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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 sources NASA NTRS DOE OSTI

DOE OSTI2026

AGFormer: Adaptive Spatiotemporal graph informed transformer for multi-reservoir inflow forecasting

Accurate reservoir inflow forecasting is crucial for effective water resource management, yet most machine learning models focus on single-reservoir prediction and overlook spatial dependencies among hydrologically connected reservoirs. Here, we propose AGFormer (Adaptive Graph-Informed Transformer), an end-to-end framework that integrates adaptive graph learning with temporal sequence modeling for multi-reservoir inflow forecasting. A shared encoder and graph attention mechanism generate reservoir-specific embeddings, which are then processed by the Transformer-based encoder–decoder for multi-step inflow forecasting. We also introduce a pretraining paradigm to learn robust temporal embeddings from misaligned historical records. Evaluated on 30 reservoirs in the Upper Colorado River Basin, AGFormer achieves superior seven-day-ahead forecasts, with NSE > 0.75 for 20 reservoirs—outperforming Encoder–Decoder LSTM, GCN+LSTM, and Transformer baselines. Adaptive graph learning captures dynamic inter-reservoir dependencies, and feature attribution aligns with snowmelt-driven hydrology. Incorporating forecasted meteorological inputs further enhances accuracy, demonstrating AGFormer’s potential to support reservoir management under dynamic hydrological conditions.

Adaptive graph learning↗

DOE OSTI2022

Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model

Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively.

Wu, Dali (ORCID:0000000231177074)↗

NASA NTRS1994

Characteristics of civil aviation atmospheric hazards

Clear air turbulence, wake vortices, dry hail, and volcanic ash are hazards to civil aviation that have not been brought to the forefront of public attention by a catastrophic accident. However, these four hazards are responsible for major and minor injuries, emotional trauma, significant aircraft damage, and in route and terminal area inefficiency. Most injuries occur during clear air turbulence. There is significant aircraft damage for any volcanic ash encounter. Rolls induced by wake vortices occur near the ground. Dry hail often appears as an area of weak echo on the weather radar. This paper will present the meteorological, electromagnetic, and spatiotemporal characteristics of each hazard. A description of a typical aircraft encounter with each hazard will be given. Analyzed microwave and millimeter wave sensor systems to detect each hazard will be presented.

Marshall, Robert E.↗

DOE OSTI2026

Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

Data imputation↗

DOE OSTI2022

Optogenetics Illuminates Applications in Microbial Engineering

Optogenetics has been used in a variety of microbial engineering applications, such as chemical and protein production, studies of cell physiology, and engineered microbe–host interactions. These diverse applications benefit from the precise spatiotemporal control that light affords, as well as its tunability, reversibility, and orthogonality. This combination of unique capabilities has enabled a surge of studies in recent years investigating complex biological systems with completely new approaches. We briefly describe the optogenetic tools that have been developed for microbial engineering, emphasizing the scientific advancements that they have enabled. In particular, we focus on the unique benefits and applications of implementing optogenetic control, from bacterial therapeutics to cybergenetics. Lastly, we discuss future research directions, with special attention given to the development of orthogonal multichromatic controls. With an abundance of advantages offered by optogenetics, the future is bright in microbial engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

KamNet: An integrated spatiotemporal deep neural network for rare event searches in KamLAND-Zen

Rare event searches allow us to search for new physics at energy scales inaccessible with other means by leveraging specialized large-mass detectors. Machine learning provides a new tool to maximize the information provided by these detectors. The information is sparse, which forces these algorithms to start from the lowest level data and exploit all symmetries in the detector to produce results. In this work we present KamNet, which harnesses breakthroughs in geometric deep learning and spatiotemporal data analysis to maximize the physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator detector searching for 0⁢νβ⁢β. Using a simplified background model for KamLAND, we show that KamNet outperforms a conventional convolutional neural network (CNN) on benchmarking Monte Carlo simulations with an increasing level of robustness. Using simulated data, we then demonstrate KamNet's ability to increase KamLAND-Zen's sensitivity to 0ν⁢ββ and 2⁢ν⁢β⁢β decay to excited states. A key component of this work is the addition of an attention mechanism to elucidate the underlying physics KamNet is using for the background rejection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Responses of the Plant Cell Wall to Sub-Zero Temperatures: A Brief Update

Our general understanding of plant responses to sub-zero temperatures focuses on mechanisms that mitigate stress to the plasma membrane. The plant cell wall receives comparatively less attention, and questions surrounding its role in mitigating freezing injury remain unresolved. Despite recent molecular discoveries that provide insight into acclimation responses, the goal of reducing freezing injury in herbaceous and woody crops remains elusive. This is likely due to the complexity associated with adaptations to low temperatures. Understanding how leaf cell walls of herbaceous annuals promote tissue tolerance to ice does not necessarily lead to understanding how meristematic tissues are protected from freezing by tissue-level barriers formed by cell walls in overwintering tree buds. In this mini-review, we provide an overview of biological ice nucleation and explain how plants control the spatiotemporal location of ice formation. We discuss how sugars and pectin side chains alleviate adhesive injury that develops at sub-zero temperatures between the matrix polysaccharides and ice. The importance of site-specific cell-wall elasticity to promote tissue expansion for ice accommodation and control of porosity to impede ice growth and promote supercooling will be presented. How specific cold-induced proteins modify plant cell walls to mitigate freezing injury will also be discussed. The opinions presented in this report emphasize the importance of a plant’s developmental physiology when characterizing mechanisms of freezing survival.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are threefold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws governing vehicle dynamics into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Using metacommunity ecology to understand environmental metabolomes

Abstract Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Issues of Long-Term Cryogenic Propellant Storage in Microgravity

Modern multi-layer insulation (MLI) allows to sharply reduce the heat leak into cryogenic propellant storage tanks through the tank surface and, as a consequence, significantly extend the storage duration. In this situation the MLI penetrations, such as support struts, feed lines, etc., become one of the most significant challenges of the tanks heat management. This problem is especially acute for liquid hydrogen (LH2) storage, since currently no efficient cryocoolers exist that operate at very low LH2 temperatures (20K). Even small heat leaks under microgravity conditions and over the period of many months give rise to a complex slowly-developing, large-scale spatiotemporal physical phenomena in a multi-phase liquid-vapor mixture. These phenomena are not well-understood nor can be easily controlled. They can be of a potentially hazardous nature for long-term on-orbital cryogenic torage, propellant loading, tank chilldown, engine restart, and other in-space cryogenic fluid management operations. To support the engineering design solutions that would mitigate these effects a detailed physics-based analysis of heat transfer, vapor bubble formation, growth, motion, coalescence and collapse is required in the presence of stirring jets of different configurations and passive cooling devices such as MLI, thermodynamic vent system, and vapor-cooled shield. To develop physics-based models and correlations reliable for microgravity conditions and long-time scales there is a need for new fundamental data to be collected from on-orbit cryogenic storage experiments. Our report discusses some of these physical phenomena and the design requirements and future studies necessary for their mitigation. Special attention is payed to the phenomena occurring near MLI penetrations.

Muratov, C. B.↗

DOE OSTI2024

Evaluating the effects of heatwave events on hydrological processes in the contiguous United States (2003–2022)

Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. Further, the spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Optical and Microphysical Retrievals of Marine Stratocumulus Clouds off the Coast of Namibia from Satellite and Aircraft

Though the emphasis of the Southern Africa Regional Science Initiative 2000 (SAFARI-2000) dry season campaign was largely on emission sources and transport, the assemblage of aircraft (including the high altitude NASA ER-2 remote sensing platform and the University of Washington CV-580, UK MRF C-130, and South African Weather Bureau JRA in situ aircrafts) provided a unique opportunity for cloud studies. Therefore, as part of the SAFARI initiative, investigations were undertaken to assess regional aerosol-cloud interactions and cloud remote sensing algorithms. In particular, the latter part of the experiment concentrated on marine boundary layer stratocumulus clouds off the southwest coast of Africa. Associated with cold water upwelling along the Benguela current, the Namibian stratocumulus regime has received limited attention but appears to be unique for several reasons. During the dry season, outflow of continental fires and industrial pollution over this area can be extreme. From below, upwelling provides a rich nutrient source for phytoplankton (a source of atmospheric sulfur through DMS production as well as from decay processes). The impact of these natural and anthropogenic sources on the microphysical and optical properties of the stratocumulus is unknown. Continental and Indian Ocean cloud systems of opportunity were also studied during the campaign. SAFARI 2000 aircraft flights off the coast of Namibia were coordinated with NASA Terra Satellite overpasses for synergy with the Moderate Resolution Imaging Spectroradiometer (MODIS) and other Terra instruments. MODIS was developed by NASA and launched onboard the Terra spacecraft on December 18, 1999 (and Aqua spacecraft on May 4, 2002). Among the remote sensing algorithms developed and applied to this sensor are cloud optical and microphysical properties that include cloud thermodynamic phase, optical thickness, and effective particle radius of both liquid water and ice clouds. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, and fundamental atmospheric research. The archived MODIS Collection 5 cloud products processing stream will be used to analyze low water cloud scenes off the Namibian and Angolan coasts during SAFARI 2000 time period, as well as other years. Pixel-level Terra and Aqua MODIS retrievals (l. km spatial resolution at nadir) and gridded (1' uniform grid) statistics of cloud optical thickness and effective particle radius will be presented, including joint probability distributions between the two quantities. In addition, perspectives from the MODIS Airborne Simulator, which flew on the ER-2 during SAFARI 2000 providing high spatial resolution retrievals (50 m at nadir), will be presented as appropriate. The H-SAF Program requires an experimental operational European-centric Satellite Precipitation Algorithm System (E-SPAS) that produces medium spatial resolution and high temporal resolution surface rainfall and snowfall estimates over the Greater European Region including the Greater Mediterranean Basin. Currently, there are various types of experimental operational algorithm methods of differing spatiotemporal resolutions that generate global precipitation estimates. This address will first assess the current status of these methods and then recommend a methodology for the H-SAF Program that deviates somewhat from the current approach under development but one that takes advantage of existing techniques and existing software developed for the TRMM Project and available through the public domain.

Platnick, Steven E.↗

DOE OSTI2023

An adaptive synchronous extraction (ASE) method for estimating intensity and footprint of surface urban heat islands: A case study of 254 North American cities

The urban heat island (UHI) effect has attracted great attention due to its potential impacts on rapidly growing urban areas. Using remotely sensed estimates of land surface temperature (LST), a large number of studies have focused on the surface UHI (SUHI) effect, which can be characterized by its two fundamental properties: intensity and footprint. The SUHI intensity reflects the LST difference between the urban area and the background reference area (BRA), and the SUHI footprint indicates the spatial extent influenced by the heat island. Currently, numerous methods have been developed to estimate the SUHI intensity and footprint, but are still greatly challenged by three main issues. Namely, the discrepancy in BRA selection criterion brings great uncertainty to the estimated SUHI intensity, the estimation of SUHI footprint is largely constrained by the predefined models, and the quantification of SUHI effect is potentially influenced by several confounding factors. Here, we proposed an adaptive synchronous extraction (ASE) method, which is capable of adaptively selecting the most optimal BRA while removing the influence of confounding factors, and achieving synchronous estimation of SUHI intensity and footprint. We applied the ASE method to 254 North American cities and conducted an in-depth comparative analysis to discuss its applicability and benefits. The main results include: (1) The ASE method avoids the limitations of existing methods in BRA selection and model presetting, and shows resilience to parameter variations. This makes the ASE method highly applicable to quantify the SUHI intensity and footprint in cities with various thermal characteristics. (2) The ASE method can better highlight the spatial, seasonal and day-night contrasts in the estimated SUHI intensity. This superiority is particularly evident when comparing it to methods based on the equal-area buffer or the simplified urban-extent algorithm. (3) Confounding factors pose non-negligible impacts on the quantification of the SUHI effect. Typically, ignoring the influence of topographic relief or missing LST data can lead to an overall overestimation of the SUHI intensity, while not removing surrounding urban areas will cause some underestimation of the SUHI intensity. In conclusion, overall, the proposed ASE method provides a new generalizable tool for quantifying the SUHI effect, which has great potentials for future studies and urban climate assessments.

54 ENVIRONMENTAL SCIENCES↗