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DOE OSTI2026

Electrify Monterey Bay

The Electrify Monterey Bay project, delivered by Central Coast Energy Services (CCES) under DOE Weatherization Assistance Program Enhancement & Innovation (E&I) funding, was designed to demonstrate scalable, low-income residential electrification across the Monterey Bay region over a 36-month performance period. The original production goal was to electrify at least 50 households; CCES exceeded this benchmark by completing electrification work in 86 households. Targeting criteria prioritized households located in disadvantaged communities and/or with an energy burden >6.5% (defined as average household energy costs divided by average household countable income). Across participating households, the average energy burden was 28%, indicating a high-need population and supporting the program’s equity and affordability objectives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Analysis on Evaluations of Monterey Bay Aquarium Research Institute’s Wave Energy Converter’s Field Data Using WEC-Sim and Gazebo: A Simulation Tool Comparison

Although many studies have validated wave energy converter (WEC) numerical models against scaled prototype experimental data, there remains a notable lack of validation using data from full-scale deployed WECs. This paper compares two numerical models of Monterey Bay Aquarium Research Institute’s Wave Energy Converter (MBARI-WEC), a two-body point absorber with an electro-hydraulic power take-off system (PTO). The models are implemented in WEC-Sim/Simscape and Gazebo Simulator. A statistical analysis of the models was performed, and field results were obtained to compare the models’ accuracy in predicting the RMS piston velocity, RMS motor speed, and mean electric power compared to field data for 56 observations across varying sea states. The Gazebo model demonstrated a closer agreement across all three parameters for a majority of the observations. When compared to the field data, the Gazebo and WEC-Sim models exhibited average mean electric power overestimations of 13% and 22%, respectively.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

MBARI WEC 2021 deployment

This dataset includes data from the Monterey Bay Aquarium Research Institute (MBARI) wave energy converter (WEC) and a nearby located Sofar Spotter buoy. The Monterey Bay Aquarium Research Institute has developed and deployed a small two-body point absorber wave energy device suitable to autonomous underwater vehicle, sensor system, and even aquaculture farm needs. For more information on the MBARI WEC see the research journal attached in the submission.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

Pioneer WEC concept design report

The "Pioneer WEC" project is targeted at developing a wave energy generator for the Coastal Surface Mooring (CSM) system within the Ocean Observatories Initiative (OOI) Pioneer Array. The CSM utilizes solar photovoltaic and wind generation systems, along with rechargeable batteries, to power multiple sensors on the buoy and along the mooring line. This approach provides continuous power for essential controller functions and a subset of instruments, and meets the full power demand roughly 70% of the time. Sandia has been tasked with designing a wave energy system to provide additional electrical power and bring the CSM up-time for satisfying the full-power demand to 100%. This project is a collaboration between Sandia and Woods Hole Oceanographic Institution (WHOI), along with Evergreen Innovations, Monterey Bay Aquarium Research Institute (MBARI), Eastern Carolina University (ECU), Johns Hopkins University (JHU), and the National Renewable Energy Laboratory (NREL). This report captures Phase I of an expected two phase project and presents project scoping and concept design results.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

MBARI-WEC September and October 2022 Field Data

This data is needed to simulate a model of the MBARI-WEC (Monterey Bay Aquarium Research Institute, Wave Energy Converter device) in a simulation environment (e.g. Gazebo) for 56 observation dates in the time between September and October 2022, and to compare the simulation outputs to the corresponding field data of the physical MBARI-WEC. There were 50 observations chosen in Sept and 6 observations in Oct. To help understand terms below, a summary of the system can be found at the github link in the downloads section below. The Gazebo MBARI-WEC model is also provided, should users wish to simulate using this platform. There are 4 *mat files included. ................................................................................................................................................................................................................................... Spectrum and Simulation Inputs: September2022_spectrum_siminputs.mat and October2022_spectrum_siminputs.mat has data needed for simulation inputs in table format. These include the ocean spectrum for an observation and operating parameters of the MBARI-WEC during that observation. They are organized as rows representing an observation and columns representing data. For example, for the September *mat there are 50 rows. The first 7 columns are Datetime, sig_waveheight, peak_period, mean_period, heaveconedoor_status, pistonpos_mean, and scale_factor: - Datetime is the date and time the observation occurred in PST - sig_waveheight is the significant wave height of the ocean spectrum during that observation in meters - peak_period is the peak period of the ocean spectrum during that observation in seconds - mean_period is the mean period of the ocean spectrum during that observation in seconds - heaveconedoor_status is the status of the heave cone doors where 0 represents the doors are open and 1 represents they are closed - pistonpos_mean is the mean position of the PTO ram (piston) in meters - scale_factor is an additional factor of 0.5 --1.4 applied to a default damping relationship The next columns are data needed to represent the ocean spectrum. First are the frequencies [Hz] labeled as "f0-f38", then the variance density [m2/Hz] labeled as "vardens0-vardens38". October2022_spectrum_siminputs.mat follows as a similar format as above, but includes a larger amount of ocean spectrum frequencies and variance density elements. ................................................................................................................................................................................................................................... Field data: The field data is found in MBARIWEC_septdata.mat and MBARIWEC_octdata.mat for the observations of September and October, respectively. These contain data in a struct format. The struct contains the following fields for each observation: PC_BattCurr, PC_LoadCurr, PC_RPM, PC_Voltage, SC_Range, SC_Velocity, DateTime, where: - PC_BattCurr is the current flowing to or from the onboard batteries in Amps - PC_LoadCurr is the current flowing to the load dump in Amps - PC_RPM is the electric/hydraulic motor shaft speed (directly coupled) in RPM - PC_Voltage is the bus voltage at the power converter in Volts - SC_Range is the PTO ram (piston) position in meters where 0 is fully retracted and 2.03 is fully extended - SC_Velocity is the PTO ram (piston) velocity in meters/sec - DateTime is the date and time of the sampled field data in each observation in PST - Electric Power is equal to: PC_Voltage*(PC_BattCurr + PC_LoadCurr) in Watts For example, upon loading MBARIWEC_octdata.mat, the aforementioned fields would be loaded, each with {6x1} cells for the 6 observations chosen in October. Within the first cell of e.g. SC_Range would be sampled data representing the field data of the MBARIWEC PTO piston position for say, one hour, of the first October observation. The corresponding field DateTime would...

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms

Abstract Increasing occurrence of harmful algal blooms across the land–water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective analysis and identification of specific conditions supporting phytoplankton growth. A novel iterative Random Forests (iRF) machine-learning model was developed and applied to two example cases along the California coast to identify key stable interactions: (1) phytoplankton abundance in response to various drivers due to coastal conditions and land-sea nutrient fluxes, (2) microbial community structure during algal blooms. In Example 1, watershed derived nutrients were identified as the least significant interacting variable associated with Monterey Bay phytoplankton abundance. In Example 2, through iRF analysis of field-based 16S OTU bacterial community and algae datasets, we independently found stable interactions of prokaryote abundance patterns associated with phytoplankton abundance that have been previously identified in laboratory-based studies. Our study represents the first iRF application to marine algal blooms that helps to identify ocean, microbial, and terrestrial conditions that are considered dominant causal factors on bloom dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Wave Energy Converter Power Take-Off Modeling and Validation From Experimental Bench Tests

This article describes the implementation of a new numerical model of the power take-off system installed in the Monterey Bay Aquarium Research Institute wave energy converter, a device developed to provide power to various oceanic research missions. The simultaneous presence of hydraulic, pneumatic, and electrical subsystems in the power take-off system represents a significant challenge in forging an accurate model able to replicate the main dynamic characteristics of the system. The validation of the new numerical model is addressed by comparing simulations with the measurements obtained during a series of bench tests. Data from the bench tests show good agreement with the numerical model. The validated model provides deeper insights into the complex nonlinear dynamics of the power take-off system and will support further performance improvements in the future.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Distributed Acoustic Sensing for Whale Vocalization Monitoring: A Vertical Deployment Field Test

Abstract There is growing interest in floating offshore wind turbine (FOWT) technology, where turbines are installed on floating structures anchored to the seabed, allowing wind energy development in areas unsuitable for traditional fixed-platform turbines. Responsible development requires monitoring the impact of FOWTs on marine wildlife, such as whales, throughout the operational lifecycle of the turbines. Distributed acoustic sensing (DAS)—a technology that transforms fiber-optic cables into vibration sensor arrays—has been demonstrated for acoustic monitoring of whales using seafloor telecommunications cables. However, no studies have yet evaluated DAS performance in dynamic, engineered environments, such as floating platforms or moving vessels with complex, dynamic strain loads, despite their relevance to FOWT settings. This study addresses that gap by deploying DAS aboard a boat in Monterey Bay, California, where a fiber-optic cable was lowered using a weighted and suspended mooring line, enabling vertical deployment. Humpback whale vocalizations were captured and identified in the DAS data, noise sources were identified, and DAS data were compared to audio captured by a standalone hydrophone attached to the mooring line and a nearby hydrophone on a cabled observatory. This study is unique in: (1) deploying DAS in a vertical deployment mode, where noise from turbulence, cable vibrations, and other sources posed additional challenges compared to seafloor DAS applications; (2) demonstrating DAS in a dynamic, nonstationary setup, which is uncommon for DAS interrogators typically used in more stable environments; and (3) leveraging looped sections of the cable to reduce the noise floor and mitigate the effects of excessive cable vibrations and strain. This research demonstrates DAS’s ability to capture whale vocalizations in challenging environments, highlighting its potential to enhance underwater acoustic monitoring, particularly in the context of renewable energy development in offshore environments.

Saw, Jaewon↗

DOE OSTI2026

Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles

The latest generation of geostationary satellites provide Earth observations similar to widely used polar-orbiting sensors but at intervals as frequently as every 5–10 min, making them ideal for studying the diurnal dynamics of land–atmosphere interactions. The NASA Earth Exchange (NEX) group created the GeoNEX datasets by collating data from several geostationary platforms, including GOES-16/17/18, Himawari-8/9, and GK-2A, and placing them on a common grid to facilitate use by the Earth science community. Here, we document the GeoNEX Coincident Ground Observations (GeCGO) dataset for terrestrial ecosystem studies and provide examples for its use. Currently, GeCGO provides GOES-16 Advanced Baseline Imager (ABI) data over a 10 km × 10 km area surrounding 1586 network sites across the Americas. GeCGO makes it easy to compare the time series of geostationary data with the diurnal ground observations, including carbon/water fluxes and aerosol optical depth, and is extensible to other regions. We also develop GeoNEXTools to facilitate analyses that require both GeoNEX data and other NASA satellite data. The objectives of this paper are to introduce GeCGO and GeoNEXTools and demonstrate their applications. First, we describe the details of GeCGO and GeoNEXTools. Second, we explain how GeCGO can be integrated with other satellite data. Finally, we showcase comparisons between GeCGO and observations from three ground-based networks. GeCGO is available at https://doi.org/10.25966/y5pe-xp41 (Hashimoto et al., 2025).

Hashimoto, Hirofumi [NASA Ames Research Center (AR↗

DOE OSTI2026

Distributed optical fibre sensing in physical oceanography: emergence and future prospects

Distributed optical fibre sensing (DOFS) is a technology that enables continuous, real-time measurements of a range of environmental parameters along the length of a fibre optic cable. In this article, we review the recently emerged applications of DOFS techniques in physical oceanography and offer a perspective on the technology's potential for future growth within the field. The introduction of DOFS to physical oceanography is contextualised with a brief history of the technology, which spun off primarily from the use of seafloor-laid optical fibres for telecommunications purposes. The key components and underpinning physics of a DOFS system are outlined and, on their basis, the suite of variables that are observable with DOFS are discussed. The implementation factors of DOFS, which include trade-offs between measurement accuracy and spatio-temporal resolutions and ranges, are also examined. The physical oceanographic applications of DOFS to date are then illustrated with case examples of four distinct DOFS techniques: distributed temperature sensing (DTS), which can provide ocean temperature observations; distributed static strain sensing (DSS) and distributed acoustic sensing (DAS), which are sensitive to temperature, cable strain and strain-associated variables, such as pressure and ocean velocity; and ultra-long-range observations of ocean currents with optical interferometry. The forthcoming prospects of DOFS in physical oceanography are considered, and are proposed to include new fibre optic-based approaches to sense ocean salinity and measure through the water column. We conclude with reflections on the future role of DOFS within the Global Ocean Observing System, and highlight the opportunities provided by the existing world-wide network of seafloor-laid optical fibres.

Naveira Garabato, Alberto C. [Univ. of Southampton↗

DOE OSTI2025

Dissolved Organic Carbon in Coastal Waters: Global Patterns, Stocks and Environmental Physical Controls

Abstract Dissolved organic carbon (DOC) in coastal waters is integral to biogeochemical cycling, but global and regional drivers of DOC are still uncertain. In this study we explored spatial and temporal differences in DOC concentrations and stocks across the global coastal ocean, and how these relate to temperature and salinity. We estimated a global median coastal DOC stock of 3.15 Pg C (interquartile range (IQR) = 0.85 Pg C), with median DOC concentrations being 2.2 times higher than in open ocean surface waters. Globally and seasonally, salinity was the main driver of DOC with concentrations correlated negatively with salinity, without a clear relationship to temperature. DOC concentrations and stocks varied with region and season and this pattern is likely driven by riverine inputs of DOC and nutrients that stimulate coastal phytoplankton production. Temporally, high DOC concentrations occurred mainly in months with high freshwater input, with some exceptions such as in Eastern Boundary Current margins where peaks are related to primary production stimulated by nutrients upwelled from the adjacent ocean. No spatial trend between DOC and temperature was apparent, but many regions (19 out of 25) had aligned peaks of seasonal temperature and DOC, related to increased phytoplankton production and vertical stratification at high temperatures. Links of coastal DOC with salinity and temperature highlight the potential for anthropogenic impacts to alter coastal DOC concentration and composition, and thereby ecosystem status.

Lønborg, Christian [Section for Marine Diversity a↗

DOE OSTI2025

The Imprint of Southern Ocean Storms on Modeled Surface Chlorophyll, Their Drivers and Satellite Biases

Southern Ocean (SO) phytoplankton chlorophyll is highly variable on sub-seasonal time scales. Although the SO is the windiest ocean basin globally, it is not conclusively understood how storms impact SO phytoplankton dynamics. Much of our existing knowledge stems from satellites, but biases due to data gaps from cloud cover and low solar angles remain unquantified. Here, we use ocean–sea-ice simulations with the Community Earth System Model to quantify the climatological 1997–2018 imprint of storms on chlorophyll and phytoplankton dynamics in the ice-free SO. Additionally, by comparing the full-field model output to synthetic satellite observations, we quantify sampling biases in satellite-derived estimates. We find that both the sign and the magnitude of the average surface chlorophyll imprint vary substantially across storms but last for at least 4 days after the storm passing. Based on our analysis, more than one third of the storms explain the majority of local non-seasonal chlorophyll variability, but satellite-derived storm imprints are often too large in magnitude. On the day of the storm passing, changes in vertical mixing predominantly cause surface chlorophyll anomalies, and reduced light availability due to enhanced cloud cover outweighs the enhanced nutrient availability due to entrainment. Interestingly, storms imprint differently on total net primary production than on surface chlorophyll, demonstrating the difficulty to derive carbon-cycle impacts from a surface-chlorophyll assessment. With SO future storm activity projected to increase, complementing satellite observations with other observing technologies, for example, profiling floats, is necessary to better constrain how storms impact biological carbon cycling in the SO.

Nissen, Cara [University of Colorado, Boulder, CO ↗

DOE OSTI2025