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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Convex Optimization with Smart Grid Examples

In this talk, we give an overview of the field of convex optimization and work through four canonical problems that relate to electrical power systems and smart grids. The purpose of these examples is to demonstrate the breadth of applications of convex optimization in energy research and to show that toy versions of these problems can be solved in just a few lines of code, indicating the scale and complexity of problems that can be tackled with a more detailed treatment. We emphasize the cvxpy modeling language as a foundational technology that enables rapid development and prototyping of convex optimization problems, allowing researchers to focus on model development rather than get caught in the weeds of numerical and code implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Predicting Geologic Behavior in Carbon Storage Projects Using Graph Neural Network

This study was invited to presented at NVIDIA's GTC conference to highlight the potential of Graph Neural Network as a novel and promising methodology for predicting pressure and saturation evolution in carbon storage projects. Carbon capture and storage (CCS) technology plays a pivotal role in mitigating greenhouse gas emissions, facilitating the transition to a low-carbon future. Effective management of subsurface reservoirs is essential to ensure the safe and efficient storage of captured carbon dioxide (CO₂). Accurate predictions of pressure and saturation over time are critical for evaluating the long-term performance and integrity of CCS projects. In recent years, Graph Neural Network (GNN) has emerged as a powerful framework for analyzing complex data in graph-structured domains. This abstract explores the application of GNN to forecast pressure and saturation evolution in carbon storage projects. Traditional numerical simulations of subsurface reservoirs have proven successful in providing pressure and saturation forecasts. However, these simulations involve massive amounts of computational effort and require extensive domain expertise for proper model calibration and validation. Graph Neural Operator offers an alternative approach that harnesses the inherent graph structure of reservoirs, where nodes represent reservoir grid cells and edges represent the geological connectivity between them.

Shih, Chung Yan↗
DOE OSTI2025

Probabilistic Diffusion Models Advance Extreme Flood Forecasting

Extreme floods pose escalating risks in a changing climate, yet forecasting remains challenging due to peak flow underestimation and high uncertainty. We introduce diffusion-based runoff model (DRUM), a probabilistic deep learning (DL) approach that advances extreme flood forecasting across representative basins in the contiguous United States. DRUM outperforms state-of-the-art benchmarks, enhancing nowcasting skill for the top 1‰ of flows in 72.3% of studied basins. Under operational scenarios, DRUM extends reliable lead times by nearly a full day for 20- and 50-year floods. When evaluated with measured precipitation, an ideal condition, recall improves by 0.3–0.4 and the early warning window extends by 2.3 days for 50-year floods. The enhancement potential varies regionally, with precipitation-driven flood zones in the eastern and northwestern US benefiting most, gaining 3–7 days in lead time. These findings highlight the transformative potential of diffusion models as a cutting-edge generative AI technique for advancing hydrology and broader Earth system sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Exascale Computing and Data Handling: Challenges and Opportunities for Weather and Climate Prediction

The emergence of exascale computing and artificial intelligence offer tremendous potential to significantly advance Earth system prediction capabilities. However, enormous challenges must be overcome to adapt models and prediction systems to use these new technologies effectively. A 2022 WMO report on exascale computing recommends “urgency in dedicating efforts and attention to disruptions associated with evolving computing technologies that will be increasingly difficult to overcome, threatening continued advancements in weather and climate prediction capabilities.” Further, the explosive growth in data from observations, model and ensemble output, and postprocessing threatens to overwhelm the ability to deliver timely, accurate, and precise information needed for decision-making. Artificial intelligence (AI) offers untapped opportunities to alter how models are developed, observations are processed, and predictions are analyzed and extracted for decision-making. Given the extraordinarily high cost of computing, growing complexity of prediction systems, and increasingly unmanageable amount of data being produced and consumed, these challenges are rapidly becoming too large for any single institution or country to handle. This paper describes key technical and budgetary challenges, identifies gaps and ways to address them, and makes a number of recommendations.

Atmosphere↗
DOE OSTI2026

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.

artificial intelligence↗
DOE OSTI2026

A physically interpretable precursor framework for sub-seasonal prediction of Northern Hemisphere flash flourishing

Flash flourishing describes rapid vegetation increases that can quickly reshape land–atmosphere exchanges and impacts on ecosystem, yet its large-scale precursors, circulation context, and sub-seasonal predictability remain poorly understood. Here, we identified onset-stage circulation regimes across northern extratropical latitudes (NEL; >30°N) using 200 and 1000 hPa geopotential height, and examined their regional expressions over eastern Asia, western North America, and Europe. Flash flourishing onset in East Asian was associated with a baroclinic circulation regime and was preceded by a North Atlantic sea surface temperature (SST) precursor at a four-pentad lead. In contrast, onset in western North American and European preferentially occurred under barotropic regimes, preconditioned by Great Plains soil moisture at three-pentad lead and North Atlantic SST at a four-pentad lead, respectively. Ridge regression forecasts revealed regime-dependent sub-seasonal predictability, with mean out-of-sample R 2 exceeding 0.3 up to lead times of two pentads in East Asia, three pentads in western North America, and four pentads in Europe. Together, these findings established a mechanistic and regionally specific framework for anticipating rapid vegetation greening at sub-seasonal timescales.

Kong, Xiangxu [Nanjing Univ. of Information Scienc↗
DOE OSTI2025

Constraining primordial non-Gaussianity from the large scale structure two-point and three-point correlation functions

Surveys of cosmological large-scale structure (LSS) are sensitive to the presence of local primordial non-Gaussianity (PNG), and may be used to constrain models of inflation. Local PNG, characterized by f NL ⁠, the amplitude of the quadratic correction to the potential of a Gaussian random field, is traditionally measured from LSS two-point and three-point clustering via the power spectrum and bi-spectrum. We propose a framework to measure f NL using the configuration space two-point correlation function (2pcf) monopole and three-point correlation function (3pcf) monopole of survey tracers. Our model estimates the effect of the scale-dependent bias induced by the presence of PNG on the 2pcf and 3pcf from the clustering of simulated dark matter haloes. We describe how this effect may be scaled to an arbitrary tracer of the cosmological matter density. The 2pcf and 3pcf of this tracer are measured to constrain the value of f NL ⁠. In LSS surveys, the effect of imaging systematics on two-point statistics is often degenerate with the PNG signal. Our proposed model employs three-point statistics primarily to break this degeneracy. Using simulations of luminous red galaxies observed by the Dark Energy Spectroscopic Instrument (DESI), we demonstrate the accuracy and constraining power of our method. Our forecast indicates the ability to constrain f NL to a precision of σf NL ≈ 22 with one year of DESI survey data, as well as the ability to constrain the imaging systematic weights in situ.

early Universe↗
DOE OSTI2024

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗
DOE OSTI2024

Vertical instability forecasting and controllability assessment of multi-device tokamak plasmas in DECAF with data-driven optimization

Abstract Reliable vertical position control will be an essential element of any future tokamak-based fusion power plant in order to reduce disruptions and maximize performance. We investigate methods to improve vertical controllability boundary determination in plasma operational space and demonstrate a data-driven approach based on direct pseudoinversion of operational space data that is rigorously quantitative, applicable in real-time plasma control systems, and physically intuitive to interpret. Applied to historical shot data from entire run campaigns on the MAST-U, KSTAR, and NSTX tokamaks, this approach, implemented in DECAF, improves vertical displacement event identification accuracy to 98.9%–100%. Further, we explore the application of a physics-based vertical stability metric as an early warning forecaster for vertical displacement events. The development of a linear surrogate model for the plasma current density profile, with a coefficient of determination of 0.992 on the training dataset, enables potential employment of this forecaster in real-time. The application of this approach on historical data from the MAST-U MU02 campaign yields a forecaster with 62.6% accuracy, indicating promise for this method when further refined and potentially coupled with other stability metrics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Evaluating Microchannel Heat Exchanger Lifetime for Concentrating Solar Power Applications Research Performance Progress Report (RPPR-1)

Microchannel heat exchanger technology is being pursued for next generation CSP concepts for primary power cycle heat addition and power cycle heat recuperation due to the high heat transfer coefficients and pressure containment advantages of small sCO 2 channels. The economics of future CSP plants as dictated by the SETO 2020 or 2030 targets depend on a heat exchanger with a 30-year lifetime (resisting creep, fatigue, corrosion, erosion) and operational characteristics such as fast ramping and the ability to withstand thermal shock. However, the lifetime and operational limits of microchannel heat exchangers operating at high temperatures, particularly those constructed from high-nickel alloys, are not well known. This uncertainty has resulted in heat exchanger vendors not being able to accurately forecast heat exchanger lifetime as required by customers, specify operational limits as required by process engineers to prevent premature heat exchanger failure, or overdesign heat exchanger which leads to higher cost than necessary.

14 SOLAR ENERGY↗
DOE OSTI2025

Hybrid Power Plants for Energy Resilience: A Case Study

As renewable energy technologies are increasingly adopted, they pose an opportunity to improve the sustainability and resilience of distributed grids, especially when their design and operation is coordinated as a hybrid power plant. When included in hybrid power plants, distributed wind turbines in particular have the potential to enhance the resilience of distributed grids in areas with good wind resource, due to their ability to provide more consistent generation and ancillary services as compared to photo-voltaic (PV) solar panels. Despite this benefit, U.S. distributed wind adoption is lower than other comparable renewable energy technologies. In this study, we seek to demonstrate how hybrid power plants that include distributed wind turbines can contribute to distribution grid resilience by meeting loads (especially critical loads) more consistently, increasing reserve capacity, and providing value to customers during outages. To demonstrate these contributions, we integrate three separate frameworks and apply them to a case study in a rural electric cooperative in Iowa. Through this case study, we simulate and compare hybrid power plant design and operation during two hazard events: a tornado that causes a 48-hour distribution outage and a winter weather event that causes a 6-hour generation outage. The inclusion of a hybrid power plant that leverages 1) increased battery duration and 2) advanced forecasting and dispatch strategies that reserve capacity leading up to a hazard event best reduce lost loads as well as diesel consumption that would otherwise be used to meet those loads during short- and long-duration hazard events. Depending on the hybrid power plant capacity and operation, we find that the outage mitigation value of a hybrid power plant (measured in value to customers to avoid an outage and avoided lost revenues for the utility) is significant in both hazard events; adding wind, solar, and battery assets to the existing system adds about $50-$100M in avoided lost load and at least $4-$8k in utility value in the tornado hazard event, and $570k-$2.2M in avoided lost load and at least $220-$650 in utility value in the winter hazard scenario. In both the tornado and winter hazard scenarios, optimizing the operation of the hybrid system for resilience can lend similar value as increasing battery duration by 5 MWh for the lower capacity systems considered.

17 WIND ENERGY↗
DOE OSTI2026

Utilization of Unmanned Aircraft Systems for Environmental Purposes at the Savannah River Site – 26578

Born in the 1950s, the Savannah River Plant was constructed as a plutonium and tritium production plant. As the Cold War era came and went, a change of direction was signaled when the name of the facility changed from Savannah River Plant to Savannah River Site (1989) – the main mission at Savannah River shifted from “production” to “cleanup.” The Department of Energy is responsible for managing the 310 square miles of land that is the Savannah River Site and the cleanup/remediation activities that occur. The Savannah River National Laboratory utilizes unmanned aircraft systems to assist with some of those environmental monitoring and remediation activities. One function of unmanned aircraft systems at the Savannah River National Laboratory is conducting aerial photography and videography. Various infrastructure evaluations have been performed with unmanned aircraft – facility rooftop inspections, water tower lock verifications, earthen dam surveys, etc. An unmanned aircraft system has been used for progress footage of remediation projects – Dunbarton Bay remediation, Saltstone Disposal Unit construction, etc. Thermal cameras on an unmanned aircraft system are used to help identify cracks/leaks in structures from vantage points not readily accessible by personnel. Virtual tours of different waste units are conducted with unmanned aircraft systems for Site groups and federal/state regulators to save costs and travel time. Additionally, advanced remote sensing technologies are used on unmanned aircraft systems at the Savannah River Site. Light Detection and Ranging uses laser pulses to measure distances to the Earth's surface or other targets to create highly detailed topographic maps that accurately portray terrain (e.g., elevation changes, slopes, and contours). Data collection with a Light Detection and Ranging unit mounted on an unmanned aircraft system is quick and efficient – large-area surveys are conducted in shorter time frames. Because Light Detection and Ranging can penetrate through foliage and vegetation to ground level, it is being used in conjunction with a watershed model to study the General Separations Area of the Site to determine stormwater flow volume and direction, monitor stream stability, and forecast river flow behavior. A hyperspectral camera captures a wide spectrum of electromagnetic wavelengths across numerous narrow bands, beyond what the human eye can see. It provides detailed spectral information about the objects or surfaces in an image, which can be used to identify and analyze materials based on their spectral signatures. A hyperspectral camera mounted on an unmanned aircraft system has been used at the Savannah River Site for non-destructive evaluation of concrete and concrete structures. Unmanned aircraft systems at the Savannah River Site are also used for the application of herbicide on identified rooftops. P-Reactor and R-Reactor are entombed nuclear reactors at the Savannah River Site. Unwanted vegetation is often present on these rooftops – over time roots can penetrate seams, cracks, and joints of the general roof and concrete caps, leading to water ingress and deterioration of the structural components. For time and cost efficiencies as well as in the interest of personnel safety, an unmanned aircraft is used to dispense herbicide on rooftop areas where vegetation is noticed to help alleviate the issues/hazards.

Lorier, Troy [Savannah River National Laboratory (↗
DOE OSTI2024

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Fourth Edition

As the world increasingly seeks low-carbon energy solutions, solar power emerges as the most abundant resource on our planet. However, the challenge of effectively harnessing this energy is crucial in the coming years. Solar energy applications such as photovoltaics, solar heating and cooling, and concentrating solar power use different technologies to capitalize on sunlight. Each system has unique capabilities and requirements, underscoring the need for reliable information about solar resources across diverse installations, from residential rooftops to large-scale power plants. This is especially important for substantial projects, often exceeding $1 billion in construction costs. Before embarking on such ventures, it is imperative to obtain accurate data concerning solar resource quality and reliability at specific sites. Developers require detailed historical information, including seasonal, daily, hourly, and, ideally, subhourly variability to effectively predict a power plant's annual performance. Without these vital data, financial analyses fall short. Moreover, with the growing adoption of distributed photovoltaics, integrating these generation sources becomes critical to maintaining grid reliability and stability. By accurately forecasting generation patterns, utilities and system operators can facilitate greater integration of solar energy, thus ensuring the operational stability of the grid. The complexity and importance of these issues have prompted the foremost experts in the field to collaborate under the auspices of the International Energy Agency's (IEA's) Photovoltaic Power Systems Programme (PVPS) Task 16 to publish this handbook, which summarizes state-of-the-art information about all these topics. The efforts focus on providing reliable data and insights that can help shape our investments in solar energy and drive a sustainable future.

14 SOLAR ENERGY↗
DOE OSTI2025

Improving tropical cyclone rapid intensification forecasts with satellite measurements of sea surface salinity and calibrated machine learning

Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is salinity, which is important for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the impact and efficacy of using salinity information in state-of-the-art forecasting, we use a statistical model consisting of a variety of machine learning (ML) methods. For salinity data, we use satellite measurements of pre-storm sea surface salinity (SSS) as a proxy for the salinity stratification. We train and test the model on various ocean basins, including the Atlantic, eastern North Pacific and western North Pacific. A calibrator is trained on top of the ML models to correct and enhance probability forecasts. The calibrator significantly improves probability forecasts relative to recent works. The ML model performance is improved with the addition of SSS in the Eastern North Pacific, western North Pacific, and the Caribbean subregion of the North Atlantic, and the overall model performance is better than previous studies. SSS decreases model skill for a model trained on the full Atlantic basin. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

hurricane↗
DOE OSTI2024

Ocean internal tides suppress tropical cyclones in the South China Sea

Abstract Tropical Cyclones (TCs) are devastating natural disasters. Analyzing four decades of global TC data, here we find that among all global TC-active basins, the South China Sea (SCS) stands out as particularly difficult ocean for TCs to intensify, despite favorable atmosphere and ocean conditions. Over the SCS, TC intensification rate and its probability for a rapid intensification (intensification by ≥ 15.4 m s −1 day −1 ) are only 1/2 and 1/3, respectively, of those for the rest of the world ocean. Originating from complex interplays between astronomic tides and the SCS topography, gigantic ocean internal tides interact with TC-generated oceanic near-inertial waves and induce a strong ocean cooling effect, suppressing the TC intensification. Inclusion of this interaction between internal tides and TC in operational weather prediction systems is expected to improve forecast of TC intensity in the SCS and in other regions where strong internal tides are present.

54 ENVIRONMENTAL SCIENCES↗