξ), n =1, i = 0;

L1

2 , n> 1, i = 0;

Li + 1 + Li

2 , n> 1,0 <i<n − 1;

Ln

2 g01(ξ)

+

Ln − 1

2 , n> 1, i = n − 1;

Ln

2 g02(ξ), i = n;

(27)

and

hm,i+N =







L1

2 g11(ξ,L1), n =1, i = 0;

L2

1

12 , n> 1, i = 0;

L2

i+1 − L2i

12 , n> 1,0 <i<n − 1;

Ln

2 g11(ξ,Ln)

+

L2

n−1

12 , n> 1, i = n − 1;

Ln

2 g12(ξ,Ln), i = n.

(28)

The coefficients wm in (26) weight the relative importance of each mth measurement in the τ profile. A relatively simple weighting prescription is used at the time of

this writing. The weighting coefficient at each depth zm is

first computed as

wm = 1

s0(zm)

, (29)

where s0(zm) is the standard deviation of measured deck

cell irradiance Es(z,λ) relative to the smoothed profile

[computed using (8–15)] over the 10 m depth interval centered at zm. This weighting gives reduced importance

to irradiance measurements made when surface irradiance

fluctuates rapidly, e.g., due to variable cloud cover, and increased importance to measurements made when incident

irradiance is relatively steady. In depth intervals where

both the surface and normalized subsurface irradiance profiles exhibit relatively large amplitude fluctuations, wm

are further multiplied by 10−6 to completely discount observations where incident irradiance variations viewed by

the deck and underwater radiometers are obviously not in

phase.

Ship shadow contamination can be visually recognized

in many irradiance profiles measured under overcast conditions. The characteristic shape of this phenomenon is illustrated in Voss et al. (1986). In profiles where ship shadow

47
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

is present, the resulting curvature in the upper layer can

distort the K and irradiance profile fits over the upper

100 m. In principle, a ship shadow model could be used

to correct the profile explicitly, but research to develop

and test the implementation of such a method is beyond

the scope of the present report. As an interim substitute,

multiply the weighting coefficients wm by 10−4 at depths

where ship shadow curvature is visually apparent. With

the surface layer boundary conditions used at present (see

below), this has the effect of passing a straight line to represent E(z,λ) from the surface intercept to the depth of

the first node selected by the operator, without regard to

any curvature that may be apparent in the measured data.

In matrix form, (26) for m = 1, . . . ,M profile observations at depths zm are combined as

τ = H

K. (30)

The M elements of the vector τ are τm = wmτ (zm), calculated from the data with (18). The elements of the M ×2N

matrix H are wmhmi, where hmi are defined in (27) for

i ≤ n, in (28) for N ≤ i ≤ n + N, and hmi = 0 for

n<i<N and i>n + N. Finally, the vector 

K is defined

as



K =





K0

K1

.

.

.

Kn

.

.

.

KN−1

∂

∂z K0

∂

∂z K1

.

.

. ∂

∂z Kn

.

.

. ∂

∂z KN−1





, (31)

i.e., the unknown vector of attenuation coefficients and

their gradients at the N nodes.

As with 

Es, K is determined by the least-squares solution to (30) as



K = HT H

−1

HT τ, (32)

which can be solved using any standard matrix inversion

program. In implementation, the singular value decomposition method and computer codes used were given by

Press et al. (1992).

To constrain the solution at the surface node, which

depends on the entire data profile below it, it is required

that

K0 ≡ K1, (33)

which implies further that

∂

∂z K0 ≡ ∂∂z K1,

≡ 0.

(34)

In other words, a layer of constant K is imposed between the surface and the depth of the first node selected

by the operator.

The bottom boundary values are not constrained, and

frequently take on unreasonable values. The solution at the

bottom node is determined only by the data in the deepest

finite element, when the data values are near the minimum

detectable level of the instrument, and consequently are

noisy. To minimize the influence of the bottom node on

the overall profile, a thin (less than 10 m) bottom element

is selected by picking the second deepest node (node index

N − 2) very close to the deepest one (index N − 1). Then

ignored is the fitted curve in this bottom boundary layer

simply by dropping node N − 1 when the fitted K and E

curves are calculated using (22) and (30).

This completes the mathematical description of the fitting procedure. In use, the first estimate bin-averaged

E

(z) profile is overlaid on the measured E(z) profile on

the computer screen. To aid the operator in selecting node

depths, profiles of bin-averaged K

(z) and smoothed deck

cell irradiance Es(z) are also displayed. If data from a

beam transmissometer or fluorometer are available, the

profiles of beam attenuation coefficient cp(z) and chlorophyll a fluorescence F(z) are displayed as a further guide to

selecting node depths. The operator then interactively selects an array of node depths, observes the resulting fit obtained using (18–32), and iteratively adjusts node depths

and refined estimates of E(0−) until a best fit is obtained.

After this procedure has been applied to all radiometer

channels, each profile of Kd(z,λ), Ku(z,λ), and KL(z,λ) is

represented by a vector of node depths zn and the associated vector 

K, as given in (31). The fitted curve for each

K(z,λ) can then be calculated using (22). To calculate

the fitted Ed(z,λ) profile, (30) is first used to calculate the

td(z,λ) profile and then

Ed(z,λ) = Ed(0−,λ)e−τd(z,λ)

. (35)

The same procedure is used to retrieve Eu(z,λ) and

Lu(z,λ) profiles from the Hermitian finite element representation.

48
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Chapter 4

Automated and Interactive Bio-Optical

Processing Package

Curtiss O. Davis

W. Joseph Rhea

Naval Research Laboratory, Washington, DC

Abstract

Underwater profiles of upwelling and downwelling irradiance, as well as upwelling radiance have been collected

from a wide variety of ocean environments. To obtain apparent optical properties from these data requires

removing data spikes, binning and smoothing, and removing possible artifacts from changes in surface illumination, ship shadow, or reflections and wave focusing. To accomplish these data processing requirements, the

AIBOP software package was developed. In the AIBOP system, the data is processed in two parts: first, an

automated de-spiking and binning routine, and second, interactive spectral processing to remove the effects

of changes in surface illumination, ship shadow, or reflection and near-surface effects such as wave focusing.

Once the data is processed, routines are provided for calculating extinction coefficients, surface radiance and

irradiance values, and remote sensing reflectance.

4.1 INTRODUCTION

Optical data has been collected with a BOPS as part of

a number of interdisciplinary oceanographic expeditions,

such as, the JGOFS EqPac and Arabian Sea programs.

The BOPS records 44 channels of data and extensive processing is required to retrieve AOPs from the measured

underwater light fields. To provide accurate, rapid processing of these large data sets, a AIBOP software package

written by W. Rhea using IDL from Research Systems,

Inc. The full processing of the data was broken down into

two distinct levels, the first one being an automated despiking and binning routine, and the second one being a

more detailed, user-interactive spectral processing section

where underwater optical effects, such as ship shadow, were

removed from the data.

The philosophy behind the development of the AIBOP

software centered on the desire to process all possible optical casts, not just those made under ideal environmental

conditions. Due to the number of different projects competing for wire time during most research cruises, often

only one optical cast was made per day. Under these conditions, it is necessary to use all possible data, even if it

contains correctable optical contamination, such as intermittent clouds, wave focusing effects, and occasionally ship

shadow or ship hull reflection. The AIBOP software was

designed specifically to remove as many of these effects as

possible.

4.2 INSTRUMENTATION

Optical data were collected with a BOPS, an updated

version of the BOPS originally developed by Smith et al.

(1984). The package is built around a Biospherical Instruments MER-1048 spectroradiometer, which measures

upwelling and downwelling spectral irradiance as well as

upwelling spectral radiance. The data collected with the

BOPS consists of 44 parameters and includes the following

measurements made by the MER:

13 channels of downwelling irradiance, Ed, ranging from 410–710 nm;

8 channels of upwelling irradiance, Eu, ranging

from 410–694 nm;

8 channels of upwelling radiance, Lu, ranging

from 410–683 nm;

Photosynthetically available radiation (PAR);

Depth; and

Instrument tilt and roll.

In addition, conductivity and temperature were measured with a Sea-Bird CTD, chlorophyll fluorescence was

measured with a Sea Tech fluorometer, and beam transmission was measured with a Sea Tech 25 cm transmissometer.

Simultaneously, a deck cell collected 4 channels of surface

downwelling irradiance, Ed(0+), ranging from 410–683 nm.

The MER acquired all the in situ data 16 times a second,

averaged it to four records a second, and sent it up the

49
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

31, 43, 44

******** DATA PROCESSING NOTES *********

Post-cruise (WJR) - Data processed using calibration file listed below.

Initial data-cleaning limited to occasional data spike or gap removal.

Data then binned to 1 meter depths.

09/91 (WJR) - Reprocessed Lu channel using time-averaged comparison between

original calibration (06/11/87) and SDSU/CHORS calibration (07/17/91).

******** VERTICAL CAST CASTCARD ********

FILE w910321a; 03-21-1991

CRUISE IDENTIFICATION..... WHITE POINT OUTFALL - 03/91

CALIBRATION FILE..........8302W10a.cfl

CALFILE DESC..............MER-1048-8302 Recalibrated for JPL 12 MAR 1991

NAME.................w910321a

DATE......................03-21-1991

STATION...................STA 1

Position.............LONG:118-20.41 W

.....................LAT :33-41.20 N

SKY CONDITION.............CLEAR

OPERATOR NAME.............WJR

SUN POSITION..............2

HANDSHAKE................. 1

ANALOG CHANNELS........... 35

FREQUENCY AVERAGED........ 1023

ANALOG CYCLES AVERAGED.... 4

START TIME................12:23:41

DOWNCAST ENDING MARKED AT RECORD 1391

UPCAST ENDING MARKED AT RECORD 2423

FINAL TIME................12:35:25

TRANSMISSION ERRORS....... 0

NORMAL TERMINATION OF DATA LOGGING.

TOTAL RECORDS RECORDED.... 2462

Record,Ed410,Ed441,Ed488,Ed520,Ed550,...

4,102.0160,118.3141,143.5586,138.1729,...

Fig. 31. An example of a working data file.

cable to a deck box and a PC which stored the data on a

hard disk.

4.3 LEVEL-1 PROCESSING

The first level of processing the BOPS data began with

a decision to use either the downcast or upcast, depending upon various criteria including absence of clouds in the

data, absence of ship shadow, and absence of data spikes

or dropouts cause by occasional pauses in the data transmission from the MER to the onboard computer. The next

step was an automated process to remove any remaining

data spikes or dropouts from the selected cast data and replace them (typically single values) with interpolated values.

The data was then checked for single points lying outside 3σ of a 7-point bin, and any outlying points were then

replaced with interpolated values. This was done to reduce much of the effects caused by wave focusing in clear

waters. This cleaned data was then mean-binned into 1 m

intervals, centered on the half meter. This completed the

level-1 processing.

4.4 LEVEL-2 PROCESSING

The level-2 processing involved interactive spectral processing of the data. First, ship roll was removed from surface light channels (when necessary) by applying a sevenpoint running mean filter to each of the four surface light

channels. Next, cloud effects were removed from the underwater light field by normalizing each underwater optical channel to either the mean or maximum of the closest

matching surface light wavelength. The decision whether

to use the mean or maximum of surface light value depended on the nature of the light field during the cast. In

general, the maximum value was used when the sky was either very clear or fully overcast. The mean value was used

only during partly cloudy conditions where bright clouds

caused a brief increase in measured light levels on either

side of the cloud with a shadow in the middle. In these

cases, the mean light value more closely matched the overall clear-sky light levels than did the maximum light value.

Also, when needed, the surface light channel records

were shifted slightly in time before normalizing the underwater data to account for the difference between the time a

cloud passed over the surface and underwater arrays. This

50
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

52, 43, 44

33.68667,-118.34016, 80.51598, 80.84930

=================================BEGIN HEADER==================================

DESCRIPTION OF FIRST LINE IN FILE (BEFORE HEADER):

Number of Header Records in this File

Number of Data Records following this header

Number of Columns within each Data Record

DESCRIPTION OF SECOND LINE IN FILE (BEFORE HEADER):

Latitude of Cast (Degrees; South=[-]; North=[+])

Longitude of Cast (Degrees; West=[-]; East=[+])

Date and Local Time of Cast (Julian Day + Local Time in percent of 24hr)

Date and G.M. Time of Cast (Julian Day + G.M. Time in percent of 24hr)

==================================CAST INFO====================================

Filename----------------------->,w910321a.mer

Cruise Identification---------->,WHITE POINT OUTFALL - 03/91

Station Identification--------->,STA 1

Date of Cast (Mon/Day/Year)---->,03-21-1991

Local Start Time (Hr:Min:Sec)-->,12:23:41

Latitude (Degrees Minutes )---->, 33-41.20 N

Longitude (Degrees Minutes)---->,118-20.41 W

Sky State During Cast---------->,CLEAR

==============================PROCESSING NOTES=================================

Post-cruise (WJR) - Data processed using calibration file listed below.

Initial data-cleaning limited to occasional data spike or gap removal.

Data then binned to 1 meter depths.

09/91 (WJR) - Reprocessed Lu channel using time-averaged comparison between

original calibration (06/11/87) and SDSU/CHORS calibration (07/17/91).

05/93 (WJR) - Ship-roll removed from all surface (Deck) channels.

05/93 (WJR) - All optical channels normalized to maxsurface (Deck) channels.

05/93 (WJR) - All optical channels smoothed to remove wave effects.

05/93 (WJR) - Ship shadow/reflection removed from affected optical channels.

08/93 (WJR) - %Transmission corrected by new calibration

08/93 (WJR) - %Transmission converted to Beam Attenuation

==========================DATA COLUMN DESCRIPTIONS=============================

Column, Column Title, Description

-------------------------------------------------------------------------------

0, Depth, Mean Depth of 1-meter record bin (meters)

1, Pts/m, Number of points averaged per bin (0=interpolated data)

2, Tilt , Instrument Tilt (degrees; range -45 to 45)

3, Roll , Instrument Roll (degrees; range -45 to 45)

4, TempC, Water Temperature (Deg C)

5, S ppt, Calculated Salinity (PPT)

6, Cond , Measured Conductivity

7, Sigma, Calculated Water Density

8, BeamC, Beam Attenuation Coefficient from 25cm Transmissometer

9, Fluor, Stimulated Fluoresence (Fluoro units; range 0 to 100)

10, PARuw, Downwelling Scalar PAR at depth (E17 quanta/sec/cm2)

11-23, Ed410-Ed710, Downwelling Spectral Irradiance at depth (uW/cm2/nm)

24-31, Eu410-Eu469, Upwelling Spectral Irradiance at depth (uW/cm2/nm)

32-39, Lu410-Lu683, Upwelling Spectral Radiance at depth (uW/cm2/nm/str)

40-43, E+410-E+683, Spectral Irradiance above sea surface (uW/cm2/nm)

=============================DATA COLUMN TITLES================================

Depth,Pts/m,Tilt ,Roll ,TempC,S ppt,Cond ,Sigma,BeamC,Fluor,PARuw,Ed410,...

=================================END HEADER====================================

0.50000, 0.00000, 1.81487, 5.48543, 0.00000, 0.00000, 0.00000, ...

Fig. 32. An example of a final data file.

51
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

shifting was used only for normalizing and was not applied

to the final surface light data. The next processing step

was to smooth out sea-surface wave effects from underwater optical channels by applying either a three-, five-, or

seven-point boxcar mean filter to all underwater optical

channels.

As a final step in casts where obvious effects such as

ship shadow, ship hull reflection, or wave focusing were

present in the downwelling irradiance (Ed) data, the upper (shallow) portion of each affected Ed channel was replaced with extrapolated, or curve-fit data. This was accomplished using a automated program that makes use of

the lower non-shadowed optical data and finds the most

accurate extrapolation to a predefined surface value estimated from the deck cell data. The extrapolation was

carried out using a least-squares polynomial fit performed

in log space. For those underwater Ed channels where

there was no corresponding above-water Ed(0+) channel,

estimated Ed(0+) values were automatically calculated using RADTRAN (Gregg and Carder 1990) for the time, day,

location, and sky conditions.

4.5 CALCULATION OF K

The diffuse attenuation coefficient for downwelling irradiance (Kd) was calculated from the Ed profile using a

least-squares fit of the log-transformed data (Smith and

Baker 1894). A 9 m window (z ± 4 m) was used for calculating Kd at each depth level. These Kd values were

not stored but rather were calculated on demand when

requested. Whenever measured chlorophyll and phaeopigment data were available, the calculated Kd values were

overplotted with Kd values as predicted by the model of

Morel (1988).

4.6 DATA FILE FORMATS

There are two file formats used by the AIBOP software

package. The first is a working format (Fig. 31), which is

used by the software during all of the processing steps.

This format keeps the data in the same configuration as

output by Biospherical Instruments data collection software. The second format is a final format (Fig. 32) which

is distributed to the public. The final format differs from

the working format in several ways:

The data column are rearranged in a more logical order (e.g., having depth as the first column),

Percent transmission from the transmissometer

is converted to beam attenuation, and

Greenwich Mean Time (GMT) is calculated for

each cast and is shown along with local time.

Also a special plotting line is added which can aid in

quickly determining the exact location and time of each

cast. All files are in comma-separated ASCII for ease of

reading by others.

4.7 LOG FILES

A log file was automatically created for each cast which

lists all of the level-2 spectral processing steps in the order

they were performed. The log file (Fig. 33) allows for later

recreation of the processed data, as well as serving as a

permanent record of the processing session. These log files

are stored separately from the data files but are available

to anyone requesting them.

MER OPTICAL PROCESSING LOG FILE

FOR: w910321a.mer

BY: WJR DATE: 05/93

PROCESSING PERFORMED ON ALL OPTICAL CHANNELS:

REMOVED_SHIP_ROLL_FROM_SURFACE

NORMALIZED_UW_TO_SURF_MAXIMUM

REMOVED_UW_WAVE_EFFECTS_(5PTS)

INDIVIDUAL OPTICAL CHANNELS PROCESSED:

1, (Ed 410), SHADOW_REMOVED_(7,13)

2, (Ed 441), SHADOW_REMOVED_(7,13)

3, (Ed 488), SHADOW_REMOVED_(11,15)

4, (Ed 520), SHADOW_REMOVED_(7,17)

5, (Ed 550), SHADOW_REMOVED_(3,13)

6, (Ed 560), SHADOW_REMOVED_(10,16)

7, (Ed 589), SHADOW_REMOVED_(5,13)

8, (Ed 633), SHADOW_REMOVED_(9,19)

9, (Ed 656), SHADOW_REMOVED_(7,15)

10, (Ed 671), SHADOW_REMOVED_(3,9)

11, (Ed 683), SHADOW_REMOVED_(2,12)

12, (Ed 694), SHADOW_REMOVED_(2,10)

13, (Ed 710), SHADOW_REMOVED_(2,10)

17, (Lu 410), NOT PROCESSED

18, (Lu 441), NOT PROCESSED

19, (Lu 488), NOT PROCESSED

20, (Lu 520), NOT PROCESSED

21, (Lu 550), NOT PROCESSED

22, (Lu 633), NOT PROCESSED

23, (Lu 656), NOT PROCESSED

24, (Lu 683), NOT PROCESSED

25, (Eu 410), NOT PROCESSED

26, (Eu 441), NOT PROCESSED

27, (Eu 488), NOT PROCESSED

28, (Eu 520), NOT PROCESSED

29, (Eu 550), NOT PROCESSED

30, (Eu 589), NOT PROCESSED

31, (Eu 671), NOT PROCESSED

32, (Eu 694), NOT PROCESSED

35, (PAR uw), NOT PROCESSED

END OF MER OPTICAL PROCESSING LOG FILE

Fig. 33. An example of a log file.

52
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

Chapter 5

The SIO Method

Eric A. Brody

University of California, Santa Barbara, Santa Barbara, California

Abstract

The SIO group has employed a bulk method for deriving AOPs from spectroradiometry profiles. The bulk

approach has been long employed in the past and much of what is known about AOPs is a result of using

this approach. The bulk method has been used in the past by the SIO group, although, they are not doing so

presently. The bulk approach fits a straight line to the log-transformed irradiance and radiance profiles within

the oceanic mixed layer. The fluxes just below the sea surface are estimated from the exponentiated intercept

and the slope gives a mixed layer averaged diffuse attenuation coefficient, Kd(z0, λ). This method provides only

a single estimate of Kd(z0, λ) and cannot be used to determine the depth dependence of Kd(z, λ). One distinct

advantage of the bulk method is that it can easily be applied using any spreadsheet program on a modest

computer (PC).

5.1 INTRODUCTION

For the SIO MER-1012 data, a very simple and conservative method, based on the traditional Smith and Baker

(1984 and 1986) method, was used. Now with the new

MER-2040 and the addition of spectral irradiance above

the surface, this approach is no longer employed and a

switch to the BBOP method has been made. A summary

of the data processing methodology is given below.

5.2 DATA PROCESSING

Preparation of the data includes averaging the data to

a 1 m bins and calculating the mixed-layer depth by visually inspecting the temperature, salinity, and beam attenuation data. In addition, an inspection for ship shadows,

wave focusing, and surface glitter is made to determine

the shallowest good depth. Sometimes the deepest depth

is not the base of the mixed layer, but the depth at which

the downwelling irradiance becames less than 10 times the

noise levels. This is especially important when solar zenith

angles are large, or cloudy conditions cause minimal surface flux.

Editors’ Note: Although this Chapter is not as detailed as the

others, the editors’ thank the author for making an effort to

make up for a lack of material from the SIO group.

In addition, the surface PAR (E0 PAR) is checked to be

more or less constant during the profiling. If PAR is less

than 0.1×1016 Q cm−2 s−1 or varied too much, then the

data is rejected. Since surface irradiance for each channel was not available, the data was not normalized to the

surface as outlined by Smith and Baker (1984).

Using the (visually) selected shallowest and deepest

depths, a least-squares regression fit is applied to the depth

versus the natural log of a given channel (see for example,

Smith and Baker 1984 and 1986). The slope is the derived diffuse attenuation coefficient (K) for the mixed layer

depth and the exponential of the intercept is the data extropolated to just below the surface (z =0 −). The ratio

of water leaving radiance to downwelling irradiance just

below the surface, Lw(0−):Ed(0−), is the remote sensing

reflectance, Rrs(0−). The Fresnel coefficient is not used to

get values at (z = 0+).

Verifcation of the data involves correlating the mixed

layer depth chlorophyll and phaeopigment or particle absorption with the Rrs(0−) and K for the mixed layer depth

and comparing it to the algorithms (Gordon et al. 1988;

Smith and Baker 1984 and 1986).

53
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Appendix A

Attendees to the First SeaWiFS

Data Analysis Round-Robin (DARR-94)

The attendees to DARR-94 are presented alphabetically.

Karen Baker

UCSD/SIO

La Jolla, CA 92093

Voice: 619–534–2350

Fax: 619–534–2996

Net: karen@icess.ucsb.edu

Eric Brody

UCSB/ICESS

Santa Barbara, CA 93106

Voice: 805–893–4449

Fax: 805–893–2578

Net: ebrody@icess.ucsb.edu

Curtiss Davis

Naval Research Laboratory

Code 7212

Washington, DC 20375

Voice: 202–767–9296

Fax: 202–404–7453

Net: davis@rira.nrl.navy.mil

Vincent Fournier-Sicre

UCSD/Scripps Oceanography

La Jolla, CA 92093–0218

Voice: 619–534–8947

Fax: 619–534–2997

Net: fournier@spode.ucsd.edu

Stanford Hooker

NASA/GSFC/Code 970.2

Greenbelt, MD 20771

Voice: 301–286–9503

Fax: 301–286–1775

Net: stan@ardbeg.gsfc.nasa.gov

John Morrow

Biospherical Instruments, Inc.

5340 Riley Street

San Diego, CA 92110

Voice: 619–686–1888

Fax: 619–686–1887

Net: morrow@biospherical.com

James Mueller

SDSU/CHORS/Suite 206

6505 Alvarado Road

San Diego, CA 92120–5005

Voice: 619–594–2230

Fax: 619–594–4570

Net: jim@chors.sdsu.edu

Margaret O’Brien

UCSB/ICESS

Santa Barbara, CA 93106

Voice: 805–893–2544

Fax: 805–893–2578

Net: mob@icess.ucsb.edu

W. Joseph Rhea

Naval Research Laboratory

Code 7212

Washington, D.C. 20375

Voice: 202–767–9296

Fax: 202–404–7453

Net: rhea@riva.nrl.navy.mil

Dave Siegel

UCSB/ICESS

Santa Barbara, CA 93106

Voice: 805–893–4547

805–893–2544

Fax: 805–893–2578

Net: davey@icess.ucsb.edu

Ray Smith

UCSB/ICESS

Santa Barbara, CA 93106

Voice: 805–893–4709

Fax: 805–893–2578

Net: ray@icess.ucsb.edu

Jens Sorensen

UCSB/ICESS

Santa Barbara, CA 93106

Voice: 805–893–4449

Fax: 805–893–2578

Net: jens@icess.ucsb.edu

Glossary

AIBOP Automated and Interactive Bio-Optical Processing

ANSI American National Standards Institute

AOP Apparent Optical Property

ASCII American Standard Code for Information Interchange

BATS Bermuda Atlantic Time-Series Station

BBOP Bermuda Bio-Optics Project

BOPS Bio-Optical Profiling System

CalCoFI California Cooperative Fisheries Institute

CHORS Center for Hydro-Optics and Remote Sensing (San

Diego State University)

COARE Coupled Ocean-Atmosphere Response Experiment

CTD Conductivity, Temperature, and Depth

c.v. coefficient of variation

CVT Calibration and Validation Team

DAAC Distributed Active Archive Center

DARR Data Analysis Round-Robin

DARR-94 The First DARR (July 1994)

DEC Digital Equipment Corporation

EC Excluding CHORS (data)

EqPac Equatorial Pacific (Process Study)

FORTRAN Formula Translation (computer language)

ftp file transfer protocol

GLOBEC Global Ocean Ecosystems

GMT Greenwich Mean TIme

gcc GNU C Compiler

GNU GNU’s not UNIX

GOMEX Gulf of Mexico Experiment

GSFC Goddard Space Flight Center

GUI Graphical User Interface

54
Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

IBM International Business Machines

ICESS Institute for Computational Earth System Science

(University of California at Santa Barbara)

IDL Interactive Data Language

IRIX Not an acronym, a computer operating system.

JGOFS Joint Global Ocean Flux Study

KQ Kd Quality (flag)

LCD Least Common Denominator (file)

MER Marine Environmental Radiometer

MOBY Marine Optical Buoy

NASA National Aeronautics and Space Administration

NRL Naval Research Laboratory

NSF National Science Foundation

PAR Photosynthetically Available Radiation

PC (IBM) Personal Computer

rms root mean squared

SDSU San Diego State University

SeaBASS SeaWiFS Bio-Optical Archive and Storage System

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SGI Silicon Graphics, Incorporated

SIO Scripps Institution of Oceanography

SIRREX SeaWiFS Intercalibration Round-Robin Experiment

SNR Signal-to-Noise Ratio

SUN Sun Microsystems

SXR SeaWiFS Transfer Radiometer

TOGA Tropical Ocean Global Atmosphere program

UCSB University of California at Santa Barbara

UNESCO United Nations Educational, Scientific, and Cultural Organizations

UNIX Not an acronym, a computer operating system.

WOCE World Ocean Circulation Experiment

Symbols

Ad(z,λ) Linear regression intercepts at the center of a fitted

depth interval for ln of Ad(z,λ) defined in (17).

Au(z,λ) Linear regression intercepts at the center of a fitted

depth interval for ln of Au(z,λ) defined in (17).

Al(z,λ) Linear regression intercepts at the center of a fitted

depth interval for ln of Al(z,λ) defined in (17).

E(λ) Spectral irradiance.

Ed(0−, λ) Incident downwelling spectral irradiance just beneath the sea surface.

Ed(z, λ) Incident downwelling spectral irradiance profile.

Es(z, λ) Vertical profile of surface irradiance.

Es,i(λ) The value of Es(z,λ) at node depth zi.

Es(zm,λ) Defined as H

Es(λ).

Es(λ) The measured irradiance vector of length M.

Eu(0−, λ) Upwelling spectral irradiance just beneath the sea

surface.

Eu(z, λ) Upwelling spectral irradiance profile.

gij Integrals of γij , defined in (24).

hmj Matrix elements defined in (28).

H Matrix of coefficients [hmj ].

K Diffuse attenuation coefficient.

Kd(z,λ) Vertical profile of the diffuse attenuation coefficient

for the downwelling irradiance spectrum.

K

d(z,λ) Kd(z,λ) determined by least squares regression over

a depth interval.

KL(z,λ) Vertical profile of the diffuse attenuation coefficient

for the upwelling radiance spectrum.

K

L(z,λ) KL(z,λ) determined by least squares regression over

a depth interval.

Ku(z,λ) Vertical profile of the diffuse attenuation coefficient

for the upwelling irradiance spectrum.

K

u(z,λ) Ku(z,λ) determined by least squares regression over

a depth interval.

Li The length of the ith element.

Lu(0−, λ) Upwelling spectral radiance just beneath the sea

surface.

Lu(z, λ) Upwelling spectral radiance profile.

M Total number of discrete data points in a vertical

radiometeric profile.

N The number of node depths.

Rrs(z,λ) Vertical profile of the remote sensing reflectance

spectrum.

wm The weighting coefficient at each depth zm.

z The vertical coordinate.

zi The depth of a particular node.

zm The depth of the mth data point in a vertical radiometric profile.

zn The node depth number (n = 0, . . . ,N − 1).

γij Hermitian cubic basis function.

λ Wavelength of light.

ξ A local depth coordinate ranging from −1at node

zi−1 to +1at node zi.

σ One standard deviation.

σt The density of sea water determined from the in

situ salinity and temperature, but at atmospheric

pressure.

σθ The density of sea water determined from the in

situ salinity and the potential temperature (θ), but

at atmospheric pressure.

τˆ(z,λ) The estimated vertical profile of the spectral optical

depth.

τ (z, λ) Vertical profile of the spectral optical depth.

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Siegel, O’Brien, Sorensen, Konnoff, Brody, Mueller, Davis, Rhea, and Hooker

The SeaWiFS Technical Report Series

Vol. 1

Hooker, S.B., W.E. Esaias, G.C. Feldman, W.W. Gregg, and

C.R. McClain, 1992: An Overview of SeaWiFS and Ocean

Color. NASA Tech. Memo. 104566, Vol. 1, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 24 pp., plus color plates.

Vol. 2

Gregg, W.W., 1992: Analysis of Orbit Selection for SeaWiFS:

Ascending vs. Descending Node. NASA Tech. Memo.

104566, Vol. 2, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 16 pp.

Vol. 3

McClain, C.R., W.E. Esaias, W. Barnes, B. Guenther, D. Endres, S. Hooker, G. Mitchell, and R. Barnes, 1992: Calibration and Validation Plan for SeaWiFS. NASA Tech.

Memo. 104566, Vol. 3, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 41pp.

Vol. 4

McClain, C.R., E. Yeh, and G. Fu, 1992: An Analysis of GAC

Sampling Algorithms: A Case Study. NASA Tech. Memo.

104566, Vol. 4, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 22 pp., plus color plates.

Vol. 5

Mueller, J.L., and R.W. Austin, 1992: Ocean Optics Protocols

for SeaWiFS Validation. NASA Tech. Memo. 104566, Vol.

5, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 43 pp.

Vol. 6

Firestone, E.R., and S.B. Hooker, 1992: SeaWiFS Technical

Report Series Summary Index: Volumes 1–5. NASA Tech.

Memo. 104566, Vol. 6, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 9 pp.

Vol. 7

Darzi, M., 1992: Cloud Screening for Polar Orbiting Visible

and IR Satellite Sensors. NASA Tech. Memo. 104566, Vol.

7, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 7 pp.

Vol. 8

Hooker, S.B., W.E. Esaias, and L.A. Rexrode, 1993: Proceedings of the First SeaWiFS Science Team Meeting. NASA

Tech. Memo. 104566, Vol. 8, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 61pp.

Vol. 9

Gregg, W.W., F.C. Chen, A.L. Mezaache, J.D. Chen, J.A.

Whiting, 1993: The Simulated SeaWiFS Data Set, Version 1. NASA Tech. Memo. 104566, Vol. 9, S.B. Hooker,

E.R. Firestone, and A.W. Indest, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 17 pp.

Vol. 10

Woodward, R.H., R.A. Barnes, C.R. McClain, W.E. Esaias,

W.L. Barnes, and A.T. Mecherikunnel, 1993: Modeling

of the SeaWiFS Solar and Lunar Observations. NASA

Tech. Memo. 104566, Vol. 10, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 26 pp.

Vol. 11

Patt, F.S., C.M. Hoisington, W.W. Gregg, and P.L. Coronado,

1993: Analysis of Selected Orbit Propagation Models for

the SeaWiFS Mission. NASA Tech. Memo. 104566, Vol.

11, S.B. Hooker, E.R. Firestone, and A.W. Indest, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 16 pp.

Vol. 12

Firestone, E.R., and S.B. Hooker, 1993: SeaWiFS Technical Report Series Summary Index: Volumes 1–11. NASA Tech.

Memo. 104566, Vol. 12, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 28 pp.

Vol. 13

McClain, C.R., K.R. Arrigo, J. Comiso, R. Fraser, M. Darzi,

J.K. Firestone, B. Schieber, E-n. Yeh, and C.W. Sullivan, 1994: Case Studies for SeaWiFS Calibration and Validation, Part 1. NASA Tech. Memo. 104566, Vol. 13,

S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 52 pp., plus

color plates.

Vol. 14

Mueller, J.L., 1993: The First SeaWiFS Intercalibration RoundRobin Experiment, SIRREX-1, July 1992. NASA Tech.

Memo. 104566, Vol. 14, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 60 pp.

Vol. 15

Gregg, W.W., F.S. Patt, and R.H. Woodward, 1994: The Simulated SeaWiFS Data Set, Version 2. NASA Tech. Memo.

104566, Vol. 15, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 42 pp., plus color plates.

Vol. 16

Mueller, J.L., B.C. Johnson, C.L. Cromer, J.W. Cooper, J.T.

McLean, S.B. Hooker, and T.L. Westphal, 1994: The Second SeaWiFS Intercalibration Round-Robin Experiment,

SIRREX-2, June 1993. NASA Tech. Memo. 104566, Vol.

16, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 121 pp.

Vol. 17

Abbott, M.R., O.B. Brown, H.R. Gordon, K.L. Carder, R.E.

Evans, F.E. Muller-Karger, and W.E. Esaias, 1994: Ocean

Color in the 21st Century: A Strategy for a 20 -Year Time

Series. NASA Tech. Memo. 104566, Vol. 17, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 20 pp.

Vol. 18

Firestone, E.R., and S.B. Hooker, 1994: SeaWiFS Technical Report Series Summary Index: Volumes 1–17. NASA Tech.

Memo. 104566, Vol. 18, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 47 pp.

57
Results of the SeaWiFS Data Analysis Round-Robin, July 1994 (DARR-94)

Vol. 19

McClain, C.R., R.S. Fraser, J.T. McLean, M. Darzi, J.K. Firestone, F.S. Patt, B.D. Schieber, R.H. Woodward, E-n. Yeh,

S. Mattoo, S.F. Biggar, P.N. Slater, K.J. Thome, A.W.

Holmes, R.A. Barnes, and K.J. Voss, 1994: Case Studies

for SeaWiFS Calibration and Validation, Part 2. NASA

Tech. Memo. 104566, Vol. 19, S.B. Hooker, E.R. Firestone,

and J.G. Acker, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 73 pp.

Vol. 20

Hooker, S.B., C.R. McClain, J.K. Firestone, T.L. Westphal,

E-n. Yeh, and Y. Ge, 1994: The SeaWiFS Bio-Optical

Archive and Storage System (SeaBASS), Part 1. NASA

Tech. Memo. 104566, Vol. 20, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 40 pp.

Vol. 21

Acker, J.G., 1994: The Heritage of SeaWiFS: A Retrospective on the CZCS NIMBUS Experiment Team (NET) Program. NASA Tech. Memo. 104566, Vol. 21, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 43 pp.

Vol. 22

Barnes, R.A., W.L. Barnes, W.E. Esaias, and C.R. McClain,

1994: Prelaunch Acceptance Report for the SeaWiFS Radiometer. NASA Tech. Memo. 104566, Vol. 22, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 32 pp.

Vol. 23

Barnes, R.A., A.W. Holmes, W.L. Barnes, W.E. Esaias, C.R.

McClain, and T. Svitek, 1994: SeaWiFS Prelaunch Radiometric Calibration and Spectral Characterization. NASA

Tech. Memo. 104566, Vol. 23, S.B. Hooker, E.R. Firestone,

and J.G. Acker, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 55 pp.

Vol. 24

Firestone, E.R., and S.B. Hooker, 1995: SeaWiFS Technical Report Series Summary Index: Volumes 1–23. NASA Tech.

Memo. 104566, Vol. 24, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, (in press).

Vol. 25

Mueller, J.L., and R.W. Austin, 1995: Ocean Optics Protocols

for SeaWiFS Validation, Revision 1. NASA Tech. Memo.

104566, Vol. 25, S.B. Hooker, E.R. Firestone, and J.G.

Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 66 pp.

Vol. 26

Siegel, D.A., M.C. O’Brien, J.C. Sorensen, D.A. Konnoff, E.A.

Brody, J.L. Mueller, C.O. Davis, W.J. Rhea, and S.B.

Hooker, 1995: Results of the SeaWiFS Data Analysis

Round-Robin (DARR), July 1994. NASA Tech. Memo.

104566, Vol. 26, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 58 pp.

58
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 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

 February 1995 Technical Memorandum

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

6. AUTHOR(S) David A. Siegel, Margaret C. O'Brien, Jens C. Sorensen,

 Daniel A. Konnoff, Eric A. Brody, James L. Mueller, Curtiss O. Davis,

 W. Joseph Rhea and Stanford B. Hooker

Laboratory for Hydrospheric Processes

Goddard Space Flight Center 95B00081

Greenbelt, Maryland 20771

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSORING/MONITORING

 AGENCY REPORT NUMBER

National Aeronautics and Space Administration

Washington, D.C. 20546–0001 TM–104566, Vol. 26

11. SUPPLEMENTARY NOTES

12a. DISTRIBUTION/AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE

David A. Siegel, Margaret C. O'Brien, Jens C. Sorensen, Daniel A. Konnoff, and Eric A. Brody: University of California at

Santa Barbara, Santa Barbara, California; James L. Mueller: San Diego State University, San Diego, California; Curtiss O.

Davis and W. Joseph Rhea: Naval Research Laboratory, Washington, DC; Elaine R. Firestone: General Sciences Corporation,

Laurel, Maryland

13. ABSTRACT (Maximum 200 words)

The accurate determination of upper ocean apparent optical properties (AOPs) is essential for the vicarious calibration of the

SeaWiFS instrument and the validation of the derived data products. To evaluate the role that data analysis methods have

upon values of derived AOPs, the first Data Analysis Round-Robin (DARR-94) workshop was sponsored by the SeaWiFS

Project during 21–23 July, 1994. The focus of this intercomparison study was the estimation of the downwelling irradiance

spectrum just beneath the sea surface, Ed (0-

,λ); the upwelling nadir radiance just beneath the sea surface, Lu (0-,λ); and the

vertical profile of the diffuse attenuation coefficient spectrum, Kd (z, λ)$. In the results reported here, different methodologies

from four research groups were applied to an identical set of 10 spectroradiometry casts in order to evaluate the degree to

which data analysis methods influence AOP estimation, and whether any general improvements can be made. The overall

results of DARR-94 are presented in Chapter 1 and the individual methods of the four groups are presented in Chapters 2–5.

The DARR-94 results do not show a clear winner among data analysis methods evaluated. It is apparent, however, that some

degree of outlier rejection is required in order to accurately estimate Lu(0-,λ) or Ed (0-,λ). Furthermore, the calculation,

evaluation and exploitation of confidence intervals for the AOP determinations needs to be explored. That is, the SeaWiFS

calibration and validation problem should be recast in statistical terms where the in situ AOP values are statistical estimates

with known confidence intervals.

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Subject Category 48

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SeaWiFS, Oceanography, Data Analysis Round-Robin, DARR, Data Analysis Methods, 58

Bio-Optical Processing, Radiance Attenuation Profiles, Irradiance Attenuation Profiles

Report is available from the Center for AeroSpace Information (CASI),

7121 Standard Drive, Hanover, MD 21076–1320; (301)621-0390

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 REPORT DOCUMENTATION PAGE OMB No. 0704-0188

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) Prescribed by ANSI Std. 239-18, 298-102

 SeaWiFS Technical Report Series

Volume 26–Results of the SeaWiFS Data Analysis Round-Robin, July 1994 Code 970.2

 (DARR-94)

 Series Editors: Stanford B. Hooker and Elaine R. Firestone