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LEVERAGING PREDICTIONS FROM NOAA’S
OCEANOGRAPHIC FORECAST MODELS
TO INCREASE ENVIRONMENTAL VARIABILITY AWARENESS
IN OCEAN MAPPING
G. MASETTI, L. A. MAYER, P. D. JOHNSON, AND J. G. W. KELLEY
BOSTON, MA - JANUARY 16, 2020
V2
OCEANOGRAPHIC FORECAST MODELS
IN OCEAN MAPPING
V0
SOUND SPEED MANAGER SMARTMAP
4
0 32
salinity
Sound
speed
Depth
Δd
5
6Ref.: Masetti, G. et al., Sound Speed Manager: An open-source application to manage sound speed profiles, International Hydrographic Review, vol. 17, pp. 31-40, 2017.
7
8
9
10
11
PYDRO UNIVERSE STAND-ALONE APPS PYTHON PACKAGES
www.nauticalcharts.noaa.gov www.hydroffice.org GitHub/PyPi/Conda
12
DISTRIBUTION
13
14
SMARTMAP ALGORITHM
15
• Constant Gradient:
• Snell Law:
Ref.: Masetti,, G., Kelley, J., Johnson, P., and Beaudoin, J., A Ray-Tracing Uncertainty Estimation Tool for Ocean Mapping, IEEE Access. IEEE, pp. 1-9, 2017.
SMARTMAP WEBGIS
16
• OFS + WOA
• Predictions
• Survey Planner
• Historic
Database
SOUND SPEED MANAGER SMARTMAP
FORECAST
Leveraging Predictions from NOAA’s Oceanographic Forecast Models to Increase Environmental Variability Awareness in Ocean Mapping
Leveraging Predictions from NOAA’s Oceanographic Forecast Models to Increase Environmental Variability Awareness in Ocean Mapping
A
B
Ref.: Masetti,, G., Smith, M., Mayer, L.A., and Kelley, J.G.W., Applications of the GoMOFS to Enhance Spatio-Temporal Oceanographic Awareness for Ocean Mapping, Frontiers in Marine Sciences, 2020.
WORST-CASE SCENARIO
OPTIMAL SOLUTION
REACTIVE-ONLY FORECAST
FULL FORECAST
THANKS!
Visit: https://www.hydroffice.org
Contact me at: gmasetti@ccom.unh.edu

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Leveraging Predictions from NOAA’s Oceanographic Forecast Models to Increase Environmental Variability Awareness in Ocean Mapping