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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Role of SPI-1 Secreted Effectors in Acute Bovine Response to Salmonella enterica Serovar Typhimurium: A Systems Biology Analysis Approach

Salmonella enterica Serovar Typhimurium (S. Typhimurium) causes enterocolitis with diarrhea and polymorphonuclear cell (PMN) influx into the intestinal mucosa in humans and calves. The Salmonella Type III Secretion System (T3SS) encoded at Pathogenicity Island I translocates Salmonella effector proteins SipA, SopA, SopB, SopD, and SopE2 into epithelial cells and is required for induction of diarrhea. These effector proteins act together to induce intestinal fluid secretion and transcription of C-X-C chemokines, recruiting PMNs to the infection site. While individual molecular interactions of the effectors with cultured host cells have been characterized, their combined role in intestinal fluid secretion and inflammation is less understood. We hypothesized that comparison of the bovine intestinal mucosal response to wild type Salmonella and a SipA, SopABDE2 effector mutant relative to uninfected bovine ileum would reveal heretofore unidentified diarrheaassociated host cellular pathways. To determine the coordinated effects of these virulence factors, a bovine ligated ileal loop model was used to measure responses to wild type S. Typhimurium (WT) and a ΔsipA, sopABDE2 mutant (MUT) across 12 hours of infection using a bovine microarray. Data were analyzed using standard microarray analysis and a dynamic Bayesian network modeling approach (DBN). Both analytical methods confirmed increased expression of immune response genes to Salmonella infection and novel gene expression. Gene expression changes mapped to 219 molecular interaction pathways and 1620 gene ontology groups. Bayesian network modeling identified effects of infection on several interrelated signaling pathways including MAPK, Phosphatidylinositol, mTOR, Calcium, Toll-like Receptor, CCR3, Wnt, TGF-β, and Regulation of Actin Cytoskeleton and Apoptosis that were used to model of host-pathogen interactions. Comparison of WT and MUT demonstrated significantly different patterns of host response at early time points of infection (15 minutes, 30 minutes and one hour) within phosphatidylinositol, CCR3, Wnt, and TGF-β signaling pathways and the regulation of actin cytoskeleton pathway.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
NASA NTRS2001

Improving the Capture and Re-Use of Data with Wearable Computers

At the Goddard Space Flight Center, members of the Real-Time Software Engineering Branch are developing a wearable, wireless, voice-activated computer for use in a wide range of crosscutting space applications that would benefit from having instant Internet, network, and computer access with complete mobility and hands-free operations. These applications can be applied across many fields and disciplines including spacecraft fabrication, integration and testing (including environmental testing), and astronaut on-orbit control and monitoring of experiments with ground based experimenters. To satisfy the needs of NASA customers, this wearable computer needs to be connected to a wireless network, to transmit and receive real-time video over the network, and to receive updated documents via the Internet or NASA servers. The voice-activated computer, with a unique vocabulary, will allow the users to access documentation in a hands free environment and interact in real-time with remote users. We will discuss wearable computer development, hardware and software issues, wireless network limitations, video/audio solutions and difficulties in language development.

Pfarr, Barbara↗
DOE OSTI2019

Effect of nanotube coupling on exciton transport in polymer-free monochiral semiconducting carbon nanotube networks

Semiconducting single-walled carbon nanotubes (s-SWCNTs) are attractive light-harvesting components for solar photoconversion schemes and architectures, and selective polymer extraction has emerged as a powerful route to obtain highly pure s-SWCNT samples for electronic applications. Here we demonstrate a novel method for producing electronically coupled thin films of near-monochiral s-SWCNTs without wrapping polymer. Detailed steady-state and transient optical studies on such samples provide new insights into the role of the wrapping polymer on controlling intra-bundle nanotube–nanotube interactions and exciton energy transfer within and between bundles. Complete removal of polymer from the networks results in rapid exciton trapping within nanotube bundles, limiting long-range exciton transport. The results suggest that intertube electronic coupling and associated exciton delocalization across multiple tubes can limit diffusive exciton transport. Here, the complex relationship observed here between exciton delocalization, trapping, and long-range transport, helps to inform the design, preparation, and implementation of carbon nanotube networks as active elements for optical and electronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Kinematic Flexibility Analysis: Hydrogen Bonding Patterns Impart a Spatial Hierarchy of Protein Motion

Elastic network models (ENMs) and constraint-based, topological rigidity analysis are two distinct, coarse-grained approaches to study conformational flexibility of macromolecules. In the two decades since their introduction, both have contributed significantly to insights into protein molecular mechanisms and function. However, despite a shared purpose of these approaches, the topological nature of rigidity analysis, and thereby the absence of motion modes, has impeded a direct comparison. We present an alternative, kinematic approach to rigidity analysis, which circumvents these drawbacks. We introduce a novel protein hydrogen bond network spectral decomposition, which provides an orthonormal basis for collective motions modulated by noncovalent interactions, analogous to the eigenspectrum of normal modes. The zero modes decompose proteins into rigid clusters identical to those from topological rigidity, while nonzero modes rank protein motions by their hydrogen bond collective energy penalty. Our kinematic flexibility analysis bridges topological rigidity theory and ENM, enabling a detailed analysis of motion modes obtained from both approaches. Analysis of a large, structurally diverse data set revealed that collectivity of protein motions, reported by the Shannon entropy, is significantly reduced for rigidity theory compared to normal mode approaches. Strikingly, kinematic flexibility analysis suggests that the hydrogen bonding network encodes a protein-fold specific, spatial hierarchy of motions, which goes nearly undetected in ENM. This hierarchy reveals distinct motion regimes that rationalize experimental and simulated protein stiffness variations. Kinematic motion modes highly correlate with reported crystallographic B factors and molecular dynamics simulations of adenylate kinase. A formal expression for changes in free energy derived from the spectral decomposition indicates that motions across nearly 40% of modes obey enthalpy–entropy compensation. Taken together, our results suggest that hydrogen bond networks have evolved to modulate protein structure and dynamics, which can be efficiently probed by kinematic flexibility analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Scaling effects of defects in fiber-reinforced composites

Material defects may be introduced willingly or unwillingly during material manufacturing and structural component fabrication stages. Their presence in the material plays a dominant role in determining the material's strength and the associate failure mechanisms. In the sense that the size and the number of defects may increase with the volume of the material, the effect of dimensional scaling may manifest itself in the dependence of material strength on volume. Or, alternatively, there may exist a scaling effect of material defects. In fiber-reinforced composites, manufacturing or fabrication defects may come in several forms: matrix voids, matrix microcracks, fiber misalignment, broken fibers, or interface disbonds, just to mention a few. These are interacting and competing defects in the sense that one type of defect may become dominant under one stress condition and another type of defect may become dominant under a different stress condition. This happens because the fiber reinforcement network, together with the distribution of defects, constitutes the prime microstructure of the composite, and there exist continued interactions between the evolving microstructure and the distribution of defects. In the process, the scaling effects of defects are complicated by this interaction. In this presentation, the scaling effects of defects in fiber-reinforced composites will be briefly discussed with the introduction of the concept of effective defects. It is then shown with the aid of some actual experimental and analysis results that the scaling effects are very much present, but they are regulated by the characteristic dimension of the composite microstructure due to the aforementioned microstructure-defect interaction effect.

Wang, A. S. D.↗
DOE OSTI2024

Structural studies of intrinsically disordered MLL -fusion protein AF9 in complex with peptidomimetic inhibitors

AF9 (MLLT3) and its paralog ENL(MLLT1) are members of the YEATS family of proteins with important role in transcriptional and epigenetic regulatory complexes. These proteins are two common MLL fusion partners in MLL -rearranged leukemias. The oncofusion proteins MLL-AF9/ENL recruit multiple binding partners, including the histone methyltransferase DOT1L, leading to aberrant transcriptional activation and enhancing the expression of a characteristic set of genes that drive leukemogenesis. The interaction between AF9 and DOT1L is mediated by an intrinsically disordered C-terminal ANC1 homology domain (AHD) in AF9, which undergoes folding upon binding of DOT1L and other partner proteins. We have recently reported peptidomimetics that disrupt the recruitment of DOT1L by AF9 and ENL, providing a proof-of-concept for targeting AHD and assessing its druggability. Intrinsically disordered proteins, such as AF9 AHD, are difficult to study and characterize experimentally on a structural level. In this study, we present a successful protein engineering strategy to facilitate structural investigation of the intrinsically disordered AF9 AHD domain in complex with peptidomimetic inhibitors by using maltose binding protein (MBP) as a crystallization chaperone connected with linkers of varying flexibility and length. The strategic incorporation of disulfide bonds provided diffraction-quality crystals of the two disulfide-bridged MBP–AF9 AHD fusion proteins in complex with the peptidomimetics. These successfully determined first series of 2.1–2.6 Å crystal complex structures provide high-resolution insights into the interactions between AHD and its inhibitors, shedding light on the role of AHD in recruiting various binding partner proteins. We show that the overall complex structures closely resemble the reported NMR structure of AF9 AHD/DOT1L with notable difference in the conformation of the β-hairpin region, stabilized through conserved hydrogen bonds network. These first series of AF9 AHD/peptidomimetics complex structures are providing insights of the protein–inhibitor interactions and will facilitate further development of novel inhibitors targeting the AF9/ENL AHD domain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Lignin-Based Polyurethanes from Unmodified Kraft Lignin Fractionated by Sequential Precipitation

In this work, lignin-based polyurethanes were synthesized from three unmodified softwood Kraft lignin fractions as the primary hydroxyl group providers which constituted >40% of the polymeric materials. High-, medium-, and low-molecular-weight Kraft lignin fractions were prepared by sequential precipitation from the acetone–methanol cosolvent by adding hexane as an antisolvent. A polyether, poly(ethylene glycol), and a polyester, poly(trimethylene glutarate), were selected as secondary polyols to form the soft segments in the lignin-based network. At a fixed NCO/OH ratio (1:1), the effects of lignin macromolecular features and their intermolecular interactions with the secondary polyols on mechanical and thermal properties of the lignin-based polyurethanes were investigated by tensile tests, DMA, SEM, and TGA. Because of the unique association behavior of macromolecular lignin, the sequential precipitation method was able to produce Kraft lignin fractions possessing very high molecular weights. The morphological study showed that the polyurethane network was assembled by cross-linking nanosized lignin complexes through urethane links. It was discovered that the mechanical properties of the lignin-based polyurethanes can be tuned by lignin molecular weight and the incorporation of a secondary polyol. Compared to poly(trimethylene glutarate), poly(ethylene glycol) interacts strongly with associated lignin complexes through hydrogen bonding. It promoted dissociation of the lignin complexes and consequently improved reactivity between lignin and diisocyanate. Moreover, at PEG/lignin = 1:4 (w/w), the soft segments formed by PEG enhanced the stiffness of the lignin-based polyurethanes; when the PEG content was increased to PEG/lignin = 2:3 (w/w), plastic deformation can be observed for the stress–strain curves of all corresponding lignin-based polyurethanes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Trust based attachment

In social systems subject to indirect reciprocity, a positive reputation is key for increasing one’s likelihood of future positive interactions. The flow of gossip can amplify the impact of a person’s actions on their reputation depending on how widely it spreads across the social network, which leads to a percolation problem. To quantify this notion, we calculate the expected number of individuals, the “audience”, who find out about a particular interaction. For a potential donor, a larger audience constitutes higher reputational stakes, and thus a higher incentive, to perform “good” actions in line with current social norms. For a receiver, a larger audience therefore increases the trust that the partner will be cooperative. This idea can be used for an algorithm that generates social networks, which we call trust based attachment (TBA). TBA produces graphs that share crucial quantitative properties with real-world networks, such as high clustering, small-world behavior, and powerlaw degree distributions. We also show that TBA can be approximated by simple friend-of-friend routines based on triadic closure, which are known to be highly effective at generating realistic social network structures. Therefore, our work provides a new justification for triadic closure in social contexts based on notions of trust, gossip, and social information spread. These factors are thus identified as potential significant influences on how humans form social ties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

A 30 kpc CHAIN OF ''BEADS ON A STRING'' STAR FORMATION BETWEEN TWO MERGING EARLY TYPE GALAXIES IN THE CORE OF A STRONG-LENSING GALAXY CLUSTER

New Hubble Space Telescope ultraviolet and optical imaging of the strong-lensing galaxy cluster SDSS J1531+3414 (z = 0.335) reveals two centrally dominant elliptical galaxies participating in an ongoing major merger. The interaction is at least somewhat rich in cool gas, as the merger is associated with a complex network of 19 massive superclusters of young stars (or small tidal dwarf galaxies) separated by ∼1 kpc in projection from one another, combining to an estimated total star formation rate of ∼5 M {sub ☉} yr{sup –1}. The resolved young stellar superclusters are threaded by narrow Hα, [O II], and blue excess filaments arranged in a network spanning ∼27 kpc across the two merging galaxies. This morphology is strongly reminiscent of the well-known ''beads on a string'' mode of star formation observed on kiloparsec scales in the arms of spiral galaxies, resonance rings, and in tidal tails between interacting galaxies. Nevertheless, the arrangement of this star formation relative to the nuclei of the two galaxies is difficult to interpret in a dynamical sense, as no known ''beads on a string'' systems associated with kiloparsec-scale tidal interactions exhibit such lopsided morphology relative to the merger participants. In this Letter, we present the images and follow-up spectroscopy and discuss possible physical interpretations for the unique arrangement of the young stellar clusters. While we suggest that this morphology is likely to be dynamically short-lived, a more quantitative understanding awaits necessary multiwavelength follow-up, including optical integral field spectroscopy, ALMA submillimeter interferometry, and Chandra X-ray imaging.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Nucleosynthesis and the rp-process

Production of elements heavier than iron, their abundance and cite of production remain an active field of research to-date. In this paper I would present a brief review of the nucleosynthesis process and then focus further on the proton capture processes with particular emphasis on the nuclear physics aspects of the rp-process. The present calculation clearly shows that the electron capture rates on waiting point nuclei are at least of similar magnitude as the competing positron decay rates under rp-process conditions. The study strongly suggests that electron capture rates form an integral part of weak-interaction mediated rates under rp-process conditions and should not be neglected in nuclear reaction network calculations as done in past.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model predictions for combining PAH mixtures in agreement with microarrays ► Predictions highly dependent on aryl hydrocarbon receptor repressor expression.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1993

OMP-26 - An interactive scheduling tool for the 26 meter subnetwork

Operations Mission Planner for the 26 Meter (26M) Antenna Subnetwork (OMP-26) is an interactive scheduling tool developed by the Jet Propulsion Laboratory (JPL) to assist in allocating the Deep Space Network (DSN) 26M antennas to support Earth-orbiting Spacecraft (SC). Prior to integrating OMP-26 into the 26M scheduling arena, the text-based, labor-intensive task lacked the sufficient means to support the 1992 mission support increase. Therefore, JPL developed the OMP-26 interactive scheduling tool to automate the time consuming tasks of the existing, manual scheduling process. OMP-26 has addressed the scheduling dilemma by assisting schedulers to identify and resolve conflicts more quickly through its graphical display features and constraint-checking editing capability. Although OMP-26 has reduced the scheduler's existing workload by easing the identification and resolution of conflicts, it will not meet the needs of another mission support increase to occur by 1995. Therefore, future plans for OMP-26 involve transitioning from a solely interactive scheduling tool to one that is highly automated and more powerful.

Rosas, Jody L.↗
DOE OSTI2023

Non-reciprocal microwave window

A non-reciprocal microwave network is provided that includes an in-line ferromagnetic element with adjoining polarizing adapters to achieve directivity via a multi-mode interaction at or near the ferrite to act as new class of 4-port circulator or 2-port isolator, with standard waveguide inputs for assembly in larger networks.

Franzi, Matthew A.↗
DOE OSTI2015

A new parameter-free soft-core potential for silica and its application to simulation of silica anomalies

A core-softening of the effective interaction between oxygen atoms in water and silica systems and its role in developing anomalous thermodynamic, transport, and structural properties have been extensively debated. For silica, the progress with addressing these issues has been hampered by a lack of effective interaction models with explicit core-softening. In this work, we present an extension of a two-body soft-core interatomic force field for silica recently reported by us [S. Izvekov and B. M. Rice, J. Chem. Phys. 136(13), 134508 (2012)] to include three-body forces. Similar to two-body interaction terms, the three-body terms are derived using parameter-free force-matching of the interactions from ab initio MD simulations of liquid silica. The derived shape of the O–Si–O three-body potential term affirms the existence of repulsion softening between oxygen atoms at short separations. The new model shows a good performance in simulating liquid, amorphous, and crystalline silica. By comparing the soft-core model and a similar model with the soft-core suppressed, we demonstrate that the topology reorganization within the local tetrahedral network and the O–O core-softening are two competitive mechanisms responsible for anomalous thermodynamic and kinetic behaviors observed in liquid and amorphous silica. The studied anomalies include the temperature of density maximum locus and anomalous diffusivity in liquid silica, and irreversible densification of amorphous silica. We show that the O–O core-softened interaction enhances the observed anomalies primarily through two mechanisms: facilitating the defect driven structural rearrangements of the silica tetrahedral network and modifying the tetrahedral ordering induced interactions toward multiple characteristic scales, the feature which underlies the thermodynamic anomalies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

SuperCDMS Event Reconstruction Using Convolutional Neural Networks [Thesis]

The SuperCDMS experiment uses cryogenic silicon and germanium detectors to search for dark matter candidates such as WIMPs (Weakly Interacting Massive Particles) streaming through the Earth. Collisions in the silicon and germanium crystals are expected to produce phonons whose thermal signatures can be measured. This thesis first describes the integration of a new Signal Distribution Unit (SDU) to the SuperCDMS data acquisition system, which allows for synchronization of multiple detectors and electronic/mechanical noise characterization via accelerometer, antenna, and AC phase measurements. From SuperCDMS detector data it is necessary to reconstruct the energies of the particle events. This thesis explores the use of Convolutional Neural Networks (CNNs) to perform this reconstruction and finds that, although they perform well, changing the noise model breaks the model and requires the neural network to be retrained. In order to mitigate this issue, a new CNN model is proposed which includes the noise Power Spectral Density (PSD) of the data as an additional input to the CNN. While it proves to be effective as a denoising algorithm, it still fails for data with a different noise model. However, including data from multiple PSDs in the neural network training sample allows it to handle data with different types of noise while still maintaining the quality of the reconstruction. Nevertheless, neural networks trained even on multiple PSDs do not robustly handle data taken with PSDs dissimilar to those in the training sample, suggesting that CNNs may need to be retrained whenever the noise environment changes in a significant way.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2011

Atypical Behavior Identification in Large Scale Network Traffic

Cyber analysts are faced with the daunting challenge of identifying exploits and threats within potentially billions of daily records of network traffic. Enterprise-wide cyber traffic involves hundreds of millions of distinct IP addresses and results in data sets ranging from terabytes to petabytes of raw data. Creating behavioral models and identifying trends based on those models requires data intensive architectures and techniques that can scale as data volume increases. Analysts need scalable visualization methods that foster interactive exploration of data and enable identification of behavioral anomalies. Developers must carefully consider application design, storage, processing, and display to provide usability and interactivity with large-scale data. We present an application that highlights atypical behavior in enterprise network flow records. This is accomplished by utilizing data intensive architectures to store the data, aggregation techniques to optimize data access, statistical techniques to characterize behavior, and a visual analytic environment to render the behavioral trends, highlight atypical activity, and allow for exploration.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Deep learning to represent subgrid processes in climate models

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

54 ENVIRONMENTAL SCIENCES↗