Engineering topics

Moisan, John R.

Publications and source records attributed to Moisan, John R..

6 records

NASA NTRS2013

Algorithm Development for Predicting Biodiversity Based on Phytoplankton Absorption

Ocean color remote sensing has provided the scientific community with unprecedented global coverage of chlorophyll a, an indicator of phytoplankton biomass. Together, satellite-derived chlorophyll a and knowledge of Phytoplankton Functional Types (PFTs) will improve our limited understanding of marine ecosystem responses to physiochemical climate drivers involved in carbon cycle dynamics and linkages. Using cruise data from the Gulf of Maine and the Middle Atlantic Bight (N 269 pairs of HPLC and phytoplankton absorption samples), two modeling approaches were utilized to predict phytoplankton absorption and pigments. Algorithm I predicts the chlorophyll-specific absorption coefficient (a*(sub ph) (m(exp 2) mg chl a(exp - 1)) using inputs of temperature, light, and chlorophyll a. Modeled r(exp 2) values (400700 nm) ranged from 0.79 to 0.99 when compared to in situ observations with approximately 25 percent lower r(exp 2) values in the UV region. Algorithm II-a utilizes matrix inversion analysis to predict a(sub ph)(m(exp -1), 400700 nm) and r(exp 2) values ranged from 0.89 to 0.99. The prediction of phytoplankton pigments with Algorithm II-b produced r(exp 2) values that ranged from 0.40 to 0.93. When used in combination, Algorithm I, and Algorithm II-a are able to use satellite products of SST, PAR, and chlorophyll a (Algorithm I) to predict pigment concentrations and ratios to describe the phytoplankton community. The results of this study demonstrate that the spatial variation in modeled pigment ratios differ significantly from the 10-year SeaWiFS average chlorophyll a data set. Contiguous observations of chlorophyll a and phytoplankton biodiversity will elucidate ecosystem responses with unprecedented complexity.

phytoplankton↗

NASA NTRS2011

An Inverse Modeling Approach to Estimating Phytoplankton Pigment Concentrations from Phytoplankton Absorption Spectra

Phytoplankton absorption spectra and High-Performance Liquid Chromatography (HPLC) pigment observations from the Eastern U.S. and global observations from NASA's SeaBASS archive are used in a linear inverse calculation to extract pigment-specific absorption spectra. Using these pigment-specific absorption spectra to reconstruct the phytoplankton absorption spectra results in high correlations at all visible wavelengths (r(sup 2) from 0.83 to 0.98), and linear regressions (slopes ranging from 0.8 to 1.1). Higher correlations (r(sup 2) from 0.75 to 1.00) are obtained in the visible portion of the spectra when the total phytoplankton absorption spectra are unpackaged by multiplying the entire spectra by a factor that sets the total absorption at 675 nm to that expected from absorption spectra reconstruction using measured pigment concentrations and laboratory-derived pigment-specific absorption spectra. The derived pigment-specific absorption spectra were further used with the total phytoplankton absorption spectra in a second linear inverse calculation to estimate the various phytoplankton HPLC pigments. A comparison between the estimated and measured pigment concentrations for the 18 pigment fields showed good correlations (r(sup 2) greater than 0.5) for 7 pigments and very good correlations (r(sup 2) greater than 0.7) for chlorophyll a and fucoxanthin. Higher correlations result when the analysis is carried out at more local geographic scales. The ability to estimate phytoplankton pigments using pigment-specific absorption spectra is critical for using hyperspectral inverse models to retrieve phytoplankton pigment concentrations and other Inherent Optical Properties (IOPs) from passive remote sensing observations.

Moisan, John R.↗

NASA NTRS2009

Phytoplankton and Climate

Ocean phytoplankton supply about half of the oxygen that humans utilize to sustain life. In this lecture, we will explore how phytoplankton plays a critical role in modulating the Earth's climate. These tiny organisms are the base of the Ocean's food web. They can modulate the rate at which solar heat is absorbed by the ocean, either through direct absorption or through production of highly scattering cellular coverings. They take up and help sequester carbon dioxide, a key greenhouse gas that modulated the Earth's climate. They are the source of cloud nucleation gases that are key to cloud formation/processes. They are also able to modify the nutrient budgets of the ocean through active uptake of inert atmospheric nitrogen. Climate variations have a pronounced impact on phytoplankton dynamics. Long term variations in the climate have been studied through geological interpretations on its influence on phytoplankton populations. The presentation will focus on presenting the numerous linkages that have been observed between climate and phytoplankton and further discuss how present climate change scenarios are likely to impact phytoplankton populations as well as present findings from several studies that have tried to understand how the climate might react to the feedbacks from these numerous climate-phytop|ankton linkages.

Moisan, John R.↗

NASA NTRS2008

The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: Coordination of Multiple Oceanic Robot Boats

Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth s climate and ecology. Ocean sensing is typically done with satellites, buoys, and crewed research ships. The limitations of these systems include the fact that satellites are often blocked by cloud cover, and buoys and ships have spatial coverage limitations. This paper describes a multi-robot science exploration software architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in-situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended deployment autonomous ocean surface vehicles, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a sliding autonomy control architecture, where the operating mode of the vessels ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.

sensor web↗

NASA NTRS2004

Ocean Carbon Flux, Transport, and Burial Within the Western and Eastern US Coastal Zones

This project has been to develop and apply a regional. eddy-resolving circulation and biogeochemistry model of both the western and eastern U.S. coastal regions, capable of simulating the processes that control the carbon cycle. Validation has been by statistical comparison with analyses from various satellite measurements, including those from EOS sensors, as well as from in situ measurements. Sensitivity studies were carried out to investigate how the coastal ecosystem and biogeochemical cycles respond to changes in climate, large-scale eutrophication from indus- trial pollution, and other anthropogenic induced changes. The research has been conducted in collaboration with research groups at UCLA. NASA/GSFC (Wallops), Rutgers, and SIO. Overall. the project was focused on several key modeling issues, each of which tie back into completing the primary task of developing a coastal carbon model for both the eastern and western US. coasts. Individual groups within the entire program are still collaborating to address these specific tasks. These include: implementation of the coupled circulation/biogeochemical model within the U.S. West Coast. including high-resolution, embedded subdomains for the Southern California Bight and Monterey Bay region; development of a biogeochemical model with resolved carbon, nitrogen and oxygen cycles; development of data assimilation techniques for use of satellite data sets; reconfiguration of the model domain to U.S. East Coast; development of coastal forcing fields: development of methods to compare the model against remotely sensed data; and, the test of model sensitivity to environmental conditions. Below, we present a summary of the progress made toward achieving these soak. Because this has been a multi-institutional, collaborative effort, we note the groups involved with particular activities.

McWilliams, James C.↗

NASA NTRS2001

Biogeochemical Response to Mesoscale Physical Forcing in the California Current System

In the first part of the project, we investigated the local response of the coastal ocean ecosystems (changes in chlorophyll, concentration and chlorophyll, fluorescence quantum yield) to physical forcing by developing and deploying Autonomous Drifting Ocean Stations (ADOS) within several mesoscale features along the U.S. west coast. Also, we compared the temporal and spatial variability registered by sensors mounted in the drifters to that registered by the sensors mounted in the satellites in order to assess the scales of variability that are not resolved by the ocean color satellite. The second part of the project used the existing WOCE SVP Surface Lagrangian drifters to track individual water parcels through time. The individual drifter tracks were used to generate multivariate time series by interpolating/extracting the biological and physical data fields retrieved by remote sensors (ocean color, SST, wind speed and direction, wind stress curl, and sea level topography). The individual time series of the physical data (AVHRR, TOPEX, NCEP) were analyzed against the ocean color (SeaWiFS) time-series to determine the time scale of biological response to the physical forcing. The results from this part of the research is being used to compare the decorrelation scales of chlorophyll from a Lagrangian and Eulerian framework. The results from both parts of this research augmented the necessary time series data needed to investigate the interactions between the ocean mesoscale features, wind, and the biogeochemical processes. Using the historical Lagrangian data sets, we have completed a comparison of the decorrelation scales in both the Eulerian and Lagrangian reference frame for the SeaWiFS data set. We are continuing to investigate how these results might be used in objective mapping efforts.

Niiler, Pearn P.↗