Chapter 19
Forecasting the Coastal Optical Properties
Using Satellite Ocean Color
Robert Arnone, Brandon Casey, Sherwin Ladner, and Dong-Shang Ko
19.1 Introduction
Algorithms for ocean color bio-optical properties from space have advanced significantly in the last 20 years. Improved alogorithms have advanced beyond chlorophyll
to characterize coastal optical properties such as absorption from phytoplankton,
colored dissolved organic matter and detritus, in addition to backscattering from the
particle distribution. These properties have provided new insights into the changing
conditions along our coast. However, for many coastal management applications
these satellite derived properties are insufficient to make real-time decisons (Arnone
and Parsons, 2004). Daily satellite ocean color imagery represents a nowcast of biooptical conditions. Although these near real-time bio-optical products can be made
available within hours of a satellite over pass, they may be inadequate for operations. In the coastal waters, changes are occuring within hours as a result of the tidal
fluxuations, river discharges, precipation, and local wind events so that the nowcast
of the bio-optical properties may not be representative of local conditions within the
24 h period. Real-time coastal decisons on processes such as:
• dissipation of a coastal plume,
• movement of a Harmful Algal Bloom,
• river plume dispersion,
• turbidity frontal movement,
• chlorophyll bloom dispersion,
• larval fish migration,
all may require hourly forecast of bio-optical properties on a daily basis.
A sensible forecast of bio-optical properties along coastal waters requires an initialization field. This field can be best represented by the nowcast from ocean color
bio-optical properties. Forecast and prediction of these properties can be defined
R. Arnone (B)
Oceanography Division, Naval Research Laboratory, Stennis Space Center, MS 39529, USA
e-mail: arnone@nrlssc.navy.mil
335
V. Barale et al. (eds.), Oceanography from Space,
DOI 10.1007/978-90-481-8681-5_19, C
Springer Science+Business Media B.V. 2010
Forecasting the Coastal Optical Properties
Using Satellite Ocean Color
Robert Arnone, Brandon Casey, Sherwin Ladner, and Dong-Shang Ko
19.1 Introduction
Algorithms for ocean color bio-optical properties from space have advanced significantly in the last 20 years. Improved alogorithms have advanced beyond chlorophyll
to characterize coastal optical properties such as absorption from phytoplankton,
colored dissolved organic matter and detritus, in addition to backscattering from the
particle distribution. These properties have provided new insights into the changing
conditions along our coast. However, for many coastal management applications
these satellite derived properties are insufficient to make real-time decisons (Arnone
and Parsons, 2004). Daily satellite ocean color imagery represents a nowcast of biooptical conditions. Although these near real-time bio-optical products can be made
available within hours of a satellite over pass, they may be inadequate for operations. In the coastal waters, changes are occuring within hours as a result of the tidal
fluxuations, river discharges, precipation, and local wind events so that the nowcast
of the bio-optical properties may not be representative of local conditions within the
24 h period. Real-time coastal decisons on processes such as:
• dissipation of a coastal plume,
• movement of a Harmful Algal Bloom,
• river plume dispersion,
• turbidity frontal movement,
• chlorophyll bloom dispersion,
• larval fish migration,
all may require hourly forecast of bio-optical properties on a daily basis.
A sensible forecast of bio-optical properties along coastal waters requires an initialization field. This field can be best represented by the nowcast from ocean color
bio-optical properties. Forecast and prediction of these properties can be defined
R. Arnone (B)
Oceanography Division, Naval Research Laboratory, Stennis Space Center, MS 39529, USA
e-mail: arnone@nrlssc.navy.mil
335
V. Barale et al. (eds.), Oceanography from Space,
DOI 10.1007/978-90-481-8681-5_19, C
Springer Science+Business Media B.V. 2010