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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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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Volumetric tumor burden and its effect on brachial plexus dosimetry in head and neck intensity-modulated radiotherapy

To determine the effect of gross tumor volume of the primary (GTV-P) and nodal (GTV-N) disease on planned radiation dose to the brachial plexus (BP) in head and neck intensity-modulated radiotherapy (IMRT). Overall, 75 patients underwent definitive IMRT to a median total dose of 69.96 Gy in 33 fractions. The right BP and left BP were prospectively contoured as separate organs at risk. The GTV was related to BP dose using the unpaired t-test. Receiver operating characteristics curves were constructed to determine optimized volumetric thresholds of GTV-P and GTV-N corresponding to a maximum BP dose cutoff of > 66 Gy. Multivariate analyses were performed to account for factors associated with a higher maximal BP dose. A higher maximum BP dose (> 66 vs ≤ 66 Gy) correlated with a greater mean GTV-P (79.5 vs 30.8 cc; p = 0.001) and ipsilateral GTV-N (60.6 vs 19.8 cc; p = 0.014). When dichotomized by the optimized nodal volume, patients with an ipsilateral GTV-N ≥ 4.9 vs < 4.9 cc had a significant difference in maximum BP dose (64.2 vs 59.4 Gy; p = 0.001). Multivariate analysis confirmed that an ipsilateral GTV-N ≥ 4.9 cc was an independent predictor for the BP to receive a maximal dose of > 66 Gy when adjusted individually for BP volume, GTV-P, the use of a low anterior neck field technique, total planned radiation dose, and tumor category. Although both the primary and the nodal tumor volumes affected the BP maximal dose, the ipsilateral nodal tumor volume (GTV-N ≥ 4.9 cc) was an independent predictor for high maximal BP dose constraints in head and neck IMRT.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Radiation-induced hypothyroidism in patients with breast cancer: a retrospective analysis of 243 cases

This study aims to estimate the incidence of hypothyroidism (HT) and to evaluate the predictors affecting the development of HT after radiotherapy (RT) for breast cancer, with a focus on radiation dose-volumetric parameters. Between 2009 and 2015, 243 patients undergoing RT for breast cancer were retrospectively analyzed. Free triiodothyronine (FT3), free thyroxin (FT4), and thyrotropin (TSH) were monitored before and after RT. The relation between the doses to thyroid gland (D{sub mean}, D{sub max}, D{sub min}), percentage of thyroid volume receiving > 10 Gy, 20 Gy, 30 Gy, 40 Gy, and 50 Gy (V10 to V50), absolute thyroid volume, and HT were analyzed. The risk of HT according to radiation fields and the other clinic factors were also evaluated. The median follow-up was 41 (range; 6 to 130) months. Sixty-seven percent of the patients received RT to the breast/chest wall and ipsilateral supraclavicular fossa. Of 243 patients, 51 (21%) were diagnosed with HT. The median time to the onset of HT was 27 (range; 5 to 64) months. There were no significant relationships observed between D{sub min} or V10 to V50 and HT. The surgery type, clinical stage, nodal status, RT field, D{sub mean}, and D{sub max} were statistically significant predictors for HT in univariate analysis. The D{sub mean} was the only prognostic factor predicting HT in multivariate analysis, and D{sub mean} > 21 Gy was a threshold value for the evolvement of HT. In this study, we present evidence that postoperative irradiation of patients with breast cancer may frequently lead to HT. Patients who have received RT for breast cancer, especially irradiation on the supraclavicular region, may require thyroid function screening after RT.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Urinary total arsenic and 8-hydroxydeoxyguanosine are associated with renal cell carcinoma in an area without obvious arsenic exposure

8-Hydroxydeoxyguanosine (8-OHdG) is one of the most reliable and abundant markers of DNA damage. The study was designed to explore the relationship between urinary 8-OHdG and renal cell carcinoma (RCC) and to investigate whether individuals with a high level of 8-OHdG would have a modified odds ratio (OR) of arsenic-related RCC. This case–control study was conducted with 132 RCC patients and 245 age- and sex-matched controls from a hospital-based pool between November 2006 and May 2009. Pathological verification of RCC was completed by image-guided biopsy or surgical resection of renal tumors. Urinary 8-OHdG levels were determined using liquid chromatography with tandem mass spectrometry (LC–MS/MS). Concentrations of urinary arsenic species, including inorganic arsenic, monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA), were determined by a high performance liquid chromatography-linked hydride generator and atomic absorption spectrometry. Level of urinary 8-OHdG was significantly associated with the OR of RCC in a dose–response relationship after multivariate adjustment. Urinary 8-OHdG was significantly related to urinary total arsenic. The greatest OR (3.50) was seen in the individuals with high urinary 8-OHdG and high urinary total arsenic. A trend test indicated that the OR of RCC was increased with one of these factors and was further increased with both (p = 0.002). In conclusion, higher urinary 8-OHdG was a strong predictor of the RCC. High levels of 8-OHdG combined with urinary total arsenic might be indicative of arsenic-induced RCC. -- Highlights: ► Urinary 8-OHdG was significantly related to urinary total arsenic. ► Higher urinary 8-OHdG was a strong predictor of RCC risk. ► Urinary 8-OHdG may modify arsenic related RCC risk.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Model development for household waste prevention behaviour

Highlights: Black-Right-Pointing-Pointer We model waste prevention behaviour using structure equation modelling. Black-Right-Pointing-Pointer We merge attitude-behaviour theories with wider models from environmental psychology. Black-Right-Pointing-Pointer Personal norms and perceived behaviour control are the main behaviour predictors. Black-Right-Pointing-Pointer Environmental concern, moral obligation and inconvenience are the main influence on the behaviour. Black-Right-Pointing-Pointer Waste prevention and recycling are different dimensions of waste management behaviour. - Abstract: Understanding waste prevention behaviour (WPB) could enable local governments and decision makers to design more-effective policies for reducing the amount of waste that is generated. By merging well-known attitude-behaviour theories with elements from wider models from environmental psychology, an extensive cognitive framework that provides new and valuable insights is developed for understanding the involvement of individuals in waste prevention. The results confirm the usefulness of the theory of planned behaviour and of Schwartz's altruistic behaviour model as bases for modelling participation in waste prevention. A more elaborate integrated model of prevention was shown to be necessary for the complete analysis of attitudinal aspects associated with waste prevention. A postal survey of 158 respondents provided empirical support for eight of 12 hypotheses. The proposed structural equation indicates that personal norms and perceived behaviour control are the main predictors and that, unlike the case of recycling, subjective norms have a weak influence on WPB. It also suggests that, since social norms have not presented a direct influence, WPB is likely to be influenced by a concern for the environment and the community as well by perceptions of moral obligation and inconvenience. Results also proved that recycling and waste prevention represent different dimensions of waste management behaviour requiring particular approaches to increase individuals' engagement in future policies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

An application of the theory of planned behaviour to study the influencing factors of participation in source separation of food waste

Highlights: ► Theory of planned behaviour (TPB) has been conducted to identify the influencing factors for participation in source separation of food waste using self administered questionnaires. ► The findings suggested several implications for the development and implementation of waste separation at home programme. ► The analysis indicates that the attitude towards waste separation is determined as the main predictors where this in turn could be a significant predictor of the repondent’s actual food waste separation behaviour. ► To date, none of similar have been reported elsewhere and this finding will be beneficial to local Authorities as indicator in designing campaigns to promote the use of waste separation programmes to reinforce the positive attitudes. - Abstract: Tremendous increases in biodegradable (food waste) generation significantly impact the local authorities, who are responsible to manage, treat and dispose of this waste. The process of separation of food waste at its generation source is identified as effective means in reducing the amount food waste sent to landfill and can be reused as feedstock to downstream treatment processes namely composting or anaerobic digestion. However, these efforts will only succeed with positive attitudes and highly participations rate by the public towards the scheme. Thus, the social survey (using questionnaires) to analyse public’s view and influencing factors towards participation in source separation of food waste in households based on the theory of planned behaviour technique (TPB) was performed in June and July 2011 among selected staff in Universiti Putra Malaysia, Serdang, Selangor. The survey demonstrates that the public has positive intention in participating provided the opportunities, facilities and knowledge on waste separation at source are adequately prepared by the respective local authorities. Furthermore, good moral values and situational factors such as storage convenience and collection times are also encouraged public’s involvement and consequently, the participations rate. The findings from this study may provide useful indicator to the waste management authorities in Malaysia in identifying mechanisms for future development and implementation of food waste source separation activities in household programmes and communication campaign which advocate the use of these programmes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Orthogonal potency analysis of mesenchymal stromal cell function during ex vivo expansion

Highlights: • MSC potencies for their different functions are not identically affected by age. • In vitro age can strongly predict MSC adipogenic and hematopoietic-supporting potencies. • MSC shape can also strongly predict for hematopoietic-supporting potency. Adult bone marrow mesenchymal stromal cells (MSCs) have cross-functional, intrinsic potency that is of therapeutic interest. Their ability to regenerate bone, fat, and cartilage, modulate the immune system, and nurture the growth and function of other bone marrow hematopoietic stem/progenitor cells have all been evaluated by transplant applications of MSCs. These applications require the isolation and expansion scaled cell production. To investigate biophysical properties of MSCs that can be feasibly utilized as predictors of bioactivity during biomanufacturing, we used a low-density seeding model to drive MSCs into proliferative stress and exhibit the hallmark characteristics of in vitro aging. A low-density seeding method was used to generate MSCs from passages 1–7 to simulate serial expansion of these cells to maximize yield from a single donor. MSCs were subjected to three bioactivity assays in parallel to ascertain whether patterns in MSC age, size, and shape were associated with the outcomes of the potency assays. MSC age was found to be a predictor of adipogenesis, while cell and nuclear shape was strongly associated to hematopoietic-supportive potency. Together, these data evaluate morphological changes associated with cell potency and highlight new strategies for purification or alternatives to assessing MSC quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Ab Initio Direct Dynamics

The reactivity and dynamics of molecular systems can be explored computationally by classical trajectory calculations. The traditional approach involves fitting a functional form of a potential energy surface (PES) to the energies from a large number of electronic structure calculations and then integrating numerous trajectories on this fitted PES to model the molecular dynamics. The ever-decreasing cost of computing and continuing advances in computational chemistry software have made it possible to use electronic structure calculations directly in molecular dynamics simulations without first having to construct a fitted PES. In this “on-the-fly” approach, every time the energy and its derivatives are needed for the integration of the equations of motion, they are obtained directly from quantum chemical calculations. This approach started to become practical in the mid-1990s as a result of increased availability of inexpensive computer resources and improved computational chemistry software. The application of direct dynamics calculations has grown rapidly over the last 25 years and would require a lengthy review article. The present Account is limited to some of our contributions to methods development and various applications. To improve the efficiency of direct dynamics calculations, we developed a Hessian-based predictor-corrector algorithm for integrating classical trajectories. Hessian updating made this even more efficient. Furthermore, this approach was also used to improve algorithms for following the steepest descent reaction paths. For larger molecular systems, we developed an extended Lagrangian approach in which the electronic structure is propagated along with the molecular structure. Strong field chemistry is a rapidly growing area, and to improve the accuracy of molecular dynamics in intense laser fields, we included the time-varying electric field in a novel predictor-corrector trajectory integration algorithm. Since intense laser fields can excite and ionize molecules, we extended our studies to include electron dynamics. Specifically, we developed code for time-dependent configuration interaction electron dynamics to simulate strong field ionization by intense laser pulses. Our initial application of ab initio direct dynamics in 1994 was to CH 2 O → H 2 + CO; the calculated vibrational distributions in the products were in very good agreement with experiment. In the intervening years, we have used direct dynamics to explore energy partitioning in various dissociation reactions, unimolecular dissociations yielding three fragments, reactions with branching after the transition state, nonstatistical dynamics of chemically activated molecules, dynamics of molecular fragmentation by intense infrared laser pulses, selective activation of specific dissociation channels by aligned intense infrared laser fields, angular dependence of strong field ionization, and simulation of sequential double ionization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine Learning-Assisted Recovery of Delicate Kinetic Information from Transient Reactor Experiments

Identifying active sites and their roles in chemical reaction steps remains a vital challenge in heterogeneous catalysis. Transient experiments offer a unique way to probe active sites and distinguish subtle kinetic features. Although physics-based analysis methods may be well-developed, they can be highly susceptible to experimental noise, and smoothing methods may erase or even distort important features; a smooth curve is not always the best curve. We demonstrate a new workflow for the direct interpretation of intrinsic kinetic information from exit flux curves measured in transient reactor experiments. This workflow contains three artificial neural networks (ANNs), including a noise reducer, a concentration predictor, and a rate predictor to analyze experimental data, followed by the virtual TAP (VTAP) physics-based reactor model and density functional theory (DFT) calculations of adsorption energies on specific sites. We use this workflow to analyze the data from experiments titrating Pt/Al 2 O 3 and Pt/SiO 2 catalysts with carbon monoxide (CO) in the temporal analysis of products (TAP) reactor. Our workflow separates the time-evolving chemical reaction and mass transfer information contained in the TAP pulse response. The existence of strong- and weak-binding sites on the Pt/Al 2 O 3 catalyst is observed in the catalyst titration experiment in the transient reactor. The structures of the strong- and weak-binding sites are then identified by using DFT calculations. We find that the Pt/SiO 2 catalyst has only strong-binding sites, which aligns with the inactive support effect of SiO 2 . We demonstrate how machine learning methods provide unique insights with high-resolution data analysis that cannot be achieved by using state-of-the-art physics-based methods.

Adsorption↗
DOE OSTI2012

Review of Rate Constants and Exploration of Correlations of the Halogen Transfer Reaction of Tri-substituted Carbon-centered Radicals with Molecular Halogens

Rate constants for the reaction (R 3C + X2 R 3CX + X ; X = F, Cl, Br, and I) are reviewed. Because of curved Arrhenius plots and negative EX values, empirical structure-reactivity correlations are sought for log kX,298 rather than EX. The well-known poor correlation with measures of reaction enthalpy is demonstrated. The best quantitative predictor for R 3C is p, the sum of the Hammett p constants for the three substituents, R . Electronegative substituents with lone pairs, such as halogen or oxygen, thus appear to destabilize the formation of a polarized pre-reaction complex and/or TS ( +R---X---X -) by -inductive/field electron withdrawal while simultaneously stabilizing them by -resonance electron donation. The best quantitative predictor of the reactivity order of the halogens, I2 > Br2 >> Cl2 F2, is the polarizability of the halogen, (X-X). For the data set of 60 rate constants which span 6.5 orders of magnitude, a modestly successful correlation of log kX,298 is achieved with only two parameters, p and (X-X), with a mean unsigned deviation of 0.59 log units. How much of this residual variance is the result of inaccuracies in the data compared with over-simplification of the correlation approach remains to be seen.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Deep Learning Estimation of Daily Ground–Level NO 2 Concentrations from Remote Sensing Data

The limited number of nitrogen dioxide (NO 2 ) surface measurements calls for the development of highly accurate approaches to estimating surface NO 2 concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO 2 concentrations over Texas in 2019. We use the deep convolutional neural network (Deep-CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO 2 estimation. Additionally, we leverage a novel approach, SHapley Additive exPlanations (SHAP), to describe how Deep-CNN understands each predictor variable. The SHAP results show that the Deep-CNN model has an advanced understanding of the dataset, revealing that TROPOMI closely captures levels of NO 2 . In addition, we show the superiority of our Deep-CNN model at estimating surface NO 2 over other well-known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO 2 concentrations, their accuracy is inferior to that of the Deep-CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO 2 estimation and ranks last among all models. Furthermore, testing the impact of sample size on model performance, we also show that, compared to other models, Deep-CNN needs more samples to trigger its strength at surface NO 2 estimation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predictive Proxies of Present and Future Lightning in a Superparameterized Model

Abstract A superparameterized climate model is used to assess the global performance of several previously proposed proxies for lightning. In particular, predictors incorporating hydrometeor (ice, graupel) profiles and convective vertical velocities are compared to observations, then used to estimate changes in flash rates with global warming. The choice of microphysics parameterization is also investigated, with all predictors showing higher correlations with Lightning Imaging Sensor/Optical Transient Detector observations when using a 2‐moment scheme compared to a 1‐moment representation. All proxies generally agree in their response to warming over tropical land, with notable decreases in Africa, the Middle East, and northern South America, but disagree over oceans and the midlatitudes. The product of convective available potential energy and precipitation predicts increases over these latter areas, as do the 2‐moment ice‐based proxies, while those of the 1‐moment model tend to show decreases, highlighting the importance of cloud microphysics when using climate models to simulate lightning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US

Abstract Burned areas in the western US have increased ten‐fold since 1980s, which are attributable to multiple factors, including increasing heat, changing precipitation patterns, and extended drought. To better understand how these factors contribute to large fire emissions (gridded monthly fire emissions >95th percentile of all the fire emissions in the western US; 0.009 Gg/month), we build a machine learning model to predict fire emissions (PM 2.5 ) over the western US at 0.25° resolution, interpreted using explainable artificial intelligence (XAI). From the predictor contributions derived from XAI, we conduct k‐means clustering analysis to identify four clusters of predictor variables representing different drivers of large fire emissions. The four clusters feature the contributions of fuel load (Cluster 1) and different levels of dryness (Cluster 2–4), controlled by fuel moisture, drought condition, and fire‐favorable large‐scale meteorological patterns featuring high temperature, high pressure, and low relative humidity. In the past two decades, large fire emissions peak in summer. However, large fire emissions increased significantly in September and October in 2010–2020 relative to 2000–2009, extending the peak large fire emissions from summer to autumn. The larger enhancements of large fire emissions during autumn compared to summer are contributed by decreased fuel moisture, along with more frequent concurrent fire‐favorable large‐scale meteorological patterns and drought. These results highlight fuel drying as a common driver supported by multiple drivers, such as warmer temperature and more frequent synoptic patterns favorable for fires, in increasing the autumn risk of large fire emissions across the western US.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Large‐Scale Statistically Meaningful Patterns (LSMPs) Associated With Precipitation Extremes Over Northern California

Abstract We analyze large‐scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k‐means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies two days before the onset, from October to March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as Northwest continental negative height anomaly, Eastward positive “Pacific‐North American Pattern (PNA),” Westward negative “PNA,” and Prominent Alaskan ridge. These four clusters, shown in multiple variables, evolve very differently and have differing links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula‐based framework for predicting PEx events, we find LSMP indices that are useful predictors of NorCal PEx events, with moisture‐based variables being the best predictors of PEx events at least 6 days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance tested. To ensure statistical rigor, the LSMPs analyzed here (with the modified acronym) include local tests of both significance and consistency, which are not always featured in the literature on large‐scale meteorological patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Updraft Width Modulates Ambient Atmospheric Controls on Convective Cloud Depth

Abstract The depth of convective clouds affects vertical transport of atmospheric constituents, influencing downstream weather and climate. Atmospheric controls on the maximum depth reached by moist convection are investigated with radar‐tracked convective cells tagged with sounding‐derived atmospheric parameters from a field campaign in central Argentina. Regression analyses show that narrow (<12‐km diameter) and wide (>16‐km diameter) cell depths respond to disparate factors, where cell areas are defined using composite reflectivity signatures. Undiluted lifted parcel indices including convective available potential energy (CAPE) and level of neutral buoyancy (LNB) are top predictors of wide cell maximum depth while mid‐tropospheric relative humidity is the top predictor of narrow cell maximum depth. Because narrow cells are more numerous than wide cells, the overall outcome of the full cell population does not strongly correlate with CAPE and LNB conditions. Tracked cells and atmospheric conditions in a simulation with 3‐km grid spacing covering the field campaign produce similar results to those observed. Narrow cells that are relatively deep have a cooler and moister mid‐troposphere with weaker free tropospheric subsidence, while relatively deep wide cells have much warmer and moister lower tropospheric conditions. These atmospheric differences are present 1 hr before cell initiation at both a fixed observing site and variable cell initiation locations. Simulated narrow cell maximum equivalent potential temperature decreases with height at a rate similar to the ambient vertical gradient, causing these cells to fall short of their LNB and supporting the view that entrainment‐driven dilution is a dominant control on their depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multidecadal Fluctuations in the Observed ENSO‐Tropical Cyclone Teleconnection

Abstract El Niño‐Southern Oscillation (ENSO) is a skillful predictor for seasonal tropical cyclone (TC) activity in most TC basins. This study examines recent changes in the observed ENSO‐TC teleconnection strength, as measured by ENSO modulation of hurricane frequency. We find that the ENSO‐North Atlantic TC teleconnection fluctuated over time, with the strongest relationship occurring from the 1980s to the mid‐2000s. In the western and eastern North Pacific, the ENSO‐TC teleconnection has strengthened in recent decades. Periods with a strong ENSO‐TC teleconnection are associated with more favorable environmental conditions for TCs, with higher values of genesis potential indices. Positive phases of the Atlantic Multidecadal Oscillation coincided with periods of strong ENSO‐TC teleconnections in the Atlantic and North Pacific basins. A weaker Atlantic ENSO‐TC relationship was associated with negative phases of the Pacific Decadal Oscillation and the North Atlantic Oscillation. This research reveals climate conditions that modulate ENSO's utility for seasonal TC prediction. Plain Language Summary El Niño‐Southern Oscillation (ENSO) is a useful predictor for seasonal tropical cyclone (TC) activity in many basins. Here we found that the strength of the ENSO‐TC teleconnection, represented as the correlation between ENSO and the number of hurricanes and accumulated cyclone energy, has changed in the historical record. The ENSO‐TC teleconnection in the North Atlantic fluctuated over time, with a weak relationship during the 1960s and 1970s and a strong relationship during the 1980s to mid‐2000s. Meanwhile, the ENSO‐TC teleconnection strengthened in the North Pacific in recent decades, with strong teleconnections after the 1980s in the western North Pacific and after the 2000s in the eastern North Pacific. Periods of strong ENSO‐TC teleconnections are associated with more favorable environmental conditions for TCs, including higher values of genesis potential indices and higher mid‐tropospheric humidity, as well as positive phases of the Atlantic Multidecadal Oscillation. Additionally, the negative phase of the Pacific Decadal Oscillation leads to strong/weak ENSO‐TC teleconnections in the eastern North Pacific and North Atlantic, respectively. Furthermore, a negative North Atlantic Oscillation is associated with a weak ENSO‐North Atlantic TC teleconnection. This research highlights variations in ENSO's effectiveness for seasonal TC prediction. Key Points The observed impact of ENSO on tropical cyclone (TC) activity exhibits multidecadal fluctuations The ENSO‐TC teleconnection was strong in the Atlantic from the 1980s to mid‐2000s and strengthened over the North Pacific in recent decades The ENSO‐TC teleconnection is stronger in the Atlantic and North Pacific basins during a positive Atlantic Multidecadal Oscillation

ENSO↗