Development and Deployment of the Surface Current Imaging Nowcast System

2021 ◽  
Author(s):  
P Steven Anderson ◽  
Seth Zuckerman ◽  
James Stear ◽  
Shejun Fan
Keyword(s):  
Real Time ◽  
Surface Current ◽  
Offshore Platform ◽  
Environmental Data ◽  
Prototype System ◽  
Surface Currents ◽  
Long Wave ◽  
Least Squares Fit ◽  
One Year ◽  
Long Wave Infrared

Abstract This paper describes the development and installation of an ocean surface current monitoring device called SCINS: Surface Current Imaging Nowcast System. We describe the process of designing and building the prototype system, installation on an offshore platform, implementation of real-time reporting, and results from one year of operations. SCINS utilizes passive long-wave infrared imaging of the ocean to derive surface currents. This is done using a time-series of images to observe the phase-speed of the ocean waves. Then, the Doppler shift of the observed waves due to the surface current is determined using a non-linear least squares fit. The primary components of SCINS are a long-wave infrared camera and a data acquisition computer. The camera is mounted several 10s of m above the water surface. The system collects imagery at 2 Hz for 5 minutes every 15 minutes, day and night, and calculates surface currents in real-time. In this paper, we describe the results from deploying SCINS on an offshore platform, Chevron's Big Foot TLP, in the Gulf of Mexico for one year of continuous data collections, including several tropical storm and hurricane events. Results are compared to environmental data to describe system performance as a function of wind, wave, and sea conditions. We describe the engineering challenges and lessons learned from designing and installing this new type of passive imaging system for offshore use. We conclude that SCINS is an effective method for measuring surface currents in the vicinity of offshore platforms, requiring very little maintenance and without the need to put any instrumentation in the water.

A REAL-TIME SIMULATION OF THE TRAJECTORY AND FATE OF SPILLED OIL AT SEA

1997 ◽  
Vol 1997 (1) ◽  
pp. 573-577 ◽  
Author(s):  
Keyyong Hong ◽  
Sun-Young Kim ◽  
Museok Song
Keyword(s):  
Real Time ◽  
Oil Spill ◽  
Center Of Mass ◽  
Coastal Region ◽  
Surface Current ◽  
Environmental Data ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Time Simulation ◽  
Spilled Oil

ABSTRACT On the basis of the physical modeling of the trajectory and fate of spilled oil at sea, an integrated computer program was developed for the real-time prediction of spilled oil behavior. The trajectory model describes the movement of the surface spill center of mass, and the trajectory is mainly governed by the velocity of the wind and the surface current. For the fate model, weathering effects of spreading, evaporation, emulsification, and shoreline interaction are included. The Windows-based GUI has a flexible preprocessor that enables one to use measured field data as well as numerically generated data. For the realtime prediction, environmental data are stored in the database. The database on tidal and oceanic currents is produced by solving a Navier-Stokes equation based on the IAF finite difference method. The measured data are used for both the boundary condition of the governing equation and the calibration of the generated current field. An oil spill accident in the south coastal region of Korea is simulated and compared with the observed data. The simulation result indicates that the developed oil spill model gives a reasonable estimation of the route of spilled oil. Further improvement of the environmental database is required for the accurate prediction of spilled oil behavior.

scholarly journals Derivation and Evaluation of Satellite-Based Surface Current

2021 ◽  
Vol 8 ◽  
Author(s):  
Jun Myoung Choi ◽  
Wonkook Kim ◽  
Tran Thy My Hong ◽  
Young-Gyu Park
Keyword(s):  
Deep Learning ◽  
Velocity Field ◽  
Real Time ◽  
Satellite Data ◽  
Surface Current ◽  
Scalar Fields ◽  
Hf Radar ◽  
Surface Currents ◽  
Dynamic Features ◽  
Basin Scale

Observations of real-time ocean surface currents allow one to search and rescue at ocean disaster sites and investigate the surface transport and fate of ocean contaminants. Although real-time surface currents have been mapped by high-frequency (HF) radar, shipboard instruments, satellite altimetry, and surface drifters, geostationary satellites have proved their capability in satisfying both basin-scale coverage and high spatiotemporal resolutions not offered by other observational platforms. In this paper, we suggest a strategy for the production of operational surface currents using geostationary satellite data, the particle image velocimetry (PIV) method, and deep learning-based evaluation. We used the model scalar field and its gradient to calculate the corresponding surface current via PIV, and we estimated the error between the true velocity field and calculated velocity field by the combined magnitude and relevance index (CMRI) error. We used the model datasets to train a convolutional neural network, which can be used to filter out bad vectors in the surface current produced by arbitrary model scalar fields. We also applied the pretrained network to the surface current generated from real-time Himawari-8 skin sea surface temperature (SST) data. The results showed that the deep learning network successfully filtered out bad vectors in a surface current when it was applied to model SST and created stronger dynamic features when the network was applied to Himawari SST. This strategy can help to provide a quality flag in satellite data to inform data users about the reliability of PIV-derived surface currents.

RxCADRE 2008: Wildfire Airborne Sensor Program - Lite uncalibrated long wave infrared image mosaics

2016 ◽  
Author(s):  
Andrew T. Hudak ◽  
Benjamin C. Bright ◽  
Robert L. Kremens ◽  
Matthew B. Dickinson ◽  
Matthew G. Alden
Keyword(s):  
Infrared Image ◽  
Long Wave ◽  
Long Wave Infrared

scholarly journals A Bi-Spectral Microbolometer Sensor for Wildfire Measurement

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3690
Author(s):  
Denis Dufour ◽  
Loïc Le Noc ◽  
Bruno Tremblay ◽  
Mathieu N. Tremblay ◽  
Francis Généreux ◽  
...  
Keyword(s):  
Low Resource ◽  
Long Wave ◽  
Radiative Power ◽  
Infrared Radiances ◽  
Moderate Scale ◽  
Long Wave Infrared ◽  
Detector Type ◽  
Experimental Burns ◽  
Fire Radiative Power

This study describes the development of a prototype bi-spectral microbolometer sensor system designed explicitly for radiometric measurement and characterization of wildfire mid- and long-wave infrared radiances. The system is tested experimentally over moderate-scale experimental burns coincident with FLIR reference imagery. Statistical comparison of the fire radiative power (FRP; W) retrievals suggest that this novel system is highly reliable for use in collecting radiometric measurements of biomass burning. As such, this study provides clear experimental evidence that mid-wave infrared microbolometers are capable of collecting FRP measurements. Furthermore, given the low resource nature of this detector type, it presents a suitable option for monitoring wildfire behaviour from low resource platforms such as unmanned aerial vehicles (UAVs) or nanosats.

Long-wave infrared sub-monolayer quantum dot quantum cascade photodetector

2020 ◽  
pp. 1-1
Author(s):  
Zhijian Shen ◽  
Zhuo Deng ◽  
Xuyi Zhao ◽  
Jian Huang ◽  
Lu Yao ◽  
...  
Keyword(s):  
Quantum Dot ◽  
Quantum Cascade ◽  
Long Wave ◽  
Long Wave Infrared
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