NASA/TM--2001-209982

In Situ Aerosol Optical Thickness Collected

by the SIMBIOS Program (1997-2000):

Protocols, and Data QC and Analysis

Giulietta S. Fargion and Robert Barnes

SAIC General Sciences Corp., Beltsville, Maryland

Charles McClain

NASA Goddard Space Flight Center, Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

March 2001

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Preface

The purpose of this technical report is to provide current documentation of the Sensor Intercomparison

and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project Office activities on in

situ aerosol optical thickness (i.e., protocols, data QC and analysis). This documentation is necessary to

ensure that critical information is related to the scientific community and NASA management. This critical

information includes the technical difficulties and challenges of validating and combining ocean color data

from an array of independent satellite systems to form consistent and accurate global bio-optical time series

products. This technical report is not meant as a substitute for scientific literature. Instead, it will provide a

ready and responsive vehicle for the multitude of technical reports issued by an operational project.

iii

Table of Contents and Author List

CHAPTER I .................................................................................................................................................. I

SIMBIOS SUN PHOTOMETER PROGRAM

Charles R. McClain and Giulietta 3. Fargion

CHAPTER 2 .................................................................................................................................................. 3

CHARACTERISTICS OF THE SIMBIOS ATMOSPHERIC INSTRUME_VTS

Christophe Pietras, Mark Miller, Robert Frouin, Ellsworth J. Welton and llya Slutsker

CHAPTER 3 ................................................................................................................................................ 1 I

CALIBRATION OF SUN PHOTOMETERS AND SKY RADIANCE SENSORS

Christophe Pietras, Mark Miller, Robert Frouin, Tom Eck, Brent Holben and John Marketon

CHAPTER 4 ................................................................................................................................................ 22

VARIANCE EQUATIONS AND UNCERTAINTY ANALYSIS FOR THE LANGLEY TECHNIQUE

Mark Miller, Mary Jane Bartholomew and 1_ M. Reynolds

CHAPTER 5 ................................................................................................................................................ 26

SUN AND SKY RADIANCE MEASUREMENTS AND DATA ANALYSIS PROTOCOLS

Robert Frouin, Brent Holben, Mark Miller, Christophe Pietras, John Porter and Ken Voss

CHAPTER 6 ................................................................................................................................................ 43

SIMBIOS PROJECT PROTOCOLS FOR PROCESSING IN SITU AEROSOL OPTICAL THICKNESS MEASUREMENTS

Ewa J. Ainsworth, Christophe Pietras, Mark Miller, Robert Frouin and Sean Bailey

CHAPTER 7 ................................................................................................................................................ 70

SATELLITE AEROSOL OPTICAL THICKNESS MATCH-UP PROCEDURES

Sean Bailey and Menghua Wang

CHAPTER 8 ................................................................................................................................................ 73

ANALYSES OF MATCH-UP RESULTS

Ewa J. Ainsworth, Christophe Pietras and Sean Bailey

CHAPTER 9 ................................................................................................................................................ 88

SEAWIFS VICARIOUS CALIBRATION: AN ALTERNATIVE APPROACH UTILIZING SIMULTANEOUS ]N SITU

OBSERVATIONS OF OCEANIC AND ATMOSPHERIC OPTICAL PROPERTIES.

Bryan A. Franz, Ewa J. Ainsworth and Sean Baily

APPENDIX A .............................................................................................................................................. 97

SEABASS DATA FILE FORMAT

P. Jeremy Werdeil, Sean Bailey and Giulietta S Fargion

Chapter 1

SIMBIOS Sun Photometer Program

Charles R. McClain 1 and Giulietta S. Fargion 2

JNASA, Goddard Space Flight Center, Greenbelt, Maryland

2SAIC General Sciences Corporation, Beltsville, Maryland

1.1 INTRODUCTION

Aerosol optical thickness (AOT) values

determined from the satellite ocean color data

provide useful information on the spatial and

temporal distributions of aerosols and are byproducts of the atmospheric corrections required to

estimation of water-leaving radiances. The

SIMBIOS Project is using in situ atmospheric data,

primarily from sun photometers, for several

purposes including:

1. validation the SeaWiFS and other ocean color

mission aerosol optical products, e.g., AOT and

AngstrOm exponent, as in (Wang et al., 2000),

2. evaluation of the aerosol models currently used

for atmospheric corrections, e.g., Gordon and

Wang (1994), and

3. development of vicarious sensor calibration

methodologies, e.g., Evans and Gordon (1994),

especially for the near-infrared bands where insitu water-leaving radiance data in the visible

from sites like the Marine Optical Buoy (MOBY:

Clark et al., 1997) cannot be used.

The principal source of in situ aerosol

observations was the Aerosol Robotic Network

(AERONET). AERONET is a network of groundbased automated sun photometers owned by

national agencies and universities (Holben et al.

1998). AERONET data provides globally

distributed, near-real time observations of aerosol

spectral optical depths, aerosol size distributions,

and precipitable water. Because the majority of the

AERONET stations are at continental locations,

SIMBIOS augmented the network with 12

additional island and coastal sites, including the

Hawaiian Islands (Lanai and Oahu), Ascension

Island, Bahrain, Tahiti, Wallops Island (midAtlantic U.S. east coast), South Korea, Turkey,

Argentina, Azores and Australia (Perth).

Deploying instruments in foreign locations usually

requires a memorandum of understanding between

NASA and the institution tending the

instrument, a process that can take many months.

The SIMBIOS Project has invested considerable

effort in modifying the CIMEL system to be more

durable for extended deployments in marine

environments.

The SIMBIOS Project also has a number of

other sun photometers including hand-held

MicroTops, ship-stablilized PREDE's, and

SIMBAD/SIMBADA radiometers. The

SIMBAD/SIMBADA radiometers collect optical

thickness data as part of the surface reflectance

measurement. The Project also has a micropulse

lidar which can be deployed on a vessel that

provides information on the vertical distribution of

aerosols. The lidar custodian is Dr. Jim Spinhirne,

the instrument designer, at GSFC who operates a

network of lidar sites. The radiometers are

calibrated in collaboration with the AERONET

Program which maintains an open-air calibration

facility and a sphere calibration laboratory at

NASA Goddard Space Flight Center (GSFC). The

calibration capability includes a French-built

polarization adapter for the integrating sphere for

calibration of polarized bands in some CIMEL

instruments. The shipboard instruments are

available to U.S. SIMBIOS investigators during

research expeditions.

The purpose of this technical memorandum is

to summarize the end-to-end sun photometer data

processing and analysis. This includes data

acquisition, calibration, quality control, and matchup comparisons with satellite data. These analyses

have been standardized across the various types of

radiometers and also includes analysis of shadowband radiometer data provided by Dr. Mark Miller

of the Brookhaven National Laboratory (BNL).

REFERENCES

Clark, D., H. R. Gordon, K. J. Voss, Y. Ge, W.

Broenkow, and C. Trees, 1997: Validation of

atmospheric correction over the oceans, J.

Geophys. Res., 102, 17,209-17217.
Evans, R. H. and H. R. Gordon 1994: CZCS

"system calibration": A retrospective

examination, J. Geophys.Res.., 99(C4), 7293-

7307.

Gordon, H.R. and M. Wang, 1994: Retrieval of

water-leaving radiance and aerosol optical

thickness over the oceans with SeaWiFS: A

preliminary algorithm, Appl. Opt., 33, 443-

452.

Holben, B.N., T.F. Eck, I. Slutsker, D. Tanre, J.P.

Buis, A. Setzer, E. Vermote, J.A. Reagan, Y.J.

Kaufman, T. Nakajima, F. Leaven, I.

Jankowiak, and A. Smirnov, 1998: AERONET

- a federated instrument network and data

archive for aerosol characterization. Remote

Sens. Environ., 66, 1-16.

Wang, M., S. W. Bailey, C. M. Pietras, and C. R.

McClain, 2000: SeaWiFS aerosol optical

thickness match-up analyses, In SeaWiFS

Postlaunch Calibration and Validation

Analyses, Part 2, NASAII'M-2000-206892,

Vol. 10., S. B. Hooker and E. R. Firestone

(eds.), NASA Goddard Space Flight Center,

Greenbelt, Maryland, 39-44.
Chapter 2

Characteristics of the SIMBIOS Atmospheric Instruments

Christophe Pietras i, Mark Miller 2, Robert Frouin 3, Ellsworth J. Welton 4, & llya Slutsker 5

ISAIC General Sciences Corporation, Beltsville, Maryland

2Department Environmental Science, Earth System Science Division, Brookhaven

National Laboratory, Upton, New York

3Scripps Institution of Oceanography, University of California, San Diego, California

4GESTC, University of Maryland Baltimore County, Baltimore, Maryland

5Science Systems and Applications, Inc., Lanham, Maryland

2.1 INTRODUCTION

The SIMBIOS pool of instruments is composed

of three types of equipment. These instruments are

used by the SIMBIOS Project and the SIMBIOS

Science Team to enhance the overall quality of

atmospheric aerosol validation data sets provided to

the SeaBASS archive. The first type is composed of

sun/sky photometers that measure the solar

irradiance and the sky radiance. The second type is

the shadow-band radiometer that measures the

diffuse and global (upper hemispheric) sky

radiance. The third type is the Micropulse Lidar

(MPL), which measures the vertical and horizontal

distribution of aerosol backscatter, extinction, and

optical depth. The photometers and radiometers

generally have several channels, with center

wavelengths from 0.3 Ixm to 1.0 _na and with

narrow bandwidths (0.01lxm). Their characteristics

are briefly summarized in Table 2.1. The

instruments are deployed by SIMBIOS or NASA

Principal Investigators on cruises and provide data

to the SeaBASS archive for SIMBIOS ocean color

validation research. Deployment policy and

procedures are available on the SIMBIOS home

page (http://simbios.gsfc.nasa.gov). This chapter

reviews each type of instrument, its characteristics,

and its advantages for the project. A description of

the protocols for the operation of these instruments

and for their data analysis is given in Chapter 3.

2.2 HAND-HELD SUN

PHOTOMETERS AND

RADIOMETERS

Hand-held sun photometers are the simplest

and the most cost-effective instruments to measure

aerosol optical thickness. They are highly

appreciated for their portability, their low cost, and

their ease of use (Figure 2.1). The SIMBIOS

Program maintains a pool of twelve MicroTops sun

photometers, manufactured by Solar Light, Inc.

(Morys, 1998), and three SIMBAD instruments

(one SIMBAD and two SIMBADA), built by the

University of Lille (Deschamps, 2000). The

acronym for SIMBAD translates from the French

as: Satellite Validation for Marine Biology and

Aerosol Determination. The SIMBAD instruments

can be used to measure the upwelling radiances

from the ocean, as well as the solar irradiance and

the sky radiance. These hand-held sun photometers

are used during ocean-color evaluation cruises to

provide aerosol optical thickness measurements for

comparison with values derived from satellite

algorithms. The MicroTops and SIMBAD

instruments have five channels, with center

wavelengths between 0.4 _tm and 1.0 lava,

according to specifications given by the World

Meteorological Organization. The center

wavelengths and bandwidths for the instrument are

given in Table 2.2. The optics of the SIMBAD

instruments are fitted with a polarizer to reduce

reflected skylight when the instrument is operated

in its ocean-viewing mode (Fougnie et al., 1999).

Both the MicroTops and SIMBAD instruments use

photodiode detectors and interference filters.

Collimators mounted in front of the optics allow a

2.5 ° full field of view for the MicroTops and a 3°

full field-of-view for SIMBAD. Auxiliary pressure

and temperature are provided by the instruments,

and Global Positioning Sensors are used to obtain

the position and time. Dark count measurements

are made by the MicroTops, using a cover for the

optics, and by the SIMBAD, using a specific dark

count mode. The dark counts allow corrections for

instrument temperature effects. The measurement

frequency for both instruments is 10 Hz. The

highest intensities measured are stored and used to
Figure2.1Microtopssunphotometer (left)andSIMBADradiometer (right).

Figure2.2CIMELsunphotometer (left)andPREDEMarkII sun photometer (right).

Figure 2.3 Fast rotating shadow-band radiometer (FRSR)
Figure2.4Micropulse Lidar (MPL)

avoid-pointing errors. The MicroTops is powered

with alkaline batteries allowing 50 hours of

operation between changes. SIMBAD is equipped

with rechargeable batteries that a allow 6 hours of

continuous use.

The MicroTops is also equipped with a

microprocessor that calculates the aerosol optical

depth, integrated water vapor, and air mass in real

time, and displays these values on a LCD screen.

The techniques for marine reflectance

measurements using the SIMBAD radiometer are

given in Deschamps et al. (2000), and those for ship

board measurements using the MicroTops sun

photometer are given in Porter et al. (2000). The

operation and maintenance protocols for the

SIMBAD and MicroTops instruments are described

in Chapter 4.

2.3 AUTOMATIC SUN

PHOTOMETER AND SKY

RADIANCE SCANING

SYSTEMS

Automatic sun photometers and sky

radiometers are more complex than their hand-held

counterparts. They allow the retrieval of aerosol

and water vapor column amounts from direct sun

measurements and the retrieval of aerosol

properties from sky radiance measurements. The

automatic instruments are often heavy and bulky

because of their power requirements, which are

generally provided by large batteries. Since they

are designed for independent, automatic operation,

they need to be weather resistant. As a

consequence, they are often expensive to buy. The

CIMEL and PREDE instruments (Fig. 2.2),

manufactured in France and Japan, respectively, are

two sun and sky scanning spectral radiometers used

by the SIMBIOS Project.

The CIMEL instruments are used in the

AERONET network to globally monitor the aerosol

optical properties over land (Holben et al, 1998).

Since 1998, the network has grown considerably

(Holben et al., 2000). It includes 12 instruments

owned by SIMBIOS project, maintained by the

AERONET group, and deployed in coastal and

island regions to monitor aerosol optical properties

over ocean. The CIMEL Electronique 318A,

manufactured in Paris, is a multi-spectral

radiometer powered by solar panels and batteries.

The silicon photodiode detectors in the CIMEL are

not temperature-controlled, and a thermistor

measures the temperature of the detector, which is

used to correct the temperature dependence of the

detectors. The PREDE POM Mark II,

manufactured in Tokyo, is a multi-spectral

radiometer that uses temperature-controlled silicon

photodiode detectors (Nakajima et al., 1996). Both

instruments have their sensor heads mounted on a

robot, allowing zenith and azimuthal motion. A

collimator located in front of the entrance optics

and an instrument designed for 10-5 stray-light

rejection allows measurements of the sky aureole 3°

from the sun. The CIMEL and the PREDE have

approximately 1.2 ° and 1.5 ° full fields-of-view,

respectively, at each measurement wavelength. The

components of the sensor heads are sealed from

moisture and desiccated to prevent damage to the

electrical components and interference filters. Ion

assisted deposition (IAD) interference filters are

used by each of the instruments. Their spectral

characteristics are given in Table 2.3. When idle,
theCIMELandthePREDEsensorheadarepointed

downward(PARK mode)to preventoptical

windowdeteriorationfromrainor particles.A

wetnesssensorin theCIMELdetectsrainevents

andinitiatestheparkmode.ForthePREDE,a

widefield of viewcameraon topof therobot

allowstheradiometer to makemeasurements when

sky is cloudfree. In addition,thePREDEis

equippedwith a sophisticatedtrackingsystem,

includingfastrespondingmotorsandanarrowfield

cameracoupledwiththe sensorhead,allowing

measurements onboarda ship,or othermoving

platform.A polarizationmodelof theCIMELis

used by the SIMBIOSProject to make

measurements of the polarizationof the sky

radianceat0.87I.tm.

2.4 FAST ROTATING SHADOWBAND RADIOMETERS

The shadow-band radiometer, equipped with

an occulting apparatus, measures the diffuse and

global (upper hemispheric) irradiance and computes

the solar irradiance as the difference between the

two. The device gets its name from the

hemispherical metal strip that rotates around the

detector and blocks the direct solar beam to yield a

signal that is from the sky only (after the effect of

the arm is included). The multiple wavelength

rotating shadow-band radiometer Harrison et al.

(1994) uses independent interference-filterphotodiode detectors and an automated rotating

shadow-band technique to make spatially resolved

measurements at seven wavelengths. The accuracy

of the direct-normal spectral irradiance

measurements made with this type of sun

photometer is comparable with those made by

narrow-beam tracking devices. A significant

advantage of the shadow-band technique is that the

global and diffuse irradiance measurements can be

used to study the solar radiation budget and the

fractional cloud cover at the time of the

measurement. The latter capability is particularly

important for satellite validation studies.

A marine version of the multiple wavelength

rotating shadow-band radiometer (Fig 2.3) has been

developed at the Brookhaven National Laboratory

(BNL) and is operated by Dr. Mark Miller. The

BNL marine version uses a slightly modified

version of the detector used for continental

applications. It has seven channels: one broadband

and six 10-nm wide channels at 415,500, 610, 660,

870, and 940 nm. For shipboard measurements,

modifications to the detector circuitry used for

continental applications are necessary because the

response time of this circuitry is too slow for use on

a moving ship. If the response time of the detector

is too slow, wave action may cause the orientation

of the radiometer to change appreciably during the

time the shadow-band is occulting the sun. The

rotation of the shadow-band itself must be

sufficiently fast for the same reason. The marine

version of the shadow-band radiometer (Reynolds

et al., 2000) is generally referred to as the BNL

Fast-Rotating, Shadow-band Radiometer (FRSR).

The response of the silicon cell in the detector used

for continental applications is faster than one

millisecond, yet the internal preamplifiers have

integrating low-noise amplifiers that slow the

overall response. For the FRSR, the response time

of the detector is made faster by reducing the

magnitude of the low-pass filter capacitors.

Laboratory tests do not show additional noise in the

measurements as a result of this modification. The

processing algorithms, which incorporate pitch, roll

and heading measurements, are key to the

instrument's ability to derive direct-normal beam

irradiance without gimbals and a gyro-stabilized

table.

A correction for the amount of sky that is

blocked by the occulting band is essential for an

accurate measurement. For the FRSR shadowband,

this correction is possible through measurement of

"edge" irradiance in the same manner as the land

version of the radiometers. The shadow irradiance

occurs when the sun is completely covered by the

shadowband, but a portion of the diffuse irradianee

is also blocked. The edge irradianee is measured

when the band is just to one side of the solar disk

and provides a good estimate of the global

irradiance minus the portion of sky that is blocked

by the shadowband at the time it blocks the solar

disk. In practice the edge irradianee is selected

from two measurements taken when the shadow is

on one side or the other of the diffuser. Generally

an average is taken, but in some cases in the early

morning or late evening, only one of the edges is

acceptable

2.5 MICRO-PULSE LIDAR

SYSTEM (MPL)

The MPL (Fig. 2.4) is a compact and eye-safe

lidar system capable of determining the range of

aerosols and clouds by firing a short prise o.f laser

light at 523 nm and measuring the time-of-flight

from pulse transmission to reception of the returned

signal (Spinhirne, 1993). The returned signal is

retrieved as a function of time, converted into range

using the speed of light, with a magnitude

6
proportional tothe amount of light backscattered by

atmospheric molecules (Rayleigh scattering),

aerosols, and clouds. The MPL achieves ANSI

eye-safe standards by using low output energies

(micro Joules, _lJ) and beam expansion to 20.32 cm

in diameter. The MPL laser pulse duration is 10 ns

with a pulse repetition frequency (PRF) of 2500 Hz

and output energies range from 1 to 6 _J depending

upon system performance and operational settings.

The high PRF allows the system to average many

low energy pulses in a short time to achieve a good

signal-to-noise ratio. The main characteristics of the

MPL are summarized in Table 2.4.

The MPL transmitter-receiver consists of a

black, 20.32 cm diameter Cassegrain telescope with

optics and electronics mounted directly below the

telescope. The laser supply and scalar (data

binning unit) are connected to the transmitterreceiver, along with a control computer. The MPL

must be operated inside a climate controlled

housing. The laser supply contains a diode pumped

Nd:YLF laser with a fundamental pulse output

wavelength of 1046 nm that is converted to 523 nm

by passage through a frequency doubling crystal

prior to transmission by the telescope. Signals are

received using the same telescope and are recorded

with a Geiger mode avalanche photodiode.

Over the years, the MPL design has been fully

characterized, and its various strengths, limitations,

and nuances are now well understood. The MPL

systems are reasonably easy to deploy to the field,

are eye-safe, have the capability to operate

continuously for extended periods of time, and can

detect and analyze various cloud and aerosol layers

(including thin cirrus and low concentration aerosol

layers). The MPL system is limited by having only

one channel for analysis purposes, and a small

field-of-view that creates a long overlap range.

However, the small field-of-view also eliminates

multiple scattering effects present in many other

lidar systems.

Procedures have been developed to correct the

raw MPL data for afterpulse detector noise

(induced by the laser pulse itself), and near-range

signal falloff caused by the telescope overlap

function. Data analysis algorithms have been

written to calibrate the MPL systems and to analyze

the resulting data. Successful analysis of the MPL

data requires aerosol optical depth measurements

from a sun photometer co-located with the lidar.

The sun photometer is used to calibrate the MPL

and is essential for completion of the data

processing routines. MPL data products include:

aerosol and cloud layer heights for each layer

detected; optical depth; extinction profile; and

extinction-backscatter ratio (sr). Optical depth

profiles for each layer can be generated by

integrating the extinction profile. An overview of

MPL data processing techniques is given in Welton

et al. (2000). Further information on MPL systems,

data processing, and current available data can be

found at the MPL-Net web site

(http://virl.gsfc.nasa.gov/mpl-netD.

REFERENCES

Deschamps, P. Y., B. Fougnie, R. Frouin, P.

Lecomte and C. Verwaerde, 2000: SIMBAD:

An advanced field radiometer to measure

aerosol optical thickness and marine reflectance,

Appl. Opt. in press.

Fougnie, B., R, Frouin, P. Lecomte, and P-Y.

Deschamps, 1999: Reduction of skylight

reflection effects in the above-water

measurement of marine diffuse reflectance.

Appl. Opt. 38, 3844-3856.

Harrison, L., J. Michalsky, and J. Berndt, 1994:

Automated multi-filter rotating shadow-band

radiometer: An instrument for optical depth and

radiation measurements, Appl. Opt. 33, 5126-

5132.

Holben, B. N., T. F. Eck, I. Slutsker, D. Tanre, J. P.

Buis, A. Setzer, E. Vermote, J. A. Reagan, Y.

Kaufman, T. Nakajima, F. Lavenu, I.

Jankowiak, and A. Smirnov, 1998: AERONET

A federated instrument network and data

archive for aerosol characterization, Rem. Sens.

Environ. 66, 1-16.

Holben, B. N., D. Tanre, A. Smirnov, T. F. Eck, I.

Slutsker, N. Abuhassan, W. W. Newcomb, J.

Schafer, B. Chatenet, F. Lavenue, Y. J.

Kaufman, J. Vande Castle, A. Setzer, B.

Markham, D. Clark, R. Frouin, R. Halthore, A.

Karnieli, N. T. O'Neill, C. Pietras, R. T. Pinker,

K. Voss, and G. Zibordi, 2000: An emerging

ground-based aerosol climatology: Aerosol

optical depth from AERONET, J. Geophys.

Res. in press.

Morys, M., F. M. Mims, and S. E. Anderson, 1998:

Design calibration and performance of

MICROTOPS II hand-held ozonemeter,

http://www.solar.com/ftp/papers/mtops.pdf,

12pp.

Nakajima T., G. Tonna, R. Rao, P. Boi, Y. L.

Kaufman, and B. Holben, 1996: Use of sky
brightnessmeasurementsfrom groundfor

remotesensingof particulate polydispersions,

Appl. Opt. 35, 2672-2686.

Porter, J. N., M. Miller, C. Pietras, and C. Motell,

2000: Ship-based sun photometer

measurements using MicroTops sun

photometers, J. Atmos. Ocean. Technol. in

press.

Reynolds, M., M. A. Miller, and M. J.

Bartholomew, 2000: Design, operation, and

calibration of a shipboard fast-rotating

shadowband spectral radiometer, J. Atmos.

Ocean. Technol. in press.

Spinhirne, J. D., 1993: Micro pulse iidar, IEEE

Trans. Geosci. Remote Sensing, 31, 48-55.

Welton, E. J., K. J. Voss, H. R. Gordon, H. Maring,

A. Smirnov, B. Holben, B. Schmid, J. M.

Livingston, P. B. Russell, P. A. Durkee, P.

Formenti, M. O. Andreae, 2000: Ground-based

lidar measurements of aerosols during ACE-2:

Instrument description, results, and comparisons

with other ground-based and airborne

measurements, Tellus B 52, 635-650.

Table 2.1 Characteristics of the SIMBIOS instruments

Channels

nanometers MicroTops SIMBAD CIMEL PREDE

315 t/

340 t/

380

400

415

440

490

500

523

560

610

660

67O

675

870

t/

t/

tg

t/

t/

t/

t/

t/

t/

t/

t/

t/

t/

v'

t/

SHADOW-BAND MPL

l/

t/

t/

t¢

940 t/ t/

1020 t/ ¢'

FOV 2.5 3 1.2 1.5 180 0.00573
Table2.2Centerwavelengths (CWL)andbandwidths(BWL)ofthechannelsofeachMicroTopsand

SIMBADradiometer.

MicroTops Ch1 Ch2 Ch3 Ch4 Ch5

Units# CWL BWL CWL BWL CWL BWL CWL BWL CWL BWL

03755 440.6 10.1 499.7 10.4 674.3 10.8 869.7 10.1 936.2 10.O

03765 440.4 10.1 499.7 10.3 674.0 10.8 870.1 I0.1 935.7 9.5

03766 440.6 10.2 499.7 10.4 674.8 10.7 865.5 10.1 935.6 9.4

03767 440.7 10.0 499.6 10.4 674.0 10.7 870.3 10.2 937.2 10.8

03768 440.6 101 499.5 10.3 674.5 10.8 870.2 10.1 936.3 9.3

03769 440.7 10.0 499.5 10.3 674.3 10.9 869.5 9.8 935.9 9.3

03770 440.6 10.1 499.7 10.4 674.5 10.9 869.9 10.2 937.1 10.8

03771 440.4 10.2 499.6 10.4 674.1 10.8 870.1 10.1 936.5 10.9

03772 440.7 10.2 499.6 10.4 674.3 10.8 870.0 10.2 935.9 10.9

03773 440.6 10.0 499.6 10.4 674.1 10.8 870.2 10.2 937.1 10.8

03774 440.5 10.1 499.8 10.4 674.4 10.8 870.6 10.0 935.9 9.3

03775 440.7 10.1 499.8 10.4 674.4 10.7 870.0 10.1 934.9 9.3

SIMBAD

Unit #

972306 442.7 11.6 491.9 11 562.2 9.0 672.0 10.0 875.2 12.7

972309 441.6 11.7 491.9 11 562.7 9.1 670.8 10.3 872.4 11.4

Table 2-3. Center wavelength (CWL) and Bandwidth (BWL) of the channels of PREDE and CIMEL sun

photometers.

PREDE Chl Ch2 Ch3 Ch4 Ch5 Ch6 Ch7

Units # CWL BWL CWL BWL CWL BWL CWL BWL CWL BWL CWL BWL CWL BWL

PS 314.5 3.0 400.3 9.6 501.0 9.6 676.0 10.1 868.8 10.3 940.4 10.2 1019.2 8.9

090063

PS 314.8 3.0 400.1 9.4 500.9 9.7 675.7 10.1 869.7 10.5 940.2 10.2 1019.3 9.0

O90O64

CIMEL Chl Ch2 Ch3 Ch4 Ch5 Ch6 Ch7 Ch8

Ulllig # CWL BWL CWL BWL CWL BWL CWL BWL CWL BWL CWL BWL CWL BWL CWL BWL

93 339.9 1.7 379.4 3.9 440.6 9.3 500.6 10.0 675.6 8.6 870.1 10.7 936.1 10.0 1021.4 10.2

106 340.0 1.8 379.4 3.9 440.4 9.9 499.0 9.8 674.4 8.6 869.6 10.6 936.1 8.7 1019.1 9.9

107 339.7 1.8 379.4 3.8 440.6 9.8 500.7 10.0 674.8 10.3 870.0 10.7 935.7 9.9 1019.5 9.8

108 339.8 1.8 379.6 3.8 NA NA 499.4 9.9 675.8 9.0 870.4 10.7 935.0 10.3 1018.8 9.9

109 339.7 1.7 379.6 3.9 440.7 9.5 500.4 9.9 674.4 8.6 869.1 10.5 935.9 9.8 1019.4 9.8

151 339.9 1.8 379.4 3.9 440.5 9.9 676.0 9.6 499.4 9.8 869.6 10.5 935.5 9.8 1020.6 10.4

155 340.1 ! .9 380.2 ! .9 440.7 8.5 501.0 9.9 674.8 10.3 869.3 10.5 935.8 8.7 1020.5 9.9

159 339.8 1.9 380.2 !.9 440.5 8.3 501.0 9.9 674,9 10.2 869.3 10.6 936.8 10.5 1020.1 9.7

160 339.9 1.8 380.2 1.9 439.8 8.5 501.3 9.9 674.9 10.2 869.7 10,5 936.3 8.8 1020A 9.8

161 339.9 1.8 380.2 1.9 440.2 9.2 501.2 9.8 674.9 10.3 869.2 10.6 935.4 8.8 1020.2 9.7

162 440.6 9.0 674.6 10.3 869.8 9.2 869.8 9.2 868.6 9.2 869.8 9.2 935.1 8.7 1019.7 9.6

191 440.5 8.3 674.9 10.3 870.0 9.3 869.8 9.3 869.7 9.2 870.1 9.2 936.0 8.7 1020.3 9.8
Table 2.4 MPL System

Parameter Name

Wavelength

Output Pulse Energy

Pulse Duration

Pulse Repetition Frequency

Field of View

APD Detector Quantum Efficiency

APD Detector Dark Count Rate

_pecifications

Parameter Value

523 + 0.12 nm

< lO_J

10 ns

2500 Hz

100 larad (0.00573 °)

40%

< 250 counts per second

Vertical Resolution 75 m

Temporal Resolution 1 minute

10
///_ 4, .z,__

Chapter 3

Calibration of Sun Photometers and Sky Radiance

Sensors

Christophe Pietras t, Mark Miller 2, Robert Frouin 3, Tom Eck 4 and Brent Holben 5, John

Marketon 4

ISAIC General Sciences Corporation, Beltsville, Maryland

2Department Environmental Science, Earth System Science Division, Brookhaven

National Laboratory, Upton, New York

3Scripps Institution of Oceanography, University of California, San Diego, California

4Raytheon ITSS Corporation, Lanham, Maryland

5Biospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland

3.1. INTRODUCTION

The main source of error in retrieving aerosol

optical thicknesses using sun photometry comes

from the determination of the TOA voltages. The

degradation of interference filters is the most

important source of the long-term changes in the

cross-calibrations. Although major improvements

have been made in the design of the filters

(interference filters fabricated using ion-assisted

deposition), the filters remain the principal factor

limiting performance of the sun photometers.

Degradation of filters necessitates frequent

calibration of sun photometers and frequent

measurements of the filter transmission or the

relative system response (Schmid et al., 1998). The

degradation of the filters mounted on the CIMEL

sun photometers have been monitored since 1993

by the AERONET Project. The decay reported by

Holben et al. (1998) for the first 2 years of a

CIMEL's operation is between 1 and 5%.

Nevertheless, the filters mounted on CIMEL

instruments are regularly replaced after 2 years of

use. The cross-calibration technique consists of

taking measurements concurrently with the

uncalibrated and the reference sun photometers.

While analyzing measurements, the quality of the

calibration has to be checked, using the following

considerations:

1. any cirrus clouds suspected to be masking the

sun, during the calibration period, need to be

reported and the corresponding data set

removed; and

2. the stability of the day needs to be checked.

Then, the average and standard deviation of

TOA voltages are computed during the entire

period of each calibration. If the standard deviation

is higher than 1%, the calibration is rejected and

will not be part of the data processing.

Absolute calibration of the sky radiometers

depends on the accuracy of the calibration of the

reference integrating sphere. The NIST-traceable

calibration of the integrating sphere at GSFC is

considered to be accurate at better than 5%. The

calibration history of SIMBAD shows a stability of

the calibration within +_.5% over three years. The

calibration of radiometers during a field experiment

is possible using a plaque, but higher uncertainties

are expected (as shown on Figure 3.4) because of

the uncertainty of the determination of the reflected

signal from the plaque.

Absolute calibration depends also on the

relative size of the entrance aperture of the sphere,

on the field-of-view of the radiometer, and on the

distance between the sphere and the radiometer.

The first absolute calibration performed at GSFC

(August 1999) was performed with the radiometer

placed one meter from the sphere. The calibration

coefficients, as shown on Figure 3.4, are higher

than those derived from later calibrations

performed using a distance reduced to 0.1 meter.

The Hardy sphere has a large aperture and allows a

flexibility in the distance setting of the radiometer.

However, the reflection of the light from the optical

bench, the wall of the room, or the edge of the

sphere may explain the higher signal measured by

the radiometer and the resulting calibration

coefficients.

This chapter will describe calibration

techniques, facilities, and protocols used for

calibrating sun photometers and sky radiometers.

11
Measurements anddataanalysesarediscussedin

Chapter 6.

3.2 CALIBRATION FACILITIES

GSFC Roof Platform

A platform has been built by the AERONET

Group on the roof of the Earth Sciences building at

GSFC (Figure 3.1) which has a nearly clear

unobstructed horizon. The roof platform allows sun

photometer cross-calibrations during every clear

(low AOT) days. Absolute calibrations using lamp

standards are generally not recommended for the

retrieval of aerosol optical thickness (AOT).

However, in the case of an instrument with a large

decrease in sensitivity over time, it may be

advisable to combine the standard lamp calibration

with solar calibration (Schmid et al., 1998). All

AERONET automatic sun photometers are set up

on this platform for cross-calibration. The roof

platform is equipped with one or more CIMEL

reference sun photometers, calibrated at Mauna Loa

Observatory (Hawaii, altitude 3400m). The

reference sun photometers are used to transfer the

calibration to field instruments by making

measurements simultaneously. After crosscalibration, these sun photometers are shipped to

designated monitoring sites or for field

experiments. The roof platform has been built at

GSFC primarily for the calibration of CIMEL

instruments, but the facility is also used to crosscalibrate other sun photometers (MicroTops,

SIMBAD, PREDE).

Radiance Calibration Facility

The instruments were calibrated in the Code

920.1 Radiance Calibration Facility (RCF), located

at NASA's GSFC. The RCF maintains instruments

and NIST-traceable calibrated sources to calibrate,

monitor, and assess the performance of remote

sensing instrumentation. To reduce the effects of

particulate contamination, the RCF is located in an

ISO Class 7 (M5.5, 10000) contamination

controlled suite. The main RCF source is a 1.8m

diameter fiberglass integrating sphere named Hardy

(Figure 3.2). The sphere interior is coated with

BaSO4 in a Poly-Vinyl Alcohol (PVA) binder over

a Krylon flat white base coat (GSFC WI 1998).

Sixteen 200W Quartz Tungsten Halogen lamps,

arranged around the 25.4cm primary exit aperture

internally illuminate the sphere. The lamps are

baffled so that radiation does not directly exit

through the aperture. Two baffled 20cm ports are

used for air circulation, with a blower drawing on

the hot air exhaust vent. Located directly above the

primary exit aperture is a 5cm diameter monitor

port with a bracket for securing a source monitor. A

source monitor was not installed for the calibrations

described in this paper. A constant current power

system (Walker et al., 1994) is used to drive the

lamps. The power system permits up to sixteen

sphere output levels by supplying power to the

appropriate number of lamps.

The Hardy sphere source is calibrated on a monthly

basis against NIST FEL irradiance standard lamps

in the visible and near-infrared spectral region (0.4

and 2.41am). The FEL irradiance calibration is

transferred to the integrating radiance source using

a transfer radiometer with an integrating sphere

irradiance collector (Mueller, et al., 1993 and

Walker, et al. 1991). Overall calibration uncertainty

(Figure 3.3) is about 1.5% in the visible region and

generally under 2.5% across the calibrated region.

The greatest contributor to the uncertainty is the

FEL lamp itself.

3.3 TECHNIQUES AND

PROTOCOLS

The ongoing calibration of sun photometers is a

necessary to account for instrument changes,

including changes in the sensitivity of detectors and

changes in the transmittances of the interference

filters. Since the time of the pioneering work of

Voltz (1959), several papers have discussed various

methods to improve the solar calibration. Schmid et

al. (1998) used lamp and solar calibrations in

conjunction with each other; O'Neill et al. (1984)

combined solar aureole and solar beam extinction

measurements; and Soufflet et al. (1992) and

Holben et al. (1998) used well-calibrated reference

sun photometers.

Calibration of the Reference Sun Photometers

Three AERONET reference sun photometers

are calibrated at Mauna Loa Observatory (MLO,

3400m) at 1-3 months intervals. The reference

instruments have the same characteristics as the

CIMEL sun photometers deployed in the field.

These MLO calibrated CIMELs are used by the

AERONET network to cross-calibrate the other

CIMELs and by the SIMBIOS Project to crosscalibrate other types of sun photometers

(MicroTops, SIMBAD, PREDE). A three month

cycle is maintained to keep one or two MLO

calibrated CIMELs on the GSFC roof platform, one

12
attheMaunaLoaObservatoryforcalibration, and

oneintransitattimesbetweenlocations.

The Langley-Bouguer tchniqueis usedto

determinethetop-of-the-atmosphere (TOA)signals

(signalsatzeroairmass)foreachchannel of each

referencesunphotometer.Holbenet al. (2000)

havereportedthecalibrationhistoryof theMLO

calibratedinstruments#101.Table3.4presentsthe

TOAsignalsthisreferenceCIMEL,derivedfrom

thesuccessivecalibrationsperformedatMaunaLoa

ObservatorysinceSeptember 1997.Sincethen,the

percentchangesin Vo,theTOA signalat zero

airmass, are-1.73%yrt, -0.69%yrl, --4.23%yr"

1,0.05%yr"l,0.71%yr1,and0.37%yr"1at440,

500,675,870,940,and1020nm,respectively.

Sun Photometer Cross-Calibration

The cross-calibration technique applied by

Holben et al. (1998) to the reference CIMEL

instruments has been extended to the complete pool

of SIMBIOS sun photometers. The reference sun

photometers are calibrated, using the LangleyBouguer technique, in high altitude conditions at

Figure 3.1 Roof platform at Goddard Space Flight Center used for transferring calibration to sun photometers.

Qu _vent

E_

Figure 3.2 Integrating Sphere Hardy used to calibrate the SIMBAD radiometer and integrating sphere source.

13
3 0%

Total eler'nerl'ts RS_

-- S_:_ndard l_mip lrrad|an¢_=

2 5%

15%

1,0_o

00"A_

0 600 I_00 1500 2000 2500

Figure 3.3 Integrating Sphere Calibration uncertainty.

Figure 3.4 Absolute calibration of the SIMBAD radiometer at GSFC and at LOA. Top figure presents the

historic calibration coefficients since March 1997. The bottom figure presents the ratio of the calibration

coefficient Ci to the coefficient Ci _obtained the first calibration (March 1 1997).

14
MaunaLoa (Hawaii). The Langley-Bouguer

techniqueand its limitationsare describedin

chaptersixof Fargionet al.(2000).Thecrosscalibrationtechniqueconsistsof takingconcurrent

measurementswith the referenceand the

uncalibratedsunphotometers.Sincethe time

differenceis lessthanoneminuteandsincethe

solarzenithangleis lowerthan70°, theTOA

voltagefrom the uncalibratedinstrumentis

calculatedrelativetotheratioofthevoltagesfrom

thereferenceinstrument,

(3.1)

where V0_f(_i -) is the TOA signal for channel 2q of

the reference CIMEL and V(2q) and V_f(_ .) are the

signals measured by the uncalibrated and reference

sun photometers. Some sun photometers have

channels (_'s) which are slightly different from any

of those in the reference instrument. In this case,

the closest channel of the reference sun photometer

is used in the calculations, and the TOA voltages

are obtained using

, -a -a )]

(3.2)

where _ is the wavelength of the channel in the

reference instrument. In Equation (3.2), the first

exponential term gives the difference in the

Rayleigh optical thickness between wavelengths _-

and 2q, the second exponential term is the difference

in the ozone optical thickness, and the third term

gives the difference in the aerosol optical thickness.

In the equation, a and x,(1 p.m) are the Angstr6m

coefficient and the aerosol optical thickness at 1 I.tm

determined from CIMEL reference measurements

using the Angstr6m law:

-° (3.3)

According to Table 3.1, most of the sun

photometers have channels that are common with

those in the reference CIMEL reference, allowing a

simple application of the cross-calibration

technique in Equation (3.1). The stability of the

aerosol extinction is not critical when the

wavelengths of the of the reference and field

instruments are slightly different. However, in this

case, the standard deviations of the TOA voltages

need to be determined over time. The protocols of

the cross-calibration are summarized below:

1. set the GMT time on both calibrated and

uncalibrated sun photometers;

2. initiate measurements as soon as the calibrated

sun photometer starts working;

3. take measurements concurrently with the

calibrated sun photometer, between 10 a.m.

and 3 p.m. local time to have suitable airmass;

4. measure the dark current to avoid temperature

effects;

5. record the sky condition in case of clouds or

thin cirrus occurrences (cloud coverage and

cloud positions in the sky); and

6. stop when the airmass reaches 3 or when the

sky condition changes.

The SIMBIOS sun photometers are routinely

cross-calibrated at least every three months or

before each field campaign. Calibrations are

performed during days with clear and stable

atmospheric conditions (AOT at 0.44 lxm typically

lower than 0.15). The uncertainties of the crosscalibration combine the uncertainties of the

calibrated reference sun photometer and the

uncalibrated sun photometer. The calibration

transfer from the reference sun photometers to

uncalibrated instruments using the cross-calibration

technique at least doubles the uncertainty of derived

V0(_.) for instruments of the same design.

According to Holben et al. (1998), the uncertainties

in AOTs obtained for cross-calibrated CIMEL

instruments are estimated to be 0.01 to 0.02 in

AOT. The uncertainties are greater when the crosscalibrated sun photometer is not of the same design

as the reference sun photometer.

Sky Radiometer Calibration

Sky radiance scanning systems are automated

instruments dedicated to measure sky radiances in

the aureole and in the principal plane of the sun.

Radiative properties of aerosols are retrieved using

an inversion algorithm of the sky radiances

(Dubovik et al., 2000, Nakajima et al., 1996) and of

the polarized component of the sky radiances

(Vermeulen et al., 2000). This section is dedicated

to the description of calibration techniques for

retrievals of sky radiances.

Radiometers, such as CIMEL and PREDE, are

calibrated for sky radiances using an integrating

sphere. The radiometer is aligned in front of the

sphere, and ten measurements are taken for each

channel. Radiances from the integrating sphere are

then integrated over the spectral responses of each

15
channel oftheradiometer. Asaresult,ratiosofraw

radiometervoltagesto the integratedsphere

radiancesare obtained. Theseratiosgive

radiometer calibrationparameter Ci(alsocalledthe

instrument responsivity)

V/ (3.4) Ci=

_ L( Z )R, (_ )dZ

where Vi is the voltage measured by channel i,

Ri(_,) is the spectral response of the channel, and

L(_,) is the spectral radiance of the integrating

sphere. The accuracy of the radiometer calibration

is dependent on the calibration of the integrating

sphere, the sphere's size, the clarity of the

calibration protocols, and the precision of the

calibration process. A two-meter integrating sphere

is available and managed by NASA GSFC

Calibration Facility (http://spectral.gsfc.nasa.gov/).

The uncertainty of the radiances provided by this

integrating sphere is estimated to be less than 5%.

The protocols for doing the absolute calibration are

summarized below:

1. align the optical axis of the radiometer with the

optical axis of the integrating sphere;

2. turn on the integrating sphere using the

maximum numbers of lamps, and wait at least

30 minutes before starting any measurements;

3. control the signal from the radiometer (If the

signal is saturated, decrease the intensity of the

light source by turning off an even number of

lamps, and wait a couple of minutes for

stabilization);

4. cover the aperture output of the sphere, the

aperture entrance of the radiometer, and take

dark measurements;

5. uncover the exit aperture of the integrating

sphere and take measurements for each

channel;

6. start again with a reduced number of lamps;

and

7. record the data and the radiances from the

calibrated integrating sphere.

3.4 CALIBRATION HISTORY

Cross-CalibrationHistory

The cross-calibration of the SIMBIOS sun

photometers has been performed at GSFC since

1998. Each sun photometer in the SIMBIOS

instrument pool has been cross-calibrated using the

same technique. Several SIMBIOS CIMELs (serial

numbers 94, 37, 27 and 101) have been used as

reference instruments and have been calibrated at

Mauna Loa. The cross-calibration is considered

complete when:

1. enough simultaneous measurements are

selected;

2. time difference is lower than 40 seconds;

and

3. TOA standard deviation over the

calibration period is less than 1%.

Table 3.3 shows the TOA voltages for each sun

photometer cross-calibrated with a reference Cimel.

The average values for the TOA voltages and their

standard deviations are given for each spectral

channel.

Sphere Calibration History

Absolute calibration of sky and above water

radiometers have been performed at GSFC since

1998 (see Table 3.2). Figure 3.4 shows the

calibration history for SIMBAD number 972306.

Calibrations were performed on August 1999,

January 2000, and March 2000. They are

compared with calibrations performed at LOA

(Lille, France) on November 1997, March 1998,

_ind June 1999. The top panel of Figure 3.5 gives

the calibration coefficients (Ci) in all channels. The

bottom panel gives the ratios of the coefficients

(Ci's)to the value from the first calibration (Cil).

The plots show that the variations are within :t.5%.

Only the calibration using the plaque has greater

variations because of the higher uncertainties in

determining the radiances from the plaque. A

greater variation is also found in the first calibration

at GSFC. This difference may have been caused by

a greater distance between the instrument and the

sphere during the calibration (1 meter) than the

distance chosen for other calibrations (0.1 meter).

Unexpected light flux from the room or from the

edges of the sphere may have contributed to the

measurement results and may explain the higher

calibration coefficient on this date. Comparable

results have been retrieved with the other SIMBAD

(number 972309) and with the CIMEL instruments.

Calibration of polarized radiometers, using the

integrating sphere Hardy, are described in a

separate chapter.

3.5 CONCLUSIONS

The techniques for calibrating the sun

photometers and sky radiometers in the SIMBIOS

pool of instruments have been developed. The

protocols for taking accurate measurements and

16
allowingaccuratecalibrationareinplaceaswell.

Sunphotometersandskyradiometerscalibrations

havebeenperformedsuccessfullybtheSIMBIOS

Projectsince1998.Thecalibrationfacilitieshave

been designedto accuratelycalibrateany

instrument.Protocolsto usethesefacilitieshave

beenrefinedto minimizetheuncertaintiesin the

measurements.Many calibrationshavebeen

performedsince1998,andthehistoryhasshown

long-termstabilityof thecross-calibrations (better

than3%) and the absolutecalibrationusing

integratingspheres(within :t5%). However,

refinementsoftheprotocolsshouldhelpreducethe

calibrationuncertainties. Thecleanroomwherethe

sphereis locatedis nowcontaminationcontrolled

forparticulatesin theair. Thismayimprovethe

stabilityofthecalibrationof theintegratingsphere.

In addition,adevice(asinglechannelradiometer

mountedinthebackof thesphere)hasbeenadded

to monitorfluctuationsin the sphereintensity

duringcalibrations. Thedevicewillhelpdetermine

thecontributionofvariationsinthesphereoutputto

theresultsof thecalibrationmeasurements. The

calibrationof the referencesunphotometerat

MannaLoaiswellknown.Thelong-termstability

of the SIMBIOScross-calibrations has been

monitoredfor threeyears.However,a detailed

studyof theuncertaintiespropagatedthroughthe

calibrationtransferhasyettobeconducted. Effects

causedby differentfields-of-view,filters,and

detectorsneedtobeaccountedforaswell.

REFERENCES

Dubovik, O., and M.D. King, 2000: A flexible

inversion algorithm for retrieval of aerosol

optical properties from Sun and sky radiance

measurements, J. Geophys. Res. (submitted).

Fargion, G., J.L. Mueller, and C.R. McClain, 2000:

Ocean Optics Protocols For Satellite Ocean

Color Sensor Validation, Revision 2. NASA

Tech. Memo, NASA Goddard Space Flight

Center, Greenbelt, Maryland, 147 pp.

Goddard Space Flight Center (GSFC) Work

Instruction 545-WI-8072.1.22, 1998: Procedure

for Formulation/Application of MS-125, A

Barium Sulfate Optical Coating Standard.

Holben, B.N., T.F. Eck, I. Slutsker, D. Tanre, J.P.

Buis, A. Setzer, et al., 1998: A federated

instrument network and data archive for aerosol

characterization, Remote Sens. Environ., 66, 1-

16.

Hoiben, B.N., D.Tanre, A.Smirnov, T.F.Eck,

I.Slutsker, N.Abuhassan, et al., 2000: An

emerging ground-based aerosol climatology:

Aerosol Optical Depth from AERONET, J.

Geophys. Res., In press

Mueller, J., C. Johnson, et al., 1993: The Second

SeaWiFS Intercalibration Round-Robin

Experiment, SIRREX-2, SeaWiFS Technical

Report Series, NASA Technical Memorandum

104566, 16, 3-4, 23-24.

Nakajima, T., G. Tonna, R. Rat, P. Boi, Y.L.

Kaufman, B. Holben, 1996: Use of Sky

brightness measurements from ground for

remote sensing of particulate polydispersions,

Appl. Opt., 35, 15, 2672-2686.

O'Neill, N.T., and J.R. Miller, 1984: Combined

solar aureole and solar beam extinction

measurement, 1: Calibration considerations,

Appl. Opt., 23, 3691-3696.

Schmid, B., P.R. Spyak, S.F. Biggar, C. Wehrli, J.

Sekler, T. Ingold, C. M_itzler, and N. Kamplr,

1998: Evaluation of the applicability of solar

and lamp radiometric calibrations of a precision

Sun photometer operating between 300 and

1025 nm, App. Opt., 37, 18, 3923-3941.

Soufflet, V., C. Devaux, D. Tanre, 1992: A

modified Langley Plot method for measuring

the spectral aerosol optical thickness and its

daily variations, Appl. Opt., 31, 2154-2162.

Vermeulen, A., C. Devaux., and M. Herman, 2000:

Retrieval of the scattering and microphysical

properties of aerosols from ground-based

optical measurements including polarization,

Appl.Opt. (submitted).

Volz, F., 1959: Photometer mit selen-photoelement

zur spectralen messung der sonnenstrahlung und

zur bestimmung der wellenlangenabhangigkeit

der dunsttrubung, Arch. Meteor. Geophys.

Bioklimatol. Ser., B 10, 100-131.

Walker J., C. Cromer, J. McLean, 1991: A

technique for improving the calibration of largearea sphere sources, SPIE Vol. 1493 Calibration

of Passive Remote Observing Optical and

Microwave Instrumentation, 224-230.

Walker, J. and A. Thompson, 1994: Improved

Automated Current Control for Standard Lamps,

J. Res. Natl. Inst. Stand. Technol. 99, 255-261.

17
CIMEL#101 CIMEL#108

10

o

g_ 6

o_ 4

_ -2

E

.,g-4

g,

u

N -8

-10

0

I I I I I I I I I

I I 1 I I I I I I

100 200 300 400 500 600 700 800 900 100C

Days frominitial cehbra_omonS_ 301997

10

8

o= 4

=o

_ 2

g -2

E

•'_ -6

U

N

N-8

-10

0

I I I I I I

%,, "_--_,,

I I I I I I I

I

100 200 300 400 500 600 700 800

D_ysfromiamalc_bra_iomonJuly21998

MicroTops #3772 SIMBAD #972306

10

8

¢'4

6

*. 2

90

"_ -2

a_

-4

io

r,)

-8-

-10

0

I I I I I I I I I I I I I I

I I I I I I I I I I I I I I

100 200 300 400 500 600 700 800 "I 00 200 300 400 500 600 700 800

Days from _al c,hbratiora on Dex. 121998 Days from ix_d ¢_hbra_om on Au_mst 20 1998

Figure 3.5 Calibration history for Cimel MLO calibrated #101, Cimel in the filed #108, MicroTops 3772, and Simbad #972306

18
Table3-1Calibrationhistory' oftheCIMEL#101atMaunaLOA(Hawaii)since1997.

Date 440 nm 500 am 675 nm 870 nm 940 nm 1020nm

1997-09-30 13843 15695 17281 13576 17189 14960

1998-03-23 13671 15626 16852 13535 17322 14973

1998-08-08 13628 15615 16525 13598 16969 15069

1998-10-29 13508 15505 16262 13514 17921 14975

1999-02-03 13605 15620 16313 13610 17194 15043

1999-02-13 13605 15620 16313 13610 17194 15043

1999-05-04 13479 15511 16270 13647 17022 15097

1999-09-11 13374 15453 16064 13660 17207 15089

1999-10-02 13416 15497 16064 13654 17578 15123

1999-12-29 13343 15405 16002 13635 17512 15019

2000-02-19 13388 15505 15894 13533 17916 15025

2000-06-11 13324 15448 15855 13605 17445 15017

Table 3.2 Calibration activities conducted at GSFC since 1998

Date Instruments Calibration Type

1998-08-20 MicroTops 3769, 3773; Simbad 972306; Prede PS090063 Sun cross calibration

1998-08-21 MicroTops 3769, 3773; Simbad 972306; Prede PS090063 Sun cross calibration

1998-10-16 MicroTops 3765, 3766, 3773; Simbad 972306; Prede PS090064 Sun cross calib

1998-11-12 MicroTops 3765, 3766, 3768, 3770 Sun cross calibration

1998-11-24 MicroTops 3772, 3773; Simbad 972306; Prede PS090064 Sun cross calibration

1998-12-14 MicroTops 3755, 3765, 3768, 2770, 3772; Simbad 972306; Suncrosscalibration

Prede PS090064

1999-01-08 Polorized Cimel 25, 92, 111 Sphere calibration

1999-01-19 Polorized Cimel 14, 43, 45 Sphere calibration

1999-03-30 MicroTops 3755, 3767, 3768, 3772 Sun cross calibration

1999-05-29 Polorized Cimel 191 Sphere Calibration

1999-06-09

1999-08-12

MicroTops 3768, 3769, 3773, 3775

Simbad 972306, 972309

Sun cross calibration

Sphere Calibration

1999-09-23 MicroTops 3765, 3768, 3772; Simbad 972306, 972309 Sun cross calibration

1999-10-28 MicroTops 3767; Simbad 972306 Sun cross calibration

2000-01-04 Simbad 972303, 972306, 972307, 972308, 972309 Sphere calibration

2000-01-14 MicroToDs 3767, 3768, 2770, 3775; Simbad 972309 Sun cross calibration

2000-03-06 Simbad 972301, 972306, 972307, 972309, 972310 Sphere calibration

2000-03-06 MicroTops 3766, 3773; Simbad 972306, 972309 Sun cross calibration

2000-07-03 Polorized Cimel 188 Sphere calibration

2000-08-18 Simbad 972303, 972306, 972308 Sphere calibration

2000-09-20 MicroTops 3755, 3765, 3767, 3768, 3772, 3773 Sun cross calibration

19
Table 3.3 Cross-calibration history conducted at GSFC since 1998 for the MicroTops and SIMBAD sun

photometers. The average values of the TOA voltages and the standard deviation are given for each sun

photometer. The reference CIMEL units used for each calibration are indicated as well as the time

difference (At) of measurements taken by the reference CIMEL and the corresponding sun photometer.

MieroTops CIMEL At

Units # Units # s

Date 440 nm 500 nm 675 nm 870 nm 940 m

03755

factory

Vo+/-AVo

- - 800 915 836 786 1400

94 14 1998-12-14 765+/-2 872+/-2 668+/-2 742+/-3 2113+/-12

101 14 1998-12-14 764+/-2 872+/-2 668+/-2 741 +/-3 1970+/-12

37 20 1999-03-30 806+/-1 880+/-1 644+/-1 782+/-2 1868+/-4

94 19 1999-03-30 807+/-1 885+/-3 643+/-1 781 +/-2 2234+/-9

37 16 2000-09-20 788+/-4 884+/-3 566+/-2 777+/-3 1852+/-21

03765

factory - - 1263 990 1147 831 1464

27 71 1998-10-16 1105+/-8 916+/-6 1246+/-8 801+/-3 1722+/-7

27 17 1998-11-12 1036+/-5 884+/-5 1350+/-13 797+/-6 1739+/-16

94 63 1998-12-14 1226+/-3 987+/-4 1148+/-6 825+/-2 2334+/-11

101 63 1998-12-14 1224+/-3 987+/-3 1148+/-6 824+/-2 2176+/- 10

94 50 1999-09-23 1235+/-3 978+/-4 1150+/-3 827+/-3 2373+/-7

37 17 2000-09-20 1237+/-5 982+/-3 1156+/-3 825+/-2 2001+/-12

O3766

factory - 1224 992 1191 807847 1546

27 80 1998-10-16 1054+/-7 906+/-9 1312+/-7 811 +/-2 1820+/-14

27 18 1998-11-12 1011+/-22 866+/-24 1389+/-22 803+/-9 1857+/-35

37 13 2000-03-06 1226+/-4 983+/- 11 1195+/-10 838+/-4 2113+/-18

106 18 2000-03-06 1212+/-13 977+/-10 1185+/-14 833+/-12 1552+/-17

03767

37 13 1999-03-30 1157+/-4 982+/-5 1190+/-4 794+/-2 2472+/-15

94 11 1999-03-30 1160+/-4 989+/-5 1190+/-3 793+/-2 2957+/-21

101 22 1999-10-28 1108+/-9 976+/-6 1183+/-6 789+/-4 2712+/-19

94 16 2000-01-14 1064+/-8 997+/-4 1209+/-2 797+/-4 2939+/-17

03768

factory

factory

1236 989 1195 807 1616

27 14 1998-11 -12 882+/-5 873+/-5 1390+/- 10 766+/-8 1564+/-9

94 26 1998-12-14 1017+/-20 987+/-2 1203+/-4 802+/-4 2628+/-15

101 26 1998-12-14 1016+/-20 986+/-2 1202+/-4 801 +/-4 2450+/-14

94 17 1999-03-30 978+/-49 988+/-4 1200+/-3 799+/-3 2644+/-14

37 21 1999-03-30 972+/-54 982+/-3 1201 +/-3 800+/-3 2212+/-10

101 23 1999-06-09 894+/- 17 980+/- 1 1204+/-2 807+/-1 2419+/-6

94 45 1999-09-23 826+/- 18 982+/-6 1206+/-6 784+/-4 2547+/-26

1999-11-11 1263 990 1147 831 1454

37 12 2000-09-20 1247+/-6 981+/-4 1205+/-5 802+/-4 2200+/-24

03769

factory 1220 979 1157 791 1419

37 15 1998-08-20 1166+/-3 977+/-5 1189+/-3 801 +/-3 1902+/-17

37 26 1998-08-21 1171 +/-8 982+/-4 1188+/-2 801 +/-2 1904+/-11

101 28 1999-06-09 1203+/-5 975+/-4 1167+/-5 798+/-3 2198+/-16

O377O

factory 1225 1002 1176 630 1819

27 44 1998-11-12 1024+/-4 892+/-3 1389+/-3 801 +/-1 2146+/-12

2O
94

101

30 1998-12-14 1213+/-3

28 1998-12-14 1210+/-4

993+_6 1165+_9

993+#6 1166+#8

821+/-5 2835+/-7

820+/-5 2642+/-6

03772

factory

27

94

101

94

37

94

1194

14 1998-11-24 942+/-8

63 1998-12-14 1160+/-8

62 1998-12-14 1157+/-8

15 1999-03-30 1192+/-5

10 1999-03-30 1190+/-5

62 1999-09-23 1178+/-3

1000 1180

868+_4 1398+_14

994+#2 1166+k6

994+_1 1166+_6

998+_3 1169+_4

991+#3 1168+k4

981+A3 1155+k4

806 1843

767+/-6 2166+/-31

804+/-3 2897+/-13

803+/-3 2700+/-10

810+/-2 3002+/-29

811+/-1 2508+/-23

793+/-2 2920+/-11

03773

factory

37

37

27

27

101

1245 991 1195

18 1998-08-20 1238+/-7 988+/-4 1219+/-5

25 1998-08-21 1244+/-8 988+/-12 1218+/-10

81 1998-10-16 1099+k17 912+/-12 1315+/-14

25 1998-11-24 993+k2 862+#2 1441+/-4

28 1999-06-09 1238+/-4 987+/-4 1198+A3

818 1443

825+/-3 1929+All

824+/-7 1919+#13

803+#5 1745+/-19

785+_3 1757+_6

827+_2 2178+/-13

03775

101 37 1999-06-09 1179+#5 982+/-4 1212+/-5 818+k3 2578+/-22

S1MBAD 440 nm 490 nm 560 nm 670 nm 870nm

Umt#

972306 Vo_-AVo

37 13 1998-08-20 391290+/- 477288+/- 404124+/- 420995+/- 305421+_

719 2026 165 2039 414

37 82 1998-08-21 388591+/- 479121+/- 406870+A 421086+/- 304820+/-

1703 3755 1305 529 388

27 107 1998-10-16 346025+/- 449951+/- 383534+k 454057+/- 300166+k

3581 1968 6934 2460 1816

94 122 1998-12-14 388269+/- 473101+# 394874+k 410455+/- 311944+_

3922 1242 935 2597 702

101 122 1998-12-14 387413+/- 472637+_ 394399+/- 410233+/- 311458+/-

3911 1195 823 2198 681

94 81 1999-09-23 376205+/- 464224+_ 391526+/- 416182+/- 300000+A

2369 815 854 933 2204

101 56 1999-10-28 376820+1- 462637+_ 387034+/- 410887+_ 302475+_

4872 1326 1614 1868 1715

106 16 2000-03-06 376901+/- 459570+_ 377800+A 404798+/- 298829+/-

1870 1825 1559 1410 1531

37 10 2000-03-06 382815+/- 465574+_ 382168+A 408538+/- 301005+/-

2311 2362 1231 961 469

972309

94

106

37

12 2000-01-14 271780+_ 385589+/- 331527+/- 402221+/- 315555+/-

946 1000 2112 2603 2890

16 2000-03-06 287566+# 413058+/- 342878+/- 411053+k 294097+/-

6927 2193 2870 4870 3823

14 2000-03-06 297359+/- 418699+/- 344590+_ 415921+/- 298063+/-

13257 3152 3315 6602 6980

21
, "tJ .4/

Chapter 4

Variance Equations and Uncertainty Analysis for the

Langley Technique

Mark Miller, Mary Jane Bartholomew, and R.M Reynolds

Department Environmental Science, Earth System Science Division, Brookhaven

National Laboratory, Upton, New York

4.1 INTRODUCTION

To accurately interpret aerosol optical thickness

(AOT) data, it is necessary to understand the

sources of measurement uncertainty and their

impact on the geophysical values produced by the

radiometer. A preliminary analysis of the variance

in the measurements of aerosol optical thickness, xx

, and the Angstrom exponent, (x, is given in this

report.

4.2 CALCULATION OF

UNCERTAINTIES

From Beer's Law:

ln(I_)=-'cxm+ln(Ixr) (4.1)

where za = zu + z_ +'rxo. The subscripts A,R,

and O indicate the contributions to the optical

thickness made by aerosols, molecular scattering

(Rayleigh) and ozone absorption. IXN is the

measured irradiance of the solar beam referenced to

a plane that is normal to the solar beam and

excluding all scattered diffuse light. IXTis top-ofthe-atmosphere irradiance referrenced to a plane

along the normal to the solar beam. With the

exception of m (under typical conditions), each

term in (4.1) is wavelength dependent, as indicated

by the subscript X. The irradiance at the top of the

atmosphere can be written as I_r =I_o ,

where I_ is the measured irradiance on a particular

day of the year during which ro is the earth-sun

distance and r is the current earth-sun distance.

Substituting this relationship into (4.1) and solving

for T_A:

_',a = l[ln (/,o)- 2 In (_-)] -'r,, - _'_o

(4.2)

Using the Lambertian or Langley method (Shaw

1983; Harrison and Michalsky 1994a), a plot of m

versus ln(IxN) can be extrapolated to m---0 to derive

In(IXT). The negative of the slope of the line is xx.

A power-law formalism is typically used to

quantify the relationship between xxx at different

wavelengths. This power law is written as

(4.3)

where a is termed the angstrom exponent.

The variance associated with the measurement

of x using the Langley method can be expressed in

the form of a variance equation for (4.2), which is

given by

I'/I :Ill dr2 = m 0.2 •_ -0,) +°'2U=) + 2, + o'_,, +o',2

m

(4.4)

It is assumed that 2o'_r_, ,]-=0 since these values

do not fluctuate on the same scale as '_..

Furthermore, ¢r2 depends on surface pressure ,=

measurements and is at least an order of magnitude

less than other variance terms and can be neglected.

The ozone optical thickness variance, ¢72 is only t'_t O

important in the vicinity of 660 nm and will be

neglected here. This leaves

22
1 2 2 2

(4.5)

which suggests that o.2 is the largest at small

zenith angles (when the sun is highest in the sky

and m approaches unity). It can be shown that

Cr

o't_,l = ...a_: (4.6)

X

so (4.5) can be written as

•, ,,m) Lt,#,oJ tl J

(4.7)

Hence, the variance in the measurement of %is

a result of the sum of the variances in the

measurements of Ixo and I_. Similarly, the

variance equation for ix is

0".2- (4.8)

which shows that o'_ is inversely proportional to

the square of the difference between the two

wavelengths that bound the calculation of of. The

greater the separation between these two

wavelengths, the less the varaince, or uncertainty,

in the determination of a.

These variances are the result of several

instrument-related factors, which are detailed

below. These factors typically contribute variance

as a percentage of the absolute measurement, so

orx, = Cl_ Ix, (4.9)

where Ct, is the estimated percent accuracy of the

measurement of Ix. Accordingly, (4.8) can be

written as

0"2,=(--_-l'_2[Cll +Ct2] (4.10)

t.m/c ,0

Thus, the variance in measurements of x results

from the sum of the squares of the percentage

accuracy in measurements of Ix0 and Ix_ and is

inversely proportional to the square of the air mass,

m. This suggests that measurements made at higher

values of air mass have inherently less variance and

are more accurate. Of course, (4.10) can be

substituted into (4.8) to yield

r¢' c,,._+c,..' ]+(<:,:+5,:,. ]]"'

, Lt Co ; t <. )i

0 a

(4.11)

which can be used to evaluate the uncertainty of

angstrom exponent measurements.

The variance equations (4.10) and (4.11)

represent a rough estimate of the uncertainties

associated with the measurements of x and ix, since

they include the assumption o'_: = C,_ I_<, which is

simplistic. Strictly speaking, (4.7) and (4.8) must

be used to provide the best possible estimates of

variance, although (4.10) and (4.11) are useful to

illustrate how the variance analysis technique be

used as a quality control metric for sun

photometers, both shadow-band and oriented

varieties. Placing a threshold on the allowable

uncertainties of x and ix reduces the scatter of data

points by eliminating observations for which the

uncertainty associated with the Langley technique

or instrument deficiencies is unacceptably large.

The terms in (4.10) and (4.11) can be computed

from the data, as can the terms in (4.7) and (4.8) for

a more rigorous analysis, and each individual data

point can be scrutinized to ensure that its

uncertainty is not so large as to make the

measurement meaningless.

An example of the use of (4.10) and (4.11) as

quality control metrics is shown in Figures (4.1)

and (4.2). These plots show x versus ix in several

different aerosol regimes over the ocean. Note that

ix is computed for different wavelength ranges.

These data were collected with the Brookhaven

National Laboratory fast-rotating, shadow-band

radiometer and the nature of the aerosol determined

by chemical analysis. The difference between

Figures (4.1) and (4.2) is that the latter has been

subject to data rejection on the basis of equations

(4.10) and (4.11). In Figure (4.2), the variance

(uncertainty) in x must be less than 0.05 and the

variance (uncertainty) in ix must be less than 0.3.

These thresholds are determined subjectively based

on experience. Note that the impact of the

uncertainty threshold is to greatly reduce the scatter

of the data in Figure (4.2) by eliminating points

23
with unacceptablyarge error bars. Most

importantly, thevariousaerosol regimesareclearly

separablein Figure(4.2)whiletheyarenotin

Figure(4.1). It shouldbeemphasizedthatthese

errorbars includethe inherentuncertaintyin

Langleytechniqueitself,as well as instrument

uncertainty.Themagnitudeof the latter,the

instrument uncertainty, isbeingfurtherboundedby

fieldandlaboratorymeasurements at thetimeof

thisreport.

Inessence, theatmospheric correctionalgorithm

attemptsto infertheresultsof Figure(4.Ia)from

Figure(4.lb). Thatis,thepower-lawextrapolation

of thedatain Figure(4.lb), using660nmasa

surrogateforthe765nmchannel intheSeaWiFS

sensor,shouldideallyproducetheresultsinFigure

(4.1a).It isclearthatapplicationof apower-law

extrapolationtothedatainFigure(4.1b)wouldnot

appearto producea graphthatlookslike Figure

(4.la).ThebasicshapeandconfigurationofFigure

(4.1b)isdifferenttheFigure(4.1a),althoughthis

differenceis largely due to measurement

uncertainty.When data points that contain

unacceptableuncertaintyarerejected,the basic

shapeandconfigurationofFigure(4.2b)isactually

quitesimilartothatinFigure(4.2a),whichimplies

thatapower-lawatmospheric correctionispossible

whenthemeasurement uncertaintyis minimized.

In summary,theseanalysesshow that the

uncertaintyintheLangleytechniqueitselfmustbe

consideredif satelliteatmosphericcorrection

validationdataarecollectedwithtraditionalsun

photometers andshadow-band raiometers.

I T I" I r

0.05 0.1

I I 1 I

0.15 0.2 0.25 0.3 0.35

AOT 862 nm

Figure 4.1.

1 T I T

o oO

+ +

m j.

0.05 0.1 0.15 0.2 0.25 0.3 0.35

AOT 862 nm

Shadow-band radiometer data showing the aerosol optical thickness at 865 nm versus the

Angstrom exponent for the two wavelengths listed on the y-axis. Several different aerosol regimes are

depicted.

24
D_ +

0.05

-_ 0

+

0

+

I I

o_

oo

+

Oo

+

I I

-I0.1 0.15 0.2

AOT 862 nm

I

0.25 0.3 0.35

2s/ < , ,

o _&o

_-_._) __. 'o 005 o_

_+ r÷

+l

0.15

I

o_

I I

+

I I I

0.2 0.25 0.3

AOT862nm

Figure 4.2. As in Figure 4.1, but after filtering on the basis of measurement uncertainty.

0.35

25
Chapter 5

Sun and Sky Radiance Measurements and Data

Analysis Protocols

Robert Frouin l, Brent Holben 2, Mark Miller 3, Christophe Pietras 4, John Porter 5 and Ken

Voss 6

1Scripps Institution of Oceanography, University of California, San Diego, California

2Biospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland

3Department Environmental Science, Earth System Science Division, Brookhaven

National Laboratory, Upton, New York

4SAIC General Sciences Corporation, Beltsville, Maryland

5School of Ocean & Earth Science & Technology, University of Hawaii, Hawaii

6physics Department, University of Miami, Florida

5.1 INTRODUCTION

This chapter is concerned with two types of

radiometric measurements essential to verify

atmospheric correction algorithms and to calibrate

vicariously satellite ocean color sensors. The first

type is a photometric measurement of the direct

solar beam to determine the optical thickness of the

atmosphere. The intensity of the solar beam can be

measured directly, or obtained indirectly from

measurements of diffuse global upper hemispheric

irradiance. The second type is a measurement of

the solar aureole and sky radiance distribution using

a CCD camera, or a scanning radiometer viewing in

and perpendicular to the solar principal plane.

From the two types of measurements, the

optical properties of aerosols, highly variable in

space and time, can be derived. Because of the high

variability, the aerosol properties should be known

at the time of satellite overpass. Atmospheric optics

measurements, however, are not easy to perform at

sea, from a ship or any platform. This complicates

the measurement protocols and data analysis. Some

instrumentation cannot be deployed at sea, and is

limited to island and coastal sites. In the following,

measurement protocols are described for

radiometers commonly used to measure direct

atmospheric transmittance and sky radiance,

namely standard sun photometers, fast-rotating

shadow-band radiometers, automated sky scanning

systems, and CCD cameras. Methods and

procedures to analyze and quality control the data

are discussed, as well as proper measurement

strategies for evaluation of atmospheric correction

algorithms and satellite-derived ocean color.

5.2 AUTOMATIC SUN

PHOTOMETER AND SKY

RADIANCE SCANNING

SYSTEMS

The technology of ground-based atmospheric

aerosol measurements using sun photometry has

changed substantially since Volz (1959) introduced

the first hand-held analog instrument almost four

decades ago. Modern digital units of laboratory

quality and field hardiness collect data more

accurately and quickly and are often equipped for

onboard processing (Schmid et al., 1997; Ehsani,

1998, Forgan, 1994; and Morys et al., 1998). The

method used remains the same, i.e., a detector

measures through a spectral filter the extinction of

direct beam solar radiation according to the BeerLambert-Bouguer law:

V(&)=Vo(_) exp[-('c(_,)M)] ts(&), (5.1)

where V(_,) is the measured digital voltage, VoO0 is

the extra-terrestrial voltage, M is the optical air

mass, z(&) is the total optical depth, A, is

wavelength, d and do are respectively the actual and

average earth-sun distances, and tg(_)is the

transmission of absorbing gases. The total optical

26
depthisthesumoftheRayleighandaerosol optical

depth.

Theearth-sundistancecorrectioniscalculated

usingtheapproximation

I_-) 2 = 1+ 0.034cos 236; J

(5.2)

where J is the number of the day of the year (Iqbal

1983).

Air mass is a function of the sun zenith angle.

Currently, the same value of air mass is used for

Rayleigh, ozone, and aerosol factors. Air mass is

calculated as

t-1

M =[cos zr0° +0.15"(93.885-0o) _z_3 (5.3)

[ 180°

where the sun zenith angle 0o is expressed in

degrees.

Sky-scanning spectral radiometers that

measure the spectral sky radiance at known angular

distances from the sun have expanded the aerosol

knowledge base. They provide, through inversion

of the sky radiance, aerosol physical properties,

such as size distribution, and optical properties,

such as the aerosol scattering phase function

(Nakajima et al., 1983, 1996; Tanr6 et al., 1988;

Shiobara et al., 1991; Kaufman et al., 1994;

Dubovik et al., 2000; and Dubovik and King,

2000). The inversion technique to calculate these

aerosol properties requires precise aureole

measurements near the solar disk and good straylight rejection. Historically these systems are

cumbersome, not weather hardy and expensive. The

CIMEL and PREDE (French and Japanese

manufacturers respectively) sun and sky scanning

spectral radiometers overcome most of such

limitations, providing retrievals of aerosol and

water vapor abundance from direct sun

measurements, and of aerosol properties from

spectral sky radiance measurements. Since the

measurements are directional and represent

conditions of the total column atmosphere, they are

directly applicable to satellite and airborne

observations, as well as to studies of atmospheric

processes. Owing to a sophisticated tracking system

with fast responding motors, the PREDE can be

installed onboard a ship, or other moving platform,

to monitor aerosol optical properties at sea. In the

following, we focus on the CIMEL system, since

the measurement protocols are similar for both

CIMEL and PREDE systems.

Description

The CIMEL Electronique 318A spectral

radiometer, manufactured in Paris, France, is a

solar powered, weather hardy, robotically pointed

sun and sky spectral radiometer. At each

wavelength, this instrument has approximately a

1.2° field-of-view (full angle) and filtered solar

aureole and sky radiance. The 33 cm collimators

were designed for 10-5 stray-light rejection for

measurements of the aureole 3° from the sun. The

robot mounted sensor head is pointed at nadir when

idle to prevent contamination of the optical

windows from rain and foreign particles. The

sun/aureole collimator is protected by a quartz

window, allowing observation with a ultraviolet

enhanced silicon detector with sufficient signal-tonoise for spectral observations between 300 and

1020 nm. The sky collimator has the same 1.2 ° field

of view, but uses an order of magnitude larger

aperture-lens system to improve dynamic range for

measuring the sky radiance. The components of the

sensor head are sealed from moisture and

desiccated to prevent damage to the electrical

components and interference filters. Eight ion

assisted deposition interference filters are located in

a filter wheel rotated by a direct drive stepping

motor. A thermistor measures the temperature of

the detector, allowing compensation for any

temperature dependence in the silicon detector.

A polarization model of the CE-318 is also

used in SIMBIOS. This version executes the same

measurement protocol as the standard model, but

makes additional hourly measurements of polarized

sky radiance at 870 nm in the solar principal plane

(Table 5.1 and 5.2).

Installation

The installation procedures for the CIMEL

instrument are summarized below. More detailed

information is available from the AERONET web

page (http://aeronet.gsfc.nasa.gov).

The site should have a clear horizon and be

representative of the regional aerosol regime. The

basic assembly is relatively simple to mount. The

cables are labeled clearly and most fit only in one

place. Once the robot is assembled, it should be

oriented so the zenith motor casing is pointing

roughly east (the metal claw to which the sensor

head is attached, then points to the west). The round

connector end of the data cable should be attached

to the sensor head, and the flat connector should be

plugged into the white CIMEL control box. Strap

the sensor head to the robot metal claw using the

silver metal band. Make sure that the face of the

27
sensorheadis flush with the edge of the metal

claw. Also, ensure that the long axis of the

collimator cross-section is perpendicular to the axis

of the zenith motor casing and claw. Verify that the

robot itself is level. Do not use the embedded

bubble level on top of the robot. Place the supplied

bubble level on top of the fiat ledge of the central

robot tubular body (below the sensor head motor)

This should be level in both the N/S and E/W axes.

Verify that the CIMEL control box "TIME" and

"DATE" are correct, i.e., that they agree with the

VITEL transmitter clock. If the Time or Date is

wrong, the CIMEL will no.___!t find the sun on a

"GOSUN" command.

Next, put the CIMEL in manual mode using

the white control box display screen. In Manual

mode, the main screen reads: "PW MAN SCN

VIEW". Do a "PARK" procedure. When "PARK"

is complete the sensor head collimator should be

pointing down, perpendicular to the ground. Place

the bubble level on the top of the metal claw arm

and verify that this is level. If not, loosen the zenith

bolt's hex nut (below the permanent bubble level on

the top of the robot) and level it by rotating the

zenith motor casing with your hand. Re-tighten the

zenith nut tightly. Important: Perform another

"PARK" procedure, or two, and make sure it is in

fact level.

Using the right 2 buttons, change the display to

read "GOSUN". Select "GO" to initiate. The sensor

head should point to the sun. The hole at the top of

the collimator should allow the sunlight to

illuminate the marker spot at the base of the

collimator. When the bright spot is on the mark, the

instrument is aligned. If it is off to the left or right,

rotate the robot base to align it. After you rotate the

robot, you will need to verify that the robot is still

level as before. Park the instrument and perform

another "GOSUN" to check that the alignment is

still good. If not, ensure that the robot is level, and

that the sensor head is level when manually parked.

One note: when you level the sensor head and do a

"GOSLrN", repeat this process a few times to be

sure of the alignment. The first "GOSUN" after

leveling is often not correct, because moving the

sensor head while leveling can temporarily offset

the robot's zeroing point. Re-parking the sensor and

doing a second "GOSUN" should yield a more

accurate alignment. Repeat this procedure until the

alignment remains accurate and consistent on

repetition.

Press "PW" then increment to 4, and place the

instrument in "AUTO" mode. The main "AUTO"

mode display should read: "PW AUTORUN

VIEW". The CIMEL should be left in this mode in

order to perform automatic measurement

sequences. The VITEL transmitter has a multi-level

menu with "TIME DATE" etc in top level, and sub

categories below each top-level item. The exact

menu structure varies with software version (2.01,

2.9, and 2.11). Refer to the version most similar to

your particular transmitter. One may operate the

VITEL display by using the control buttons. To

initiate an action, press the "SET-UP" button, then

press the "SCROLL" button repeatedly to view the

categories in the current menu level. To choose any

subcategory, press the "SELECT" button when the

desired feature is shown in the display window. To

change a parameter use the right 2 buttons

"CHANGE" and "ENTER'". At any time, one may

return to the previous (higher) menu level by

pressing the "SET-UP" button.

Measurement Protocols

The radiometer makes only two basic

measurements, either direct solar flux, or sky

radiance. Each type of measurement involves

several programmed sequences.

Direct sun measurements are made in eight

spectral bands distributed between 340 and 1020

nm (440, 670, 870, 940 and 1020 nm are standard).

Each measurement requires approximately 10

seconds. A sequence of three such measurements

are taken 30 seconds apart creating a triplet

observation per wavelength. Triplet observations

are made during morning and afternoon Langley

calibration sequences and at standard 15-minute

intervals in between (Table 5.1). The time

variation of clouds is typically much greater than

that of aerosols, and therefore significant variation

in the triplets may be used to screen cloud

contaminated measurements from the data.

Variability over the 15-minute interval also allows

another check for cloud contamination at a lower

frequency. Sky measurements are performed at

440, 670, 870 and 1020 nm (Table 5.1). A single

spectral measurement sequence (Langley sky) is

made immediately after the Langley air mass direct

sun measurement, with the sensor pointed 20 ° from

the sun. This is used to assess the stability of the

Langley plot analysis (O'Neill et al. 1984). Two

basic sky observation sequences are made,

"almucantar" and "principal plane". The objective

of these sequences is to retrieve size distribution,

phase function and aerosol optical thickness (AOT).

This is approached by acquiring aureole and sky

radiance observations spanning a large range of

scattering angles, relative to the sun's direction,

assuming a constant aerosol profile.

An almucantar is a series of measurements

taken at the same sun elevation for specified

28
azimuthanglesrelativeto theSunposition.The

rangeof scatteringanglesdecreaseasthesolar

zenithangledecreases, thusalmucantar sequences

madeatanopticalairmassof 2,ormore,achieve

scatteringanglesof 120°, or larger.Scattering

anglesof 120°aretypicalof manysun-synchronous

viewingsatellites,andthusa measureof the

satellitepathradianceis approximatedfromthe

groundstation.Duringanalmucantar measurement,

observationsfroma singlechannel aremadein a

sweepataconstant elevationangleacrossthesolar

diskandcontinuethrough360° of azimuth in about

40 seconds (Table 11.2). This is repeated for each

channel to complete an almucantar sequence. A

direct sun observation is also made during each

spectral almucantar sequence. More than four

almucantar sequences are made daily at optical

airmasses of 4, 3, 2 and 1.7, both morning and

afternoon. An almucantar sequence is also made

hourly between 9 AM and 3 PM local solar time for

the standard instrument and skipping only the noon

almucantar for the polarization instrument. The

standard principal plane sky radiance measurement

sequence is similar to the almucantar sequence, but

the sensor scans in the principal plane of the sun,

and therefore all angular distances from the sun are

scattering angles, regardless of solar zenith angle.

This measurement pointing sequence begins with a

sun observation, moves 6° below the solar disk then

sweeps through the sun's principal plane, taking

about 30 seconds for each of the four spectral bands

(Table 5.2). Principal plane observations are made

hourly when the optical airmass is less than 2 to

minimize the variations in radiance due to the

change in optical airmass.

Polarization measurements of the sky at 870

nm are an option with this instrument. The

sequence is made in the principal plane at 5°

increments between zenith angles of-85 ° and +85 °.

The configuration of the filter wheel requires that a

near-IR polarization sheet be attached to the filter

wheel. Three spectrally matched 870 nm filters are

positioned in the filter wheel exactly 1200 apart.

Each angular observation is a measurement of the

three polarization filter positions. An observation

takes approximately 5 seconds and the entire

sequence about 3 minutes. This sequence occurs

immediately after the standard measurement

sequence in the principal plane.

Data Analysis

We are following the procedures established

for the AERONET program (Holben et al, 1998)

(Table 5.3). These algorithms impose a processing

standardization on all of the data taken in the

network, facilitating comparison of spatial and

temporal data between instruments.

Table 5.1. Measurement sec

BASIC DIRECT SUN

Triplet Observation

Spectral

Range nm

340 to 1020

340 to 1020

tences of the CIMEL Sun/Sky scanning spectral radiometer.

Target No. Obs. Obs. Application

Sun

Sun

Standard

Measurement

Sequence

Langley

340 to 1020 Sun

1 each _,

Three direct

sun

Variable:

depends on

da_, ien[[th

16, am&

PM between

m7&2

Interval

~ 8 sec. for. 8 L

3 @ 30 sec. apart, 1

min total

Ea. 15 min m=2 AM

to m=2 PM

m=7 - 5, incr of.5 m

m=5 - 2, incr. of.25

340 to 1020 Sun

BASIC SKY 440 to 1020 Sky 1 each _, none

Langley sky 440 to 1020 Sky 16 between m=7 - 5,.5;

m 7 & 2 m=5- 2,.25

Almucantar 440 to 1020 Sky 72 >8/day: m= 4, 3, 2,

(Table 2) 1.7 hrly 9AM to

3PM

Polarization 870 Sky 42 hourly

(Table 2) m=3 AM to m=3

PM

Principal Plane 440 to 1020 Sky 42 hourly

(Table 2) m=3 am to m=3 PM

AOT, Pw, ot

AOT, Pw,

& cloud

screening

AOT, Pw, oc

Langley, Cal.,

AOT, Pw,

Sky Radiance

Stability of

Ln[[l), Plot

Size Dist. and

P(0), AOT, c_

Size Dist. and

P(0)

Size Dist. and

P(0) AOT, a

29
The archivalsystemallowsthe SIMBIOS

communityto accesseithertherawor processed

dataviainternetfor examination, analysisand/or

reprocessing, asneeded,throughtheAERONET

web page: aeronet.gsfc.nasa.gov:8080. The

algorithms, inputs,corrections, andmodelsusedin

computing the aerosol optical thickness,

precipitablewater(Pw),spectral irradiance, andsky

radianceinversionsarereferencedin Table15.3.

Thealgorithmscomprisetwoprincipalcategories;

timedependent retrievalssuchasAOTandPw,and

skyradianceretrievalsuchassizedistribution,

asymmetryparameter, singlescatteringalbedoand

complexindexofrefraction.Asnewandimproved

approachesandmodelsareacceptedwithinthe

communitythe processingmay be applied

uniformlytothenetwork-wideatabase.

Sky radiance Inversion Products

Optical properties of the aerosol in the

atmospheric column are retrieved by two inversion

algorithms: that of Nakajima et al. (1983, 1996) and

the new algorithm developed by the AERONET

Project (Dubovik and King 2000; Dubovik et al.

2000).

a) Inversions by the Nakajima et al. 's (1983, 1996)

algorithms

The code inverts sky radiance in two ways:

1. simultaneously at four wavelengths (440; 670;

870 and 1020 nm) in the aureole angular range

(scattering angle between 2.g ° and 40 o ;

. separately at each of four wavelengths (440;

670; 870 and 1020 rim) in the whole solar

almucantar (scattering angle greater than 2.8 °) -

-option "single channel inversion".

The inversion assumptions are that aerosol

particles are homogeneous spheres with a fixed

index of refraction: n (_,) = 1.45, k(Z) = 0.005. The

dr(r)

retrieved variables are: _ (in lam'3/lam'2), the

din r

volume particle size distribution in range of sizes:

0.057 tam < r < 8.76 lam, the scattering optical

thickness at 440,670,870,1020nm, and the phase

function at 440, 670, 870 and 1020rim (including an

asymmetry parameter).

b) Inversions by the new AERONET code (Dubovik

and King 2000; Dubovik et al. 2000)

The code inverts _o(_) and sky radiances

simultaneously at four wavelengths (440; 670; 870

and 1020 nm) in the whole solar almucantar

(scattering angles greater than 2.8°). Aerosols are

assumed to be homogeneous spheres, but the index

of refraction is not fixed.

Table 5.2. Almucantar and Principal Plane sequences for the standard and polarization instruments.

ALMUCANTAR

Azimuth angle relative to

sun

PRINCIPAL PLANE:

Standard

Scattering Angle from sun

(negative is below the sun)

Sun

0°

0

(*)

6.0, 5.0, 4.5, 4.0, 3.5, 3.0, 2.5, 2.0, -2.0,-2.5, -3.0, -3.5, -4.0, -4.5, -5.0,

-6.0, -8.0, - 10.0, - 12.0, - 14.0, - 16.0, - 18.0, -20.0, -25.0, -30.0, -35.0, -40.0,

-45.0, -50.0, -60.0, -70.0, -80.0, -90.0, - 100.0, - 110.0, - 120.0, - 130.0,

-140.0, -160.0, -180.0

Duplicate above sequence for a complete counter clockwise rotation to --6

-6.0, -5.0, -4.5, -4.0, -3.5, -3.0, -2.5, -2.0, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0,

6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0,

50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0, 140.0

PRINCIPAL PLANE:

Polarization

Scattering Angle from sun

(negative is in the anti

solar direction)

-85.0, -80.0, -75, -70, -65.0, -60.0, -55.0, -50.0, -45.0, -40.0, -35.0, -30.0,

-25.0, -20.0, -15.0, -I0.0, -5.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0,

45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0

30
Table 5.3 Procedure of the AERONET Pro

Variabl% algorithm or correction

Basic Computations

Rayleigh Optical Depth, "_

refractive index of air

depolarization factor

Solar Zenith Angle, 0o

Earth sun distance, d

Ozone amount, 03

Aerosol optical air mass, m_

Rayleigh optical air mass, mr

O_optical air mass, mo

Corrections

Temperature, T

Water Vapor for 1020 AOT

Rayleigh, all wavelenbe&s

O_ abs. coef. _, > 350 nm

O_ abs. coef. Z,< 350 nm

Time, t

Conunents

Input elevation in m

Table lookup by 5° lat. long.

-0.25%/°C for 1020 nm

specific for each inst.

from Pw retrieval,Lowtran

from elevation

CIMEL, UTC, DAPS time

stamps, +1 second

ranl

References

Penndorf, 1957

Edlen, 1966

Young, 1980

Burcholtz, 1995

Michalsky, 1988

Iqbal, 1983

London et al., 1976

Kasten and Young, 1989

Kasten and Young, 1989

Komhyr et al., 1989

Hamamatsu Inc. and Lab

measurements

Kneizys et al, 1988

Vigroux, 1953

Bass and Paur, 1984

Refer to Homepage

Retrievals

Spectral direct Sun AOT,Langley Beer's Law Shaw, 1983

Plots

Pw: (a, k, Vo) Modified Langley

Size Dist., Phase function From spectral sky radiance

Procedures

Cloud Screening Thresholds, _ AOT & t

AOT, Pw, Wavelength Exp.

Size Dist., Phase function, g

Climatology, Direct Sun

Climatology, Sky

Bruegge et al., 1992;

Reagan et al., 1992

Nakajima et al., 1983

Dubovik and King, 2000

Smirnov et al., 2000

Refer to Homepage

Refer to Homepage

dV(r)

The retrieved variables are (in tam

din r

3/tamZ), the volume particle size distribution in the

range of sizes 0.05 tam < r < 15 tam, and the

volume concentration, volume mean radius,

standard deviation, and effective radius for total (t),

fine (f), and coarse (c) modes.

Note that the fine and coarse mode variables

de(r) is bican be used only if the retrieved

In r

modal. There is no automatic check for bi-modality.

Also retrieved are the real and imaginary parts of

the complex refractive index, re(Z) = n(_,) - i k(k),

(1.33 < n(k) < 1.6; 0.0005 < k(Z,) < 0.5) at

440,670,870, and 1020nm, the single scattering

albedo, and the phase function (including its

asymmetry parameter) at 440, 670, 870, and 1020

nm. It is assumed that particles in the range 0.05-

0.6 tam are fine mode and those in the range 0.6-15

tam are coarse mode aerosols (Dubovik et al.,

2000). This definition is not completely correct in

all size distributions. Nevertheless, experience has

shown it to hold true in the majority of practical

cases.

Quality Control

The AERONET z° (A) quality assured data

are cloud screened following the methodology of

Smirnov et al. (2000), and here we present just a

brief outline of the procedure. The principal filters

used for the cloud screening are based on temporal

variability of the zo (A), with the assumption being

that greater temporal variance in z° is due to the

presence of clouds. The first filter is a check of the

variability of the three _o values measured within a

one-minute period. If the difference between

31
minimumandmaximum"r_(_,) within this one

minute interval is greater than 0.02 (for r_ less than

0.667) or 0.03 z_ (for z_ greater than 0.667) then

the measurement is identified as cloud

contaminated. Then the time series of the

remaining "ra (,71,) are analyzed for the presence of

rapid changes or spikes in the data. A filter based

on the second derivative of the logarithm of "¢_(_,)

as a function of time is employed to identify rapid

variations which are then filtered as observations

affected by cloud. Other secondary order cloud

screening and data quality checks are also made and

these are described in detail in Smirnov et al.

(2000). Unscreened data is fully available from the

AERONET homepage. Automatic cloud screening

of the almucantar and principal plane data is done

by checking the distributions of data about the solar

disc for symmetry and smoothness.

5.3 SKY RADIANCE

DISTRIBUTION CAMERA

SYSTEMS

Camera systems for sky radiance distribution

are useful to collect the entire hemisphere of sky

radiance data in a quick manner. The resulting data

images usually contain the sun, so that the

measurement geometry can be determined

accurately and unambiguously. Also images can be

checked for cloud contamination and other

measurement artifacts more easily than can be done

with data from scanning systems. The limitation of

camera systems is that the dynamic range of the

whole scene must be contained in each image.

Therefore, the camera system must have large

dynamic range and there has to be a method of

attenuating the direct sunlight before it strikes the

imaging optics. To get a complete sky radiance

distribution, including the solar aureole, it may be

necessary to have an auxiliary system to measure

the sky radiance near the sun (Ritter and Voss,

2000).

In addition, a sky radiance system, fitted with

polarizers, can measure the Stokes parameters

dealing with linear polarization (Voss and Liu,

1997). These additional parameters are useful for

investigating the polarization properties of the

atmospheric aerosols, and improving the aerosol

optical models.

One of the most important areas of the sky

radiance distribution to measure is the area near the

horizon, opposite the sun, in the principal plane (the

plane containing the sun and the zenith direction).

This portion of the sky contains information on the

large scattering angle portion of the atmospheric

aerosol phase function, and is very important for

determining the aerosol optical properties relevant

to atmospheric correction for ocean color satellites.

The second very important region of the sky is

the solar aureole, the region near the sun. Because

the aerosol scattering phase function is strongly

peaked in the forward direction, information in this

region is important in determining the aerosol

single scattering albedo. Techniques for converting

sky radiance measurements to aerosol properties

have been described in Wang and Gordon (1993),

Gordon and Zhang (1995) and Zhang and Gordon

(1997a, b). An example of a camera system for sky

radiance distribution is described in Voss and

Zibordi (1989). The system described has been

upgraded, for greater dynamic range, with a cooled

CCD array. The basic system consists of a fisheye

lens, a spectral/polarization filter changer, and a

digital camera. To block direct sunlight from hitting

the array, an occulter is manually adjusted to

shadow the fish-eye lens. The size of the occulter is

approximately + 20 ° of the almucantar when the

sun is at 60 ° zenith angle; the effect of the occulter

is obvious in data images shown in Liu and Voss

(1997). Four spectral filters select the wavelength

range to be measured. Polarization filters are used

to collect 3 planes of polarization in data images.

These images can be combined to determine the

linear polarization stokes vectors.

Measurement Protocols

Obviously the first order requirement is that the

field of view of the camera system be as

unobstructed as possible, and that the measurement

site be located in an appropriate place with respect

to the ships stack exhaust. If the whole field of view

cannot be clear (as is usually the case), then one

should try for a clear hemisphere, where data

between obstructions in the other hemisphere can

be used for checking the sky symmetry.

As the desired objective is the aerosol

scattering parameters, the sky must also be cloud

free. Clouds cause two problems. The first is easy

to detect and is the direct effect of having the bright

cloud in the scene (in particular on the almucantar

or principle plane). Almost any cloud will

overwhelm the effect of aerosols in determining the

sky radiance. This effect of clouds is usually quite

evident in the sky radiance image. The second

problem is the indirect effect of clouds, while not

directly causing a problem, shadowing aerosols and

reducing the skylight caused by aerosol scattering.

32
Thissecondeffectismoredifficultto handle and

places a more stringent requirement on the state of

cloudiness during a measurement sequence. This

effect can often be quite visible when the

atmospheric aerosol loading is high, causing light

beams to be evident in the aerosol layer. For these

reasons, measurements with clouds present should

be avoided if at all possible.

The maximum scattering angles existing in the

sky radiance distribution occur near the horizon in

the principle plane opposite the sun. For a given

solar zenith angle, the maximum scattering angle is

given by adding n/2 to the solar zenith angle. Since

knowledge of the aerosol phase function at large

scattering angles is important for the atmospheric

correction process, measurements of the sky

radiance distribution should be taken when the sun

is at large zenith angles. The optimum angle is a

compromise between getting large scattering angles

and working too close to the horizon where

multiple scattering effects are large (because of

long optical paths through the atmosphere). A solar

zenith angle of 600 has been chosen as optimum,

because of these constraints. Concurrent with the

sky radiance measurements, it is important to

measure the aerosol optical depth. By combining

the aerosol optical depth and sky radiance

distribution, the aerosol scattering properties can be

determined, together with the single scattering

albedo of the aerosols (Wang and Gordon, 1993;

Gordon and Zhang, 1995; Zhang and Gordon,

1997a).

Data Analysis Protocols

Data reduction of the sky radiance data is very

straightforward, and is described in Voss and

Zibordi (1989). Basically with camera images, the

data reduction process consists of simple image

processing. Each image is multiplied by an absolute

calibration factor and by an image that corrects for

camera lens roll-off. This last factor is very

important with a fisheye lens, as the important

portion of the image is near the edge where the rolloff can become very significant. Once the image

has been converted to radiometric data, specific

areas can be selected for further analysis. In

particular the almucantar and principal plane can

easily be extracted for use in inversion routines.

Reduction of the sky radiance data to get the

polarization properties is slightly more

complicated. The current method is described in

Voss and Liu (1997). Basically the Mueller matrix

of the camera system is described as interacting

with the Stokes vector of the skylight. There are

three orientations of a linear polarizer in the system

providing three separate Mueller matrices

describing the camera system. For each sky

direction (a pixel in the camera images), these

Mueller matrices and the resultant intensities

measured by the camera form a set of simultaneous

equations with the unknowns being the sky Stokes

vectors. For each pixel, these equations are inverted

to obtain the Stokes vector of the skylight. While

these images have been evaluated qualitatively (Liu

and Voss, 1997), work is currently being done to do

more quantitative inversions following the methods

of Zhang and Gordon (1997b).

5.4 HAND-HELD SUN

PHOTOMETERS

These instruments offer the simplest and most

cost-effective means to collect data on aerosol

optical thickness at sea. They are based on the

measurement of the solar beam intensity, and

therefore, the direct atmospheric transmittance.

From this transmittance, after proper correction for

attenuation by air molecules, the aerosol optical

thickness may be obtained (Equation 5.1). The

technique is straightforward in principle. It is

difficult for an observer to point the photometer at

the sun accurately from a moving platform, but this

difficulty is obviated with modern-day instruments.

The interest of these instruments also resides in the

fact that, in most of the oceans, aerosol optical

thickness measurements at the time of satellite

overpass are sufficient to verify the atmospheric

correction of ocean color (Schwindling et al. 1998).

They allow one to estimate, via the Angstrom

coefficient, the "pseudo" phase function of the

aerosols (the product of the single-scattering albedo

and the phase function), a key atmospheric

correction variable.

Many types of sun photometers have been built

and are available commercially. In the following,

we focus on two instruments, the MicroTops sun

photometer, manufactured by Solar Light, Inc., and

the SIMBAD radiometer, built by the University of

Lille. The NASA SIMBIOS Program maintains a

set of these instruments for use during ocean-color

evaluation cruises. The objective is to collect

accurate aerosol optical thickness measurements

during the ship cruises for comparison with values

derived from satellite algorithms.

a) MicroTops

The Solar Light, Inc. MicroTops sun

photometer is a hand held radiometer used by many

investigators throughout the world. The popularity

33
of MicroTopssunphotometers isduetotheirease

of use,portability,andrelativelylow cost.The

instrumentshavefivechannelswhosewavelength

canbeselectedbyinterferencefilters.In orderto

follow the specificationsgivenby the World

MeteorologicalOrganization(WMO), the

wavelengthsaretypicallychosenat440,500,675,

870nm,withanadditional channel at940nmto

deriveintegratedwatervaporamounts.If an

additional sunphotometer isavailablethenit isalso

desirableto makemeasurements at 380and1020

am.

The MicroTops sun photometersuse

photodiodedetectorscoupledwithamplifiersand

A/Dconverters. Thecollimatorsaremountedina

castaluminumblockwitha2.5° fullfieldof view.

The MicroTopssun photometerhas built-in

pressureandtemperaturesensorsandallowsfora

GPSconnectiontoobtainthepositionandtime.A

built in microprocessor cancalculatetheaerosol

opticaldepth,integratedwatervapor,andairmass

in realtimeanddisplaythesevaluesona LCD

screen.Temperatureeffectsarecorrectedbytaking

darkcountmeasurements withthelid coveredon

startup.Furtherinformationon MicroTopssunphotometers canbefoundinMorys(1998).

b) SIMBAD

The SIMBAD radiometer was designed by the

University of Lille to measure both aerosol optical

thickness and diffuse marine reflectance, the basic

atmospheric correction variables. The radiometric

measurements are made in 5 spectral bands

centered at 443, 490, 560, 670, and 870 nm. The

ocean surface and the sun are viewed sequentially.

The same 3° field-of-view optics interference

filters, and detectors are used in both ocean- and

sun-viewing modes. A different electronic gain,

low and high, is used for each mode, respectively.

A specific mode allows measurement of the dark

current. The optics are fitted with a vertical

polarizer to reduce reflected skylight when the

instrument is operated in ocean-viewing mode

(Fougnie et al., 1999). The polarizer does not affect

the sun intensity measurements, because direct

solar radiation is not polarized.

Attached to the instrument is a GPS for

automatic acquisition of geographic location at the

time of measurement. Also acquired automatically

are pressure, temperature, and view angles.

Frequency of measurements is 10 Hz. In sunviewing mode, only the highest intensity measured

over one second is kept to avoid sun-pointing errors

on a moving platform. Data is stored internally and

downloaded onto diskette at the end of the day, or

cruise. The instrument is powered with batteries,

allowing 6 hours of continuous use. In normal use

during a cruise (see below), the internal memory

and batteries allow for 3 months of operations

without downloading data or recharging the

batteries.

Installation and Maintenance

The MicroTops and SIMBAD instruments

need to be pointed towards the sun manually. The

sun is correctly aligned when its image appears in

the cross hair on a small screen (MicroTops) or on

a target (SIMBAD). After 10-20 minutes of

practice the user will become familiar with the

pointing procedure and the process will become

second nature. It is important to get familiar with

this pointing procedure on land as ship based

measurements require more skill.

The exterior of the instrument lenses can

accumulate salt spray and should be inspected and

cleaned if needed. For the open ocean, salt is the

primary contaminant. Under these conditions, a

lens tissue can be wet with clean (filtered if

possible) water or ethanol and used to remove the

salt, then a dry lens tissue used to remove

remaining water drops.

Faulty electronics pose a potential problem that

is not always easy to detect when using MicroTops

instruments. In the past it has been found that a

leaky capacitor lowered the power and created

erratic behavior for the shorter wavelengths where

more gain is required. One can also get some idea

of the instrument stability by taking numerous

measurements with the lid covered. The voltage on

all five channels should be less than + 0.03 mV and

the variability will give some idea of the noise

present in the photometer. If the values are greater

then the unit should be sent back to the

manufacturer for repair.

Measurement Protocols

During stable conditions (land or calm seas)

pointing the radiometers at the sun is

straightforward and most of the measurements will

be accurate. Under rough ocean conditions,

pointing at the sun can become the major source of

uncertainty, with many of the measurements being

off the sun. The measurements that are off the sun

will have higher apparent aerosol optical depths,

artifacts that bias the average positively. For data

acquired under rough sea conditions, repeated

measurements of aerosol optical depths are

typically distributed in a comet shaped pattern, with

34
a clusterof lowervaluesanda tailextendingto

highervalues.In thesecases,thesmalleroptical

depthvaluesare more accurate and the larger

values, which are likely due to pointing error, must

be removed in post processing. Since many

measurements may be discarded in post processing,

it is suggested that 25 or more measurements

should be made within a short period of time (less

than 5 minutes).

In general, the SIMBAD instrument is used

alternatively in sun- and ocean-viewing mode. The

sun intensity measurements also allow one to

compute down-welled solar irradiance accurately in

clear sky conditions, or when the sky is partly

cloudy (<30%) with the sun not obscured by

clouds. The modeled values of solar irradiance are

used to normalize water-leaving radiance

measurements.

The recommended protocol is to make

consecutively one "dark" measurement, three

measurements in sun-viewing mode, one "dark"

measurement, three measurements in oceanviewing mode, one "dark" measurement, three

measurements in sun-viewing mode, and one

"dark" measurement. It requires about 15 minutes

to collect a complete data set (ocean, sun, optical

zero), including deploying the instrument and

logging ancillary data (wind speed, sea state, cloud

cover, etc.).

In order to expedite the measurements, the

MicroTops averaging time should be set to one and

the sampling down to six samples. The shorter

sampling periods will speed the measurements and

no averaging will improve the chances that at least

some of the measurements are accurately pointed at

the sun. After making the measurements, post

processing is needed to remove the high values that

occur from misalignment with the sun. Once the

large values have been removed, the remaining

values should be averaged which will reduce

electronic noise.

Temperature tests have shown that the aerosol

optical thickness derived from MicroTops is

strongly dependent on the temperature (Porter et al.

2000). Being out in the sun for 1-2 minutes can

change the instrument's temperature, and thus

affect the aerosol optical depth measurement. In

order to avoid this effect, the MicroTops should be

turned off and on frequently during the

measurement period. It is recommended that the

MicroTops be shut off and on every 10 seconds

when making measurements, or after every 2

continuous measurements. On the other hand,

temperature variation effects are negligible in the

SIMBAD measurements.

On several instances we have found

condensation to be a problem when radiometers

were stored in an Mr-conditioned room prior to

making measurements in the humid marine

atmosphere. Condensation may occur outside the

SIMBAD radiometer, but can also occur inside the

MicroTops (i.e. it is not always possible to wipe it

off). To avoid water condensation, the instrument

should be placed in the sun to warm to temperatures

higher than ambient temperatures prior to making

the measurements. It is suggested to leave the

instruments in the sun for 15-20 minutes before the

measurements. The temperature can be monitored

in the MicroTops to ensure enough warming has

occurred. This procedure presupposes that the

instrument has been calibrated at the elevated

temperature level.

For MicroTops the latitude and longitude and

time should be set either manually, or by

connecting the GPS receiver directly to the

radiometer. Using either method, the time can be

set to within one second of the correct time. The

latitude and longitude can also be stored in the

MicroTops for measurements at fixed sites. For

SIMBAD the geographic location and time are

automatically acquired at the beginning of each

acquisition in "dark", sun-viewing, and oceanviewing modes.

In order to maintain the quality of the aerosol

optical thickness measurements, the procedures

suggested above should be followed and the

radiometers should be calibrated at least twice a

year (more frequently if the calibration site is not

stable -see Chapter 6). When possible, it is also

advisable to make measurements with two

instruments. This redundancy will help to

determine if any problems are occurring.

Data Analysis

In order to derive aerosol optical thickness

measurements, 1) the bad values need to be

removed, 2) the air mass should be calculated, and

3) the molecular, ozone, water vapor and trace gas

optical depths should be removed.

To remove the bad values, the data should be

plotted and large values should be eliminated

manually, if they are not part of a systematic trend.

Poor pointing artifacts will appear as noise, while

real aerosol variations will have a more systematic

behavior when plotted as a short time series. This

visual inspection and removal of large values

should be done for each channel, and it should not

be assumed that removing all bad data points in one

channel will remove all bad data for all channels. In

this process a final optical depth variability of 20%

35
of the final average value or 0.025 may be

permitted when the optical depths are below 0.08.

This approach may slightly bias the data to lower

values but it will remove the unrealistic larger

values that would occur if the data were not

filtered.

In the standard processing, the direct

atmospheric transmittance T(_) = exp(- _(_) m)

and, thus, the total optical thickness x(g) is obtained

from the sun intensity (or voltage) V measured by

the radiometer and the calibration constant Vo, by

solving the Beer-Lambert-Bouguer equation (Eq.

5.1). The protocols used within the SIMBIOS

Project to calculate AOT are described below in

Sect. 5.6.

5.5 FAST-ROTATING

SHADOW-BAND

RADIOMETERS

An estimate of _'a can be made from calibrated

measurements of the solar beam irradiance, EN(_,),

at normal incidence when there are no clouds in

front of the solar disk. Two sun photometer designs

are commonly used to measure EN(g): a narrowbeam detector mechanically pointed at the solar

disk and a wide-field-of-view radiometer with a

solar occulting apparatus. The first type of sun

photometer requires careful angular positioning and

can provide additional information about the

forward scattering phase functions that help

characterize the aerosol constituents. In contrast, a

radiometer equipped with an occulting apparatus,

known as a shadow-band radiometer, measures the

diffuse and global (upper hemispheric) irradiance

and computes E_(g) from the difference between

the two. The device gets its name from the

hemispherical metal strip that rotates around the

detector and blocks the direct solar beam to yield a

signal that is from the sky only (after the effect of

the arm is included).

The multiple wavelength rotating shadow-band

radiometer (Harrison et al., 1994) uses independent

interference-filter-photodiode detectors and an

automated rotating shadow-band technique to make

spatially resolved measurements at seven

wavelength pass-bands. The uncertainty of the

direct-normal spectral irradiance measurement

made with this type of sun photometer is

comparable with that made by narrow-beam

tracking devices. A significant advantage of the

shadow-band technique is that the global and

diffuse irradiance measurements can be used to

study the solar radiation budgets and the fractional

cloud cover at the time of the measurement. The

latter capability is particularly important for

satellite validation studies. In the SIMBIOS

context, direct solar and diffuse sky irradiances are

critical terms for correcting down-looking in-water

radiometers for self shading (Gordon and Ding

(1995).

A marine version of the multiple-wavelength

rotating shadow-band radiometer has been

developed at the Brookhaven National LaboratGry

(BNL). The BNL marine version uses a slightly

modified version of the detector used for

continental applications. It has seven channels: one

broadband silicon detector and six ten-nm-wide

channels at 415, 500, 610, 660, 870, and 940 rim.

Modifications to the detector circuitry used for

continental applications are necessary because the

response time of the original circuitry is too slow

for use on a moving ship. If the response time of

the detector is too slow, wave action may cause the

orientation of the radiometer to change appreciably

during the time the shadow-band is occulting the

sun. The rotation of the shadow-band itself must be

sufficiently fast for the same reason. The marine

version of the shadow-band radiometer is hereafter

referred to as the BNL Fast-Rotating, Shadow-band

Radiometer (FRSR). Implicit in this terminology is

that the FRSR is a multi-filter or "spectral"

radiometer.

The response of the silicon cell in the detector

used for continental applications is faster than one

millisecond, yet the internal preamplifiers have

integrating low-noise amplifiers that slow the

overall response. The response time of the detector

is made faster for marine applications by reducing

the magnitude of the low-pass filter capacitors.

Laboratory tests do not show additional noise in the

measurements as a result of this modification. The

processing algorithms, which incorporate pitch, roll

and heading measurements, are key to the

instrument's ability to derive direct-normal beam

irradiance without gimbals and a gyro-stabilized

table.

Installation and Maintenance

The installation location of the instrument on a

ship must be carefully selected. Ideally, FRSRs

should be mounted in an exposed location as high

as possible and free of nuisance shadows from

other objects. This is often difficult. Radiation

measurements on a ship always need to consider

errors from the ubiquitous masts and antennas. A

ship's communication antennas have highest

vertical priority as do the running lights, and one

must be careful of radar beams that can cause

36
severelectronicnoise.Oncea suitablelocation

hasbeenfoundandtheinstrumentmounted,the

diffusershouldberinsedwithdistilledwaterand

wipedwithamoistenedclothatleastonceperday.

TheFRSRis typicallymountedasa partof a

portable radiation packagethat includes

independent broadbandsolarandIR radiometers.

Theglassdomesontheseradiometersshouldbe

rinsedwith distilledwaterandwipedwith a

moistenedcloth.

Calibrationis themostessential eementof a

radiationmeasurement program.A thoroughand

on-goingcalibrationprocessirequiredbeforethe

FRSR can make accurate radiometric

measurementsat sea. To insureaccurate

measurements, therearetwoimportant elementsfor

FRSRmeasurement protocol:calibrationof the

instrumentcircuitry,whichincludestemperature

stabilizationof thedetectorduringmeasurements,

anddeterminationftheextra-terrestrial constants.

Data Analysis

The shadow-band radiometer must properly

measure the global and diffuse irradiances from

which the direct-beam solar irradiance is derived by

the subtraction as

E,_ (_.)= E, ()t,)-Esky (_), (5.4)

where Esun(_,) is the direct-beam solar incident

irradiance projected onto a horizontal plane, Es(_,)

is the global irradiance incident on the horizontal

plane, and Es_/(_,) is the diffuse incident irradiance

from non-forward scattering. The global irradiance,

Es(,_), is measured when the band is out of the field

of view and the sensor is exposed to full sunlight.

The irradiance normal to the incident solar beam is

determined as

(z) =e, (X)sec0o, (5.5)

A correction for the amount of sky that is

blocked by the occulting band is essential for an

accurate measurement. An automatic correction for

the shadowband is possible through measurement

of "'edge" irradiance as is done with the land-based

shadow-band radiometers. The shadow irradiance,

Eshadow(&), occurs when the sun is completely

covered by the shadowband, but a portion of the

diffuse irradiance is also blocked. The edge

irradiance, Eedge(,_), is measured when the band is

just to one side of the solar disk and provides a

good estimate of the global irradiance minus the

portion of sky that is blocked by the shadowband at

the time it blocks the solar disk. In practice,

Eedge(,_,) is selected from two measurements taken

when the shadow is on one side or the other of the

diffuser. Generally an average is taken, but in some

cases in the early morning or late evening only one

of the edges is acceptable. It is easy to show that

the fully corrected direct solar incident irradiance is

E,_()_)=E, ag,()_)-E,_o_(,_ ). (5.6)

With the fast-rotating technique, an advantage

of using (5.6) to determine Esun(,_) is that the edge

and shadow measurements are made in a very short

time, which reduces noise significantly, especially

on partly cloudy days. Also, if the electronics have

a constant bias, the bias is removed by the

subtraction. On a moving platform, some

smoothing of the data is necessary. It was found

that simple averages over a two-minute period (16

sweeps) would reduce the sampling uncertainty by

a factor of approximately 4, and yield worst-case

measurement uncertainties of about 5 Wm -2 for the

global values and less than 1 Wm -2 for the shadow

value. For perspective, two minutes is the

approximate time for the sun to move by one

diameter across the celestial sphere. A discussion

and an example of the effectiveness of the twominute averaging process is shown in Reynolds et

al. (2000).

The shadow-band theory must be modified for

a moving platform when the head might not be on a

horizontal plane. Three measurement quantities for

each channel are computed from the two-minute

mean voltages: the global signal, v'c, the shadow

signal, U's, and the edge value, O'e. The primes

indicate the measurement is referenced to the plane

of the head, which can be different than a horizontal

plane. The two global measurements,oc_ and ucz,

are combined to produce the best estimate of global

voltage, o'c. The mean shadow voltage is _'s. The

edge value is selected from the two-minute

composite sweep using an objective algorithm that

accounts shadow width dependence on solar zenith

and relative azimuth angles. The objective selection

of the edge voltage uses one or a mean of both edge

measurements to get the best estimate of tfE. The

voltage due to direct-beam irradiance falling onto

the plane of the instrument is given by

U 'H = 1) E' -- 1) S' . (5.7)

This equation automatically corrects for the

sky that is blocked by the shadow-band and also

37
removes any bias term in the calibration equation.

An important point in (5.7) is that the right-hand

quantities are measured in a few tenths of a second,

while the shadow crosses the diffuser. In such a

short time interval the ship attitude changes

insignificantly and interference from moving clouds

is minimized. The diffuse component of the

irradiance signal is computed from

1_•u = U_' ' (5.8) -1J H.

As we have stated previously, _u is relatively

unaffected by small amounts of platform motion.

The exact azimuth and elevation of the solar beam

relative to the head must be computed from the

following variables measured externally:

{¢_h,Oh }= f(O_s,Oe,OR,Otr,O r ) (5.9)

where {c_oh}are the solar azimuth angle and solar

zenith angle relative to the plane of the head, o_ is

the mean heading of the ship in true coordinates, ¢e

is the ship mean pitch, and ORis the corresponding

mean roll over the two-minute period. The relative

solar azimuth and zenith angles in geographic

coordinates, as seen by the observer, are _ and0r.

Equation (5.6) uses three two-dimensional

coordinate transformations in heading, pitch, and

roll to shift the solar beam vector to a coordinate

system aligned with the FRSR head. The matrix

u'ansformation technique is well known and

discussed in many textbooks on matrix algebra.

Once c_, and Ohare known, the calibration table can

be consulted and an interpolated correction value,

Z(o_ oh ), can be derived.

The direct beam intensity on a horizontal plane

relative to the instrument, _u, is converted to a

direct-beam intensity into a plane normal to the

solar beam using the relationship

13H

UN = (5.10)

z(ah,oh)cos

The global and horizontal voltages are recomputed for the Earth frame of reference:

"L)H --" _')N COS0r (5.11)

1)o =1) H +1) u (5.12)

The calibration equation is used to compute Es,

Esky, Es_n, and EN from U'G, _'u, U'x, and V'N

respectively. From these terms, the Beer-LambertBouguer law (equation 5.1) can be used for

estimating the calibration constant or _'d;t).

Cloud filtering is the most important challenge

for FRSR data processing. Because the FRSR

operates autonomously, cloud observations are

naturally part of the signal that must be processed

to obtain z. The cloud filter that is currently used is

based on two steps: computing signal statistics over

windows of periods of less than two hours and

using these statistics to judge the quality of the

observation under consideration. If the standard

deviation of the observations in a two-hour moving

window is less than 0.05, a subjectively defined

threshold, and the observation at the center of the

window is also less than 0.05, the central

observation is accepted. The underpinning of this

cloud filtering technique is that z is relatively

constant over a period of two hours, while the cloud

signal is highly variable. This approach has proven

relatively successful, although improvements in the

filter are expected in the future.

5.6 SIMBIOS PROJECT AOT

EXTRACTION PROTOCOLS

The SIMBIOS Project is concerned with ocean

color satellite sensor intercomparison and merger

for biological and interdisciplinary studies of the

global oceans. Imagery from different ocean color

sensors can now be processed by a single software

package using the same algorithms, adjusted by

different sensor spectral characteristics, and the

same ancillary meteorological and environmental

data. This enables cross-comparison and validation

of the data derived from satellite sensors and,

consequently, creates the continuity of ocean color

information in temporal and spatial scales. The next

step in this process is the integration of in situ

obtained ocean and atmospheric parameters to

enable cross-validation and further refinement of

the ocean color methodology.

Atmospheric correction of satellite radiances

and, in particular, estimation of aerosol effects on

the upwelling radiance at the top of the atmosphere

is one of the most difficult aspects of satellite

remote sensing. Merging of aerosol properties

obtained from in situ observations with these

derived by sensor algorithms creates exceptional

opportunities to validate and improve the

atmospheric correction. There are many

uncertainties associated with in situ measurements

themselves. They include sun photometer or

radiometer calibration and operation problems,

inadequate handling by people and cloud

contamination. When matching against atmospheric

38
properties obtained by a satellite sensor, additional

uncertainties come into play which are caused by

different viewing angles by the satellite and

surface instruments and by time discrepancies when

both instruments acquire their observations. In the

case of the atmosphere, these uncertainties are

considerable.

Therefore, the fine calibration of sun

photometers and radiometers is needed as well as

the best possible (and uniform from instrument to

instrument) correction of obtained measurements.

Finally, having multiple observations and from

different sun photometers and radiometers is

required to cross-validate the quality of in situ data,

extract measurements of high stability and

confidence and compare them against satellite

sensor estimates with a larger degree of certainty.

Extraction of in situ A OTs

The Project has recently implemented its own

correction strategy for instrument voltages

corresponding to AOTs. The approach ensures a

uniform AOT processing for all instruments

making the AOTs comparable amongst the

instruments and between instruments and satellite

sensor AOTs derived by means of the atmospheric

correction. Also, the method uses a consistent set of

tuning variables, such as ancillary data,

concurrently applied for the correction of satellite

radiances. Therefore, some stages of the satellite

and in situ data processing are identical,

contributing to increasing confidence in the matchups.

Firstly, separate procedures retrieve sun

intensity measurements, V(Z,), from individual sun

photometers and, in case of the shadow-band

radiometer, processed direct-beam irradiances

(corresponding to the IH(_) term from the section

5.4). The following processing is uniform for all

instruments, however, of course considers distinct

spectral wavelengths used by the sensors. The

Beer-Lambert-Bougner law (equation 11.1) can be

written as follows:

d 2

V(A) = V0(A)*(_- 1 exp(-M (O.)xR (A))

*exp(-M (0.)z. (2))* exp (-M (0o)x° (A))

(5.13)

where xR and zo are the molecular (Rayleigh) and

ozone and aerosol optical thickness, respectively,

and the other terms have been previously defined.

The equation (5.14) assumes that the signal, V(A),

captured by a sun photometer is measured when the

instrument is pointing directly into the sun and that

gaseous absorption is only due to ozone.

The earth-sun distance adjustment, (do/d) 2 and

air mass, M, are calculated using equations 5.2 and

5.3, respectively. Currently, the same value of air

mass is used for Rayleigh, ozone, and aerosol

factors.

The desired variable _o is extracted from

equation 5.13 by calculating all other variables. The

following estimations of earth and atmospheric

parameters to obtain AOT coincide with the

SeaWiFS satellite sensor correction, including the

choice of meteorological and ozone ancillary data.

Calculations of the Rayleigh optical thickness

apply the most contemporary atmospheric pressure

readings obtained from the spatial and temporal

approximation of daily global pressure maps

provided by the National Centers for

Environmental Prediction. The Rayleigh optical

thickness is extracted as follows:

A p

zs(A)=kt_y(A), e 799s.9,__,

eo

where, ks, y is defined as

(5.14)

k_.y (k)= 28773.597886 *

l.0e-' [8342 I 3 + 2406030 l- 15997 ,

" = - "= l '

1 38.9 -

130 - 2----- T-

(5.15)

where A is the altitude, P is the current atmospheric

pressure, and P0 is the standard atmospheric

pressure of 1013.25 hPa (Kasten and Young 1989).

The ozone optical thickness is acquired from

spatial and temporal approximation of daily

satellite global measurements of ozone amounts.

Preferably, ozone data come from Total Ozone

Mapping Spectrometer (TOMS). If TOMS data are

unavailable, ozone counts from TIROS Operational

Vertical Sounder (TOVS) are used. Finally, if

TOVS data are missing, ozone climatology files are

applied. The ozone optical thickness is calculated

from the ozone amount, Dobson, using a scaling

factor ko:(k),

Dobson

z°(A)=k°z(_')* l_' (5.16)

where koz(k) is expressed below for the following

spectral bands (Nicolet at al., 1981):

1

/_. -4 o

39
= ( 315,340,380,400,415,440,443,490,500,

560,610,660,670,675,862,870,936,1020);

koz(_,)= ( 1.35,0, 0.00025,0.00065,0.00084,

0.0034,0.00375,0.02227,0.0328,0.10437,

0.12212,0.05434,0.04492,0.0414,0.00375,

0.0036,0,0).

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Tanrt, D., C. Devaux, M. Herman and R. Santer,

1988: Radiative properties of desert aerosols

by optical ground-based measurements at solar

wavelengths. J. Geophys. Res., 93, 14223-

14231.

Tanrt, D., C. Deroo, P. Duhaut, M. Herman, J.-J.

Morcrette, J. Perbos, and P.-Y. Deschamps,

1990: Description of a computer code to

simulate the signal in the solar spectrum: the

5S code. Int. J. Remote Sensing, 11,659-668.

Young, A.T., 1980: Revised Depolarization

Corrections for Atmospheric Extinction,

Applied Optics, 19, 3427-3428.

41
Vigroux, E., 1953: Contributiona retude

experimentalede rabsorptionde roxone,

AnnalesdePhys.,8,709.

Volz, F.E., 1959: Photometermit Selenphotoelement zurspektralen Messung der

Sonnenstrahlung und zer Bestimmung der

Wallenlangenabhangigkeit der Dunsttrubung.

Arch. Meteor. Geophys Bioktim. B10, 100-

131.

Voss, K.J. and Y. Liu, 1997: Polarized radiance

distribution measurements of skylight: I.

system description and characterization.

Applied Optics, 36, 6083-6094.

Voss, K. J., 1989: Electro-optic camera system for

measurement of the underwater radiance

distribution. Optical Engineering, 28, 241-247.

Voss, K. J. and G. Zibordi, 1989: Radiometric and

geometric calibration of a spectral electro-optic

"fisheye' camera radiance distribution system.

J. Atmos. Ocean. Tech.., 6, 652-662.

Wang, M. and H. R. Gordon, 1993: Retrieval of the

columnar aerosol phase function and singlescattering albedo from sky radiance over the

ocean: simulations. Applied Optics, 32,4598 -

4609.

Zhang, T. and H. R. Gordon, 1997a: Columnar

aerosol properties over oceans by combining

surface and aircraft measurements: sensitivity

analysis. Applied Optics, 36, 2650 - 2662.

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phase matrix from polarized sky radiance over

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36,7948 - 7959.

42
Chapter 6

SIMBIOS Project Protocols for Processing In Situ

Aerosol Optical Thickness Measurements

Ewa J. Ainsworth _, Christophe Pietras _, Mark Miller 2, Robert Frouin 3 and Sean Bailey 4

ISAIC General Sciences Corporation, Beltsville, Maryland

eDepartment Environmental Science, Earth System Science Division, Brookhaven

National Laboratory, Upton, New York

3Scripps Institution of Oceanography, University of California, San Diego, California

4Future Tech Corporation, Greenbelt, Maryland

6.1 INTRODUCTION

In situ measurements from sun and sky

radiometers are extremely useful for vicarious

calibration of satellite ocean color sensors and

validation of atmospheric correction algorithms.

An atmospheric property which has been applied in

the analyses is aerosol optical thickness (AOT).

AOT values are obtained from a direct solar signal

and sky radiances processed using latest radiometer

calibration parameters and corrected for gas

absorption and molecular scattering. AOT levels

calculated from satellite sensor observations are a

by-product of the atmospheric correction (Gordon

and Wang, 1994). In situ and satellite-obtained

AOT values are quality screened and compared

against each other to create a reliable set of matchups. The term "match-ups" relates to the results of

the AOT comparisons. The match-ups are explored

to draw conclusions about the satellite sensor

calibration and the suitability of the set of aerosol

models applied in the sensor atmospheric

correction.

Uncertainties Associated With AOT Measurements

There are several uncertainties associated with

sun and sky radiometer measurements which need

to be carefully investigated. Some of the reasons

for these uncertainties stem from inconsistencies in

the radiometer calibration, in the processing of raw

radiometer observations, and from purely erroneous

observations. The accuracy of the correction of

direct sun intensity measurements for sun

photometers and of diffuse and global solar beam

irradiances for sky radiometers is important in the

derivation of AOT values from the digital counts

captured by the instruments. Before the current

study, individual primary investigators using their

own correction parameters frequently performed

this processing. As a result, the correction

techniques and the derived AOT values were not

universally comparable. A streamlined

measurement correction method has now been

introduced for all radiometers (Chapter 5). Some

stages of the in situ and satellite data processing are

actually interchangeable, thus contributing to an

increase in confidence in the match-ups. There can

be various reasons for erroneous radiometer

observations. Many errors occur due to the

difficulty for an investigator to point a hand-held

instrument directly into the sun, especially from a

ship's deck. Occasionally, some spectral bands are

missing from a measurement set. Depending on the

nature of the flaws, the defects can be uncovered by

analyzing the behavior of the measurement in a

temporal sequence of observations and in the

spectral distribution of the measurements.

Uncertainties Associated With Comparing Data

Sets From Different Sources

Radiometer and satellite sensor observations of

aerosols are different in nature. Radiometer

measurements are obtained from the ground and a

single location at a time. Satellite observations are

performed from space at the top of the atmosphere.

For SeaWiFS, a 30 sec viewing duration provides

100 km of observation around a ground target

(Gregg et al., 1994). There usually are several to a

few hundred AOT measurements collected from

each sun or sky radiometer per day. The number of

daily measurements depends on the instrument and

investigator. Automatic radiometers and hand-held

sun photometers operate differently. Some

radiometers work from a moving platform, such as

a ship, and some need to be stationary. On the

other hand, there normally is one ocean color

satellite observation of the same spot on the ground

43
perday(theremaybetwoormore observations in

higher latitudes). Thus, a large number of in situ

radiometer observations need to be compared

against information carried by a single satellite

image.

There are a few conditions which adversely

influence the comparisons of AOT values derived

from ground radiometers and satellite sensors.

These include:

1. the variability in the measurement styles

employed for each ground radiometer and

investigator;

2. cloud contamination;

3. time differences between the satellite and

ground instrument observations; and

4. viewing geometry differences between the

satellite and ground observations.

The SIMBIOS Project has a continuously

growing collection of in situ AOT measurements

stored in both the SeaBASS and AERONET

databases. These AOT measurements are obtained

from coastal and ship-borne radiometers around the

globe and form large and diverse data sets. AOT

observations are acquired from different

investigators and different sun and sky radiometers

where the operators apply their individual styles

while taking the measurements. Temporal

distributions of daily AOT observations vary

substantially from data set to data set and cannot be

generalized. Automatic sun and sky radiometers,

CIMEL and Shadow-band, usually take

measurements within proportional time intervals

through a day. The frequency of observations

obtained from hand-held sun photometers,

SIMBAD and MICROTOPS II, is influenced by

individual operator preferences and their judgement

of clear sky conditions.

Both ground radiometer and satellite sensor

atmospheric measurements suffer from the frequent

problem of cloud contamination. The fractional

global cloud cover decreases from 50% at the

equator to just over 40% at 17° to 30 ° latitude.

Then, the cloud cover increases steadily from mid

to higher latitudes reaching around 80% at 64*

latitude and more towards the poles (Paltridge and

Platt, 1976). Cloud contamination significantly

limits the extent of radiometer and satellite

measurements of global aerosols. Fractional cloud

contamination of ground-radiometer and satelliteimage-pixel observations masks aerosol signatures

and causes erroneous or unreliable AOT match-up

results. Therefore, cloud screening is a vital

operation in the processing of both types of aerosol

information.

It takes a satellite a split second to observe a

geographical location where a sun or sky

radiometer is operational. It is highly unlikely that

the ground radiometer performs its measurements

exactly at the same time as the satellite. The time

difference between the two observations introduces

an uncertainty in comparisons of both types of

observations. This uncertainty arises from

changing atmospheric conditions, which may

include cloud and aerosol plume movements.

Earth orbit and the scanning mechanism define

a satellite viewing geometry. For example,

SeaWiFS flies in a sun synchronous orbit at 705 km

with a descending equator crossing at 20 min past

noon, local time. SeaWiFS scans across the Earth's

surface from west to east (Barnes et al., 1995).

Each scan contains 1,285 contiguous pixels over a

116.6 ° scan angle centered at nadir. Each pixel is

nominally square with a side length of 1.6 mrad,

which corresponds to 1.13 km at nadir. When

SeaWiFS samples away from nadir, the path length

between the instrument and the Earth increases as

well as the pixel size.

Sun photometers, on the other hand, capture

photometric intensity of the direct solar beam.

They are pointed from the Earth's surface precisely

into the sun disk. Sky radiance scanning systems,

which include fast rotating Shadow-band

radiometers, obtain solar intensity values indirectly

from diffuse and global (upper hemispheric) solar

beam irradiance by measuring the solar aureole and

sky radiance distributions.

Therefore, both ground and satellite instruments

observe different paths through the atmosphere

which may have significantly different optical

properties. There may be clouds or cloud edges

observed by a satellite over a sun photometer

location while the photometer, looking at the sun,

may see a clear sky. Conversely, a radiometer sun

measurement may be contaminated with clouds

while a satellite pixel centered on the location Of

the radiometer may be cloud free. In the same

manner, the satellite and ground sensors may

observe different aerosol types.

General Strategy For Processing of A OT

Observations

With large and diverse AOT in situ data sets

available for the match-ups, a complete and

effective processing of radiometer and satellite

observations is vital. A processing strategy has

been designed and implemented to compare in situ

observations with those of SeaWiFS (Ainsworth et

al., 2000). The strategy is aimed to:

44
minimizethematch-upuncertainties; and

makefulluseofthelargevolumeanddiversity

oftheexistingdatasets.

Match-upuncertaintiesarise both from

uncertaintiesassociatedwith sun and sky

radiometer measurements andfromuncertainties in

comparingtwo datasetscomingfromdifferent

sources.TheSIMBIOSProjecthascreatedocean

opticsprotocolsfor satelliteoceancolorsensor

validation(Fargionand Mueller,2000). The

protocolsestablishstrictly definedinstrument

calibrationprocedureswhichare describedin

Chapter5. The SIMBIOSProjecthasalso

implementeditsownprocessingstrategytoconvert

radiometerdigital count measurementso

geophysical AOTvalues.Thisstrategyisdescribed

in Chapter5. Theapproachprovidesa uniform

algorithmfor obtainingAOT levelsfor all

radiometerswhich makes AOT measures

comparableamongtheinstrumentsandbetweenthe

instrumentsandthesatellitesensor.Directsun

intensitymeasurementsand direct-beamsolar

irradiances, incaseoftheShadow-band raiometer,

arecorrectedformolecularandozonescattering, air

mass,andEarth-sundistance.Thealgorithmusesa

consistent setoftuningvariablesandancillarydata

of atmosphericozoneandpressureconcurrently

appliedfor thecorrectionof satelliteradiances.

Therefore, sectionsof theradiometer measurement

processingarethesameasportionsoftheSeaWiFS

processing.Thisapproacheliminatesmostof the

inconsistencies in theatmosphericcorrectionof

radiometer observations, thusinturncontributingto

higherconfidenceinthematch-ups.

Thescreeningof radiometerAOTvaluesis

designedto intercepttheremainingerroneousor

badlyprocessedmeasurements byanalyzingtheir

spectraldistributionsand behaviorwithintime

sequencesof AOTobservations. Becauseof the

uncertaintiesinvolvedin comparingsatelliteand

groundradiometerAOT information,thesetwo

distinctdatasetsareprocessedindividually:

groundradiometerAOT measurements are

temporallyscreenedthroughout aday;and

satellite-derivedAOT observationsare

spatiallyscreenedwithinanimagewindow

correspondingto theradiometergeographical

location.

The qualityof the temporalscreeningof

radiometer AOTobservations isoftendependent on

the amount and daily spread of AOT

measurements. Thescreeningis easierwith a

largernumberof measurements andwhentheir

spreadthroughadayismoreregular.However,as

shownbelow, distributionsof daily AOT

observations cannotbegeneralizedforthevariety

of radiometersand operatorsinvolvedin the

SIMBIOSProject.A significantpartof thisstudy

isconcernedwiththesearchfor a methodwhich

will work the best with any daily AOT

distributions. TheanalysisofAOTtimesequences

is currentlythe only availablecloudscreening

methodforradiometer data.Theapproachassumes

thatclearAOTobservations cannotundergorapid

changesin time andspace,exceptfor narrow

plumeswhichareunfortunatelydiscardedasclouds

duringtheprocessing(Smirnovetal.,2000).The

smoothness criterion,whichlimitssuddenincreases

anddecreasesin AOTmeasurements, is usually

implementedby placingthresholdson AOT

variationsthroughouta day or within smaller

temporalsequencesof AOT observations.The

currentstudyhasinvestigateda numberof cloud

screeningapproacheswhichwill be described

below. Cloudscreeningemployedwithsatellite

imageryis a part of the sensorprocessing

algorithms.ForSeaWiFS,a thresholdis seton

albedoat865nmtomaskoutthepixelswhichare

contaminated bycloudsandice.

Uncertaintiesarisingfromtimeandviewing

geometrydifferencesbetweensatelliteandground

observationsare tackledby analyzinglarger

temporal andspatialAOTsequences. Onlythose

atmospheresareextractedwhicharerelatively

stableintimeandspace.ThederivedAOTmatchuppointsarethencharacterized bytheuniformity

andclarity of atmosphericconditions. Strict

algorithmchecksfor cloudcontaminationand

limitsareputonthespatialandtemporalaerosol

variabilities.Thisstrategymayrejectmanyvalid

AOT radiometerand satellitemeasurements.

However, it alsoincreasesthematch-upconfidence

inviewof significantuncertainties associatedwith

comparinginformationfromthetwosourcesby

ensuringthatonlyunambiguous aerosolexamples

arecomparedbetweensatelliteandradiometer

observations. Theseunambiguousmatch-upsare

thencrediblenoughtoprovideconfidentstatistics

onthedeviationinthesatellitesensorgainsandto

validatetheappliedsetof aerosolmodels(Shettle

andFenn,1979).

FortheSIMBIOSProject,auniversal strategy

has been designedand implementedto

automaticallyandeffectivelyscreentemporalsets

of in situ AOT daily observations. The screening

removes cloudy and erroneous AOT measurements

and the measurements which exhibit questionable

atmospheric properties. It also studies the

uniformity of the atmosphere around the time of the

45
satelliteover-flight. Discreteradiometerspatial

coveragesareconsideredforinstrumentsoperated

fromamovingplatform.Thisaccountsforchanges

in thegeographical situationsof themeasurement

sites.Finally,meandailyin situ AOT measures axe

calculated for the time around the satellite overflight which are then compared against screened

AOT values derived from satellite data.

The in situ AOT screening strategy has been

designed to accommodate aerosol measurements

obtained from all SIMBIOS sun photometers and

sky radiometers, as well as for different radiometer

operation styles and atmospheric conditions.

Several algorithms have been investigated to

identify their abilities to comprehensively and

efficiently screen in situ temporal observations for

cloudy and faulty measurements. Satellite derived

AOT values are uniformly screened within spatial

image windows. The windows are centered on the

geographical coordinates of AOT in situ

measurements which passed the temporal

screening. The windows have a standard ground

coverage irrespective of their format: Local Area

Coverage (LAC), High Resolution Picture

Transmission (HRPT), or Global Area Coverage

(GAC). The screening applies a set of pixel

exclusion criteria used operationally to derive

SeaWiFS standard ocean and atmospheric products

and includes the albedo-threshold cloud masking.

The uniformity of the atmosphere within the

windows is established based on the same

conditions as used for the temporal AOT in situ

measurements. Consequently, temporal and spatial

atmospheric properties are tested in a similar

fashion for radiometer and satellite AOT

observations.

This supports the comparability of the two data

sets. The occurrences of aerosol distributions

which are overly variable spatially and/or

temporally are removed from the match-ups. For

the remaining satellite image windows, average

AOT measures are calculated within the windows

and then compared against the means of the

corresponding screened in situ AOT values.

6.2 PROCESSING OF IN SITU

AOT DATA SETS

Quality assurance for in situ AOT

measurements in the context of comparison against

satellite-derived aerosol optical thickness values

encompasses all activities concerned with

radiometer calibration (Chapter 3); correction of

sun intensity measurements (for sun photometers)

and diffuse and global solar beam irradiances (for

sky radiometers) to obtain AOT levels from digital

counts captured by the instruments (Chapter 5);

screening of AOT values and extraction of matchup points. This chapter concentrates on the last

point from this list, the screening of radiometer

AOT values and extraction of in situ points for

comparison with satellite-derived aerosol

properties. The processing operates on radiometer

data which have already been calibrated and

converted to AOT geophysical values. The

screening of in situ AOT data sets includes the

following explicit tasks:

1. the elimination of measurements with

incomplete bands; the elimination of cloudcontaminated, erroneous, badly calibrated and

badly corrected measurements; and the

exclusion of atmospheres which significantly

vary around the time of the satellite over-flight;

2. uniform and compliant processing for AOT

data obtained from different radiometers and

investigators; and

3. the averaging of temporal AOT sequences

within stationary or discrete radiometer spatial

coverages which correspond to ground

geographical locations of the satellite sensor

observation path.

The major processing steps involved in

screening of in situ AOT values are shown in

Figure 6.1. The consecutive stages of the in situ

AOT screening strategy are described in the

following sections.

6.3 REMOVAL OF

MEASUREMENTS WITH

MISSING BAND

INFORMATION

The in situ AOT screening strategy uniformly

processes measurements from any sun photometer

and sky radiometer equipped with at least three

bands within the ocean-color visible and nearinfrared range of the electromagnetic spectrum.

Different ground radiometers actually operate

within different spectral bands. After the matchups are obtained, a simple conversion is applied

which maps instrument bands to corresponding

satellite sensor bands, if these bands are different

(Chapter 9). This enables a consistent comparison

of radiometer and satellite aerosol optical

information derived from the same portions of the

spectrum.

46
Occasionally, somebandinformationismissing

fromindividualin situ measurements. Reasons for

these missing bands include temporary radiometer

and measurement faults, errors in information

storage, and measurement downloading problems

from the instrument. To successfully interpolate

satellite spectral bands using radiometer band

measurements, most of the radiometer band

information should be present. Therefore, the

current strategy discards the entire measurement if

there are missing bands that are within the spectral

range of ocean color.

6.4 ELIMINATION OF

CLOUD CONTAMINATED AND

ERRONEOUS

MEASUREMENTS

Removal of cloud contaminated and erroneous

measurements is fundamental to limiting the

uncertainties associated with comparisons between

radiometer- and satellite-derived aerosol properties.

The objective for the algorithm design is that the

screening procedure needs to work uniformly and

efficiently with in situ AOT data obtained from

different sun and sky radiometers and from

different investigators. However, the creation of a

unified approach to the analysis of AOT time

sequences is not straightforward because of the

variability of measurement styles employed for

each instrument and operator. To test this unified

approach, the results of multi-platform experiments

are used where AOT data come from a number of

sun photometers and sky radiometers operated

concurrently.

AOT measurements are temporally screened

throughout a day which enables extraction of

observations that undergo rapid changes in time.

These variable AOT counts can represent either:

1. cloudy atmospheric conditions;

2. incorrectly performed measurements; or

3. sudden aerosol plumes.

Although it is difficult to differentiate among

these three conditions with AOT information alone,

they are all undesirable for matching against

satellite-obtained AOTs. This is because: cloud

contamination masks aerosol information; failed

observations cause distorted AOT measurements;

and sudden aerosol plumes create spatial and

temporal variations in atmospheric characteristics.

The variations from aerosol plumes contribute to

the uncertainties in comparing radiometer and

satellite-derived aerosol properties due to time and

viewing geometry differences between the two

types of observations.

Automatic sun and sky radiometers, such as

Shadow-band and CIMEL, normally provide both

cloud-free and cloud-contaminated data. They

rarely fail to perform their measurements correctly.

Observations taken from hand-held sun

photometers occasionally fail due to the difficulties

in pointing the instrument into the solar disk from

an unstable ship platform. With hand-held sun

photometers, an operator's incorrect judgment of

clear skies and other unfavorable measurement

conditions can also result in cloud-contaminated

observations. Effects of cloud contamination and

erroneous observations are not different in terms of

temporal AOT irregularities. Therefore, both of

these conditions are treated in the same manner so

as to limit the uncertainties concerned with

radiometer and satellite-derived AOT comparisons.

The boundary between cloud-contaminated and

cloud-free AOT measurements can be very fuzzy.

It is most desirable to eliminate all clouds,

including those most troublesome, like high cirrus,

and leave all aerosol types for the benefit of further

comparisons with aerosol properties attained from a

satellite. Unfortunately, with the limited AOT

information, some cloud measurements, including

thin stable cirrus, are found to be indifferent from

clear AOT observations. The ability to differentiate

clouds using ocean color sensors is also imperfect.

Only a very weak cloud contamination, such as

high thin cirrus, may not contribute to notable

errors in the match-ups.

Cloud screening and removal of erroneous

measurements relies on the analysis of time

sequences of AOT observations. The approach

assumes that clear AOT values cannot undergo

rapid changes in time and space, except for narrow

plumes which are discarded as clouds during the

processing. The smoothness criterion, which limits

sudden increases and decreases in AOT

measurements, is usually implemented by placing

thresholds on AOT variations throughout a day or

within shorter AOT sequences. The current study

examines a few statistical and signal processing

methodologies to find the most comprehensive and

effective method to screen in situ AOT data.

Although only direct sun observations from the

CIMEL sun and sky radiometer have been used so

far, the instrument also measures sky radiances in

solar almucantar and plane parallel. From the

diffuse solar radiation, properties of aerosol

scattering optical thickness, aerosol size

distribution and phase function can be calculated.

Although sky radiances are not captured each time

47
asunobservationisperformed, itmaybebeneficial

forfuturestudiesto considerCIMELskyradiance

measurements forthepurposeofcloudscreening.

A setofAOTobservations for asingledayis

shownin Figure6.2. In this figure,CIMEL

measurements weretakenfromtheAERONETand

SIMBIOSsiteatBahrain.SIMBAD,Shadow-band

andMicroTopsmeasurements werecollectedby

differentoperatorsatthesamelocationduringan

oceaniccruiseexperiment.This experiment,

AEROSOLS'99, wasconductedatthebeginningof

1999.Measurements takenbyautomaticsunand

sky radiometers, CIMELandShadow-band, are

uniformlyspreadthroughout theday. Frequency

anddailydistributionofobservations capturedwith

hand-heldsun photometers,SIMBAD and

MicroTops,fully dependon an individual

investigator'sschedule,atmosphericconditions,

andjudgmentoftheclearskies.Fortheseanalyses,

the validationof the cloud screeningis

accomplishedusinga MicropulseLidar(MPL)

whichoperatesat 523nm(seeChapter2). The

MPL detectscloud-baseand layer boundary

altitudes(to thelimit of signalattenuation) and

measuresseveralphysicalandradiativeparameters

including scattering/extinctioncross-section

profilesand opticalthickness.The validation

proceduresdescribedhereuseMPL'snormalized

relativebackscatter dataprocessedbyJuddWelton

fromtheMPL-NetProjectthatareavailablefor

fromtheGSFCMPLwebsite. In addition,the

SIMBIOSproject has a databaseof AOT

observationsobtained from Shadow-band,

MicroTops,andSIMBADradiometersduringthe

AEROSOLS'99and IndianOceanExperiment

(INDOEX)campaignsin theAtlanticandIndian

Oceans,respectively.Theradiometerscollected

datafromtheNOAAshipR/VRonaldH.Brown.

TheMPLwasalsooperatedonboardtheship.

However,CIMEL measurementscannot be

validatedusingtheMPL.Thefollowingsections

will describecloud screeningand erroneous

measurement removalalgorithmstudiedfor the

match-upapplication.

Statistical Estimates Of Daily AOT Variation

The CIMEL sun and sky radiometer was the

first type of instrument from which data were

analyzed and matched against SeaWiFS derived

AOT values (Wang et al., 2000). An algorithm was

then designed to screen daily CIMEL observations.

CIMEL AOT measurements are proportionally

spaced in time but contain a large share of cloudcontaminated data. The CIMEL algorithm only

accepts days which are statistically stable. These

days are expected to have cloudless atmospheric

conditions, since clouds moving through the sky

increase the variability in the CIMEL

measurements. The stability of the atmosphere is

examined within the time from 3 hours before to 3

hours after the SeaWiFS over-flight. Even stricter

conditions are then imposed on the AOT swivels

within +1 hour off the SeaWiFS over-flight. The

constraints on the atmospheric variability are

shown in Figure 6.3, where r denotes a sequence of

AOT observations and SO is the satellite over-flight

time.

This statistical screening of the temporal

distribution of in situ AOT daily levels is

independent of the instrument type and the

measurement style. The only requirement is that

the number of measurements performed per day

within +1 hour off the satellite over-flight needs to

be higher than two. The algorithm evaluates

relative uniformity of the atmosphere over several

hours. Comparatively minor variations in AOT

values will not be distinguished, although they can

indicate cloud contamination. However, a single

occurrence of a relatively significantly cloudy or

erroneous measurement will cause the whole day to

be classified as cloudy and rejected.

To reduce drawbacks associated with the

measurement generalization, this statistical test can

also be applied within smaller temporal segments of

the AOT observation sequences. A sliding filter of

a certain radius of R - the number of AOT

observations - is passed through a daily AOT

sequence, as sketched in Figure 6.4. AOT statistics

are computed within the filter. If c_ < 0.1 and _ <

"t

0.2 then the central AOT measurement within the

filter is accepted as cloudless and valid. When the

whole day of observations is processed, the

remaining cloud-free measurements are further

analyzed. The analysis is based on the assumption

that there can be some passing clouds in a good

measurement day, which is otherwise

atmospherically stable. These cloudless AOT

measurements can then compare well against

satellite-derived aerosol estimates. If there are

more than two clear AOT measurements within

+lhour off the satellite over-flight, they are again

checked for the overall stability of the atmosphere

within the two hour interval. If the atmosphere is

temporally uniform, the cloud-free measurements

are averaged and their mean observation time is

computed.

Compared to generalized daily statistical

screening, the method based on the analysis of

measurement segments within a sliding filter is

48
moresensitiveto local variations within AOT

observations. Thus, it can detect less conspicuous

cloud contamination. Also, the approach only

rejects segments of AOT measurements which

contain cloudy and erroneous data, not the whole

day's worth of observations.

The drawback of the sliding filter method is that

it is sensitive to the temporal spacing of the

measurement, therefore, its application depends on

the instrument type and the way the measurements

are performed. To obtain comparable screening

results verified against MPL observations, a

different filter radius had to be applied for different

sun and sky radiometers. The screening of

Shadow-band measurements used the filter with

R=5; for the MicroTops sun-photometer, R=5; for

the SIMBAD instrument, R=2; and for the CIMEL

sun-photometer, R=3. Figure 6.5 shows the

comparison of MPL backscatter data and AOT

statistical screening for different radiometers using

the sliding filter method with the corresponding

radii. MPL, Shadow-band, MicroTops, and

SIMBAD were operated concurrently from aboard

the NOAA ship R/V Ronald H. Brown which took

part in the AEROSOLS'99 campaign. The CIMEL

measurements were captured on a different day and

come from the AERONET/SIMBIOS site at

Bahrain. For the Bahrain measurements, there are

no MPL data to validate CIMEL's AOT screening.

The filter radii were specially fitted to suit the style

of hand-held sun photometer and automatic

radiometer operation during the AEROSOLS'99

and INDOEX cruises. Different investigators and

atmospheric conditions may cause the optimal

parameters of the method to differ.

For the purposes of the SIMBIOS Project, there

are limitations associated with the statistical

screening of AOT variations, and a search for the

optimal algorithm for elimination of cloudcontaminated and erroneous measurements

continues. The aim of this search is to find an

algorithm that provides a uniform and standardized

AOT processing for all instruments and data sets.

Derivative Estimates Of A OT Variation

The numerical differentiation of digitized

signals has found many applications in analytical

signal processing. In s!_nals containing peaks, the

amplitude of the nW derivative is directly

proportional to the amplitude of a peak and

inversely proportional to the n_ power of the peak's

width. Therefore, differentiation can be applied to

discriminate against broad temporal features in

favor of narrow components, such as sudden cloud

signatures and erroneous AOT measurements.

The first derivative of a signal is the rate of

dz

change of the signal, x, with time, t, that is n

dt

which is interpreted as the tangent of the slope to

the signal at each point in time. The second

derivative is a measure of the curvature of the

signal. It provides information on the rate of

change of the slope of the signal between convex

and concave, and the location of inflection points.

Higher derivatives enable the detection of weak

signal variations in the presence of noise and strong

background interference, such as general daily

AOT trends. The numerical expression to calculate

the first derivative is given in Equation 6.1 as

• Ti+ 1 -- T i

_ - (6.1)

ti+t - ti

Derivatives are used to evaluate the smoothness

of daily AOT observations. In the current study,

first, second, and third derivatives of daily AOT

sequences are analyzed. Limits are imposed on

variations in the magnitude of the three derivatives.

Also, a methodology implemented in the

operational quality control of CIMEL data for the

AERONET database is studied (Smirnov et al.,

2000). In this technique, a norm of the second

derivative, as defined in Equation 6.2, and the

logarithmic second derivative are restricted by

thresholds. Threshold values are obtained from

experience with differentiating AOT temporal

sequences for different optical atmospheric

conditions. For the three-derivatives study,

thresholds are established for different sun and sky

radiometers through the evaluations of screened

results with MPL backscatter data. For the

CIMEL-originated quality control strategy,

thresholds are directly borrowed from the

AERONET algorithm.

(6.2)

A significant drawback of the AOT sequence

differentiation is that derivatives and derivative

norms are calculated relative to the time of AOT

capture; therefore, the method is sensitive to the

temporal spacing of the measurements. These

algorithms are better suited to work with AOT data

obtained from automatic sky radiometers, such as

Shadow-band and CIMEL, because their

measurements are proportionally spaced through

time. With data from hand-held sun photometers,

unequal spread of AOT daily observation causes

49
under-representation or exaggerationof extracted

peakfeatureswithin the signal. This forces

determinationf individual thresholdsfordifferent

instrumentsanddatasetswhichin turnlimitsthe

abilitytogeneralizatethealgorithm.

Figure 6.6 highlightsthe difficultiesin

generalizingtheAOTscreeningmethodbasedon

derivatives.Thefiguredisplayscreeningresults

for theShadow-bandradiometer, MicroTopssun

photometer, andSIMBADsunphotometerwhich

acquiredconcurrent dataduringtheAEROSOLS'99

campaignandanindependent CIMELradiometer.

Thealgorithmappliedlimitsonvariationsin the

first,secondandthirdderivatives.Thisparticular

screeningusedthesamethresholdvaluesforallthe

instruments. Thethresholdforthefirstderivativeis

0.5,fortheseconderivativeit is 100,andforthe

thirdderivativeisit 100,000.Thescreeningresult

canbecomparedwiththeMPLresponseandthe

performanceof thestatisticalalgorithmpresented

inFigure6.5. Figure6.6showsthatinthecaseof

Shadow-bandand CIMEL radiometers,when

comparedto the MPL backscatterdata,many

cloudyAOTmeasurements wereleft outby this

trial versionof thescreeningprocedure.With

MicroTopsandSIMBAD,toomanyobservations

wereclassifiedascloudcontaminated andremoved.

Therefore,thesecommonthresholdscouldnot

benefitalltheinstruments.

Similar trials were performedwith the

AERONETalgorithm.A normof the second

derivativeandthelogarithmicsecondderivative

wererestrictedby theCIMEL-originatedquality

controlthresholds.Themethodscreenedwell

ShadowbandAOTobservations whichwasverified

againsttheMPLbackscatter information.It also

appearedtoworksuccessfullywithCIMELdatafor

whichthestrategywasdesigned.However,the

algorithmfailed at screeningMicroTopsand

SIMBAD AOT measurements.The imposed

thresholdsforcedtheclassificationof all cloudy

andcloudlessobservationsacloudcontaminated

and their removalfrom the dataset. The

derivative-based screeningapproachcanworkwell

withparticularAOTsequences, mainlythosefrom

automaticradiometers, however,the algorithm

requiresvaryingthresholdsfor differenthand-held

instrumentsanddatasets.

The derivativemethodcan be used in

combinationwith a slidingfilter, asshownin

Figure6.4. Boththe first, second,and third

derivative test and the second-derivative

AERONETtestcanbeappliedwithinthefilter.A

commonthresholdis imposedon derivative

functionsfortheentireAOTsequencewithinthe

slidingfilter coverage.Thefilter expandsthe

coverageof thetemporalAOTstabilitylimitsas

comparedto usingthe first, second,andthird

derivativesalone(the highestthird derivative

combines information from four AOT

measurements). Thefilteralsodefinesthetl andt2

limitsfortheAERONETtestintheintegrationin

Equation6.2. Correspondinglogarithmicsecond

derivativesarethencomputedwithinthefilter.The

filtermethodenablesimmediatelocationof cloudy

orerroneousAOTmeasurements withinthedaily

observationsequence. TheresultofShadow-band

screeningusingderivativethresholdvaluesdefined

previouslyandaslidingfilterwithR=2isdisplayed

in Figure6.7. Unfortunately, the slidingfilter

radiusneedsagaintobeindividuallyestablishedfor

AOTdatafromdifferentradiometersanddatasets.

AsshowninFigure6.7,theresultsoftheAOT

screeningalgorithmbasedon AOT sequence

derivativelimitscompareswellagainstMPLdata.

However,the methodcan not be generalized

enoughtobeusedin asimpleandintegratedform

withallsunandskyradiometersanddatasets.

Fourier Filter

A daily AOT measurement sequence can be

treated as a time-domain signal and its plot as a

time-amplitude representation of the signal. The

Fourier transform enables the representation of the

signal in the frequency domain. The Fourier filter

is a type of smoothing function which limits high

frequency components of the signal from the

signal's frequency domain representation. It is

assumed that the frequency components of the

genuine signal, in our case a cloudless AOT daily

sequence, fall predominantly at low frequencies.

Noise in the signal, defined as sudden increases and

decreases in AOT measurements which indicate

cloudy and erroneous observations, falls primarily

at high frequencies. High frequencies are then

eliminated from the signal's power spectrum and

the inverse Fourier transform of the signal is

computed.

The Fourier filter has turned out to be useful for

smoothing daily sequences of AOT measurements.

It enables the entire signal to be represented only by

lower frequencies which reduces the amplitude of

the cloudy and erroneous AOT peaks. The general

trends in the signal at different frequencies can be

analyzed. However, the algorithm is only useful

for stationary signals where the frequencies do not

vary with time. In the Fourier transform, as signal

time information does not exist, it is impossible to

determine when in time certain frequency

components appear. Because of this, high

frequency measurements cannot be located and

50
eliminatedfromthetime-domainAOTsequence.

Consequently,heFourierfilterwasabandonedasa

screeningmethodfor cloudyanderroneousAOT

measurements. An exampleof AOTobservation

smoothingusingtheFourierfilteris displayedin

Figure6.8.

,_ngstrOmComponent Variability

The spectral distribution of /_ngstr6m

components for the 12 aerosol models used in the

atmospheric correction of SeaWiFS ocean color

imagery is shown in Figure 6.9. The values of the

/_ngstr_m component range from -0.05 to 1.5. It

has been studied whether the/_,ngstr6m coefficient

of cloudy AOT measurements can be separated

from pure aerosol/_ngstr6m values. The/_ngstrt_m

coefficient can be calculated in two ways. The first

method is to linearly fit the /_,ngstr6m value

through all sun and sky radiometer spectral bands

within the <440 nm, 870 nm> domain. The second

method is to compute the/_,ngstr6m coefficient, or,

for each spectral band, _, within the same domain

relative to the 865 nm band according to Equation

6.3,

In("r(_') /

_v(865)

/

a(_,) = }. (6.3)

Limiting the spread in the _ngstr6m component

to <-0.05, 1.5> does not eliminate cloudy and

erroneous AOT measurements. Clouds can also be

represented by low/_ngstr6m component values as

verified with the MPL backscatter data. Therefore,

the test on /_ngstrSm component variability is not

used to eliminate cloud-contaminated and

erroneous AOT observations.

Wavelet Transform

A daily AOT measurement sequence is again

treated as a time-domain signal. The wavelet

transform combats the problems encountered with

the Fourier transform by simultaneously providing

time and frequency representation of a signal

(Combes et al, 1989). The transform is used to

analyze non-stationary signals. Sequences of AOT

observations are non-stationary because they

contain time-varying frequency components.

The wavelet transform supports a multiresolution analysis. A signal is analyzed at

different frequencies and time intervals at variable

frequency and time resolutions. The transform

provides good time resolution at high frequencies

and good frequency resolution at low frequencies.

This is based on two observations. The first being

that a high frequency component can be better

located in time than a low frequency component.

Secondly, a low frequency component can be better

located in the frequency domain compared to a high

frequency component. With the wavelet transform,

frequency components existing at any given time

interval can be extracted and analyzed separately.

The mathematical representation of the wavelet

transform uses two parameters of translation and

scale (Masters, 1995). The translation relates to the

location of a window which is shifted through the

signal and convolutes the signal with its window

function. The scale parameter substitutes for the

frequency parameter. High scales (low

frequencies) dilate the signal and correspond to

global information about the signal. Low scales

(high frequencies) compress the signal and provide

detailed information on hidden patterns in the

signal. There are many examples of mother

wavelet functions which can be used as the

prototype for the windows in the convolution

process. Child wavelets are dilated or compressed

and shifted versions of the mother wavelet. These

mother wavelet functions include the Morlet and

the Mexican hut functions. The current study uses

the Daubechies wavelet because a discrete wavelet

transform based on the Daubechies function is a

built-in function in the Interactive Data Language

(IDL) software. The present application is fully

implemented in IDL.

The discrete wavelet transform, DWT, is

implemented through subband coding. The

procedure starts by passing the signal through a

half-band digital Iowpass filter with impulse

response H(n) and a half-band highpass filter with

impulse response G(n). The half-band lowpass

filter removes all frequencies in the signal that are

above half of the highest frequency contained

within the signal while the half-band highpass filter

removes the frequencies below half of this highest

frequency. After passing the signal through the

half-band filters, half of the samples in the signal

can be eliminated according to Nyquist's rule

because the signal now has half of the original

bandwidth. Discarding every second measurement

subsamples the original AOT sequence by 2. The

result of the half-band Iowpass filter is further

passed through the same lowpass and highpass

filters from the first step and subsampled by 2 for

further decomposition. The application of the

lowpass and highpass filters in this manner

continues until the signal can not be subsampled

further. At each iteration, the decomposition halves

the signal time resolution because there is only half

the number of samples left in the signal. The

51
successivedecompositionalso doublesthe

frequencyresolutionbecausethebandwidthof the

new signal spans only half of the previous

interation's band width. The subband coding of an

AOT sequence, _, which contains the highest

frequency, f, equal to x radian per second is

visualized in Figure 6.10.

To apply the subband coding effectively, the

number of samples in the sequence (i.e. the number

of daily AOT measurements) is required to be a

power of 2. Because this is usually not the case, the

sequence must be padded with zeros to produce the

required number of samples. The discrete wavelet

transform of a MicroTops II AOT observation

sequence is shown in Figure 6.11.

The discrete wavelet transform of the

MicroTops AOT observations shown in Figure 6.11

has 64 coefficients. The locations of the

coefficients are related to the positions of the

results of the lowpass and highpass filtering as

explained in Figure 6.10. The last 32 coefficients

of the wavelet are the results of the first level

highpass filtering. The 16 coefficients to their left

are the product of the second level highpass

filtering of the first level lowpass result. The 8

coefficients to their left are the outcome of the third

level highpass filtering of the second level lowpass

response. Within the 6tb level filtering, there are

only 2 coefficients where one is a single highpass

and the other a single Iowpass result. The single

lowpass result is the first coefficient (coefficient 0)

in the wavelet representation. The frequencies

which are most prominent in the original signal

appear as high amplitudes in that region of the

discrete wavelet transform which includes those

particular frequencies. It can be seen from Figure

6.11 that the lowest frequencies in the AOT

measurement sequence, around 1164t_ of the

maximum frequency, are the most prominent in the

MicroTops AOT distribution from the 11_ of

March 1999. The second most common frequencies

within this signal are between 1/16 th and 1/8th of the

maximum frequency. High frequencies occur less

often in this data set.

Low frequency components within AOT daily

distributions can be easily located in the frequency

domain because they appear as initial coefficients

in the signal's discrete wavelet representation. The

most prominent frequencies within AOT sequences

are found to be the lowest frequencies which appear

in the transform as high wavelet peaks. The high

frequency components are better located in the time

domain because the AOT time representation

exhibits a lot of cloudy or erroneous measurement

peaks.

Because cloud-contamination and erroneous

observations result in rapid AOT changes in time,

these variations can be correlated to higher

frequencies within the AOT daily distributions.

The Fourier filter analysis shows that clear AOT

measurements are actually represented by a small

fraction of frequencies in the lower end of the

spectrum. The wavelet transform provides a good

frequency resolution at low frequencies, therefore,

the AOT signal can be studied in detail at these

lower frequencies while the measurements

corresponding to higher frequencies can be safely

discarded as cloudy or erroneous. The frequency

content associated with consecutive AOT

observations is studied using the discrete inverse

wavelet transform. The inverse transform is

applied to the AOT signal within discrete frequency

ranges. Only those transform coefficients are

considered which correspond to successive

frequencies of interest in the signal. The

coefficients represent the result of highpass filtering

the original signal and highpass filtering the

lowpass response from the previous level's

convolution. The coefficients from each level's

highpass filtering are used to produce the inverse

transform of the AOT sequence while all other

coefficients are set to zero.

The inverse wavelet transforms of a Shadowband signal from the lowest up to the highest

frequency components are displayed in Figure 6.12.

The top left-hand-side graph in the figure shows a

daily distribution of Shadow-band AOT

measurements. The top right-hand-side graph gives

the discrete wavelet transform of this AOT

distribution. From the wavelet representation it can

be seen that the most prominent frequencies in the

original signal are the lowest frequencies between

11256 th and 1/128 th of the signal's highest

frequency. Graphs in the following rows give

inverse wavelet representations of the original

Shadow-band measurements. The graph on the left

in the second row represents the inverse wavelet

transform of the AOT sequence confined to the

lowest and most _rominent frequencies. This is the

result of the 8_ level averaging of the AOT

observations with the lowpass filters. The graph

illustrates that these lowest frequencies are

representative of all daily AOT measurements.

These principal frequencies give a very coarse

approximation of the trend in the original AOT

signal and correspond to broad temporal features,

which for AOT data, are commonly the clear-sky

atmospheric background. The graph on the right in

the second row represents the inverse wavelet

transform of the AOT distribution limited to the

frequencies between 1/128 _ and 1/64 th of the

52
highestfrequencyobtainedfrom the 7th level

convolution.TheAOTsequencerepresented inthe

6thlevelfrequenciesbetween1/64_ and1/32'_of

thehighestfrequencyis shownontheleft-handsideof thethirdrowof graphs.Thefollowing

graphsin thefourthandfifthrowsshowthesignal

at increasinglylarger domainsof higher

frequencies. Thefinalright-hand-side graphinthe

lastrowrepresentstheAOTsequencerestrictedto

abandwidthY2uptothehighestfrequencypresent

inthesignalafterthe1stlevelhighpassconvolution.

The inversewaveletrepresentationat these

frequenciesfalls to zero for some AOT

measurements becausethemeasurements wihinthe

sequencedo notcontainsuchhighfrequencies.

Conversely, highpeaksin thisinversetransform

characterizehighfrequencyAOTsignals,suchas

theAOTobservations takenaround2pm.

The inversewaveletrepresentationf AOT

sequenceswithin differentfrequencyranges

illustratesthosefrequencieswhichare present

withineachpartofthesignaldefinedbysubsequent

AOT measurements.The inversewavelet

transformstraddleszeroif theAOTobservations do

not containthecorrespondingfrequencyranges.

Peaks in the inversetransformrelate to

measurements whichare inundatedwith these

frequencies.The waveletpropertiesmakeit

possibletoexamineinverseAOTsignalsforhigher

undesirablefrequenciesandto find a frequency

borderlinebetweenclearandcloud-contaminated

measurements.

Theborderlinefrequencybetweenclearand

cloud-contaminatedor erroneous AOT

measurementswas studiedusing data from

differentsunandskyradiometersanddifferent

investigators.MPLbackscatter informationwas

usedfor validationof the waveletscreening

strategyfor the Shadow-band, MicroTopsand

SIMBADinstruments.NoMPLbackscatter data

wereavailableforCIMELmeasurements. Agood

estimationofinversewaveletvalueswhichstraddle

zerowasfoundbylimitinginversetransformpeaks

to <-0.01,0.01>in amplitude.Thefrequency

resolution available within the wavelet

representation dependsonthenumberofsamplesin

thesignal.Thehigherthenumberof dailyAOT

measurements the moredetailedthe frequency

resolutionat lowerfrequencies.The absolute

frequenciesextractedfromtheAOTsequenceare

fractionsofthemaximumfrequencypresentwithin

the signal. Therefore,for eachdaily AOT

observationthefrequencyresolutionofthewavelet

transformvarieswiththenumberof measurements

and absolutefrequencyvaluesdependon the

maximumfrequencypresent.Theseconditions

coupledwith the inversewaveletfrequency

spectrumof consecutiveAOT observations

distinctlydefinetheAOTsignal.It is therefore

alwayspossibletoplacea borderlineonthesame

proportionof frequenciesdefinedbylowpassand

highpassconvolutionlevels. Althoughthe

borderlineabsolutefrequencyvaluesare then

specifically tailored for individual AOT

distributions,the choicecriterionis always

constant.

Figure 6.13 illustratesMPL backscatter'

informationand Shadow-band, MicroTopsand

SIMBADoriginalandscreenedmeasurements for

thesamedayduringtheINDOEXcruise.Also,

originalandscreeneddiurnalCIMELobservations

are givenfor the Bahrainsite sharedby the

AERONETandSIMBIOSProjects.Theborderline

frequencyusedin thescreeningisjustabove1⁄2Mr2

of thehighestfrequencypresentin eachAOT

signal,specificallyY23_v7, whereM isthenumberof

highpassandlowpassfilteringlevelsdefinedbythe

subbandcodingin Figure6.10. The AOT

measurements whichcontainfrequenciesbetween

the borderlineand the highestfrequencyare

removedas cloud-contaminated or erroneous.

Thesemeasurements arefoundaspeaksin the

inversewaveletrepresentationof the AOT

distribution corresponding to these frequencies.

The MPL backscatter data illustrates that 10

March 1999 was a very clear day in the Indian

Ocean over the NOAA ship R/V Ronald H. Brown

except for a few scattered clouds about 8am, after

10am, and around 1l:30am. These clouds show up

as red spots in the backscatter plot. The wavelet

screening eliminated these cloud-contaminated

aerosol peaks from the Shadow-band

measurements. From the MicroTops data, cloudy

and erroneous AOT observations were deleted

because they appeared as outliers with noticeable

temporal variabilities. A set of SIMBAD AOT

measurements was ruled out as cloud-contaminated

or erroneous either because of its sudden increase

in AOT values or its correspondence to the

l l:30am cloudy period. Frequent Shadow-band

observations demonstrated a rising trend in the

AOT distribution for this day from 9am towards the

afternoon. It may have been possible to classify

SIMBAD measurements past 1lam as clear if there

had been more observations available. Finally,

unstable AOT data were excluded from the CIMEL

measurements captured on a different day and at a

different geographical location. Both sudden

increases and falls in CIMEL AOT values after

9am gave an inconsistent representation of the

atmosphere and were all removed.

53
TheborderlinefrequencyofjustaboveV2rare of

the highest signal frequency was found to be the

most suitable for wavelet screening of cloudcontaminated and erroneous AOT daily

observations. This borderline frequency value was

validated against MPL backscatter data which

provided a stringent basis for elimination of cloudy

measurements and extraction of uniform

atmospheric conditions. The approach provides the

same AOT processing irrespective of instruments

and operator preferences. AOT measurements

taken from any sun and sky radiometers and from

within any current and future data sets can be

screened using identical wavelet algorithm

parameters.

Although the borderline frequency of just above

V2r_w of the highest frequency within the AOT

sequence fits the best, more or less stringent

borderline frequencies can also be applied for

different screening purposes. Higher borderline

frequencies, for example, will force the acceptance

of additional degrees of temporal atmospheric

variability and may pass some lightly cloudy or

erroneous AOT measurements.

Concluding, the wavelet approach can be used

for temporal screening of any sets of oceanic,

coastal, and continental aerosol measurements. The

method uniformly removes cloud-contaminated and

erroneous AOT observations from data originated

from any sun and sky radiometers and using any

instrument operation schemes. The overall

algorithm accuracy can be adjusted by varying a

single parameter of borderline frequency. The

borderline frequency is set as a universal ratio

number, however, its absolute value is dependent

on the amount of daily AOT measurements and the

maximum frequency present in the AOT signal.

The method relies on the inverse wavelet estimation

of the frequency spectrum within consecutive AOT

observations. The borderline frequency validated

against MPL backscatter information is just above

I/2M_of the highest frequency present in each daily

AOT sequence. However, other numbers for the

borderline frequency can also be chosen for

different AOT screening applications.

6.5 EVALUATION OF

SCREENING ALGORITHMS

The purpose of screening strategies is to

uniformly eliminate cloud-contaminated and

erroneous AOT measurements from sequences of

AOT observations which come from different sun

and sky radiometers and data sets. There are three

algorithms which have been found useful for this

implementation. These algorithms are based on

statistical and derivative estimates of daily AOT

variations as well as the wavelet screening which

relies on elimination of measurements characterized

by large amplitudes in the frequency range.

Comparison Of Screening Algorithms

The statistical algorithm investigates stability of

the atmosphere within the entire AOT measurement

period under consideration. The method can be

consistently applied to AOT information from any

instrument or data set. Its accuracy is limited by

the constant thresholds imposed on the AOT signal

variability because the statistical significance of

different atmospheric features changes with the

number of samples present in the dataset. In its

basic form, the method is not a cloud-screening and

erroneous-observation-removal approach because it

may reject a whole day's measurements based only

on a single AOT outlier. The modified version of

the statistical algorithm which uses a sliding filter is

a truer observation screening strategy, however, it

cannot be universally applied to different

instruments and data.

The derivative-based algorithms are very

sensitive to temporal distributions of AOT

observations. They work well with AOT

measurements captured within equal time spans

throughout a day. Such AOT data are usually

obtained from automatic radiometers. The method

requires different thresholds for different

instruments and, in case of hand-held sun

photometers, different data sets.

Finally, the wavelet transform approach requires

the adjustment of a single general parameter of the

borderline frequency ratio which is universal for all

instruments and data sets. The wavelet strategy

investigates both time and frequency distributions

within the AOT signal. Therefore, it is able to

extract frequency-dependent uniform atmosphere

periods and relate them to consecutive AOT

measurements in the time domain of the signal.

Figure 6.14 illustrates the comparison of daily

AOT screening results performed by the three

algorithms. The wavelet screening approach used

the strict borderline frequency of just above 1⁄2Mrz of

the highest frequency in the signal. The statistical

method adopted the sliding filter utility. The

derivative-based approach applied the norm of the

second derivative, as defined in Equation 6.2, and

the logarithmic second derivative as inspired by the

AERONET quality assurance algorithm. The

wavelet approach shows relative tolerance to small

variations within AOT measurements whereas the

statistical method appears less flexible. The

54
derivative-based algorithmseemsto accept AOT

observations which may indicate temporally

variable atmospheric conditions.

All three in situ AOT screening strategies can

be used successfully with radiometer aerosol

measurements. However, as explained in detail

earlier in this chapter, there are some limitations as

to the application and accuracy of the statistical and

derivative estimates of AOT variations. On the

other hand, wavelet-based screening can be applied

uniformly with any instruments or data sets and the

method is well suited to extract AOT measurements

which are characteristic of periods of atmospheric

stability.

Comparison Against AERONET Quality Assurance

A comparison study was performed between the

wavelet approach to AOT screening and the partly

interactive quality assurance procedure created by

the AERONET Project (Smirnov et al., 2000). The

purposes of both SIMBIOS and AERONET

screening strategies are different. The AERONET

algorithm is designed to analyze CIMEL

observations exclusively and preserve as much

aerosol information as possible for the global

aerosol studies. The aim of the SIMBIOS approach

is to confidently compare in situ AOT

measurements against satellite-obtained AOT

values. The SIMBIOS screening method must

operate for different instruments and strictly

remove all observations suspected of being cloudcontaminated, erroneous or originated in

atmospherically unstable periods.

Figure 6.15 shows an original set of daily

CIMEL AOT measurements captured at the

Goddard Space Flight Center AERONET site, thus

it is not a coastal SIMBIOS site and is more

characteristic of continental aerosols. The Figure

also gives the results of the AERONET AOT

quality assurance and the wavelet transform

screening process using the stringent borderline

frequency of just above 1⁄2_v2 and the less strict

version of VzTM of the highest frequency. It is seen

that the AERONET approach accepts all low-invalue AOT measurements, within both stable and

unstable atmospheric periods. The more rigid

version of the wavelet algorithm only defines as

clear those AOT observations which originate

within temporally uniform atmospheric conditions.

The last plot in the figure illustrates that the wavelet

approach can virtually imitate the AERONET result

by increasing the borderline frequency.

Wavelet screening results are produced using

multi-band exclusion criteria Which are explained

in detail in Section 6.6. If the AERONET AOT

quality assurance applies a single spectral band to

select clear AOT values, the additional difference in

the results can be explained from stricter multiband inspection of diurnal AOT variabilities used

by the wavelet algorithm.

In general, the final average of daily cloud-free

AOT values is similar for both SIMBIOS and

AERONET algorithms. However, the SIMBIOS

protocol must be stricter in removing variable

aerosol conditions due to its specific goal to

provide in situ data for calibration and validation of

satellite retrievals.

6.6 CHOICE OF SPECTRAL

BANDS USED IN AOT

SCREENING

The initial scheme for elimination of cloudy and

erroneous AOT measurements was applied with

CIMEL sun and sky radiometer data (Wang et al.,

2000). This strategy was based on statistical

estimates of AOT variations throughout a day and

only used one spectral band (870 nm) of the

CIMEL radiometer to analyze those variations.

The assumption was that the 870 nm band

sufficiently defined clouds and possible erroneous

measurements.

When choosing spectral bands for AOT

screening, several possibilities need to be

considered:

• the band calibration and AOT processing;

• the magnitude of the AOT value; and

• the spectral signature of erroneous, cloudcontaminated, and variable atmosphere AOT

observations.

Despite clearly defined protocols (Fargion and

Mueller, 2000), sun and sky radiometers with badly

calibrated bands are in use and mistakes can be

made in converting raw instrument measurements

to geophysical AOT values. If only one or two

bands are effected, it may be still useful to process

AOT observations for removal of cloudy and

erroneous data. It is beneficial to make available as

many AOT measurements as possible to the

temporal screening algorithm to enable a better

judgment of daily aerosol distributions. If the

problem occurs within the single band which is

being used for AOT screening, then useful

measurements may need to be rejected and the

scheme may lose valuable information. Therefore,

may be better not to dedicate a single and specified

band for AOT screening purposes.

55
In general,thespectral distribution of nonabsorbing AOT values monotonically increases

from the near-infrared towards the visible range of

the spectrum. Therefore, AOT measurements are

lowest in magnitude around 870 nm compared to

the other spectral ranges within the ocean color

domain. Consequently, temporal AOT variations

detected in the near-infrared are significantly

accentuated in the visible. Also, some atmospheric

conditions may appear clear and stable in the nearinfrared while the visible spectrum may indicate the

underlying atmospheric variability.

It may therefore be appropriate to use more

than one spectral band to screen AOT observations

for cloud-contamination and erroneous data. The

870 nm band alone may be insufficient in some

cases to reliably remove clouds and erroneous

measurements from diurnal AOT observations.

The AOT screening strategies introduced in the

current chapter impose their temporal stability

criteria on both the single band around 870 nm,

depending on the sun or sky radiometer, and the

following fusion of bands:

• for the CIMEL, at least two bands must pass

the screening criteria; and

• for all other instruments, more than half of the

bands must pass the screening criteria.

All cloud-contamination and erroneous

measurement screening results given in this chapter

are based on the multi-band exclusion criteria.

Many studies and MPL backscatter validation

indicate that the removal of cloud-contaminated

and erroneous AOT measurements is more stable

when more than half of the bands pass the

screening criteria for Shadow-band, MicroTops,

and SIMBAD instruments. However, despite the

lack of MPL backscatter validation data, it appears

that CIMEL calibration is very stable and two

bands are sufficient to satisfy the exclusion criteria.

Compared to AOT screening using only the 870 nm

band, the multi-band approach produces less AOT

match-up points with satellite data because less

AOT observations pass the criteria. However, the

AOT match-ups obtained with the multi-band

analysis are more accurate in the visible range of

the spectrum. This result is given and described in

more detail in Chapter 8.

Concluding, it is possible to screen daily AOT

measurements for cloud-contamination and

erroneous observations using just a single spectral

band of the instrument. The 870 nm band is the

most recommended. A combination of a few bands

can also be used. The application of multi-band

screening criteria is stricter and it decreases the

number of AOT measurements available for

matching against satellite-derived aerosol

properties. However, the multi-band approach is

less sensitive to instrument calibration errors and

mistakes in raw observation processing. This study

indicates that the multi-band approach can also

contribute to the extraction of spectral signatures of

cloud-contaminated and erroneous AOT

observations as well as AOT values which are

characteristic of variable atmospheres and

otherwise cannot be detected with single band

analysis. However, these benefits of multi-band

screening need to be further investigated.

6.7 SPECTRAL

DISTRIBUTION ANALYSIS

Temporal measurement screening removes

cloud-contaminated and erroneous AOT

measurements from each set of daily AOT

sequences. It involves AOT analysis only within a

single or few radiometer spectral bands. The more

AOT observations that comprise a daily AOT

sequel, the more accurate the screening is.

Therefore, all valid AOT observations are used in

the temporal screening. No spectral distribution

analysis of individual measurements has been yet

performed and measurements which contain flaws

in data in specific bands can be present which

indicate badly calibrated or processed band

information. Additionally, some in situ AOT

observations involve continental types, absorbing,

or questionable aerosols with large /_.ngstr_Sm

component values. These aerosols cannot be

successfully matched against satellite-derived AOT

measures because the ocean color atmospheric

correction algorithms only include aerosol models

with a low Angstr6m component, up to 1.5 in

value. Therefore, the aim of the current stage in the

in situ AOT processing for match-ups with satellite

aerosol properties involves spectral examination of

individual AOT measurements which pass the

temporal screening test.

The spectral distribution analysis of AOT

measurements encompasses studies of the

A,ngstrt_m component for successive sun or sky

radiometer bands within the ocean color spectrum.

Only those AOT observations are investigated

which passed the temporal screening test and thus

represent correct and cloud-free aerosol

measurements. Although some temporal screening

algorithms, such as the wavelet transform, are most

effective with the entire set of all dally AOT

observations, it is now efficient to limit the amount

of AOT data to those which come from a certain

period around the time of the satellite over-flight.

This is because the final daily in situ AOT point to

56
be comparedagainst the satellite-derived AOT

value is calculated by averaging AOT

measurements. These are the measurements which

pass all the tests and are captured within a short

time span surrounding the satellite observation of

their geographical location. This short time span is

chosen as 5:1 hour in the current application.

For each AOT observation, a generalized

/_ngstrtm component value is first obtained. The

generalized ,_ngstrtm coefficient is computed by

finding a slope for a linear fit of the natural

logarithm of AOT values at consecutive spectral

bands to the natural logarithm of the corresponding

bandwidths expressed in micrometers. The

,_ngstr6m value is only calculated within the sun

and sky radiometer <440 nm, 870 nm> spectral

range. The linear fit method establishes the overall

trend in the spectral distribution of the AOT

observation in subsequent bands. The minimum

value of the generalized _ngstrtm component is

limited to -0.05 for well calibrated and processed

oceanic or coastal AOT measurements. Secondly, a

condition is introduced which places a maximum

value limit on the ,/kngstrtm component. This

condition regards specific t_,ngstrtm coefficients

which are computed for each spectral band within

the <440 nm, 870 nm) domain relative to the

865nm band according to Equation 6.3. The

maximum/l, ngstrtm condition limits the value of

the component to 2.5 for each radiometer band

within the domain. It has been found that this

maximum /_,ngstrtm condition calculated for

specific spectral bands is sufficient for bounding

the AOT spectral distribution and no additional

maximum value restriction needs to be placed on

the generalized Angstrtm component. The

maximum limit on the specific ,_ngstrtm

coefficients eliminates AOT observations with

questionable spectral distributions which may

originate from bad radiometer calibration, errors in

raw measurement processing, and undesirable

atmospheric conditions. Limiting the s_read of

generalized and band-specific Angstrtm

component values enables rejection of in situ AOT

observations characterized by questionable spectral

distributions. These dubious distributions may

indicate problems with calibration of individual

spectral bands or their correction to geophysical

values. These distributions can also be caused by

aerosol types or other atmospheric conditions

usually not accounted for in the processing of ocean

color imagery. AOT measures derived in course of

atmospheric correction of satellite data over oceans

are bound by the aerosol models used in the

processing. The models describe rather idealized

aerosol types understood to exist over the oceans.

Consequently, limiting in situ AOT measurements

to only those which exhibit the desirable spectral

characteristics ensures more confident comparisons

of radiometer AOT values with satellite-obtained

aerosol properties. These comparisons can then be

used to draw conclusions about satellite sensor

calibration and the accuracy of aerosol model

propagation from the near-infrared towards the

visible spectra. However, a less spectrally

restricted collection of AOT radiometer

measurements may be necessary to establish

suitability of the set of aerosol models applied in

the atmospheric correction of ocean color data.

This is because aerosol types over the oceans can

be more variable than the predictions from the

idealized aerosol models. A statistical ability to fit

all valid atmospheric conditions is required to

accurately perform the atmospheric correction of

ocean color imagery.

6.8 ANALYSIS OF TEMPORAL

AND SPATIAL AEROSOL

VARIABILITIES

So far, all daily valid AOT measurements have

been temporally screened to remove cloudcontaminated and erroneous observations. The

remaining AOT measures have been limited to

those captured around the time of the satellite overflight. They have also been spectrally analyzed to

eliminate badly calibrated and processed

observations as well as questionable aerosol

representations unsuitable for matching against

satellite-derived aerosol properties. The successful

AOT measurements which pass these tests are

further investigated. Sun and sky radiometers which

collect AOT information can be mounted on a

permanent station in a coastal region or on an island

(e.g. AERONET/SIMBIOS CIMEL sun

photometers) or used from a moving ship platform

(e.g. Shadow-band, MicroTops, and SIMBAD

radiometers). For a radiometer operated from a

moving platform, the instrument's varying spatial

coverages are considered. This is because AOT

observations from different geographical locations

need to be associated with different spatial

windows extracted from satellite imagery.

Discretezation of radiometer spatial coverage is

established relative to the ground resolution of the

ocean color sensor, size of image window extracts,

and estimation of spatial variability of the

atmosphere. To produce the results of this study,

the atmosphere is considered approximately

uniform along a 15km long ship track and the

radiometer measurements obtained within +1 hour

57
ofthesatelliteover-flight are clustered into sets of

15km spatial coverage. However, the algorithm

implementation is general enough to introduce the

spatial extent of along-track atmospheric uniformity

as a variable to the AOT processing procedure.

The described strategy separates sets of AOT

measurements which are associated with discrete

spatial coverages for sun and sky radiometers

operated from a moving platform. For stationary

instruments, a single AOT set is considered with a

constant spatial coverage which encompasses all

observations obtained within a short time span

surrounding the satellite over-flight. Presently, this

time span is +1 hour. To be able to confidently

match the extracted sets of AOT measurements

against satellite-derived AOT values, atmospheric

conditions around the time of the satellite overflight above the radiometer must be stable.

Therefore, within each AOT set, temporal

uniformity of aerosol properties is investigated.

The condition for aerosol invariability corresponds

to the statistical AOT screening requirement for t_ <

0.1 and _ < 0.2 as described in section 6.4. This

"r

additional atmospheric uniformity test is needed

because the previous temporal and spectral

measurement analyses, although they remove

cloudy, flawed, and unsuitable AOT observations,

may still pass clear and valid AOT data which are

characteristic of quickly changeable aerosol

distributions. The whole set of AOT measurements

is rejected if the atmosphere is found unstable. In

conclusion, this stage of the AOT measurement

processing removes AOT data sets which represent

aerosol conditions temporally varying around the

time of the satellite over-flight. Discrete spatial

coverages of the atmosphere are considered for

radiometer observations performed from a moving

platform. For stationary instruments, aerosol

stability is tested within their constant geographical

locations.

6.9 IN SITU AOT POINTS

If there are any AOT observations left after this

final stage of in situ AOT processing, they should

be valid, cloud-free, well calibrated and corrected,

come from the range of aerosol distributions widely

common in atmospheric correction of ocean color

imagery, and be representative of spatially and

temporally stable atmospheric conditions. If there

are two or less AOT measurements left for each

temporal and spatial set, this AOT set is rejected. If

more than two AOT observations within each set

pass all the tests, they all contribute to the final

AOT data point. This single AOT point of in situ

information is consequently matched against an

individually processed satellite-derived AOT data

point obtained for the corresponding time and

geographical location. To obtain an in situ AOT

point, AOT measurements are averaged for all

spectral bands. Also, the mean capture time and

geographical location are computed for the in situ

point. Averaging the sets of AOT measurements

completes the processing of in situ AOT

observations and provides reliable sun and sky

radiometer information for comparisons with

aerosol properties which have been theoretically

deduced from satellite imagery. The distribution of

in situ AOT measurements can only be analyzed

temporally and, for instruments based on a moving

platform, within one spatial dimension. The

distribution of satellite-derived AOT values can on

the other hand be only studied spatially because of

the large gaps between ocean color sensor revisit

times over the same geographical location. These

gaps prohibit detailed monitoring of temporal

aerosol changes. Consequently, many of the

obtained in situ AOT points may not match with

their corresponding satellite-derived aerosol

observations because the atmosphere may not

appear as clear and stable spatially as it appears

temporally. Also, appropriate satellite coverages

may be missing. Nevertheless, the described

processing of in situ AOT measurements creates a

uniform and reliable processing scheme for

observations from all sun and sky radiometers and

the final AOT points can be confidently matched

against satellite-derived aerosol properties.

6.10 CONCLUSIONS

The objective of the satellite sensor calibration

and atmospheric algorithm validation requires the

highest confidence in the in situ and satellite AOT

data sets used in the comparisons. There are many

uncertainties involved in assessing AOT measures

from ground radiometers, aerosol data processing,

and in comparing AOT values against satellitederived observations. The SIMBIOS Project

defined uniform and comprehensive strategies for

radiometer protocols and AOT data processing for

the match-ups. These strategies enable the

minimization of AOT comparison uncertainties and

make use of the large volumes and diversity of the

collected in situ AOT information.

58
J Ca_ratedandcorrectedin s_uAOTobservations I

I

I RemovalofmeasurementawithmissingbandinformationforoceancolorwavelenglhsI

I

I Tamporalmeasurementscreeningthroughaday I

Eliminal_on olcloudcontaminaled anderroneousmeasurements z

/

|

statistical, demalive,waveletransform-basedor otheralgorithms

I

l Foreachtimespanaroundthesatellle over-flight {e.g.+-Ihour) J

]

I Spectraldislributionanalysis(e.g.-0.05<=Angslrom<= 2.5) i

Ramovalofrneasuremantswhicharebadlycalibrated, corrected, andrepresentqueslJonabWaerosolsI

CIMEL S]MBAO,SHADOWBAND,MICROTOPS

i.

stationaryinstrumants I hand.heldinstruments

[

I Separateanalysisof eachdiscretespatialcoverage I

I Each mom$oremonl saquon¢a ¢orrospondl to _e ground gooorzPhlCal bcation olthe sarah o.r-_hlI

Removal (e.g.stable ofsequences a_ovphericwhich conditi_a represent wtthiltern +-Iporaly h:stddev variable <=0.01aerosol andvar<=conditions 0.2) II

I

I AveragingofAOTIfthere values, areatleas13measurementsleft: I timeofobservation, andgeographical location I

Figure 6.1. Sequence of in situ AOT screening operations.

SHADOWBAND Date: t999--01-21 Band: 500nm

2.0

1.5

I'-

o t.o

0.5

0.0

10

0.08

. • , . . _ , . . . , . . , • . . , . .

.__C_.__.T___.._[Z,A._ , . .

12 14 16 16 20

TIME

$1MBAD Dote: 1999-01-20 Bond: 443nm

. . . . . . . . . . . . . . . . . . . . . . . . . . .

O.lOF

o.o5l

0.00 [

22

MICR'OTOPS DaLe: 1999-01--21 Bond: 440nm

0.151 " - "* • '" ' " --

15 16 17 18 19 20 21

lINE

CIMEL DaLe: 1999-01-19 Bond: 440nm

0.30

0.06

I--

o 0.0,

0.02

0.00

15,2

............ i , . , i , , . , . . .

15.4 15.6 15.8 16.0 16,2 16.4 16.6

'rllvlE

0.25

0,20

0.15

0.101

0.05

0.00 . , , i . , , i

8 1o

lIME

Figure 6.2. Daily sequences of AOT measurements for four sun and sky radiometers.

12

59
Figure 6.3. Statistical screening of daily AOT measurements.

R

iwFigure 6.4. Screening of AOT daily observations with a sliding filer method.

Jan 19 - Ltdar Data

O

2 4 6 8 10 12 14 16 18 20

Hours UTC

0.00 0.25 0.60 0.75 1.00

Normalized Relative Backscatter

22

"9

"8

-7

"6

-5

"4

-3

-2

-1

24

60
SHADOWBAND Date: 1999-01-19 Band: 50Onto

0.8 ' '

o,8

0.2

0.0 ........

10 12 14 11-3 1_ 2.0 22

flUE

St'4ADOWBAND Date: 1999-01 - 19 Band: 50Onto

0.8 ................_ ................ _....... T

o,6

r_

0.2

£

0.0

10

_,,_,.---.---._

12 14. 16 18 20

n_E

SIIdBAD Date: 1999-D'-19 Band: 44-3nm

0. O-.r-'_

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0 t'LSO

o.020

0.010

0,000

16.05

,.,-- \

0.0,.r'<J

, . . . , .... i .... ,

16.10 16.15 16.20

"I1ME

22

_ 0.040

_ 0.050

Q

oo2o

-.,.

QCHO

0.£K30

16,Oh

16.25 16.30

SIMBAD Dote: t999-0"-19 Band: 443nm

16.1o 16.1_ 16.2.o 15.2_ 16.50

TIME

Figure 6.5. MPL data, original radiometer AOT observations,

measurements using the sliding filter method.

MICROTOPS Date: t999-01-19 Band:

_.! 2

0.10

O,08

O.Og

(I .04-

0.02

Q.O0 ....... .

12 14. 1B 18 20

TIWE

MICRDTOPS Date: 1999-01- 19 Band:

o.,oF

..,oo -4

4 4Onto

122

440nm

22

--.. 0.0417

$-

'o.oo i- ..........

12 14- IS 18 20

TIldE

CIMEL Dote: 1999-02-02 Band: 44Ohm

0.401:..................................................

o.loP

0.0o_....................................................................

6 7 B 9 ID 11 12 13

TIME

CIMEL Date: 1999-02-02 Band: 44Onto

0.'10 ...............................................................

o.zo

0.10

0,00 ........... , .....

6 7 8 9 IO 11 12 13

TIME

and corresponding statistically screened AOT

61
I_ICROTOPS Date, lggg-ol-lg Band: 44Onto

0.12

0.6

0.4

0.2

0.0

Io

SHADOWBAND Date: 1999-01--19 Band: 50Onrn

0.8

L I J

12. 14 18 20 22:

O.9

o.6

_0.4

r_

_ 0.2

10

0.050

0.040

0.030 5

,<

"riME

SHADOWBAND Date: 1999-01--19 Band: 50Dnm

. , , - .

12 14 16 1[_ 20

7ME

SlktBAD Bate: 1999-.01-19 Band: 4¢3nm

_,,_,..__._---------_ %

22

16.25

SlktBAD Date: 1999-01-19 B'_nd: 4,1-3nm

oo,o[

0.010 [

0.0£0 I_

0•010

O.OLO ..................

16.05 16.10 16,15 16,20 16,30

lIME

16,05 16.10 16.1_, 16,20 16:2..__ 1E.3D

TIME

0,I0

0.06

0.04

0.02

0.0(1

12 14- 16 18 20 7._

TIME

UICROFOPS DaLe.: 1999-0!-19 Band: ¢41Dnm

0.12k ........

,=, " : ]'-_-V

c, O.04k

_o.o2 _-

0.00_ ......

12 14.

• _ . . . , . . . , . . .

18 18 2,0 22

TIMF

CIMEL Date: 1999-02-02 Band: ¢40nm

0.40 _ .......................... ' ..................

0.10_

0.'30 I:. .........................................

6 7 8 9 10 11 12

]1ME

CIMEL Date: 1999-02-02 Band: _.40nm

13

o.4o [_

I_ o._o_-

".-- 0,10 E0.00_ ..................................................................

6 7 8 9 10 I1 12

TIME

Figure 6.6 Original radiometer AOT observations and corresponding AOT measurements screened using

the derivative algorithm with a common threshold value for all the instruments.

62
0.6

0.4

0.2

0.0

$HADOWBAND Date:

0.8 " "

0 12

1999-01-19 Band: 500nm

14 16 18 20

TIME

22

LI.J

I.-

n_

_J

0

SHADOWBAND Date:

0.8 " '

0.6

0.4

0.2

0.0

1999-01-19 Band: 500nm

0 12 14 16 18 20 22

lqME

Figure 6.7 Original Shadow-band AOT observations and corresponding AOT measurements screened using

the derivative algorithm within a sliding filter.

i

M_r 40 - Ltdar Date

0 2 4 6 8 10 12 14 16 18 20 22 24

Hours UTC

I ' I ' I ' _ I' ' I

0.0 0.2 0.3 0.5 0.6

Relative Attenueted Backscatter

63
SHADOWBAND Date:

1.5

1.0

0.5

1999-01-23 Bond: 50Ohm

/

0.0 .............

8 10 12 1¢

]]ME

16 18 20

>-

L3

z

Ld,

,9,

h

3:

0

.-I

,=,,

n/

,9

po

SHADOWBAND Date: 1999-01-23 Band: 500nm

1.5 .................

1.o

0.5

.0 ..i,,,i...,..,i...,..

8 10 12 1¢ 16 18 20

_ME

Figure 6.8 Original Shadow-band AOT observations and the corresponding result of inverse Fourier

transform having 90% of high frequencies eliminated.

2.0

1.5

co 1.0

E

_5

O

" 0.5

Q.

0,0

Angstrom Coefficients from SeaWiFS Aerosol Models

.... I ' '' ' ' ' ' ' ' I .... ' ' ' ' ' I ' '' ' ' ' ' ' ' I ' ' ' .... ' ' I

TS0

T99

....Tg0

C,50 .... _____m_ ......

C70

M50 ......................................................................................................

cgo _.=.=--. =-_ .......................................... =..=.=...- M70

Mgo ........... =-.=.=.=."_ ............. =..= ..... =.=.=. ..... -_.-_ .... C99

.................................................................... M99

O99

--0.5 .... i ......... i ......... ! ......... I ......... i.

400 500 600 700 800

Wavelength (nm)

Figure 6.9. _gslz6m components for the 12 aerosol models used in the atmospheric correction of

SeaWiFS imagery.

64
,c........-_-_( H M'c,GH M-_'c..... GH _'c,GH'c ,G'c)

M = log 2N

1

i i

lowpass filter HT #<o,_. [ [ G'r/.._,_

(averaging) J I

highpass filter

(detailing)

Figure 6.10. Subband coding implementation of the discrete wavelet transform.

S

MICROTOPS Date: 1999-0.3- 11 Bend: 44Onto

0.25 .......... - ........r-........... -_ , _-............

0.15

0.10

0.05

0.00

2

• . . I . . . i , . . i . . . = . . .

4 S 8 I(]

riME

WAVI "LET tronsform Dote: 1999-03-11 Bend: 440nm

0 20 4D 6(] 80

COEFFICIENTS

Figure 6.11. Discrete wavelet transform of MicroTops daily measurements.

1.2 :

1.0

0.8

0,6 :

_ 0.1-

0.2

0.0

-0.2 i

-20

65
0,8

_06

04-

0.2

O0 ,

TO

SHADOWBAND Dole: 1999-01- I_l Bond: 500nm

....... , . . , . - ....

12 1,1 16 I_ 20 22 -_O

TIM_

SHADOWBANO Dote: 1999-01-t8 Bond: 500nm

0,25

0 IO_

00511 " S '/

10 12 14 16 18 20 22 10

TIME

SHADOWE_ND Oote: I099-01 - 18 Bond: 50Onto

3°0,

_, _ -oo_-

- ........--_ -0 ,o

DUe -015

_O tO

02 I

0_

00

0,1 "

-r12

)2 14 16 18 20 22

TIME.

SHADOWI_I_NO Doh,_: 1999-01-18 Bond: 500nm

....-- i°°i

10

_ 0.2

Do

-02

-o,L

WAVELET transform Dote: 1999-01-1B Bond: 50Ohm

0 50 I00 150 200 250 300

CO[_'¢ICIENI_

SI'L_DOWeAND Dote: 1999-0t-1B Bond: 50Ohm

12 I¢ 16 I_l 20

SHADOWBAND Dote: 1999-01-18 Bond: 50Onto

22

12 14 16 18 20 22

TbME

SHADOWBAND _ote: 1999-01-18 Bond: 5OOnm

12 14 t6 1B 20 22 t2 14 16 18 20 22

TIME TIME

SHADOWBANO Dote: _999-0_-1B Bond: 5OOnm SI_ADOWB_ND Dole: 1999-0t-IB Bond: 5OOnm

c,6_

04_--

t- oo!

b

-o_L

_0 IZ 14 16 18 20 22 10 12 14 16 IB 20 22

TIM_ TIME

Figure 6.12. Original set of daily AOT measurements from the Shadow-band radiometer, discrete wavelet

transform of the AOT signal, and inverse wavelet transforms of the signal for the consecutive frequency

components from the lowest up to the highest.

66
SHADOWBAND Date: 1999-03-10 Bond: 50Onto

0.2b

0,20

0.15

O,IO

0.05

0,00

¢

0.25

MICROTQPS Date: Ig99-03-10 Band: 44Onto

............. SI

5 8 10 12 1¢ 2

gUE

3ttADOWBAN[) Date" 1__J9-03-10 Bond: 50Ohm

0.2.0

_0.15

0,1o

0,05

0.001

,3.15

0.10

0.05

6 8 10 12

lIME

SIMBAD Dake: 1999-0.3-10 Band: 4-43rlrn

...... __7 7/'/'//_=

, , , i , , , i , , ,

6 8 10

"BNE

SIMBAD DaLe: 1999-0.3-10 Bend: 443nm

0.00

¢ 12

12

0.15

_ 0.10

•._. 0.05

F0

<

, i , , , i , , i . • , .

4 6 B 10

13UE

4 5 _ 10

TIME

12

MICROTQPS Dote: 1999-03-10 Bond: 44Onto

0.25 _ ..... " ....

o,oEO.OOb ...................

2 4 6 8 10

CIMEL Date: 199.9-01-27 Band: 44Onto

0.50

0.443

0.30

0,20

0,10

0.00

¢

0.50

0.40

_J 0,50

_. 0.20

0.10

0/30

. . . , . . . , ........

6 8 10 12

]l_E

CIMEL Dote: 1999-01-27 Band: 44Onto

f

12

1¢

(5 6 I0 12 14

]]UE

Figure 6.13. MPL backscatter, original radiometer AOT observations, and corresponding AOT

measurements screened using the wavelet algorithm.

6?
0.6

._0.4

0.2

0.8

I_ o.6

P,,

r_

0.4

_ 0.2

0.0

10

CIMELDate: 1996-07-05 Bond: 870nm

0.8 ..........................

0,0 . • .J. -_

10 12 14- I 6 18 20 22

]]ME

CIMEL Date: 1996-07-05 Bond: 87Ohm

• . . , . . . , . . . , . . . , . . . , . . . , - . .

.4

-4

]

1

-1

-i

.t

24

12 14. 16 1B 20 22 24

]tME

CIMELDate: 1996-07-05 Bond: 870nm o.8 ...........................

0.6

z_ 0._-

8

0.2

0.0

10

• . . %'T':_, . , _--_,"- ....... I. , q.l. :.: :;::-.:_._.

12 14- 16 18 20 22 24-

lIME

CIMEL Date: 1996-07-05 Bond: 87Onto

0,8 " " ' " " " ' " " " ' " " " ' " " " ' • " " ' " - "

0.6

0.4

t- 0.2

o f

0.0 . "_:_ ............... : .=- -; .---'--

10 12 14- Ifl 1B 20 22 2¢

TII_E

Figure 6.14. Original set of daily AOT measurements from the CIMEL radiometer at Goddard Space Right

Center and the results of the AERONET quality assurance of these observations, strict borderline-based

wavelet screening, and relaxed borderline wavelet screening.

CIMEL Dote: 1999-01-30 Bond: 440nm

0.40

0,_0

_0,20

0,10

I_ o._

0.20

_0,I0

.. ........ , ......... , ......... , ......... , ......... , ......... .

0,00 f ...... , ......... *......... , ......... , ......... , ........

II 7 I! 9 10 11 12

TIME

ClMEL Dote: 1999-01-30 Band: 4J,.0nm

0.40 ........ ' ......... ' ......... ' ......... ' ......... ' .........

O._O .........................................................

7 8 g 10 11 12

ClklEL Dote: 1999-01-30 Bond: 440nm

0.40 ......... ' ......... ' ......... ' ......... '......... ' .........

 o.4-

_ o._-

_ °.,°[

7 8 g 10 11

CIMEL Dote: 1999-01-30 Bond: 44Onto

I 0.401 ......... ' ......... ' ......... " ......... "......... ' ......... .

o.._o_- _

_ 0,00 ........ , ......... , ......... , ......... , ......... , .......

7 S 9 10 11

12

t2

Figure 6.15. Original set of daily AOT measurements from the CIMEL radiometer at the

AERONET/SIMBIOS site at Bahrain and the results of wavelet, statistical, and derivative-based screening

of these observations.

68
REFERENCES

Ainsworth, E. J., S. W. Bailey, R. A. Barnes, R. E.

Eplee, Jr., F. S. Patt, B. A. Franz, C. Hsu, C.

Pietras, W. D. Robinson, M. Schmidt, M.

Wang, P. J. Werdell, and C. R. McClain, 2000:

Three years of experience with SeaWiFS

calibration, Pacific Ocean Remote Sensing

Conference (PORSEC), 5-8 December, Goa,

India.

Barnes, R. A., A. W. Holmes, and W. E. Esaias,

1995: Stray light in the SeaWiFS radiometer,

NASA Tech. Memo. 104566, Vol. 31, S. B.

Hooker, E. R. Firestone, and J. G. Acker, Eds,

NASA Goddard Space Flight Center, Greenbelt,

Maryland.

Combes, J.M., Grossmann, A., Tchamitchian, Ph.,

1989, Wavelets. Time-Frequency Methods and

Phase Space, Springer-Verlag.

Fargion, G. S., and J. L. Mueller, Eds., 2000:

Ocean optics protocols for satellite ocean color

sensor validation, Revision 2, NASA Tech.

Memo. 209966, NASA Goddard Space Flight

Center, Greenbelt, Maryland.

Gordon, H. R., and M. Wang, 1994: Retrieval of

water-leaving radiance and aerosol optical

thickness over the oceans with SeaWiFS: A

preliminary algorithm, Appt. Opt., 33,443-452.

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.

Masters, T., 1995, Numerical Recipes in C++,

Academic Press.

Paltridge, G. W., and C. M. R. Platt, 1976:

Radiative processes in meteorology and

climatology, in Developments in Atmospheric

Science, Elsevier, New York.

Shettle, E. P., and R. W. Fenn, 1979: Models for

the aerosols of the lower atmosphere and the

effects of humidity variations on their optical

properties, AFGL Technical Report, AFGL-TR79-0214, Air Force Geophysics Lab., Hanscomb

AFB, Mass.

Smirnov, A., B. N. Holben, T. F. Eck, O. Dubovik,

and I. Slutsker, 2000: Cloud-screening and

quality control algorithms for the AERONET

database, Remote Sens. Environ., 73, 337-349.

Wang, M., Bailey, S., Pietras, C., McClain, C.R.

and Riley, T., 2000, SeaWiFS Aerosol Optical

Thickness Matchup Analysis, in McClain, C.

R., E. J. Ainsworth, R. A. Barnes, R. E. Eplee,

Jr., F. S. Patt, W. D. Robinson, M. Wang, and S.

W. Bailey, SeaWiFS Postlaunch Calibration and

Validation analyses, Part 1, NASA Tech. Memo.

2000-206892, Vol. 10., S.B. Hooker and E.R.

Firestone, Eds, NASA Goddard Space Flight

Center, Greenbelt, Maryland, 2000.

69
Chapter 7

Satellite Aerosol Optical Thickness Match-Up

Procedures

Scan Bailey 1 and Menghua Wang 2

1Future Tech Corporation, Greenbelt, Maryland

2University of Maryland Baltimore County

7.1 INTRODUCTION

The validation of ocean color sensors requires

the use of in situ (field collected) data sets. The

SIMBIOS and SeaWiFS Projects have sponsored

numerous principal investigators (PIs) to collect in

situ optical and pigment data for the purpose of

comparing values to those derived from OCTS,

POLDER, MOS, SeaWiFS and future instruments

in the SIMBIOS Porgram. The match-up design

described here uses field data stored in the

SeaWiFS Bio-optical Archive and Storage System

(SeaBASS), match-up analysis software, and a

plotting and statistics package to validate SeaWiFS

derived products.

A key method for validating satellite data is to

compare those values with coincident

measurements taken in situ. NASA has sponsored

field research activities to build a database of

optical, pigment, and related in situ data for use in

validating the derived products (McClain et al.

1992, 1998). These data, which are stored in the

SeaWiFS Bio-optical Archive and Storage System

(SeaBASS), have been made available to those

assisting in the SeaWiFS algorithm development

and validation effort, particularly the SIMBIOS

Science Team and the Moderate Resolution

Imaging Spectroradiometer (MODIS) Oceans

Team. This chapter discusses the match-up

procedures for the validation process.

7.2 METHODS

These procedures are in place for the SeaWiFS

instrument and will be used for other missions

supported by the SIMBIOS Project.

In Situ Match-Up Data Files

In situ data are compared to satellite image files

by matching the two data sources in time and space.

Each in situ data set is first summarized in a single

ASCII file, known as a "match-up" file. The

match-up file adheres to the SeaBASS data format

described above. The data included in the matchup file are aerosol optical thickness, location, and

measurement times. For all incoming data, proper

descriptive documentation and, possibly,

interactions with the data provider, are required to

assure the usefulness of the in situ data for

matchups (Fargion and Mueller, 2000).

Match- Up Procedure

For the SeaWiFS and SIMBIOS Projects, the

match-up procedure includes using both UNIX

shell scripts (CSH) and Interactive Data Language

(IDL). These are used to compare individual

SeaWiFS Hierarchical Data Format (HDF) files to

in situ data records of individual measurements,

i.e., stations. To generate the initial list of

SeaWiFS files for consideration, the SeaWiFS

Project's HDF file database is queried for files that

spatially and temporally match records in the in situ

data source. The pixel and line number of the

SeaWiFS file that matches the in situ location is

determined. A region encompassing 101 by 101

pixels centered on the matched pixel is extracted

from each of the resulting initial level-lA (L1A)

files (GA; LAC; and high resolution picture

transmission, HRPT). These extracted data files are

saved to a new file for local disk storage.

For all valid L1A matches, a level-2 (L2)

product is generated from the extract file for further

analysis. A comprehensive suite of SeaWiFS L2

products are generated, including all %(k) and

ancillary data (ozone, windspeed, and atmopherie

pressure). In the generation of the L2 products, the

Siegel near infrared correction is applied, as well as

the sun glint and out-of-band corrections. As with

the extracted L1A files, the L2 files are saved to

local disk storage. For each successful L2 file

generated, a record including the path to the L2 file,

the matched pixel and line number, and associated

latitude and longitude are written to a file. This list

70
file and the in situ match-up file are used as input to

the IDL program where the match-up exclusion

criteria "are applied and the matchup plots and

statistics are generated.

Match- Up Exclusion Criteria

Only a small percentage of the candidate

SeaWiFS files become final valid matches. A

number of exclusion criteria have been formulated

to provide an objective set of points for SeaWiFS

validation that removes invalid or redundant data

from consideration. The approach presented here is

a result of numerous iterations. Nevertheless,

future modifications to the existing set of exclusion

logic is likely as a better understanding of both the

SeaWiFS and in situ data is attained.

The first exclusion criterion applied is a time

difference between the in situ record and the

satellite overpass. A time window of ±180 min

from the satellite overpass is because this window

is the time period of reasonable illumination in

most situations and, presumably, constant

atmospheric conditions.

In the current match-up set, the majority of

candidate SeaWiFS files, i.e., those that coincide

with in situ stations, are eliminated from

consideration as a result of the SeaWiFS image

pixels being "flagged" (excluded) usually because

of clouds and stray light, although other factors are

often present. Pixels are excluded if any of the

following flags are applied: atmospheric correction

failure, land, sun glint, total radiance above the

knee value, high satellite zenith angle, stray light,

clouds or ice, and low LwN (555).

If a match-up passes the temporal exclusion

criterion, the valid SeaWiFS pixels are averaged for

a region encompassing the matched pixel and

simple statistics are recorded. The match-up

approach uses a 21x21 pixel "box" for LAC

resolution data and a 5x5 pixel box for GAC data.

Since GAC resolution is a subsampling of LAC

resolution by a factor of 4, these two boxes are

nearly equivalent in areal extent.

Once the match-up code has reported all

unflagged matches, the routine reviews the records

for further refinement. Calculations currently used

for the exclusion of points and the matchup values

include:

1. 1.Minimum number of valid pixels: At least

fifty percent of the non-land pixels in the box

considered from the SeaWiFS image must be

valid (unflagged) to avoid contaminated data.

2. SeaWiFS file reduction: If there are GAC,

onboard LAC, and HRPT matches

.

.

corresponding to the same in situ point, the

order of preference is onboard LAC, HRPT,

and GAC. When multiple files of the same

preferred type match a single in situ record, the

closest temporal match is selected. This

selection is necessary when multiple HRPT

stations or multiple GAC swaths cover the in

situ point. Permanent real-time HRPT stations

are given precedence over other HRPT sources.

Multiple in situ measurements per SeaWiFS

file: Along-track measurements are included

in the in situ match-up files where adjoining

measurements are greater then 12 km apart.

Large coefficient of variation elimination:

Satellite matchups with extreme variation

between pixels in the SeaWiFS "box"

(coefficient of variation, or the standard

deviation divided by the mean value, > 0.2) are

excluded. These typically represent frontal

regions or other anomalies (e.g., cloud edges)

in the SeaWiFS imagery, which make the

match-up validity questionable. Presently, this

test is applied to "_a(865).

Once all the exclusion criteria are applied,

statistics are generated for each comparison. These

are written to an ASCII file. The final matched

data set is saved as an IDL saveset" (Table 1) for

future reference and analysis.

REFERENCES

Fargion, G. S., and J. L. Mueller, Ocean Optics

Protocols for Satellite Ocean Color Sensor

Validation, Revision 2, NASA TM 2000-20996,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 184 pp.

Hooker, S. B., C. R. McClain, J. K. Firestone, T. L.

Westphal, E. Yeh, and Y. Ge, 1994: The

SeaWiFS bit-optical archive and storage system

(SeaBASS), Part 1, NASA Tech. Memo.

104566, Vol. 20, S. B. Hooker and E. R.

Firestone, Eds., 27 pp.

McClain, C. R., W. E. Esaias, W. Barnes, B.

Guenther, D. Endres, S. B. Hooker, B. G.

Mitchell, and R. Barnes, 1992: SeaWiFS

Calibration and Validation Plan, NASA Tech.

Memo. 104566, Vol. 3, S. B. Hooker and E. R.

Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 43 pp.

71
McClain,C. R., M. Darzi, R. A. Barnes, R. E.

Eplee, J. K. Firestone, F. S. Patt, W. D.

Robinson, B. D. Schieber, R. H. Woodward,

and E. Yell, 1996: SeaWiFS Calibration and

Validation Quality Control Procedures, NASA

Tech. Memo. 104566, Volume 38, S. Hooker

and E. Firestone. Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, 64 pp.

McClain, C. R., M. L. Cleave, G. C. Feldman, W.

W. Gregg, S. B. Hooker, and N. Kuring, 1998:

Science quality SeaWiFS data for global

biosphere research, Sea Technology, 39: 10-16.

McClain, C. R., and G. Fargion, 1999: SIMBIOS

Project 1998 Annual Report, NASA Tech.

Memo. 1999-208645, NASA Goddard Space

Flight Center, Greenbelt, Maryland, 105 pp.

TABLE 7.1: Fields stored in match-up results summary file.

Name Description

CRUISEID

ENV_YEAR

ENV_MONTH

ENV_DAY

ENV_HOUR

ENV_MINUTE

ENV_SECOND

ENV_LAT

ENV_LON

ENV_LW(_,)

ENV_ES(X)

ENV KI:M90

ENV_CHL

TDIFF

SAT_FILE

SAT_COUNTS

SAT_SOLZ

SAT_SOLA

SAT_SENZ

SAT_SENA

SAT_LW_(_,)

SAT_ES_(_,)

SAT_NLW_(_,)

SAT_TAUA_(_)

SAT_ANGSTROM_555

SAT_EPSILON

SAT_OZONE

SAT_WINDSPEED

SAT_PRESSURE

SAT_CHL_OC2

SAT_K_490

SAT (X)_STATS

ENV_ES(_,)_CALC

-Cruise identifier

-Year of in situ measurement

-Month of in situ measurement

-Day of in situ measurement

-Hour of in situ measurement

-Minute of in situ measurement

-Second of in situ measurement

-Latitude of in situ measurement

-Longitude of in situ measurement

-In situ Lw at wavelength (_,)

-In situ Es at wavelength (Z,)

-In situ Kd490

-In situ chlorophyll concentration

-Time difference between in situ measurement and satellite overpass

-Satellite extract filename

-Number of valid pixels in 3x3 box surrounding in situ lat/lon

-Solar zenith angle at satellite overpass

-Solar azimuth angle at satellite overpass

-Satellite zenith angle at satellite overpass

-Satellite azimuth angle at satellite overpass

-Satellite Lw at wavelength (2L)

-Satellite Es at wavelength (_)

-Satellite Lwn at wavelength (2L)

-Satellite aerosol optical thickness at wavelength (_,)

-Satellite angstrom exponent between 865-555 nm

-Satellite epsilon value

-Ancillary Ozone value

-Ancillary windspeed

-Ancillary atmospheric pressure

-Satellite-derived chlorophyll using OC2v2 (operational algoithm at the

time of this writing)

-Satellite derived Kd490

-Simple statistics on satellite measurements (min, max, stdev)

-Calculated in situ Es using Gordon clear sky model

72
Chapter 8

Analyses of Match-Up Results

Ewa J. Ainsworth l, Christophe Pietras I and Scan Bailey 2

ISAIC General Sciences Corporation, Beltsville, Maryland

2Future Tech Corporation, Greenbelt, Maryland

8.1 INTRODUCTION

Match-ups between AOT points obtained from

in situ observations and satellite-derived AOT

levels are analyzed to:

• validate in situ AOT screening strategies;

• verify calibration of satellite sensor nearinfrared bands; and

• establish the effectiveness of the applied suit of

aerosol models.

The results of the match-ups are assessed using

statistical estimates. However, the conclusive result

appraisal is more flexible because of the

uncertainties involved in comparing the two types

of data and because of the uncertainties inherent in

the measurements themselves. The uncertainties in

comparing satellite- and in situ-derived AOT

information were described in detail in Chapter 6.

The accuracy of different in situ AOT screening

strategies can only be determined approximately

from the match-ups because both compared AOT

measures are not absolute and are under scrutiny. A

general statistical trend in the satellite band

calibration is evaluated. Finally, the aerosol model

suitability is estimated from the comparison of

spectral distributions of AOT levels for satellitederived and in situ measurements.

Match-ups presented in this chapter have been

obtained for the SeaWiFS-derived aerosol

properties and AOT levels calculated from

AERONET and SIMBIOS sun and sky radiometer

data. The match-up results are not conclusive.

Research into vicarious calibration of the SeaWiFS

instrument using in situ AOT observations and

validation of the atmospheric correction algorithm

is continuing.

The purpose of this chapter is to use the

preliminary results to describe a variety of matchup analysis techniques which have been developed

and implemented during the current study and show

some graphical examples of these techniques.

These analyses can be performed on any matched

satellite and in situ AOT data points. They can thus

apply to different ocean color sensors and sun and

sky radiometers. The match-up analysis algorithms

have been implemented in IDL.

8.2 ACCURACY OF THE

MATCH-UPS

The figures displayed in this chapter give

match-up results and additional information on the

match-up statistics. There are four sets of

information included:

• maximum error in matching satellite and in

situ-ob_ned AOT points (MaxDiff);

• percentage of match-up points over the y = x

line (Up);

• slope of the linear fit between two sets of AOT

points which is approximated using X2 error

statistics (Slope); and

• inaccuracy of AOT match-ups (Error).

Estimation of inaccuracies in match-ups

between satellite-derived AOT values and in situ

measurements is difficult. The inaccuracy

assessment needs to take into consideration the

intrinsic uncertainty of the instrument, the satellite

and the sun or sky radiometer; varying magnitudes

in AOT values; and the statistical validity of the

AOT match-up set.

The uncertainties associated with AOT

measurements are different for different

instruments. The uncertainty of CIMEL sun

photometer measurements is estimated at i'0.015

for AOT values (Chapter 3). Current calculations

roughly assume that the acceptable measurement

error for both satellite and sun/sky radiometer AOT

levels is i-0.02. Therefore, the total error in

comparisons can be up to i-0.04 in AOT fraction.

Consequently, if AOT match-up points for the

satellite and in situ instruments differ by 0.04 or

less, the points are considered identical. Also, a

0.04 constant is subtracted from differences

73
betweenAOTobservations which are larger than

0.04.

For low AOT values, small magnitude errors

create a large percentage of the actual AOT

measure. The AOT magnitude problem has not

been eliminated from the current results despite the

AOT normalization. Consequently, current

descriptions of error statistics depend on

magnitudes of AOT values within the match-up set.

Because the spectral distribution of non-absorbing

AOT values monotonically decreases from short

wavelength visible bands towards near-infrared

bands, near-infrared bands have lower AOT values

than visible bands. Consequently, the error statistics

for different bands is not fully uniform and reliable.

This especially occurs when there are low AOT

values in band 870nm. Finally, the larger the sets of

AOT match-ups, the more reliable the statistics.

8.3 VALIDATION OF IN SITU

AOT SCREENING

STRATEGIES

Chapter 7 reviewed in situ AOT processing

algorithms which included temporal screening

methods designed to eliminate cloudy and

erroneous measurements. In Figure 8.1, match-up

results are compared for two of the screening

strategies. The figure shows SeaWiFS-derived

AOT levels matched against in situ AOT points

which have been screened using the statistical and

wavelet-based approaches. The comparison is made

for AOT measurements captured at the Bahrain

AERONET/SIMBIOS site by a CIMEL sun

photometer. The screening methods applied only a

single spectral band, the 870nm band. The

comparison is made for the SeaWiFS bands which

are the closest to the CIMEL bands.

The comparison in Figure 8.1 illustrates that

both statistical and wavelet approaches to screening

cloud-contaminated and erroneous measurements

obtain roughly equivalent comparison results for

AOT measurements derived from the CIMEL sun

photometer. The wavelet method rejected some

observations characterized by high levels of AOT

which were accepted by the statistical approach.

Aerosol observations obtained from the CIMEL

instrument are uniformly spread throughout a day

and occur within proportional time intervals.

Usually, there are just above 12 measurements

captured within + 3 hours surrounding the satellite

over-flight. For daily in situ AOT observations

evenly distributed in time, all three screening

strategies (statistical and derivative-based estimates

of daily AOT variations as well as the wavelet

algorithm) recommended in Chapter 6 Section 6.4

appear to give comparable match-up results.

However, all three approaches are not universal for

different automatic and hand-held sun and sky

radiometers and a variety of in situ AOT data sets

obtained by different investigators. The wavelet

screening is the only algorithm applicable across all

the instruments and in situ data sets.

The accuracy of the screening strategies for

cloudy and erroneous observation removal cannot

be firmly verified from the match-up results. This is

because the comparisons are performed against

SeaWiFS-obtained AOT levels and ocean color

algorithms which are themselves being

investigated. There are no reliable validation data

for the match-ups. The accuracy of the screening

strategies can be directly verified in two ways:

• By comparisons against Micropulse Lidar

(MPL) backscatter data described in Chapter 6.

MPL backscatter information has not been

available for the AERONET/SIMBIOS

CIMEL sun photometers.

• By comparisons of screened in situ AOT

measurements obtained simultaneously from

different instruments. These comparisons will

be described later in the current chapter.

The accuracy of AOT match-ups has also been

analyzed in terms of the number of sun and sky

radiometer spectral bands used in the screening of

cloud-contaminated and erroneous measurements.

The issues concerned with the choice of spectral

bands used in the AOT screening are described in

section 6.6 of this document. The match-ups with

SeaWiFS for in situ CIMEL AOT observations

screened using the wavelet approach are displayed

in Figure 8.2. The top set of two graphs gives the

result for the screening version which used only the

870nm band. The bottom set of two graphs displays

the consequences of applying multiple spectral

bands in the screening. In the second case, at least

two bands are used to eliminate cloudy and

erroneous AOT measurements from CIMEL

observations.

74
Spectral distribution of '_n s'/Ju AOT

, i , , • i , • , ! , , , i • , •

(X) - ClMEL BAHRAIN 1997-98

E

._ 0.40

=

0.30

o

_ 0.20

g

_ 0.10

E

0.40

:2 O.3O

¢,

-6

I

_ O.2O

o

0.10

0.8 '

(r_) - CIMEL BAHRAIN 1999

0.6

_ 0.4

0.2

0.0 ......... , , , I , _ ,

200 400 600 800 1000 1200

Wovelen_th

MoxDiff:O.19 Up:13_. Slope:O.54 Error:-12.13_

0.50' ........ i ......... , ......... , ......... , ........

X ×

g<

x' x x

0.00 ........ I ......... I ......... I_,,, ..... 1 ........

0.00 O. ! 0 0.20 0.30 0.40 0.50

StotistFcol screening lit =_Jt; oerosol-optlcol-thickness 443nm

MoxDiff:0.09 Up:45_, Slope:0.76 Error:l.75_

0.50 ......... _ ......... i ......... , ......... , ........

0.00 ........ i ......... _ ......... j ......... I ........

0.00 O. 10 0.20 0.30 0.40 0.50

Stolist;coI screening ;r_ ¢Lthv. oeronol-opticol-thickness 67Onto

0.6

==

e

u

.c

I

,_ 0.4

0.2

Spectral distribution of SeoWiFS derived AOT

0.8 ' ' ' _ • ' • , , • ,

0.50

0.0

2OO

, , , I , , , i , . . I i i , I , ,

4OO 60O 8OO 1000

Wove_

MaxDiff:O.16 Up:l 8_ Slope:O.59 Error:- 7.44_

. ........ i ......... i ......... i ......... i ........

1200

0.40

i 0.30

0.00 O. 10 0.20 0.30 0.40 0.50

Stoti0ticoI screening /w. Id_t_ oerosol-ol_.icol-thicktles I 49Ohm

MoxDiff:0.10 Up:86_ Slope:O.91 Error:3.89_

0.501- ......... ,......... ,......... _ .................,

_ o._o_

_ o._o_

oX

o.2o 

_ 0.I0f

0.00 0.10 0.20 0.30 0.40 0.50

StotisticoI screenlrKJ !w. zritu ooto=ol-opUcot-thickneiI 865nm

Figure 8. I a Match-up results between SeaWiFS-obtained AOT values and AOT measurements captured in

Bahrain by a CIMEL sun photometer. The in situ CIMEL observations were screened using the statistical

based approach and a single spectral band centered at 870nm.

75
Spectral distribution of /,_ s_t¢ AOT

0.601- • , . I • . , i . • • ! . • • ! • . • .

I

(x) - CIMEL BAHRAIN 1997-98

- 999

0.50

•Fi00 0.30

0.20

O'lO I

0.00 I- , , i = = = I = = = I = _ . I , , .

E

0.4o

_=

g o._

I

I

_ 0.20

_ 0.10

200 400 600 800 1000 1200

Wavelength

MaxOiff:O.18 Up:I 1_ Slope:O.58 Error:-lO.27_

0.50 ......... , ......... , ......... , ......... _........

×

 ,"xxxx

x'"

0.00 ,,,,,,,, , ........ | ..........................

0.00 O. 10 0.20 0,30 0.40 0.50

Wavelet $Cfgleni_j ht _ oerosol-opticol-thickness 443nm

MaxDiff:O.09 Up:50_ Slope:O.94 Error:2.26_

rn ×

×

0.00 ...........................................

o,oo 0.10 0.20 0.30 0.40 0.50

Wavelet s¢reenincJ _rn _ oerosol-optlcol-thicknegs 67Onto

E

0.40

=

_ 0.30

o

I

"5 O.20

!

_ 0.10

Spectral distribution of SeoWiFS derived AOT

O.60F • • • , ' ' • , ' ' ' i ' • • i ' . ,

,, 0"50 I

_=o.4or

0.30

o.2o[

0"1° I

o,00 I- ............ I , ,

200 400 600 800 1000

Wavelength

MaxDiff:O.13 Up:22_ Slope:O.67 Errer:-6.00_

0.50 _.'

E

=

u

•g o._o_

I

o

_ o.2o E

£

_ o.mE

........ i ......... ! ......... i ......... i ........

X

X X

XX

1200

0.00 I;

0.00 O. 10 0.20 0.30 0.40 0.50

Wavelet screenin 9 ht _ oerolol--opt;col--thlckneml 49Ohm

MaxDiff:O. 10 Up:88_ Slope: 1.18 Error:4.1SZ

0.501 ......... , ......... , ......... , ......... _........

4o.40

0.20

_ 0.10

O.O0 0.10 0.20 0.30 0.40 0.50

Wavelet screening lw, _ aerosol-optical-thickness 865nm

Figure 8.2b Match-up results between SeaWiFS-obtained AOT values and AOT measurements captured in

Bahrain by a CIMEL sun photometer. The in situ CIMEL observations were screened using the wavelet

based approach and a single spectral band centered at 870nm.

76
0.50

MoxDiff:O. 18 Up: 1 1% Slope:O.58 Error:- 10.27%

0.40

E

c

0.3o

S

u= 0.20

0.10

////_x _x x

0.00 ........ , ......... _......... h......... ,.........

0.00 0.10 0.20 0.30 0.40 0.50

Wavelet screening/870nm band l'n, s'/,tu AOT 443nm

MoxDiff:O. 1 0 Up: 10% SJope:O.95 Error:-4.88%

0.50 ......... ' ......... ' ......... ' ......... ' .........

0.00 ........ J......... I,, ....... _......... ,........

0.00 0.10 0.20 0.30 0.40 0.50

Wavelet screening/multiple bends [_ M.t_ AO]- 445nm

0.40

E

_g

0.30

i----

m

0.20

0.40!

E

if)

0.30

6

<

O9

u_ 0.20

0.10

0.40

E

_g

0.30

" 0.20

0.10

MoxDiff:O. lO Up:88% Slope: 1.18 Error:4.15%

0.50 ' ........ ' ......... ' ......... , ......... , .........

X

-SI_.

o,oo,/...................

0.00 0.10 0.20 0.30 0.40 0.50

Wavelet screening/B7Onm band I_ ._i.tlz AOT 865nm

MoxDiff:O. 1 0 Up: 1 00% Slope: 1.,30 Error:3.99%

0.50 ' ........ , ......... , ......... ," ....... , .........

×

o.oo .T.

0.00 0.10 0.20 0.30 0.40 0.50

Wavelet screening/multiple bonds l_. s4,t_ AOT 865nm

Figure 8.2. Match-up results between SeaWiFS-obtained AOT values and AOT measurements captured in

Bahrain by a CIMEL sun photometer. The measurements were screened by the wavelet-based algorithm

using a single 870nm spectral band (top two graphs) and the multi-band approach (bottom two graphs).

Figure 8.2 shows that multi-band screening

eliminates many in situ AOT points from the

match-up set because they do not pass the multiband criteria for cloud-free and valid observations.

The comparison in the 865nm band suggests that

many valuable in situ AOT points may have been

removed. However, the results in the 443nm band

illustrate that most of those in situ AOT points are

rejected which do not extrapolate well towards the

visible spectra using the applied set of aerosol

models. It therefore indicates that some properties

of cloudy and erroneous aerosol measurements are

better differentiated using the visible range of the

spectrum. Near-infrared AOT measurements alone

may be insufficient to temporally screen sun and

sky radiometer data and the combination of nearinfrared and visible bands may be ideal. Because

AOT spectral distributions usually decrease

monotonically from the visible towards the nearinfrared spectrum, larger AOT magnitudes in the

visible range can also help to detect subtleties in

AOT temporal variations.

More research is needed into the spectral

expression of features associated with cloudcontaminated and erroneous AOT measurements.

This initial study indicates that the application of

multi-band AOT temporal screening can remove in

situ AOT observations which are suspected of

being cloudy or invalid. AOT measurements

screened using the multi-band method can then

produce more reliable results in comparisons with

satellite-obtained AOT values. These improved

match-up results can be obtained without upgrading

the applied set of aerosol models.

77
8.4 MATCH-UP RESULTS

Figure 8.3 illustrates the results of comparisons

between SeaWiFS-obtained AOT values and in situ

AOT points derived from CIMEL measurements.

Four CIMEL sites are included in the match-ups.

Because of the difficulties in accessing a database

of level 1 SeaWiFS imagery, the match-ups are

only shown for CIMEL observations which are

screened using the statistical algorithm (without the

sliding filter utility described in Chapter 6) and the

single 870nm spectral band. In situ AOT results are

converted into SeaWiFS spectral bands and only

those bands are displayed which are the closest to

the original CIMEL bands.

Figure 8.3 shows that the AOT match-ups

compare relatively well for SeaWiFS and in situ

points in the near-infrared 865nm band. There is,

however, a systematic overestimation by SeaWiFS

in 865nm band match-ups of around 0.023 in the

AOT value. SeaWiFS overestimates AOT measures

relative to CIMEL observations in 85% of nearinfrared cases. The 8.4% error in the near-infrared

band match-ups is elevated due to outliers and low

AOT levels which cause the error to be high

compared to the magnitude of the AOT values

themselves. The slope of the linear fit between the

pairs of SeaWiFS and in situ measurements closely

approaches I for the 865nm band match-ups. As the

wavelength decreases towards the visible spectrum,

the lower the slope value for the match-ups and the

more the SeaWiFS algorithm underestimates AOT

measures relative to CIMEL observations. For the

visible 443nm band, the SeaWiFS processing

underestimates 77% of AOT match-up points and

the significant spread of the points results in a low

slope of 0.61 for the linear fit between the satellite

and in situ data sets.

Starting from relatively accurate AOT matchups in the high wavelength near-infrared bands, the

discrepancy in the match-ups increases with

decreasing wavelengths. Figure 8.4 presents

spectral distributions of daily AOT CIMEL

measurements obtained at the

AERONET/SIMBIOS sites in Bahrain and San

Nicolas. The figure also shows these days'

distributions of in situ and corresponding SeaWiFSderived AOT match-up points. The AOT spectral

distribution shown for Bahrain represents the most

common case in which SeaWiFS slightly

overestimates the AOT compared to CIMEL

measurements in the 865nm band and significantly

underestimates the AOT in lower wavelength

visible bands. The San Nicolas example shows

SeaWiFS overestimation of AOT levels in most of

the spectral bands when compared to the sun

photometer observations. In general, SeaWiFS

AOT spectral distributions appear to be defined by

much flatter curves than in situ AOT measures.

This strongly contributes to the inaccuracies in

AOT match-ups.

8.5 ANALYSES OF SENSOR

NEAR-INFRARED BANDS

Aerosol model selection accuracy and AOT

value estimation performed by the atmospheric

correction algorithm depend on the relative and

absolute calibrations of the near-infrared bands. For

the SeaWiFS algorithm, aerosol models are

established through ratios of single scattering

aerosol reflectance in the 765nm and 865nm bands

(Eplee et al., 2000). The relationship between nearinfrared band calibrations has been here

investigated in more detail.

For the AOT match-up points from Figure 8.3,

an AngstrOm coefficient has been calculated for the

765nm band. The coefficient is obtained relative to

the 865nm band following Equation 6.3 which was

defined in Chapter 6. CIMEL AOT values are

interpolated to the SeaWiFS bands using a linear fit

in the logarithmic space within the entire CIMEL

ocean color spectral range. The Angstrtm matchups are displayed in Figure 8.5. The figure shows

that Angstrtm levels are significantly

underestimated in the 765nm band by the SeaWiFS

algorithm when compared to in situ data. Only 12%

of the Angstrtm coefficients for SeaWiFS-defined

AOT values are higher than the Angstrtm

components obtained from in situ observations. The

spread of Angstrtm levels for ClMEL

measurements is larger, (0.0, 2.0), than for

SeaWiFS-derived AOT values, (0.0, 1.0).

Consequently, the linear fit between the two sets of

Angstr6m match-up points has a significantly

flattened slope and a bias. The AngstrOm match-ups

indicate inaccuracies in the calibration of the

SeaWiFS near-infrared bands.

78
Spectral distribution of SeaWIFS derived AOT

0.8 .... , - - • , • • • , - . • , . . -

Spectral distribution of _,_ s/,_, AOT

• ' " " '(-x " ' " " ' " " I' ) CIMEL BAHRAIN 1998

(D) - CIMEL BAHRAIN 1999

(A) - CIMEL BERMUDA 1999

(¢.) - CIMEL KAASHIDHOO 199

(4-) - CIMEL SAN NICOLAS

0.0

200 400 600 800 I000 1200

Wavelength

0.50

:_ 0.40

u_

_ O.3O

I

o

.u

0.20

e

o

P O.lO

MoxDiff:O.19 Up:23_ Slope:O.61 F'rror:-4.24R

......... i ......... i ......... i ......... i .........

[] x

× ×

A

0.1_ ........ , ......... , ......... , ......... , ...... ,,

0.5(

E

0.4(

_ 0.2,(1

I

o

/ 0.20

8

_ O.lO

0._

O.IXl

0.00 0,10 0.20 0.30 0.4.0 0.50

/t_ ,_'(ft¢ oero'.ol-opticol-thlckness 443nm

MoxDiff:O. 13 Up:48_ Slope:O.83 Error:5.32_.

......... i ......... J......... i ......... i .........

×

+ []

__ , ,

O. 10 0.20 0.30 0.40 0.50

I_ ._ aerosol-optical-thickness 67Onto

0.8

0.6

=.

I

8 o.4

!

_ 0.2

0.50

o

0.40

.v

_ 0.317

0.20

0.10

?

u

g

I

O.IX)

0.50

0.40

0.30

0.20

0.10

0.00

0.00

0.O

200 400 600 800 1000 1200

Wavelength

MaxDiff:O.16 Up:291¢ Slope:O,66 F'rror:-0.79_

+ [] ×

x xx

0.00 0.10 0.20 0.30 0.40 0.50

/it s_._ oerosol--optlcol-thlcknes,, 4gOnm

MoxDiff:O.14 Up:85_ Slope:0,98 Error:8.40_

.. ........ i ......... i ......... i ......... i .........

+ []

...... i ......... i ......... i ......... i ...... ,,,

0.10 0.20 0.30 0.40 0.50

/_ s'_ aerosol-optical-thickness 865nm

Figure 8.3. Match-up results between SeaWiFS-obtained AOT values and AOT measurements captured by

different CIMEL sun photometers. The CIMEL observations were screened by the statistical algorithm

using a single spectral band centered at 870nm.

79
Spectra( distribution of trL s_ AOT measurements 1998-12-08

0.25

0.20

015

i o_o

d.

005

0.00

200

02SI

0.20

l

0._51

0,10

005

0.00

400 600 B00 1000 1200

Wavelengttl

AOT motchup points 1998-12-08

(*)-Se_WiFS

I998

Spectrol distrlbutio_ of $_ stt_ AOT measurements 1998-05-18

0201

i 0.05

- O00I

20O

020F

400 600 800 1000 1200

Wavelenglh

AOT motchup points 1998-O5-18

0,10FOOSP

0OO

20O

(0)-CI_£L SAN t_COL,_S }gg8

(0)-$eowirs

200 400 60{.1 800 1000 t 200 400 500 800 1C'30 1200

Woveklngt h Wo_etengLh

Figure 8.4. Spectral distribution of in situ AOT daily measurements (top graphs) and distributions of

corresponding in situ and SeaWiFS-derived AOT match-up points.

E

tO

r,-

v

O

nv--

cO

O

Z

<

bo

b_

O

0)

t.o

MaxDiff:1.51 Up:12% Slope:0.253 Error:-9.290%

,.3 ......... I ...... '''1 ......... I .... ' .....

&Elt - ClMEL BAHRAIN 1998

- CIMEL BAHRAIN 1999

- CIMEL BERMUDA 1999

)) - CIMEL KAASHIDHO0 1998

- CIMEL KAASHIDHOO 1999

- CIMEL SAN NICOLAS 1998

2

1 /

+

o

[]

o [],,x

lJhlJ ,illJ,tlllllttt,,J,i,l=1|il,,l_ll

0 1 2 3

I_ s_.t_ ANGSTROM(765nm)

-1

Figure 8.5. Angstr6m coefficient match-ups for SeaWiFS-obtained and in situ AOT values captured by

different CIMEL sun photometers and screened using the statistical algorithm and a single spectral band

centered at 870nm.

8O
The /_ngstr6m match-ups show that there are

some AOT SeaWiFS and in situ points which are

very close for both types of observations. These

AOT points have been extracted from the match-up

set. They are employed to evaluate the accuracy of

the extrapolation of the aerosol path radiance

towards the visible spectra using existing aerosol

models. The actual condition for the selection of

AOT match-up points is that their Angstrtm

coefficient in the 765nm band should not differ by

more than 0.2 in the A,ngstrtm value for SeaWiFSand in situ-derived AOT levels. Match-ups in six

SeaWiFS visible bands for these particular AOT

points are presented in Figure 8.6. Although there is

a small number of similar ]kngstrtm AOT match-up

points, the figure illustrates that for these AOT

points the match-up accuracy is high, even in the

low wavelength visible bands.

Figure 8.6 clarifies that, even for close

A,ngstrtm AOT match-up points, the

underestimation by the SeaWiFS algorithm of AOT

levels in the low wavelength visible spectra is still

observable. In the blue bands, only two AOT

match-ups from a single CIMEL site at San Nicolas

are higher for SeaWiFS than for in situ data. These

two match-up points do not provide a statistically

reliable alternative to the remaining match-ups. It

can be speculated that flat spectral distributions of

the existing aerosol models prohibit precise

extrapolation of the aerosol path radiance to the

visible spectra.

8.6 CROSS-INSTRUMENT

VALIDATION OF IN SITU AOT

POINTS.

As the AOT match-ups are associated with large

degrees of uncertainty (major match-up uncertainty

issues are discussed in Chapter 6), a validation of in

situ AOT points against measurements obtained

from two or more radiometers can give a more

reliable basis for the comparisons. During cruise

experiments there may be a number of sun

photometers and sky radiometers performing

simultaneous AOT observations. Concurrent AOT

points obtained by screening AOT observations for

the match-ups can be validated against one another.

If these in situ AOT points agree approximately, the

results of the match-ups with satellite-derived AOT

levels can provide a more confident basis for

satellite sensor calibration and algorithm validation.

Figure 8.7 displays four screened in situ AOT

points which are obtained from MICROTOPS II

and SIMBAD sun photometer measurements. The

measurements were performed during

AEROSOLS'99 and INDOEX cruises on four

different days. The maximum difference between

the AOT observation time by both instruments was

thirty minutes. The maximum spatial difference

between the geographical locations where both sets

of AOT measurements were obtained was 15km.

The MICROTOPS II and SIMBAD observations

which went into these AOT points were screened

with the wavelet-based algorithm to remove cloudcontaminated and erroneous measurements. The

multi-band version of the screening was applied.

It can be seen that the AOT distributions for in

situ points coming from both sun photometers agree

relatively closely for all ocean color bands common

between the instruments. Therefore, these points

create a solid basis for match-ups with satellitederived AOT measures. Due to the current

problems with accessing the database of SeaWiFS

level 1 imagery, only two of the cross-instrument

validated in situ AOT points were matched with

satellite-derived AOT estimates. The match-up

results for the two points are illustrated in Figure

8.8. These results are not suitable to draw

statistically viable conclusions about the accuracy

of the satellite sensor calibration and the

atmospheric correction algorithm. However, the

match-ups demonstrate the soundness of the crossinstrument validation method for in situ AOT

points. They demonstrate with high certainty that

the existing satellite calibration and atmospheric

correction can provide exceptionally accurate

estimates of AOT values in all ocean color bands in

certain favorable conditions. In this case, in situ

AOT points have relatively flat spectral

distributions which are easily approximated by the

SeaWiFS aerosol models.

The screening algorithm applied to produce the

in situ AOT points, the multi-band version of the

wavelet technique, contributed to this flawless

result. The multi-band wavelet screening proved to

be very strict in eliminating cloudy and erroneous

AOT measurements and left few points for matchups with satellite-derived AOT values. It also

showed the most reliable and comprehensive. The

wavelet algorithm was successfully validated for

different sun and sky radiometer data against MPL

backscatter information (Chapter 6) and provided

more accurate match-up results with SeaWiFS.

81
E 0.25

,¢

= 0.20

T o.15

o

0

t 0.10

e

o=

0.05

E

_n 0,00

SeoWiFS/ln ,¢/,_u, AOT points with similar ANCSTRObI(765nm)

MoxDiff:O.05 Up:20?. Stope:O.97 Error:-0.43%

" ix)"- cIMEL_,,N' 1998............

(0) - ClMEL SAN NICOLAS 199_)_

VI.O .............

Ec 0.25;

_3

0.20

U

T o.15

? 0.10

0.05

0.0o

MaxDiff:O.03 Up:20_ Slope:l.05 Error:O.OOg

.... t i i .... i

[] 0

, , , , , , ..............

0.00

E 0.25

Ot

,d"

= 0.20

U

.u

0.15

I 0.10

0

0.05

0.00

0.05 0.10 0.15 0.20 0.25

l_ s_ttL aerosol-optlcal-thickness 412nm

MaxDiff:O.03 Up:20% Slope:l.08 Error:O.O0_

t .... , .... i .... i ....

0

.... = , ....... , , , E ....

E 0.25

un

_3

= 0.20

0.15

o

o=

t 0.10

g 0.05

i 0.00

0.00 0.05 0.10 0.15 0.20

lw, ,,_u. aerosol-optical-thickness 49Onto

MoxDiff:O.04 Up:60% Slope:1.13 Error:O.O0_

.... i .... t .... = .... i ....

O.(Xl

b.25

0.05 0.10 0.15 0.20 0.25

I_ _ oerosol-optlcol-thickness 555nm

0.00

E 0.25

0

,_ o.20

IU

T o.15

.__

I 0.10

0.05

m 0.00

0.05 O. 10 O. 15 0.20 0.25

]_ s'/.Cu, aerosol-optical-thlckness 443nm

MoxDiff:O.03 Up:40_ SIope:l.lO Error:O.OOg

0.00

E 0.25

0

0.20

U

5_

T o.15

0

_) 0.10

i_ 0.05

_ 0.00

0.00

0.05 0.10 0.15 0.20 0.25

lw. ¢/J'= aerosol-optlcol-thickness 51Onto

MoxDiff:O.04 Up:70_; SIope:1.13 Error:O.O0_

0.05 0.10 0.15 0.20 0.25

./_t _ aerosol-optical-thickness 67Onto

Figure 8.6. Match-up results for AOT points characterized by a similar value of the ,_ngstrGm coefficient at

765nm for SeaWiFS-obtained and in situ measurements. The in situ AOT observations were captured by

different CIMEL sun photometers and screened using the statistical algorithm and a single spectral band

centered at 870nm.

82
I--

0

<

MICROTOPS II In situ AOT

0.25 ......... ' ......... ' ......... ' .......... "........ ' .........

0.20

0.15

0.10

0.05

0.00 i..........................................................

400 500 600 700 800 900 1000

Wavelength

MaxDiff:O.056 Up:75% Slope:l .234 Error:O.O00%

0.25

E 0.20

o.15

0.10

en

'5

0.05

0.00

0.00

.... i .... i • '.. • ! .... i ....

o/

/

0.05 0.10 0.15 0.20 0.25

MICROTOPS II In _'i,tu 443nm

I--

o

SIMBAD In situ AOT

0.25! ....................................... , ...................

0.20

0.15

0.10

0.05

0.00 .........................................................

400 500 600 700 800 900 1000

Wovelength

MoxDiff:O.028 Up:75% Slope: 1.122 Error:O.O00%

0,25 .... ' .......... " .... '''

Ec 0.20

0

O_

.It

0.15

0.10

m

0.05

0.00

0.00

.//_ . . , .... , .... J .... i ....

0.05 0.10 0.15 0.20 0.25

MICROTOPS l} In M,tu 49Onto

MoxDiff:O.016 Up:O% Slope:l.008 Error:O.O00%

0.25 ..... " .... " " " " ' .... ' ....

Ec 0.20

o

(D

0.15

o.1o

0.05

0.00

0.00 0.05 0.10 0.15 0.20

MICROTOPS II In sit_ 67Onto

MaxDiff:O.025 Up:25% Siope:0.962 Error:O.O00%

0.25

E 0.20

0

CO

0.15

o.1o

133

0.05

. 0.00

).25 0.00

.... 1 .... i .... i .... i ....

0

0.05 0.10 o.15 0.20 0.25

MICROTOPS II I_ s'/,t_ 87Ohm

Figure 8.7. Validation of MICROTOPS II and SIMBAD AOT points for the measurements obtained within

a maximum of thirty minutes and 15km apart for both sun photometers. Both sets of the AOT observations

were screened using the multi-band version of the wavelet-based algorithm.

83
Spectrot distribution of (n _ AOT

0.25:................ ii'i_-_t_Rdib_;g,_s'iuI_O

x

0._ ....... i ......... , ......... , ......... , ......... i ........

400 500 600 700 800 900 1000

Wovtlee_JLh

MoxDiff:O.02 Up:50_ Slope:f.23 Error:0.00]¢

0.20

0.15

_0.10

1

0.05

!°iil I

0.00 0.05 0.10 0.15 0,20 0.25

/w, _ ooroso_-opU¢o_-thickne_l 44,._1m

MoxOiff:O,02 Up:lO(_ Slope:1.25 Error:O.OOZ

...................7

°t "

!iiii .......

0.00 0,05 0,|0 0.15 0.20 0.23

ht _ oeroQxd-optJctd-thlckmms 67Ohm

Spectrot distribution of $eoWiFS derived AOT

0.25 t ......... ' ......... ' ......... ' ......... ' ......... ' .........

II 0.20

_o,0_ .... ×

,/%

E

o

m

•t 0.20 I"

J

_ o.loI8

s

_ o._

0.00 L

o.o_IT

o.ool .........i.........i.........i.........l,.,,,.,..,........

400 500 600 700 800 900 '_000

Wove_th

MoxDiff:O.02 Up:50_ Slope:1.24 Error:O.OO%

0251

0.00 005 010 0.15 0.20 0.25

/_ _ oe¢oIol-op4Jcot-th_ckneIS 490¢mn

MoxDiff:O.03 Up:lO0_ Slope:f.25 Error:O.00_

0.251 .... '

020 F

0151"

0101"

0.05 F

0.OOL .... , .... , .... _ .... , ....

0.00 0.05 0.10 0.15 0.20 0.25

/w. _ ollrolo4-optlcol-th_ck_mls 86,emm

Figure 8.8. Match-up results between SeaWiFS-obtained AOT values and in situ AOT points crossvalidated between MICROTOPS II and SIMBAD sun photometers. The MICROTOPS II and SIMBAD

observations were screened using the multi-band version of the wavelet-based algorithm.

8.7 ADDITIONAL ANALYSES

OF MATCH-UP RESULTS

AOT match-up results between satellite-derived

and in situ observations can be further investigated

in a variety of ways. A useful study is to find out

whether the AOT comparisons are unbalanced by

several aberrant factors which can contribute to

uncertainties in comparisons of the two types of

data and inaccuracies in the atmospheric correction

algorithm. These factors include higher wind

speeds and atmospheric variabilities over the

match-up geographical locations at the time of

satellite imagecapture, higher sun zenith angles or

sensor viewing angles, and larger time

discrepancies between measurements performed by

ground and satellite instruments. Figure 8.9

displays the match-up results from Figure 8.3 in the

865nm band and the correlations between the

match-up errors in this band and the following:

• time difference;

* standard deviation of the _(765,865) parameter

(ratio of the single scattering aerosol

reflectance at the two bands) calculated within

84
SeaWiFSHRPTor LACwindowsof 21x21

pixelscenteredat theCIMELgeographical

locations;

standardeviationof AOT valuesobtained

withintheSeaWiFSwindows;

solarzenithangle;and

sensorzenithangle.

The figure showsthat thereare not any

noticeable correlations between match-up

inaccuracies andthesefactors.

AOTmatch-uptimeseriescanhelpdiscover

seasonal trendsin AOTdistributionsandpossible

miscalibrationsof temporarilystationedsun

photometersor sky radiometers. Figure 8.10

presentsmatch-uptime seriesfor the CIMEL

instrumentsoperational in SanNicolas.For this

case,unfortunately, a smallnumberof match-up

pointsprohibitsany viableanalyses.However,

betterstatisticsareexpectedwithfuturematch-up

results.

In caseof suspiciousmatch-upoints,the

spectralandtemporaldistributionof sunandsky

radiometer dailymeasurements whichcomprisethe

in situ match-up point can be displayed and

analyzed. Figure 8.11 shows distributions of

SIMBAD sun photometer measurements obtained

during the INDOEX cruise.

8.8 CONCLUSIONS

This chapter has introduced a number of

techniques used to analyze match-ups between

satellite-derived and in situ AOT levels. The results

of AOT match-ups were investigated and

recommendations were made for cases where AOT

measurements were screened using statistical and

wavelet-based algorithms as well as single band

(870 nm) and multi-band approaches. The matchups were compared directly enabling the estimates

of the absolute calibration of the satellite sensor's

865nm near-infrared band. Also, the accuracy of

the relative near-infrared band calibration was

studied using the Angstr6m coefficient value of the

765nm band. The fitness of the aerosol models for

extrapolation of the aerosol path radiance from the

near-infrared to visible spectra was investigated for

close ]kngstrfm satellite and in situ match-up cases.

More reliable match-up results were obtained using

cross-instrument-validated in situ AOT points.

Finally, several additional match-up analysis

methods were outlined.

The SeaWiFS AOT match-up results with in

situ radiometer observations and /_ngstr6m

coefficient studies indicate that:

There are inaccuracies in relative and absolute

calibrations of the SeaWiFS near-infrared

bands; and

SeaWiFS aerosol models have flat spectral

distributions which are insufficient to correctly

extrapolate a large part of extracted nearinfrared AOT measures towards the visible

spectra.

Nevertheless, further studies are needed to

draw more decisive conclusions about sensor and

atmospheric correction accuracies. The following

investigations are suggested:

• Application of the wavelet-based AOT

screening technique to remove cloudcontaminated and erroneous measurements.

• Application of the multi-band version of the

screening algorithm to assure the removal of all

questionable AOT measurements.

• Application of all available in situ AOT data

sets derived from a variety of sun and sky

radiometers and geographical locations which

include coastal sites as well as open ocean.

• Further analysis of match-ups for crossinstrument validated in situ AOT points.

• Application of an alternative "absolute"

vicarious calibration of the SeaWiFS

instrument using in situ measurements of

water-leaving radiances as well as AOT values

(the first results of this technique are described

in Chapter 9 of this publication).

REFERENCES

Eplee, R. E. Jr., C. R. McClain, 2000: MOBY Data

Analysig for the Vicarious Calibration of

SeaWiFS Bands 1-6, NASA Tech. Memo.

2000-206892, In McClain, C. R., E. J.

Ainsworth, R. A. Barnes, R. E. Eplee, Jr., F. S.

Patt, W. D. Robinson, M. Wang, and S. W.

Bailey, SeaWiFS Postlaunch Calibration and

Validation Analyses, Part 1, S. B. Hooker and

E. R. Firestone, Eds, NASA Goddard Space

Flight Center, Greenbelt, Maryland.

85
0.50

0.25

0.20

0.15

.P

0,10

0.05

0.0o

I

o

E

I0

.6

i

o

_5

I

.P

E

c

an

"6

!1

_5

MoxDiff:O.14 Up:85Z SIope:O.98 Error:8.40Z

.... | .... ! .... i .... i .... i ....

0.15

0.10

0 0

0

y°, , , , i .... L .... m .... | .... l ....

0.00 0.05 O.10 O,15 0.20 0.25 0,50

CIMEL AOT 865nm

CORRELATION COEFFICIENT = -0.15782880

0

-0.05

%

0

0

0.05 0

oo^ oo

_hV ?. .9... 0.oo_o:_---o---_ ......................

0

0

0

• . . I . . , I . . . I . - . I , , . j , . . I . . .

0.00 0.02 0.04 0.06 0,08 0.10 0.12 _.14

Stdl)ev for gl_lilon within the SeoWIFS window

CORRELATION COEFFICIENT = -0.35188753

0.15 ......... ' ......... ' ......... _......... _......... , .........

0

0.10 0 0

0

0

0.05 0

^ 0 0 0 0

I v o o o_ o]

r o °o o o _0",8- J

o.oo ..o ...... .............o.....

? . <'. <>, .t

-0 05,- .....................................................

0 10 20 50 40 50 60

$olor Zenith Angle for the SIoWiF5 window

E

CO

.6

o

/5

0.15

0.10

CORRELATION COEFRCIENT = -0.49717481

• • • I • _ -'1 • • • I • • - I'.', -'1 • • • I • • •

<>

0 O

0

0

0.05 0

o o i9,

o.oo................... o.......og_ ............. o

0 o

0

-0.05 - • • , ........... , • - • , .......

-80 -60 -40 -20 0 20 40 60

Time difference between dora coptm'e in minutes

CORRELATION COEFFICIENT = 0.34739194

0.15 _''',''"', .... _ .... , .... , .... , ....

o

= 0

O.lO o

i o

E 0

0.05 0

© 00^ 0 0

.6 _ ov <>oc_

• o oo. _ <b_>o i 0oo.......°¢.._..._z......,........% ............

0

-0.05 ,,--, .... , .... , .... , .... , .... , ....

0.000 0,005 0,010 0.015 0.020 0.025 0.050 0.035

StdDev for AOTe ot 865nm within the S_o_iFS window

CORRELATION COEFFICIENT = 0.20269331

0.15 ......... ' ....... '" " ' ......... ' .........

o

_. o._o %

£ 0

0.05 0

© 0 ^0 O0

0.00 .- "_ ............

N }" 0 0

-0.05 .....................................

20 50 40 50 60

Sensor Zenith Angle for the SeoWiFS window

Figure 8.9. Correlations between match-up errors and SeaWiFS imaging and processing characteristicsfor

in situ AOT observations captured by different CIMEL sun photometers and screened using the statistical

algorithm and a single spectral band centered at 870nm.

86
0.40

0.30

E

0.20

0.10

0.00

0.40

0.,30

oo 0,20

0.10

AOT MATCHUP TIME SERIES

(O)-CIMEL SAN NICOLAS i

(,)-S_WirS

........ , ......... i ......... i ......... I ........

12/17/97 03/27/98 07/05/98 _0/13/98 01/21/99

Dote

AOT MATCHUP TIME SERIES

(O)-OMEL SAN NICOLAS

(,)-Seowirs

0.oo _, . ................................ .,J___

121171'97 03/27/98 07105/98 lO1131'9B Ol/21/Q9

Dote

Figure 8.10 Time series of AOT match-up points for CIMEL instruments o_rational in San Nicolas.

CIMEL AOT observations were screened using the statistical algorithm and a single spectral band centered

at 870nm.

0.20

0.15

I-.-

o 0.10

<

0.05

SIMBAD Dote: 1999-02-19

0.00 ..............................................

400 500 600 700 800 900

WovetencJth

SIMBAD Dote: 1999-02-19

0.20

0.15

E

r.

o

m 0.10

I,,..-

0

0.05

0.00 ...................

04:00 06:00 08:00 I0:00 12:00 14:00

Time

Figure 8.11 Spectral and temporal distributions of daily SIMBAD measurements captured during the

INDOEX cruise.

87
Chapter 9

SeaWiFS Vicarious Calibration: An Alternative

Approach Utilizing Simultaneous In Situ Observations

of Oceanic and Atmospheric Optical Properties.

Bryan A. Franz l, Ewa J. Ainsworth I and Sean Baily 2

1SMC General Sciences Corporation, Beltsville, Maryland

2Futuretech Corporation, Greenbelt, Maryland

9.1INTRODUCTION

We will first describe an approach for

estimating the top-of-atmosphere (TOA) reflectance

using simultaneous measurements of in situ waterleaving radiance and aerosol optical thickness at the

SeaWiFS wavelengths. This approach can be used

to predict the radiance that should be observed in

each of the eight SeaWiFS bands, thereby

providing a mechanism for direct calibration of the

sensor. We apply this technique to the vicarious

calibration of SeaWiFS and contrast the results with

those obtained for the operational SeaWiFS

calibration, through comparison with independent

in situ measurements of water-leaving radiances

and aerosol optical thickness.

9.2 ALGORITHM

DESCRIPTION

We begin with the definition of the reflectance,

p =_L/laoFo, where L is the radiance in a given

solar and viewing geometry, Fo is the

extraterrestrial solar irradiance, and 120 is the

cosine of the solar zenith angle. The total

reflectance measured at the top of the oceanatmosphere system, p,(A), can be written as in

Equation 9.1.

In the equation, p,(_) is the reflectance

resulting from multiple scattering by air molecules

in the absence of aerosols, p,(_) is the reflectance

resulting from multiple scattering by aerosols in the

absence of air molecules, pr,(2g is the multiple

interaction term between molecules and aerosols

(Deschamps et al., 1983), pg(3,) is the direct

reflectance of solar rays from the sea surface to the

sensor (Sun glint or glitter), py(_.) is the reflectance

at the sea surface that arises from sunlight and

skylight reflecting from whitecaps on the surface

(Gordon and Wang, 1994a), and Pw(;g is the waterleaving reflectance, which is the desired quantity in

ocean color remote sensing. The t(A) term is the

atmospheric diffuse transmittance (Wang, 1999)

that accounts for the effects of propagating a

diffuse light source from the sea surface to the

TOA. Similarly, T(A) is the direct transmittance

which accounts for the effects of propagating a

beam of light from the sea surface to the TOA. The

tg(A) term represents the gaseous transmittance,

which accounts for absorption due to ozone,

oxygen, and water vapor.

The calibration process involves computing the

TOA reflectance from known and measured

components of Equation 9.1, and comparing the

predicted value with the observed value from

SeaWiFS to derive a calibration gain and possibly

an offset. In practice, the molecular scattering

signal is well understood and can be accurately

computed (Gordon et al., 1988). The white cap

signal is generally small, and it can be predicted

using statistical relationships and ancillary wind

speed data (Gordon and Wang, 1994a).

Furthermore, the residual uncertainties can be

minimized by using observations with low surface

wind speeds. The Sun glint term can be predicted

from solar and viewing geometry and ancillary

wind field data (Cox and Munk, 1954), but in

practice it is easily avoided by limiting observations

to geometries which do not allow for direct

reflectance of the solar rays into the sensor.

Avoiding Sun glint also alleviates the need to know

the direct transmittance. The gaseous transmittance

can be predicted from ancillary data on ozone and

water vapor concentrations, solar and viewing

geometries, and the spectral band passes of the

sensor (Gordon and Wang, 1994b; Ding and

Gordon, 1995; Gordon, 1995).

88
P, (2,) = [,Or (2,) + Pa (2) + p,o (?t) + T(2,)pg (2,) + t(2)p/(2,) + t(2,)pw(2,)]t,(2,), (9.1)

This leaves the water-leaving reflectances,

aerosol and Rayleigh-aerosol terms, and the diffuse

transmittance to be derived from in situ

measurements or additional assumptions.

In practice, the water-leaving reflectance

values at a given location can be derived from in

situ optical measurements of the upwelling

radiance. The remaining terms in Equation 9.1

require some knowledge or assumptions about the

aerosol type and concentration. This includes the

diffuse transmittance term, which is dominated by

molecular scattering effects but is weakly

influenced by the aerosols (Wang, 1999).

The standard approach used by the SeaWiFS

project to account for the influence of aerosols in

the vicarious calibration (Barnes et al., 2000;

Robinson and Wang, 2000; Eplee and McClain,

2000) has been to make two assumptions: 1) that

the gain at 865nm is known and fixed at the prelaunch value, and 2) that the aerosol type near the

calibration site is characterized by an average

maritime model (Robinson and Wang, 2000). The

first assumption essentially fixes the aerosol

concentration, while the latter assumption

determines the relative calibration between the 765

and 865nm channels and the extrapolation of

aerosol reflectance to the visible bands. This has

been the calibration technique employed for all

SeaWiFS processing to date.

The focus of the present work is to develop a

technique which eliminates or reduces the aerosol

assumptions by making use of aerosol optical

thickness measurements which have been collected

in conjunction with in situ water-leaving radiances.

What we require is a method to relate

measurements of aerosol optical thickness to total

multiple-scattering aerosol reflectance, including

the effect of Rayleigh-aerosol interaction. In the

single-scattering approximation, the aerosol

reflectance, p=(Z), can be computed as

oJ0(2,) (A)Po(2,,o)

pa_(2,) = (9.2)

4/ o/t

where the subscript s denotes single-scattering. In

Equation 9.2, o_(Z) is the single-scattering aerosol

albedo, "ca(Z)is the aerosol optical depth, Pd'Z,_9) is

the scattering phase function for a scattering angle

of _9, and/ao and # are the cosines of the solar and

view zenith angles, respectively. The aerosol

optical thickness at each wavelength can be

obtained from available in situ measurements, but

the single scattering albedo and scattering phase

function require knowledge of the aerosol type.

A simple approach to characterize the aerosol

type is to define the/_,ngstrtm coefficient, ¢x(_.l,L2),

as

(9.3)

Using this characterization, we can compare

the measured A,ngstrOm coefficient to the AngstrOm

coefficients associated with a set of aerosol models,

and from the models with similar Angstrtm

coefficient we can retrieve the single-scattering

albedo and scattering phase function. In general,

the measured A.ngstrSm coefficient will fall

between two of the models, so we compute

Equation 9.2 for each model (call them p,=I(Z) and

p,_z(Z)) and we define a mixing ratio, R, to

interpolate between the two models. Before we

interpolate, however, we take advantage of the

model relationships developed by Gordon and

Wang (Gordon and Wang, 1994b) to translate the

single-scattering aerosol reflectances for model i to

multiple-scattering aerosol reflectance with

Rayleigh-aerosol interaction, [p_(Z)+p_(Z)]i. Thus,

we can now compute the total aerosol reflectance as

in Equation 9.4.

p,(_,) + p.,(Z) = R[p=(2) + p,,(2)]. + (1- R)[po(Z) + p.,(Z)]z

(9.4)

In a similar way, we can compute the

Rayleigh-aerosol diffuse transmittance for each

aerosol model (Wang, 1999) and interpolate to

retrieve the total diffuse transmittance.

9.3 APPLICATION TO SeaWiFS

CALIBRATION

The primary calibration site for the SeaWiFS

project is the MOBY buoy (Clark et al., 1997)

located off the coast of Lanai, Hawaii. Since 1996,

the MOBY buoy has been continuously collecting

upwelling radiance measurements at fine spectral

resolution through the visible wavelength regime.

MOBY measurements are collected for each

satellite overpass, and processed to provide waterleaving radiances at each of the SeaWiFS visible

band passes (412 - 670 nm). The water-leaving

radiances in the two SeaWiFS near infrared (NIR)

89
bandscanbeassumedtobenegligible, orestimated

fromacase1model(Siegel etal.,2000).

In 1998,theSIMBIOSProjectbeganoperating

a CIMELsunphotometer atLanaiaspartof the

AerosolRoboticNetwork,AERONET(Holbenet

al.,1998;Holbenetal.,2000;Wangetal.,2000).

CIMELAOTpointsarefirstextractedusingthe

wavelet-based multi-bandscreeningapproach(see

Chapter6). CIMELsunphotometermeasures

aerosolopticalthicknessat sevenwavelengths

(340,380,440,500,670,870,and1020nm).For

thesedatatobeusefulasinputsforthecalibration

techniquedescribedherein,an estimateof the

opticalthicknessat eachof theSeaWiFSbands

mustbedetermined. Todothisweapplyasimple

linearfit to log(za)vslog(3,).Usingthisfit, we

interpolatethe z_(_) to the SeaWiFS nominal

wavelengths. If the residuals from this linear fit are

more than five percent of the measured value, the

record is rejected.

We have identified 38 SeaWiFS scenes over

MOBY for which there exist contemporaneous

aerosol measurements at Lanai, and which pass the

standard SeaWiFS exclusion criteria for vicarious

calibration (Eplee and McClain, 2000).

Additionally, measurement were excluded if the in

situ measured "r_ (865) was less than 0.02 or the

wind speed was greater than 5 m/s. The threshold

on _, (865) was set to account for the uncertainty in

the calibration of the CIMEL aerosol optical

thickness retrievals. The AERONET group reports

an uncertainty of +/- 0.01 in "r_(3,)(Holben et al.,

1998). The wind speed threshold was set to reduce

uncertainties in the atmospheric correction

algorithm, as the Rayleigh and whitecap radiances

are both wind speed dependent. This left only 5

calibration points. Using the inversion technique

described above on these 5 points, we have

computed a set of vicarious gains for all eight

SeaWiFS bands. These alternative gains are listed

with the operational gains for SeaWiFS

reprocessing #3 in Table 9.1.

9.4 RESULTS AND DISCUSSION

As reproduced from Bailey et al. (Bailey et al.,

2000), the SeaWiFS bio-optical match-up results of

Figure 1 were processed using the operational

gains. A comparison of the coefficients in Table 9.1

would suggest that the assumption of unity for the

gain in band 8 is an overestimate on the order of

5%. Overall the results of the match-ups with the

alternative gains, as presented in Figure 9.2, show

that bio-optical comparisons between in situ

measurements and SeaWiFS derived values are not

greatly affected by this change of calibration.

Comparisons of the statistics by band are found in

Table 9.2. From the comparison of the alternative

gains to the operational gains, we find a slight

improvement in bands 4 and 5 and a slight

degradation in the bio-optical match-up results for

band 1.

The results of AOT match-ups between in situ

and SeaWiFS measurements indicate that the

alternative vicarious gain coefficients can cause

improvements in the outcome of SeaWiFS

atmospheric correction in terms of AOT levels.

This is because the alternative calibration method

eliminates a priori assumptions on band 8

calibration and aerosol type over MOBY, which

strongly influence aerosol determination in the

SeaWiFS processing. Figure 9.3 shows the

comparison of match-ups obtained using the

operational gains, top two graphs, and the

alternative gains, bottom two graphs. In situ AOT

points were extracted using the statistical screening

on the single CIMEL 870nm band (see Chapter 6).

Only the results in the 443nm and 865nm bands are

displayed.

For the operational gains, the slope of a linear

fit between both types of AOT observations is close

to 1 in the 865nm band and the intercept indicates a

small shift of 0.02 in the AOT value. The

application of alternative gains causes the slope of

the linear fit in the 865nm band to decrease,

however, the intercept is the same and the overall

inaccuracy of match-ups is reduced. For the

alternative gains, the large majority of AOT points

are closely clustered along the y=x line and there

are only a few outliers which adversely influence

the statistics.

The alternative SeaWiFS calibration method

uses existing aerosol models to provide the estimate

of the multi-scattering aerosol reflectance with

Rayleigh-aerosol interaction. These aerosol models

have been noticed before to exhibit AOT spectral

distributions which are flatter than the AOT

distributions obtained from in situ measurements

(Chapter 7). Therefore, in situ AOT values are

generally underestimated by the SeaWiFS

algorithm in the shorter visible wavelengths. This

underestimation is decreased but is still significant

when the alternative calibration gains are applied in

the SeaWiFS processing, as shown for the band at

443nm in Figure 9.3. In this band, the inaccuracy of

the match-ups is decreased somewhat with the

application of the alternative gain set, and the slope

of the linear fit between in situ and SeaWiFS AOT

measurements is only slightly improved.

Figure 9.4 illustrates Angstrrm coefficient

match-ups computed for the 765nm band relative to

90
the865nmband,whicharethebandsusedtoselect

anaerosolmodelintheSeaWiFSprocessing. The

alternativecalibrationapproachforcedanumberof

SeaWiFSAOTmeasurements toproduceA.ngstr6m

coefficients that more closely approximate the

coefficients of in situ observations. The inaccuracy

of A,ngstr6m coefficients computed with the

alternative calibration is half of the inaccuracy

produced with the operational gains. However, this

improvement was not sufficient to produce

substantially better AOT match-ups in the visible

bands.

The improved AOT match-up results indicate

that there are inaccuracies in the relative and

absolute calibrations of the SeaWiFS near-infrared

bands. However, the spectral shape of existing

aerosol models may cause more problems in

estimating atmospheric contribution to the signal at

the top-of-the-atmosphere than do these calibration

errors. It has been noted that SeaWiFS aerosol

models commonly have flatter spectral distributions

than those obtained from in situ AOT

measurements. Because of this, the aerosol path

radiance models produce underestimated AOT

values when extrapolated from the near-infrared

spectra to the SeaWiFS visible bands. Before final

conclusions can be drawn, however, additional

studies should be performed using a larger set of in

situ data to confirm these results. These studies may

include the use of global aerosol measurements to

determine the NIR calibration of SeaWiFS,

independent of the MOBY observations. Some

enhanced screening of the aerosol measurements

may also be necessary, as discussed in Chapter 7 of

this document.

9.5 CONCLUSIONS

We have described a procedure to predict TOA

reflectance, and hence calibration gains, from wellknown atmospheric components and simultaneous

measurements of water-leaving radiance and

aerosol optical thickness. Relative to the standard

SeaWiFS calibration technique, this procedure

eliminates the requirements for a priori knowledge

of the calibration at 865nm, and it does not require

an assumption of aerosol type. Instead, we have

used the measured aerosol optical thickness to

define the aerosol concentration, and we have used

the measured /_ngstr6m coefficient to characterize

the aerosol type.

The major limitation of this approach is that we

still must use aerosol models to relate the measured

aerosol optical thicknesses to aerosol reflectances,

and models may not be accurate or uniquely

defined with respect to /_ngstr6m coefficient.

Model uncertainties can be minimized, however, by

selecting sites where historical evidence would

suggest that the aerosols are generally homogenous

and well characterized by existing models.

The major advantage of incorporating aerosol

optical thickness measurements into the calibration

process is that we can now derive an independent

estimate of the calibration at 765 and 865nm. The

results of our preliminary analysis compare well

with a growing body of evidence that the SeaWiFS

865nm channel overestimates the TOA radiance by

4 to 10% (Barnes et al., 2000). The gains retrieved

using this alternative method produce minor

changes in the SeaWiFS retrieved water-leaving

radiances, but a marked improvement in the

SeaWiFS retrieved aerosol optical thickness and

Angstrt_m exponent.

REFERENCES

Bailey S. W., C. R. McClain, P. Jeremy Werdell,

and Brian D. Schieber, 2000: Normalized

Water-Leaving Radiance and Chlorophyll a

Match-up Analyses. In: McClain, C. R., R. A.

Barnes, R. E. Eplee, Jr., B. A. Franz, N. C.

Hsu, F. S. Patt, C. M. Pietras, W. D. Robinson,

B. D. Schieber, G. M. Schmidt, M. Wang, S.

W. Bailey, and P. J. Werdell, SeaWiFS

Postlaunch Calibration and Validation

Analyses, Part 2. NASA Tech. Memo. 2000-

206892, Vol. 10, S. B. Hooker and E. R.

Firestone, Eds., NASA Space Flight Center,

Greenbelt, Maryland, 45- 52.

Barnes, R.A., R. E. Eplee Jr., W. D. Robinson, G.

M. Schmidt, F. S. Patt, S. W. Bailey, M. Wang,

and C. R. McClain, 2000: The calibration of

SeaWiFS on orbit, Proc. SPIE, 31 July - 4

August.

Barnes R. A., R. E. Eplee, W. D. Robinson, G. M.

Schmidt, F. S. Patt, S. W. Bailey, M. Wang, C.

R. McClain, 2000: The calibration of

SeaWiFS, Proc. 2000 Conf. on

Characterization and Radiometric Calibration

for Remote Sensing, Logan, Utah, September

19-21.

Clark D. K., H. R. Gordon, K. K. Voss, Y. Ge, W.

Brokenow, and C. Trees, 1997: Validation of

atmospheric correction over oceans, J.

Geophys. Res., vol. 102, 17209-17217.

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CoxC. andW. Munk,1954:Measurements of

roughnessoftheseasurfacefromphotographs

of theSun'sglitter,J,Opt.Soc.Am.,44,838-

850.

Deschamps, P.Y.,M.Herman, andD.Tanre,1983:

Modelingof theatmosphericeffectsandits

applicationto theremotesensingof ocean

color,Appl.Opt.,vol.22,3751-3758.

DingK.andH. R. Gordon, 1995: Analysis of the

influence of 02 A-band absorption on

atmospheric correction of ocean-color imagery,

Appl. Opt., vol. 34, 2068-2080.

Eplee, R. E. Jr., C. R. McClain, 2000: MOBY Data

Analysis for the Vicarious Calibration of

SeaWiFS Bands 1-6, NASA Tech. Memo.

2000-206892, In McClain, C. R., E. J.

Ainsworth, R. A. Barnes, R. E. Eplee, Jr., F. S.

Patt, W. D. Robinson, M. Wang, and S. W.

Bailey, SeaWiFS Postlaunch Calibration and

Validation Analyses, Part 1, S. B. Hooker and

E. R. Firestone, Eds, NASA Goddard Space

Flight Center, Greenbelt, Maryland.

Gordon, H. R., 1995: Remote sensing of ocean

color: a methodology for dealing with broad

spectral bands and significant out-of-band

response, Appl. Opt., vol. 34, 8363-8374.

Gordon, H. R., J, W. Brown, R. H. Evans, 1988:

Exact Rayleigh scattering calculations for use

with Nimbus 7 Coastal Zone Color Scanner,

Appl. Opt., vol. 27, 862-871.

Gordon H. R. and M. Wang, 1994a: Influence of

oceanic whitecaps on atmospheric correction of

ocean-color sensor, Appl. Opt., voi. 33, 7754-

7763.

Gordon H. R. and M. Wang, 1994b: Retrieval of

water-leaving radiance and aerosol optical

thickness over the oceans with SeaWiFS: a

preliminary algorithm, Appl. Opt., vol. 33,

443-452.

Holben, B. N., T. F. Eck, I. Slutsker, D. Tanre, J. P.

Buis, A. Setzer, E. Vermote, J. A. Reagan, Y.

Kaufman, T. Nakajima, F. Lavenu, I.

Jankowiak, and A. Smirnov 1998: AERONET

A federated instrument network and data

archive for aerosol characterization, Rem.

Sens. Environ., 66, 1-16.

Holben, B. N., D. Tanre, A. Smirnov, T. F. Eck, I.

Siutsker, N. Abuhassan, W. W. Newcomb, J.

Schafer, B. Chatenet, F. Lavenue, Y. J.

Kaufman, J. Vande Castle, A. Setzer, B.

Markham, D. Clark, R. Frouin, R. Halthore, A.

Karnieli, N. T. O'Neill, C. Pietras, R. T. Pinker,

K. Voss, G. Zibordi, 2000: An emerging

ground-based aerosol climatology: Aerosol

Optical Depth from AERONET, J. Geophys.

Res., In press.

Robinson W. D. and M. Wang, 2000: Vicarious

Calibration of SeaWiFS Band 7, NASA Tech.

Memo. 2000-206892, In C. R. McClain, E. J.

Ainsworth, R. A. Barnes, R. E. Eplee, Jr., F. S.

Patt, W. D. Robinson, M. Wang, and S. W.

Bailey, SeaWiFS Postlaunch Calibration and

Validation Analyses, Part 1, S. B. Hooker and

E. R. Firestone, Eds, NASA Goddard Space

Flight Center, Greenbelt, Maryland.

Siegel, D. A., M. Wang, S. Moretorena, and W.

Robinson, 2000: Atmospheric correction of

satellite ocean color imagery: the black pixel

assumption, Appl. Opt., vol. 39 no. 21, 3582-

3591.

Wang, M., 1999: Atmospheric correction of ocean

color sensors: Computing atmospheric diffuse

transmittance, Appl. Opt., vol. 38, 451-455.

Wang, M., S. Bailey, C. Pietras, C. R. McClain, and

T. Riley, 2000: SeaWiFS Aerosol Optical

Thickness Match-up Analysis, NASA Tech.

Memo. 2000-206892, In C. R. McClain, R. A.

Barnes, R. E. Eplee, Jr., B. A. Franz, C. H.

Hsu, F. S. Patt, C. M. Pietras, W. D. Robinson,

B. D. Schieber, G. M. Schmidt, M. Wang, S.

W. Bailey, and P. J. Werdell, SeaWiFS

Postlaunch Calibration and Validation

Analyses, Part 2, S. B. Hooker and E. R.

Firestone, Eds, NASA Goddard Space Flight

Center, Greenbelt, Maryland.

92
10.00

1.00

,,=

g,

0.10

0.01

0.01

........ , L_(12)N4_. ............

....... I ........ i

O.10 1.00 10.00

10.00 1 ........ , ,L7_!443!.-_

1.00_- •W

I

O.01L-{ ...........................

0.01 0.10 1.00 10.00

10.00

1.00

g_

0.10

0.01

0.01

Lw.(4go)

........ I ........ • _1 ........ .

i ....... I ........ I ........ i

0.10 1.00 10.00

10.00

1.00

0.10

0.01

0.01

L_(SlO)

........ I ........ ! ........

................ I ........

0.10 1.00 10.0(

L,_(555)

10.00 1 ........ , '_"

1°°1_ E .u,r L,_-

_ 0.10 °i _ •

0.01 _ , ........ , ........

0.01 0.10 1.00 10.00

In Sire

Chlorophyll m

'°°°r.................7

,°°f . ._: ""

0.01 0.10 1.00 10.00

In Sire

Figure 9.1. In situ comparison with SeaWiFS retrieved normalized water-leaving radiances when using

operational vicarious gains.

93
10.00

1.00

Ol

0.10

0.01

........_!,?2j

).01 0.10 1,00 10.00

0.01 I:

0.01 0.10 1.00 10.00

10.00

1.00

Of)

u.

to

0.1o 1.00 10.00

0.10

o.01

0.01

L._(555)

, , , ,,,,, , , ..............

0.10 1.00 10.00

In Sire

10,00

1.00

#

0.10

0.01 0.10 1.00 10.00

/n $1m

Figure 9.2. In situ comparisons to SeaWiFS retrieved water-leaving radiances when using alternative,

AOT-based vicarious gains.

94
E 0"50 I

0.40

0.30

b--

Z

_ o.2o

tO

0.10

O

(g

U'I

E

0.40

_r

i-

<o 0.30

_z

._, 0.20

Z

_ 0.10

g,

0.00

MoxDiff:O.19 Up:23% Slope:O.61 Error:-4.24%

.................. oxi i ........ i..............

0.00 ....... , ......... , ......... , ......... , ........

0.00 0.10 0.20 0.30 0.40 0.50

In er/.&'_,aerosol-optical-thlckness 443nm

MoxDiff:O.15 Up:18% Slope:O.67 Error:-2.80%

0.50 .........'.........'.........'.........'.........

o /x÷

f x

4" 4"/ oX X

A ;:'O

0.00 0.10 0.20 0.30 0.40 0.50

In s'Lt_ oerosol-opticol-thickness 443nm

I--

Z

OC

U3

u.

o

E

tO

0O

U3

_z

0

Z

U3

b_

u3

MaxDiff:O.14 Up:85_= Slope:O.98 Error:8.40%

0.50 ' ........,.........,.........,.........,........

0.40

0.30

X

0.20 + +o []

a -IF--'-

, _x/e" (m-c.,__ ,_,

O. 10 _ (=) - C=WEL_WUO* ,_9

+ _{_X (o) - (_=ELKaAS_B-_Olg9_

o.oo ........ ,................ (1.).-..£';E.._..:...c_...,gn,

0.00 0.10 0.20 0.30 0.40 0.50

lw. s'/._u, oerosol-optical-thlckness 865nm

MoxDiff:O. lO Up:64% Slope:O.87 Error:5.22%

0.50 ......... ' ......... ' ......... ' ......... ' ........

0.40

0.30

0.20 ° X

4- 4.

+ _ (x)- ow[L S*W=N ,_m

0,10 (_) - oui_ _m_ut_ ,_

((,) - CIMELv,/u_tot,.to0t_!

(.) - OliB. to_S_l[:l_O0 _i_

o.oo ........................ .(.*.).Tp¥._L...s_.._._...,p._!

0.00 0.10 0.20 0.30 0.40 0.50

In.s'i#u,oerogol-optical-thickness 865nm

Figure 9.3. Comparison of AOT match-ups in the 443nm and 865nm bands for the operational and

alternative set of SeaWiFS calibration gains.

SeaWiFS

E}and

Gain

._actor

Table 9.1 SeaWiFS

Band 1 Band 2

1.005726 3.995034

vicarious | _in coefficients usin_ the inverse technique.

Band 3 Band 4 Band 5 Band 6 Band 7 Band8

3.968053 3.989528 ).995204 0.953351 0.923352 3.955989

95
r_

o

z

z

(D

_2

MaxDiff:1.51 Up:12% Slope:0.255 Error:-9.290%

, ........ 1 '' '' '' '' '1 ' ...... ''1 .........

x) - ClMEL BAHRAIN 1998

[3) - CIMEL BAHRAIN 1999

b) - CIMEL BERMUDA 1999

O) -- CIMEL KAASHIDHOO 1998

*) - CIMEL KAASHIDHOO 1999

+) - CIMEL SAN NICOLAS 1998

1

0 +

, i , i t i i i i I ......... I , , i i , i iii I ........

0 1 2

/r_ sitt_ ANGS-rROM(765nm)

r_

o

t_

z

MoxDiff:l.07 Up:33% Slope:0.367 Error:-4.373%

3 ......... t ......... i ......... i ........ ,

x) - CIMEL BAHRAIN 1998

El) - ClMEL BAHRAIN 1999

&) - CIMEL BERMUDA 1999

O) - CIMEL KAASHIDHOO 1998

*) - CIMEL KAASHIDHOO 1999

+) -- CIMEL SAN NICOLA_ 1998

+ +.,_Y_,O X X o /'

^o *

+ +

...... ,,l I,,1,,,,,,11,,,,,,1

0 1 2 3

f_, s%tt_ ANGSTROM(765nm)

-1

1

Figure 9.4. Comparison of aerosol/_ngstr6m coefficients in the 765nm band relative to the 865nm band for

the AOT match-up points and the operational and alternative set of SeaWiFS calibration gains.

Table 9.2 Statistical comparison between operation vicarious gain matchup results and the alternative

vicarious _ain matchup results.

SeaWiFS Band SeaWiFS :In situ Ratio Std. Dev. R-square N

All Match-ups

Alternative Vicarious Calibration Gains

1 0.7135

2 0.9176

3 1.0203

4 1.0876

5 1.1278

0.3085

0.3040

0.2859

0.3140

0.3182

0.6478

0.7618

0.7514

0.7011

0.8387

209

228

233

221

233

Operational Vicarious Calibration Gains

0.7929

0.9621

0.3255

0.3333

0.7175

0.7641

0.7559

192

202

1.0122 0.2970 207

4 1.0793 0.3275 0.5975 195

5 1.0993 0.3385 0.6952 207

Case 1Ma_h-ups

Al_rn_ive Vicarious Calibration

1 0.8273 0.1997 0.8218 118

2 0.9576 0.2296

3 1.0140 0.2211

0.8313 140

0.7575 157

4 1.0672 0.2338 0.4432 143

5 1.1031 0.2772 0.3822 146

Operational Vicarious Calibration

1 0.8520 0.2146 0.8132 125

0.9628 0.2407 0.8282 145

0.9906 0.2171 0.7508 156

1.0403 0.2308 0.3392 143

5 1.0560 0.2697 0.3454 150

96
Appendix A

SeaBASS Data File Format

P. Jeremy Werdell I, Sean Bailey I and Giulietta S. Fargion 3

1Science System and Applications Inc., Lanham, Maryland

2Future Tech Corporation, Greenbelt, Maryland

3SAIC General Sciences Corporation, BeltsviUe, Maryland

SeaBASS HEADERS

Each header begins with a '/' and every data

file opens with 'begin_header'. The headers are

then listed in any order, as long as the list ends with

'end_header'. A value of 'NA' (not available or

applicable) is assigned to any header where

information cannot be provided. Data files with

missing headers will not be accepted for submission

to SeaBASS. A description of each follows.

'Data file name' simply provides the name of

the data file. 'Affiliations', 'investigators', and

'contact' provide information on the contributing

researchers. The primary investigator is listed first,

followed by any associate investigators. Commas

separate multiple entries, and white spaces and

apostrophes are not allowed. 'Contact' is the

electronic mail address of the contributor.

'Experiment', 'cruise', and 'station' record

information on the long-term experiment (if

available), the specific cruise, and the station within

the cruise. For each of the latter, an entry of

'SIMBIOS' is not permitted. 'Documents' refers to

cruise reports, logs, and associated documentation

that provide additional information about the

experiment or cruise. 'Calibration_files' points to

additional file(s) that contain the coefficients and

techniques used to calibrate the instruments used in

data collection. The files referred to by

'documents' and 'calibration_files' must

accompany the data files at the time of submission.

'Data_status' describes the condition of the

data file, accepting values of preliminary, update,

and final. 'Preliminary' is used when the data are

submitted for the first time and the investigator

intends to analyze the data further. 'Update'

indicates the data are being resubmitted and

informs the Project that an additional resubmission

will occur in the future. 'Final' is used when the

investigator has no intention of revisiting the data

set. 'Data_type' describes the general collection

method of the data. Accepted values include: 'cast'

for vertical profiles (e.g. optical packages, CTD);

'flow_thru for continuous data (e.g. underway flow

through systems); 'above_water' for above surface

radiometry data (e.g. ASD, SIMBAD); 'sunphoto'

for sunphotometry data (e.g. MicroTops, PREDE);

'mooring' for moored data and buoy data; 'drifter'

for drifter and drogue data; 'scan' for discrete

hyperspectral measurements (e.g., absorption

spectra); and 'pigment' for laboratory measured

pigment data (fluorometry, spectrophotometry,

HPLC). 'North_latitude', 'south_latitude', 'east_long

itue', 'west_longitude', 'start_date', end_date',

'start_time', and 'end_time' provide information on

the location, date, and time data were collected.

Each entry should be the extreme value for the

entire data file. For example, 'north_latitude' refers

to the coordinate furthest north data in the file were

collected. 'Start_time' and 'end_time' refer to the

earliest and latest time-of-day data in the file were

collected. The latter do not refer to the time data

collection began and data collection ceased,

respectively. Latitude and longitude are listed in

decimal degrees, with coordinates north of the

equator or east of the Prime Meridian set positive

and south of the equator or west of the Prime

Meridian set negative. Dates have the format

'YYYYMMDD'. Times have the format

'HH:MM:SS' and are listed in Greenwich Mean

Time (GMT).

'Cloud_percent', 'wave_height', 'wind_speed',

'secchi_depth", 'measurement_depth', and

'water_depth' provide ancillary information about

the station, when available. 'Wave_height' and

'water_depth' have units of meters and

'wind_speed' has units of meters per second. A

value for 'measurement_depth' is included when the

file contain data collected at a discrete depth (e.g.,

bottle samples or buoy/moored radiometers).

'Fields' names each of the columns of in situ

data presented below the headers. Each entry

describes the data in a one column, and every

column must have an entry. 'Units' provides the

units for each column of data. Every value in

'fields' must also have an equivalent entry in

97
'units'.'Missing'refersto a nullvalueusedasa

placeholder for anymissingdatapoint.Eachrow

of datamustcontainthesamenumberof columns

as definedin the 'fields' header. 'Delimiter'

indicateshowthecolumnsof dataaredelimited.

Accepteddelimitersincludetabs,spaces,and

commas,butonlyonedelimiteris permittedper

datafile. Finally,if theinvestigatorswishto

includeadditionalcommentsabouthedatafile,

theymaydoin thewithintheheaderboundaries.

Linesof commentsbeginwith a '!' andmay

includeanyandalltextcharactersandwhitespace.

Commentcommentsincludeadditionancillary

informationaboutthedatafile,seaandskystates,

difficultiesencounteredduringdatacollection,

methodsofdatacollection,instrumentsued,anda

descriptionof nonstandardSeaBASSfield name

includedinthedatafile. Alistanddescriptionof

theSeaBASSmetadataheadersiavailableonline

athttp://seabass.gsfc.nasa.gov/seabass_header.html.

Thislistisupdatedregularly.

FIELD NAMES AND UNITS

In an effort to ensure compatibility within the

SeaBASS data archive, and to facilitate the

development of the expanded version of the

SeaBASS database, a standard set of caseinsensitive field names and units has been adopted.

While the list of standardized field names is

reasonably comprehensive, it cannot account for all

the possible data types one might wish to provide to

the SeaBASS archive. If a data type to be

submitted to SeaBASS does not fall under one of

the predefined standard field names, the

investigator may still include the data. Note that

the standardized set is updated as the need arises

(e.g. a data parameter is commonly submitted or

queried). Non-standard data will be archived,

however, the data values will not be ingested into

the online database. The data will be retrievable,

but only with the original archived file, not as a

separate dataset. A list of the standardized field

names and units is available online at

http://seabass.gsfc.nasa.gov/cgi-bin/stdfields.cgi.

This list is updated regularly (Table 1A).

Table 1A.

Fieldname

;eaBASS Standardized Fieldnames and Units as of June 2000. (###.# = wavelen_:th).

Units Description

t###.# /m Total absorption coefficient

aaer###.# l/m

ag###.#

altitude

ad###.# l/m

aclg###.# l/m

1/m

m

am unitless

angstrom mitless

AOT###.# mifless

ap###.# l/m

aph###.# l/m

a*ph###.# 1/m

At

_b#_.#

3incount

degreesC

l/m

_one

_p###.# l/m

Absorption coefficient of atmospheric aerosols

Absorption coefficient of detrius

Absorption coefficient of detrital+gelbstoff

Absorption coefficient of CDOM

Altitude (above sea level)

Airmass (calculable from time/position)

Angstrom exponent

Aerosol optical thickness

Absorption coefficient of particles

Absorption coefficient of ph_,toplankton

2hi a-specific aph

n,ir temperature

Backscatter

:###.# l/m

:loud _b Percent cloud cover

:ond aunho/cm

:lepth

Ed###.# aW/cm^2/nm

colts

aE/cm^2/s

aW/cm^2/nm

EdGND

Epar

Es###.#

EsGND volts

b/umber of records averaged into a bin

Particle scattering coefficient

Beam attenuation coefficient

Conductivity

Depth of measurement

Downwelling irradiance

Dark current values for Ed sensor

Profiled Photos_cnthetic Available Radiation

Downwelling irradiance above the surface

Dark current values for Es sensor

98
Eu###.# _W/cm^2/nm

volts EuGNI)

FO#_.#

Kd###.#

KI###.#

uW/cm^2/nm

1/m

l/m

Knf###.# l/m

Kpar 1/m

Ku###.# l/m

2sky###.# uW/cmA2/nm/sr

Lt###.# uW/cmA2/nm/sr

2u###.# uW/cmA2/nm/sr

LuGND volts

Lw###.# uW/cmA2/nrn/sr

2wn###.#

latf

Oz

PAR

JW/cmA2/nm/sr

1E/m^2/sr/s

tobson

__Jcm^2/s

pitch te_rees

pp aagC/mgchla/hr

_ressure 1bar

_ressure atm aabar

_###.# _r

none

RelAz

RI###.#

roll

Rpi###.#

Rrs###.#

anifless

degrees

l/sr

:le_rees

anitless

1/sr

sal PSU

sample none

SenZ degrees

sigmaT

SN

SST

kg/m3

none

de_reesC

Llpwellin s irradiance

Dark current values for Eu sensor

Extraterrestrial Solar irradiance

Diffuse attenuation coefficient for downwelling irradiance

Diffuse attenuation coefficient for upwelling radiance

Diffuse attenuation coefficient for natural fluorescence of chl a

Diffuse attenuation coefficient for PAR

Diffuse attenuation coefficient for upwelling irradiance

Sky radiance

rotal water radiance

Upwelling radiance

Dark current values for Lu sensor _

Water leaving radiance

qormalized water leaving radiance (Nlw=Lw * Fo/Es)

natural fluorescence of chl a

.'olumn Ozone

Photosynthetic Available Radiation measured at the sea surface

de_rees

Instrument pitch

Primary Productivity

Water Pressure

Atmospheric pressure

Eu/Lu (equal to Pi in diffuse water)

Data quality flag...arbitrar), analyst specific value

[rradiance reflectance (Re=Eu/Ed)

Sensor azimuth angle, relative to the solar plane (for above

radiometers)

Radiance reflectance (RI=Lu/Ed)

Instrument roll

Radiance reflectance with PI

Remote sensing reflectance (Rrs=Lw/Ed)

Salinity

Sample Number

Sensor zenith angle (for above water radiometers)

Density- 1000k_/m3

Instrument serial number - should be in documents...

;timf volts

SZ m Secchi disk depth

SZA

tilt

trflns

SPM

de_rees

%

e/L

L

nm

m/s

de_reesC

mm

xtg/m^3

volfilt

wavelength

windspeed

Wt

Wvp

Pigments

Mlo

_.nth

Asta

Sea Surface Temperature

Stimulated fluorescence of chia

Solar zenith angle (calculable from time/position)

Instrument tilt

?recent transmission

rotal Suspended Particulate Material

Volume Filtered

Wavelength of measurement

Wind Speed

VCater temperature

Water vapor

Alloxanthin

HPLC Antheraxanthin

HPLC Astaxanthin

watel

99
At

beta-beta-Car

beta-eta-Car

beta-psi-Car

But-fuco

Cantha

CHL

Chl_a

Chl_b

Chl_c

Chlide_a

12hlide_b

:roco

Diadchr

Diadino

Diato

Dino

DV_ChI_a

DV_ChI_b

Echin

P-t-8-carot

Et-chlide_a

Et-chlid¢_b

_ta--eta-Car

Fuco

Hex-fuco

Lut

Lyco

Me-chlid¢_a

Me-chlide_b

Mg..DVP

Monado

Neo

P-457

Perid

PHAEO

Phidc_a

Phid¢_h

Phide_c

phythl-chl_c

Phytin_a

?hytin_b

?hytin_c

?ras

?yrophytin_a

Dyrophytin_b

?yrophytin_c

Siphn

Siphx

rpg

Vauch

Viola

de_ecsC

ms/m^3

ms/m^3

ms/m^3

ms/m^3

m_/m^3

ms/m^3

m_m^3

ms/m^3

mfffm^3

ms/m^3

ms/m^3

n_m^3

n_/m^3

n_/m^3

_8/m^3

_/m^3

_/m^3

_8/m^3

_8/m^3

_/m^3

rn_/m^3

_n_/m^3

ms/m^3

ms/m^3

m_/m^3

ms/m^3

rag/m^3

m_m^3

roB/m^3

rag/m^3

m_m^3

ms/m^3

m[/m^3

m_/m^3

ms/m^3

ms/m^3

m_/m^3

m_/m^3

ms/m^3

m[/m^3

mg/m^3

m[/m^3

m_m^3

rag/m^3

rig/m^3

_ns/m^3

rn_/m^3

:rig/m^3

:rig/m^3

ms/m^3

rng/m^3

Air temperature

ZdPLC Beta,beta-Carotene

E-IPLCBeta,eta-Carotene

FIPLC Beta,psi-Carotene

_IPLC 19'-Butaonoyloxyfucoxanthin

FIPLC Canthaxanthin

Pluorescncc/spectrophotometric derivedchlorophylla

_IPLC Chlorophylla

FIPLC Chlorophyllb

FIPLC Chlorophyllc

FIPLC Chlorophyllidea

bIPLC Chlorophyllideb

[-IPLCCrocoxanthin

FIPLC Diadinochrome

FIPLC Diadinoxanthin

[-IPLC Diatoxanthin

HPLC Dinoxanthin

HPLC DivinylChorophylla

HPLC DivinylChorophyllb

HPLC Echinenone

[-IPLC Ethyl-apo-8'-carotene

HPLC Ethyl Chlorophyllide a

[-1PLC Ethyl Chlorophyllide b

HPLC Eta-eta-Carotene

HPLC Fucoxanthin

HPLC 19'-Hexanoyloxyfucoxanthin

HPLC Lutein

HPLC Lycopene

HPLC Methyl Chlorophyllide a

HPLC Methyl Chlorophyllide b

HPLC MI[ 2,4 divinyl pheoporphyrin a5 monomethyl ester

HPLC Monadoxanthin

HPLC Neoxanthin

HPLC P-457

HPLC Pcridinin

Pheaopisment

HPLC Pbeophorbidea

HPLC Pbeophorbideb

HPLC Pheophorbidec

HPLC Phytylated Chlorophyll c

HPLC Pheophytin a

_IPLC Pheophytin b

_IPLC Pheophytin c

_IPLC Prasinoxanthin

_IPLC Pyropheophytina

FIPLC l_¢ropheophytinb

[-IPLCPyropheophytinc

[-IPLCSiphonein

FIPLC Siphonaxanthin

rot_ (sum of all)pigments

[-IPLCVaucheriaxanthin-ester

[-IPLC Violaxanthin

100
Time, Location

date

day

hour

lat

Ion

minute

rag/m^3

YYyymrn..dd

rid

hh

W

. :legrees

degrees

mn

month mo

second ss

station none

time hh:mm:ss

_,eal" YYYY

HPLC Zeaxanthin

Sample date

Sample Day

Sample Hour

Sample Julian Day (Day 'of Year)'

Sample Latitude

Sample Longitude

Sample Minute

Sample Month

Sample Second

Sample Station

Sample time

Sample Year

101
GLOSSARY

A/D

ALSCAT

AERONET

AOL

AOT

ARGOS

ASCII

AMT

AMT-3

AMT-5

AMT-6

AMT-7

Analog-to-Digital

ALPHA and ScatteringMeter (Nora: the symbol ct corresponds to c(k),the beam

attenuation coefficient, in present usage.)

Aerosol Robotic Network

Airborne Oceanographic Lid&

Aerosol Optical Thickness

Not an acronym: the name given to the data collection and location system on NOAA

Operational Satellites

American Standard Code for Information Inter- change

Atlantic Meridionai Transect

The ThirdAMT Cruise

The FifthAMT Cruise

The Sixth AMT Cruise

The Seventh AMT Cruise

BSI

CCD

CERT

CSH

Biospherical Instruments, Inc.

Charge Coupled Device

Calibration Evaluation and Radionmtric Tearing

UNIX "C-shell" script programming utility

DAS

DU

DUT

DVM

Data Acquisition Sequence

Dobson Unit of total ozone

Device Under Test

Digital Voltmeter

FEL

FOV

FWHM

Not an acronym; a type of standard lamp for irradian_ and radiance calibration

Field-of-View

Full-Width at Half-Maximum

GAC

GASM

GMT

GOES

GPIB

GPS

GSFC

GlobalArea Coverage

GeneralAngle ScatteringMeter

Greenwich Mean Time

GeostationaryOperationalEnvironmentalSamllite

General Purpose Interface Bus

Global Positioning System

Goddard Space Flight Center

HDF

HRPT

Hierarchical Data Format

High Resolution Picture Transmission

IFOV

IOP

IR

Instantaneousfield-of-view

Inherent Optical Properties

Infrared

LIA

LAC

Level-IA SeaWiFS data product with navigation information

Local Area Coverage

MERIS

MPL

MOBY

MODIS

MOS

Marine Environment Research Imaging Spectroradiometer (French)

Micropulse Lidar

Marine Optical Buoy

Moderate Resolution Imaging Spectmradiometer

Moderate Resolution Imaging Spectrometer

102


NASA

NIST

NOAA

H

POLDER

QC

SeaBAM

SeaBASS

SeaWiFS

SIMBIOS

SIRREX

SIRREX-7

SNR

UPS

UV

UVB

National Aeronautics and Space Administration

National Institute of Standards andTechnology

National Oceanic and Atmospheric Administration

Ocean Color andTemperature Sensor (Japanese)

Principal Investigator

Polarization and Directionality of the Earth Reflectance (French)

Quality Control

SeaWiFS Bio-optical Algorithm Mini-workshop

SeaWiFS Bio-optical Archive and Storage System

Sea-viewing Wide Field-of-view Sensor

Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic

Studies

SeaWiFS lntercalibration Round-Robin Experiment

The Seventh SIRREX

Signal-to-Noise Ratio

Un-intermptable Power Supply

Ultraviolet

Ultraviolet-B

103
REPORT DOCUMENTATION PAGE J Form Approved OMB No. 0704-0188

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources,

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this

collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, t 215 Jefferson

Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

March 2001 Technical MemorandglIl

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

In Situ Aerosol Optical Thickness Collected by the SIMBIOS

Program ( 1997-2000): Protocols, and Data QC and Analysis

6. AUTHOR(S)

Giulietta S. Fargion, Robert Barnes, and Charles McClain

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES)

Goddard Space Flight Center

Greenbelt, Maryland 20771

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS (ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

970

8. PEFORMING ORGANIZATION

REPORT NUMBER

2001-01672-0

10. SPONSORINGIMONITORING

AGENCY REPORT NUMBER

TM--2001-209982

11. SUPPLEMENTARY NOTES

G. Fargion and R. Barnes: SAIC General Sciences Corp., Beltsville, Maryland

12a. DISTRIBUTION I AVAILABILITY STATEMENT

Unclassified-Unlimited

Subject Category: 48

Report available from the NASA Center for AeroSpace Information,

7121 Standard Drive, Hanover, MD 21076-1320. (301) 621-0390.

13. ABSTRACT (Maximum 200 words)

12b. DISTRIBUTION CODE

The purpose of this technical report is to provide current documentation of the Sensor Intercomparison

and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project Office activities

on in situ aerosol optical thickness (i.e., protocols, and data QC and analysis). This documentation is

necessary to ensure that critical information is related to the scientific community and NASA management. This critical information includes the technical difficulties and challenges of validating and

combining ocean color data from an array of independent satellite systems to form consistent and

accurate global bio-optical time series products. This technical report is not meant as a substitute for

scientific literature. Instead, it will provide a ready and responsive vehicle for the multitude of technical reports issued by an operational project.

14.SUBJECTTERMS

SIMBIOS, aerosol optical thickness, ocean color data.

17. SECURITY CLASSIFICATION

OF REPORT

Unclassified

NSN 7540-01-280-5500

18. SECURITY CLASSIFICATION

OF THIS PAGE

Unclassified

19. SECURITY CLASSIFICATION

OF ABSTRACT

Unclassified

15. NUMBER OF PAGES

103

16. PRICE CODE

20. LIMITATION OF ABSTRAC1

UL

Standard Form 298 (Rev. 2-89)

Prescribed by ANSI Std, Z39.18

298-102