Moline Mark A.

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Moline
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Mark A.
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  • Article
    Remote environmental monitoring units : An autonomous vehicle for characterizing coastal environments
    (American Meteorological Society, 2005-11) Moline, Mark A. ; Blackwell, Shelley M. ; von Alt, Chris ; Allen, Ben ; Austin, Thomas ; Case, James ; Forrester, Ned C. ; Goldsborough, Robert G. ; Purcell, Mike ; Stokey, Roger P.
    In oceanography, there has been a growing emphasis on coastal regions, partially because of their inherent complexity, as well as the increasing acknowledgment of anthropogenic impacts. To improve understanding and characterization of coastal dynamics, there has been significant effort devoted to the development of autonomous systems that sample the ocean on relevant scales. Autonomous underwater vehicles (AUVs) are especially well suited for studies of the coastal ocean because they are able to provide near-synoptic spatial observations. These sampling platforms are beginning to transition from the engineering groups that developed and continue to improve them to the science user. With this transition comes novel applications of these vehicles to address new questions in coastal oceanography. Here, the relatively mature Remote Environmental Monitoring Units (REMUS) AUV system is described and assessed. Analysis of data, based on 37 missions and nearly 800 km of in-water operation, shows that the vehicle’s navigational error estimates were consistently less than 10 m, and error estimates of mission duration, distance, velocity, and power usage, once the vehicle was properly ballasted, were below 10%. An example of the transition to science is demonstrated in an experiment conducted in 2002 in Monterey Bay, California, where the vehicle was used to quantify critical horizontal length scales of variability. Length scales on the order of tens to hundreds of meters were found for the region within 25 km of the coastline, which has significant implications for designing proper sampling approaches and parameterizing model domains. Results also demonstrate the overall utility of the REMUS vehicle for use by coastal oceanographers.
  • Article
    Bioluminescence intensity modeling and sampling strategy optimization
    (American Meteorological Society, 2005-08) Shulman, Igor ; Kindle, J. C. ; McGillicuddy, Dennis J. ; Moline, Mark A. ; Haddock, Steven H. D. ; Nechaev, D. A. ; Phelps, Michael W.
    The focus of this paper is on the development of methodology for short-term (1–3 days) oceanic bioluminescence (BL) predictions and the optimization of spatial and temporal bioluminescence sampling strategies. The approach is based on predictions of bioluminescence with an advection–diffusion–reaction (tracer) model with velocities and diffusivities from a circulation model. In previous research, it was shown that short-term changes in some of the salient features in coastal bioluminescence can be explained and predicted by using this approach. At the same time, it was demonstrated that optimization of bioluminescence sampling prior to the forecast is critical for successful short-term BL predictions with the tracer model. In the present paper, the adjoint to the tracer model is used to study the sensitivity of the modeled bioluminescence distributions to the sampling strategies for BL. The locations and times of bioluminescence sampling prior to the forecast are determined by using the adjoint-based sensitivity maps. The approach is tested with bioluminescence observations collected during August 2000 and 2003 in the Monterey Bay, California, area. During August 2000, BL surveys were collected during a strong wind relaxation event, while in August 2003, BL surveys were conducted during an extended (longer than a week) upwelling-favorable event. The numerical bioluminescence predictability experiments demonstrated a close agreement between observed and model-predicted short-term spatial and temporal changes of the coastal bioluminescence.