Demonstration of a Real Time Capability to Produce Tidal Heights and Currents for Naval Operational Use: A Cast Study for the West Coast of Africa (Liberia)

This report documents an existing capability to produce operationally relevant products on sea level and currents from a tides/storm surge model for any coastal region around the world within 48 hours from the time of the request. The model is ready for transition to the Naval Oceanographic Office (NAVOCEANO) for potential contingency use anywhere around the world. A recent application to naval operations offshore Liberia illustrates this. Mississippi State University, in collaboration with the University of Colorado and NAVOCEANO, successfully deployed the Colorado University Rapidly Relocatable Nestable Tides and Storm Surge (CURReNTSS) model that predicts sea surface height, tidal currents and storm surge, and provided operational products on tidal sea level and currents in the littoral region off south-western coast of Africa. This report summarizes the results of this collaborative effort in an actual contingency use of the relocatable model, summarizes the lessons learned, and provides recommendations for further evaluation and transition of this modeling capability to operational use

Barotropic Tidal Predictions and Validation in a Relocatable Modeling Environment

Under funding from the Office of Naval Research (ONR), and the Naval Oceanographic Office (NAVOCEANO), the Mississippi State University Center for Air Sea Technology (CAST) has been working on developing a Relocatable Modeling Environment(RME) to provide a uniform and unbiased infrastructure for efficiently configuring numerical models in any geographic/oceanic region. Under Naval Oceanographic Office (NAVO-CEANO) funding, the model was implemented and tested for NAVOCEANO use. With our current emphasis on ocean tidal modeling, CAST has adopted the Colorado University's numerical ocean model, known as CURReNTSS (Colorado University Rapidly Relocatable Nestable Storm Surge) Model, as the model of choice. During the RME development process, CURReNTSS has been relocated to several coastal oceanic regions, providing excellent results that demonstrate its veracity. This report documents the model validation results and provides a brief description of the Graphic user Interface (GUI)

Nonlinear Gulf Stream Interaction with the Deep Western Boundary Current System: Observations and a Numerical Simulation

Gulf Stream (GS) separation near its observed Cape Hatteras (CH) separation location, and its ensuing path and dynamics, is a challenging ocean modeling problem. If a model GS separates much farther north than CH, then northward GS meanders, which pinch off warm core eddies (rings), are not possible or are strongly constrained by the Grand Banks shelfbreak. Cold core rings pinch off the southward GS meanders. The rings are often re-absorbed by the GS. The important warm core rings enhance heat exchange and, especially, affect the northern GS branch after GS bifurcation near the New England Seamount Chain. This northern branch gains heat by contact with the southern branch water upstream of bifurcation, and warms the Arctic Ocean and northern seas, thus playing a major role in ice dynamics, thermohaline circulation and possible global climate warming. These rings transport heat northward between the separated GS and shelf slope/Deep Western Boundary Current system (DWBC). This region has nearly level time mean isopycnals. The eddy heat transport convergence/divergence enhances the shelfbreak and GS front intensities and thus also increases watermass transformation. The fronts are maintained by warm advection by the Florida Current and cool advection by the DWBC. Thus, the GS interaction with the DWBC through the intermediate eddy field is climatologically important.NCC2-1371Approved for public release; distribution is unlimited

Advancing coastal ocean modelling, analysis, and prediction for the US Integrated Ocean Observing System

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here by permission of Taylor & Francis for personal use, not for redistribution. The definitive version was published in Journal of Operational Oceanography 10 (2017): 115-126, doi:10.1080/1755876X.2017.1322026.This paper outlines strategies that would advance coastal ocean modeling, analysis and prediction as a complement to the observing and data management activities of the coastal components of the U.S. Integrated Ocean Observing System (IOOS®) and the Global Ocean Observing System (GOOS). The views presented are the consensus of a group of U.S. based researchers with a cross-section of coastal oceanography and ocean modeling expertise and community representation drawn from Regional and U.S. Federal partners in IOOS. Priorities for research and development are suggested that would enhance the value of IOOS observations through model-based synthesis, deliver better model-based information products, and assist the design, evaluation and operation of the observing system itself. The proposed priorities are: model coupling, data assimilation, nearshore processes, cyberinfrastructure and model skill assessment, modeling for observing system design, evaluation and operation, ensemble prediction, and fast predictors. Approaches are suggested to accomplish substantial progress in a 3-8 year timeframe. In addition, the group proposes steps to promote collaboration between research and operations groups in Regional Associations, U.S. Federal Agencies, and the international ocean research community in general that would foster coordination on scientific and technical issues, and strengthen federal-academic partnerships benefiting IOOS stakeholders and end users.2018-05-2

Ocean observations in support of studies and forecasts of tropical and extratropical cyclones

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Domingues, R., Kuwano-Yoshida, A., Chardon-Maldonado, P., Todd, R. E., Halliwell, G., Kim, H., Lin, I., Sato, K., Narazaki, T., Shay, L. K., Miles, T., Glenn, S., Zhang, J. A., Jayne, S. R., Centurioni, L., Le Henaff, M., Foltz, G. R., Bringas, F., Ali, M. M., DiMarco, S. F., Hosoda, S., Fukuoka, T., LaCour, B., Mehra, A., Sanabia, E. R., Gyakum, J. R., Dong, J., Knaff, J. A., & Goni, G. Ocean observations in support of studies and forecasts of tropical and extratropical cyclones. Frontiers in Marine Science, 6, (2019): 446, doi:10.3389/fmars.2019.00446.Over the past decade, measurements from the climate-oriented ocean observing system have been key to advancing the understanding of extreme weather events that originate and intensify over the ocean, such as tropical cyclones (TCs) and extratropical bomb cyclones (ECs). In order to foster further advancements to predict and better understand these extreme weather events, a need for a dedicated observing system component specifically to support studies and forecasts of TCs and ECs has been identified, but such a system has not yet been implemented. New technologies, pilot networks, targeted deployments of instruments, and state-of-the art coupled numerical models have enabled advances in research and forecast capabilities and illustrate a potential framework for future development. Here, applications and key results made possible by the different ocean observing efforts in support of studies and forecasts of TCs and ECs, as well as recent advances in observing technologies and strategies are reviewed. Then a vision and specific recommendations for the next decade are discussed.This study was supported by the National Oceanic and Atmospheric Administration and JSPS KAKENHI (Grant Numbers: JP17K19093, JP16K12591, and JP16H01846)

Adjusting Neural Network to a Particular Problem: Neural Network-Based Empirical Biological Model for Chlorophyll Concentration in the Upper Ocean

The versatility of the neural network (NN) technique allows it to be successfully applied in many fields of science and to a great variety of problems. For each problem or class of problems, a generic NN technique (e.g., multilayer perceptron (MLP)) usually requires some adjustments, which often are crucial for the development of a successful application. In this paper, we introduce a NN application that demonstrates the importance of such adjustments; moreover, in this case, the adjustments applied to a generic NN technique may be successfully used in many other NN applications. We introduce a NN technique, linking chlorophyll “a” (chl-a) variability—primarily driven by biological processes—with the physical processes of the upper ocean using a NN-based empirical biological model for chl-a. In this study, satellite-derived surface parameter fields, sea-surface temperature (SST) and sea-surface height (SSH), as well as gridded salinity and temperature profiles from 0 to 75m depth are employed as signatures of upper-ocean dynamics. Chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a concentrations. Different methods of optimizing the NN technique are investigated. Results are assessed using the root-mean-square error (RMSE) metric and cross-correlations between observed ocean color (OC) fields and NN output. To reduce the impact of noise in the data and to obtain a stable computation of the NN Jacobian, an ensemble of NN with different weights is constructed. This study demonstrates that the NN technique provides an accurate, computationally cheap method to generate long (up to 10 years) time series of consistent chl-a concentration that are in good agreement with chl-a data observed by different satellite sensors during the relevant period. The presented NN demonstrates a very good ability to generalize in terms of both space and time. Consequently, the NN-based empirical biological model for chl-a can be used in oceanic models, coupled climate prediction systems, and data assimilation systems to dynamically consider biological processes in the upper ocean