Timothy S. Moore
University of New Hampshire
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Applied Optics | 2013
P. Jeremy Werdell; Bryan A. Franz; Sean W. Bailey; Gene C. Feldman; Emmanuel Boss; Vittorio E. Brando; Mark Dowell; Takafumi Hirata; Samantha Lavender; Zhongping Lee; Hubert Loisel; Stephane Maritorena; Frédéric Mélin; Timothy S. Moore; Timothy J. Smyth; David Antoine; Emmanuel Devred; O. Hembise; Antoine Mangin
Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.
Eos, Transactions American Geophysical Union | 2002
Anthony K. Liu; Yunhe Zhao; Wayne E. Esaias; Janet W. Campbell; Timothy S. Moore
Historically ocean surface feature tracking analyses have been based on data from a single orbital sensor collected over the revisit interval of a lone low-Earth orbital satellite. Today ocean surface layer currents are being derived by performing feature tracking using data from a number of sensors on different satellites. Satellite ocean color data provide important insight into the marine biosphere by quantifying certain fundamental properties—for example, phytoplankton pigment concentration and marine primary production—on a global scale. In addition, satellite ocean color data can also be used as a tracer for measuring ocean surface layer currents, because the ocean color signal comprises information from a deeper water depth, from 10 to 30 m, than surface signatures such as sea surface temperature.
Frontiers in Marine Science | 2017
Timothy S. Moore; Colleen B. Mouw; James M. Sullivan; Michael S. Twardowski; Ashley M. Burtner; Audrey B. Ciochetto; Malcolm N. McFarland; Aditya R. Nayak; Danna Paladino; Nicole Stockley; Thomas H. Johengen; Angela W. Yu; Steve Ruberg; Alan Weidemann
There is a growing use of remote sensing observations for detecting and quantifying freshwater cyanobacteria populations, yet the inherent optical properties of these communities in natural settings, fundamental to bio-optical algorithms, are not well known. Towards bridging this knowledge gap, we measured a full complement of optical properties in western Lake Erie during cyanobacteria blooms in the summers of 2013 and 2014. Our measurements focus attention on the optical uniqueness of cyanobacteria blooms, which have consequences for remote sensing and bio-optical modeling. We found the cyanobacteria blooms in the western basin during our field work were dominated by Microcystis, while the waters in the adjacent central basin were dominated by Planktothrix. Chlorophyll concentrations ranged from 1 to over 135
Remote Sensing of Environment | 2009
Timothy S. Moore; Janet Campbell; Mark Dowell
{\mu}g/L
Remote Sensing of Environment | 2015
Colleen B. Mouw; Steven Greb; Dirk Aurin; Paul M. DiGiacomo; Zhongping Lee; Michael S. Twardowski; Caren E. Binding; Chuanmin Hu; Ronghua Ma; Timothy S. Moore; Wesley J. Moses; Susanne E. Craig
across the study area with the highest concentrations associated with Microcystis in the western basin. We observed large, amorphous colonial Microcystis structures in the bloom area characterized by high phytoplankton absorption and high scattering coefficients with a mean particle backscatter ratio at 443nm greater than 0.03, which is higher than other plankton types and more comparable to suspended inorganic sediments. While our samples contained mixtures of both, our analysis suggests high contributions to the measured scatter and backscatter coefficients from cyanobacteria. Our measurements provide new insights into the optical properties of cyanobacteria blooms, and indicate that current semi-analytic models are likely to have problems resolving a closed solution in these types of waters as many of our observations are beyond the range of existing model components. We believe that different algorithm or model approaches are needed for these conditions, specifically for phytoplankton absorption and particle backscatter components. From a remote sensing perspective, this presents a challenge not only in terms of a need for new algorithms, but also for determining when to apply the best algorithm for a given situation. These results are new in the sense that they represent a complete description of the optical properties of freshwater cyanobacteria blooms, and are likely to be representative of bloom conditions for other systems containing Microcystis cells and colonies.
Deep-sea Research Part I-oceanographic Research Papers | 2007
Nicholas A. Johnson; Janet Campbell; Timothy S. Moore; Michael A. Rex; Ron J. Etter; Craig R. McClain; Mark Dowell
Remote Sensing of Environment | 2014
Timothy S. Moore; Mark Dowell; Shane R. Bradt; Antonio Ruiz Verdú
Remote Sensing of Environment | 2012
Timothy S. Moore; Mark Dowell; Bryan A. Franz
EPIC3(Reports of the International Ocean-Colour Coordinating Group (IOCCG) ; 15), Dartmouth, Nova Scotia, B2Y 4A2, Canada., International Ocean-Colour Coordinating Group, 156 p., pp. 1-156, ISBN: ISSN 1098-6030 | 2014
Shubha Sathyendranath; Jim Aiken; Séverine Alvain; Ray Barlow; Heather Bouman; Astrid Bracher; Robert J. W. Brewin; Annick Bricaud; Chris W. Brown; Áurea Maria Ciotti; Lesley Clementson; Susanne E. Craig; Emmanuel Devred; Nick J. Hardman-Mountford; Takafumi Hirata; Chuanmin Hu; Tihomir S. Kostadinov; Samantha Lavender; Hubert Loisel; Timothy S. Moore; Morales Jesus; Cyril Moulin; Colleen B. Mouw; Anitha Nair; Dionysios E. Raitsos; Collin S. Roesler; Jamie D. Shutler; Heidi M. Sosik; Inia Soto; Venetia Stuart
Geophysical Research Letters | 1984
P. M. Kintner; J. LaBelle; Michael C. Kelley; L. J. Cahill; Timothy S. Moore; R. L. Arnoldy