Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Controllable synthesis of cobalt oxide nanoflakes on three-dimensional porous cobalt networks as high-performance cathode for alkaline hybrid batteries

Highlights: • Construct self-supported porous Co networks. • Porous Co/CoO composite films show high capacity and good cycling life. • Porous conductive metal network is favorable for fast ion/electron transfer. - Abstract: Herein we report porous three-dimensional cobalt networks supported CoO nanoflakes by the combination of successive electro-deposition methods. The electrodeposited Co networks have average large pores of ∼5 μm and all the branches are composed of interconnected nanoparticles. CoO nanoflakes with thickness of ∼15 nm are uniformly coated on the Co networks forming self-supported Co/CoO composite films. The as-prepared Co/CoO composite films possess combined properties of porous structure and strong mechanical stability. As cathode for alkaline hybrid batteries, the Co/CoO composite films exhibit good electrochemical performances with high capacity of 83.5 mAh g{sup −1} at 1 A g{sup −1} and stable high-rate cycling life (65 mAh g{sup −1} at 10 A g{sup −1} after 15,000 cycles). The hierarchical porous architecture provides positive roles in the enhancement of electrochemical properties, including fast electronic transportation path, short diffusion of ions and high contact area between the active material and the electrolyte.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Tau covariance patterns in Alzheimer's disease patients match intrinsic connectivity networks in the healthy brain

According to the network model of neurodegeneration, the spread of pathogenic proteins occurs selectively along connected brain regions. We tested in vivo whether the distribution of filamentous tau (measured with [ 18 F]flortaucipir-PET), fibrillar amyloid-β ([ 11 C]PIB-PET) and glucose hypometabolism ([ 18 F]FDG-PET) follows the intrinsic functional organization of the healthy brain. We included 63 patients with Alzheimer's disease (AD; 30 male, 63 ± 8 years) who underwent [ 18 F]flortaucipir, [ 11 C]PIB and [ 18 F]FDG PET, and 1000 young adults (427 male, 21 ± 3 years) who underwent task-free fMRI. We selected six predefined disease epicenters as seeds for whole-brain voxelwise covariance analyses to compare correlated patterns of tracer uptake across AD patients against fMRI intrinsic connectivity patterns in young adults. We found a striking convergence between [18F]flortaucipir covariance patterns and intrinsic connectivity maps (range Spearman rho's: 0.32-0.78, p < .001), which corresponded with expected functional networks (range goodness-of-fit: 3.8-8.2). The topography of amyloid-β covariance patterns was more diffuse and less network-specific, while glucose hypometabolic patterns were more spatially restricted than tau but overlapped with functional networks. These findings suggest that the spatial patterns of tau and glucose hypometabolism observed in AD resemble the functional organization of the healthy brain, supporting the notion that tau pathology spreads through circumscribed brain networks and drives neurodegeneration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Nanopore networks in colloidal silica assemblies characterized by XCT for confined fluid flow modeling

Fluid flow through nanopore structures has exhibited different behavior than that described by Darcy's law, derived from pore networks of micrometer scale. Here, pressure assemblies of colloidal silica spheres of specified diameters from 94 to 620 nanometers were prepared as model nano-porous media. The smaller silica spheres tended to form random packing, but some substantial ordering was found in the packing of the larger silica spheres. X-ray computed tomography (XCT), with a voxel resolution of 16 nm, was used to characterize the nanopore networks. The porosity of the Nano-XCT pore network (34%) was less than the total porosity of the assemblies as determined by X-ray attenuation (52%), possibly due to the limits of voxel resolution during segmentation. Fluid flow in the nanopore networks was simulated using the single-phase Lattice Boltzmann Method, and the simulated permeability was compared with the empirical and experimental values. Based on our characterization, although a well-ordered packing of silica spheres was not achieved, it was found that the nanopore networks in the colloidal silica assemblies had pore size distributions corresponding to the particle sizes. The simulated permeability was less than the experimental measurement for water flow, but the complex packing of silica spheres and surface chemistry issues need to be considered in future research.

02 PETROLEUM↗
DOE OSTI2013

Networks of recyclable material waste-picker’s cooperatives: An alternative for the solid waste management in the city of Rio de Janeiro

Highlights: ► In the marketing of recyclable materials, the waste-pickers are the least wins. ► It is proposed creating a network of recycling cooperatives to achieve viability. ► The waste-pickers contribute to waste management to the city. - Abstract: The objective of this study is to discuss the role of networks formed of waste-picker cooperatives in ameliorating problems of final disposal of solid waste in the city of Rio de Janeiro, since the city’s main landfill will soon have to close because of exhausted capacity. However, it is estimated that in the city of Rio de Janeiro there are around five thousand waste-pickers working in poor conditions, with lack of physical infrastructure and training, but contributing significantly by diverting solid waste from landfills. According to the Sustainable Development Indicators (IBGE, 2010a,b) in Brazil, recycling rates hover between 45% and 55%. In the municipality of Rio de Janeiro, only 1% of the waste produced is collected selectively by the government (COMLURB, 2010), demonstrating that recycling is mainly performed by waste-pickers. Furthermore, since the recycling market is an oligopsony that requires economies of scale to negotiate directly with industries, the idea of working in networks of cooperatives meets the demands for joint marketing of recyclable materials. Thus, this work presents a method for creating and structuring a network of recycling cooperatives, with prior training for working in networks, so that the expected synergies and joint efforts can lead to concrete results. We intend to demonstrate that it is first essential to strengthen the waste-pickers’ cooperatives in terms of infrastructure, governance and training so that solid waste management can be environmentally, socially and economically sustainable in the city of Rio de Janeiro.

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

Ruta graveolens water extract inhibits cell-cell network formation in human umbilical endothelial cells via MEK-ERK1/2 pathway

Highlights: • RGWE inhibits HUVEC ability to form tubes on matrigel in vitro. • RGWE interferes, through MEK/ERK, with the VEGF-mediated signaling that leads to angiogenesis. • RGWE is proposed as a new therapeutic tool to prevent pathological angiogenesis. Angiogenesis is a process encompassing several steps such as endothelial cells proliferation, differentiation and migration to form a vascular network, involving different signal transduction pathways. Among these, ERK1/2 signaling mediates VEGF-dependent signaling pathway. Here we report that the water extract of Ruta graveolens (RGWE), widely known as a medicinal plant, is able to impair in a dose-dependent manner, cell network formation without affecting cell viability. Biochemical analysis showed that the major component of RGWE is rutin, unable to reproduce RGWE effect. We found that RGWE inhibits ERK1/2 phosphorylation and that this event is crucial in cell network formation since the transfection of HUVEC with a constitutively active MEK (caMEK), the ERK1/2 activator, induces a robust cell network formation as compared to untransfected and/or mock transfected cells and, more importantly, caMEK transfected cells became unresponsive to RGWE. Moreover, RGWE inhibits VEGF and nestin gene expression, necessary for vessel formation, and the caMEK transfection induces their higher expression. In conclusion, we report that RGWE is able to significantly impair vessels network formation without affecting cell viability, preventing ERK1/2 activation and, in turn, down-regulating VEGF and nestin expression. These findings point to RGWE as a potential therapeutic tool capable to interfere with pathologic angiogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. Furthermore, the Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Encoder–decoder neural network for solving the nonlinear Fokker–Planck–Landau collision operator in XGC

An encoder–decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker–Planck–Landau collision operator. This is part of the governing equation in the massively parallel particle-in-cell code XGC, which is used to study turbulence in fusion energy devices. The neural network emphasizes physics-inspired learning, where it is taught to respect physical conservation constraints of the collision operator by including them in the training loss, along with the ℓ 2 loss. In particular, network architectures used for the computer vision task of semantic segmentation have been used for training. A penalization method is used to enforce the ‘soft’ constraints of the system and integrate error in the conservation properties into the loss function. During training, quantities representing the particle density, momentum and energy for all species of the system are calculated at each configuration vertex, mirroring the procedure in XGC. This simple training has produced a median relative loss, across configuration space, of the order of 10 –4 , which is low enough if the error is of random nature, but not if it is of drift nature in time steps. The run time for the current Picard iterative solver of the operator is O(n 2 ), where n is the number of plasma species. As the XGC1 code begins to attack problems including a larger number of species, the collision operator will become expensive computationally, making the neural network solver even more important, especially since its training only scales as O(n). Here, a wide enough range of collisionality has been considered in the training data to ensure the full domain of collision physics is captured. An advanced technique to decrease the losses further will be subject of a subsequent report. Eventual work will include expansion of the network to include multiple plasma species.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Toughening of Thermoresponsive Arrested Networks of Elastin-Like Polypeptides To Engineer Cytocompatible Tissue Scaffolds

Formulation of tissue engineering or regenerative scaffolds from simple bioactive polymers with tunable structure and mechanics is crucial for the regeneration of complex tissues, and hydrogels from recombinant proteins, such as elastin-like polypeptides (ELPs), are promising platforms to support these applications. The arrested phase separation of ELPs has been shown to yield remarkably stiff, biocontinuous, nanostructured networks, but these gels are limited in applications by their relatively brittle nature. Here, a gel-forming ELP is chain-extended by telechelic oxidative coupling, forming extensible, tough hydrogels. Small angle scattering indicates that the chain-extended polypeptides form a fractal network of nanoscale aggregates over a broad concentration range, accessing moduli ranging from 5 kPa to over 1 MPa over a concentration range of 5–30 wt %. These networks exhibited excellent erosion resistance and allowed for the diffusion and release of encapsulated particles consistent with a bicontinuous, porous structure with a broad distribution of pore sizes. Furthermore, biofunctionalized, toughened networks were found to maintain the viability of human mesenchymal stem cells (hMSCs) in 2D, demonstrating signs of osteogenesis even in cell media without osteogenic molecules. Furthermore, chondrocytes could be readily mixed into these gels via thermoresponsive assembly and remained viable in extended culture. These studies demonstrate the ability to engineer ELP-based arrested physical networks on the molecular level to form reinforced, cytocompatible hydrogel matrices, supporting the promise of these new materials as candidates for the engineering and regeneration of stiff tissues.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Roles of Alkali Metals and Ionic Networks in Directing the Formation of Anionic Metal–Organic Frameworks

Integrating different types of fundamental chemical interactions to direct the self-assembly processes and to control the dimension of the network formed by each type of interactions is a promising strategy to develop advanced functional materials. Here, the ionic interaction of alkali metals (Na, K, Cs) towards multi-functional 2,2’-disulfo-4,4’-oxydibenzoic acid is utilized in two solvent systems, aqueous-amide and uro-amide, to synthesize six novel anionic framework materials with dramatically different features and dimensionalities: (Na 3 [Zn 4 O(L 4- ) 3 (e-murea)(H 2 O) 3 ](H 2 N(CH 3 ) 2 +) 3 (CPM-s1), [Zn 4 O(L 4- ) 3 (DMF)]((NH 2 (CH 3 ) 2 +) 6 ) (CPM-s2), Na 6 [(Zn 2 OH 2 ) 3 (L 3- ) 6 ](H 2 O) 3 (CPM-s3), K 3 [Zn 2 (L 4- ) 2 ](H 2 N(CH 3 ) 2 +)(e-murea) 2 (H 2 O) 3 (CPM-s4), K 4 [Zn(L 3- ) 2 ](H 2 O) 2 (CPM-s5), and Cs 6 [Zn(L 4- ) 2 ](H 2 O) 2 (CPM-s6), L 4- = 2,2’-disulfo-4,4’-oxydibenzoate, DMF = N,N-dimethylformamide, e-murea = 1,3-dimethyl-1-2-imidazolidinone). In CPM-s1 to CPM-s6, the alkali-sulfonate ionic bonding networks transformed from 0D clusters with Na + to 1D chains and 2D sheets with K + , and 3D network with Cs + . These vastly differing ionic domains influenced the coordination modes and spatial arrangements of the metal-carboxylate linkers, which consequently, directed the dimensionality of the coordination networks from 3D with Na + to 1D with Cs + . CPM-s1 and CPM-s2, made from tetrameric Zn 4 O clusters are rare examples of anionic MOF-5-type structures. CPM-s3 features an unprecedented hexameric Zn 6 cluster. Even though a large portion of the charge-balancing cations in these materials would hamper hydrogen bonding networks, some of them still exhibit very high ionic conductivity (e.g. 1.25 x 10 -3 S cm -1 in CPM-s3), suggesting the potential of these materials as solid-state electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electroreduction-Driven Formation and Connectivity of Polyoxometalate Coordination Networks

We present the synthesis of metal oxide coordination networks based on Preyssler-type polyoxoanions ([NaP 5 W 30 O 110 ] 14– and [NaP 5 MoW 29 O 110 ] 14– ) bridged with metal–aquo complexes ([M(H 2 O) n ] m+ , M m+ = Co 2+ , Ni 2+ , Zn 2+ , Y 3+ ), induced by electrochemical reduction. Networks bridged with first-row transition metals are isostructural with a previously reported Co-bridged structure, while the Y 3+ -bridged structure is new. All networks feature an uncommon binding motif of the metal cation to the oxygen atoms at cap positions, which we hypothesize is due to increased electron density at the cap upon reduction. Oxidation of a Zn 2+ -bridged network resulted in a new structure in which Zn 2+ –O cap bonds are lost, indicating the importance of reduction in the connectivity of these polyoxometalate-based coordination networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Water Network Shape-Dependence of Local Interactions with the Microhydrated –NO 2 – and –CO 2 – Anionic Head Groups by Cold Ion Vibrational Spectroscopy

We report the structural evolutions of water networks and solvatochromic response of the CH 3 NO 2 – radical anion in the OH and CH stretching regions by analysis of the vibrational spectra displayed by cryogenically cooled CH 3 NO 2 – ·(H 2 O) n=1–6 clusters. The OH stretching bands evolve with a surprisingly large discontinuity at n = 6, which features the emergence of an intense, strongly red-shifted band along with a weaker feature that appears in the region assigned to a free OH fundamental. Very similar behavior is displayed by the perdeuterated carboxylate clusters, RCO 2 – ·(H 2 O) n=5–7 (R = CD 3 CD 2 ), indicating that this behavior is a general feature in the microhydration of the triatomic anionic domain and not associated with CH oscillators. Electronic structure calculations trace this behavior to the formation of a “book” isomer of the water hexamer that adopts a configuration in which one of the water molecules resides in an acceptor–acceptor–donor (AAD) (A = acceptor, D = donor) H-bonding site. Excitation of the bound OH in the AAD site explores the local network topology best suited to stabilize an incipient –XO 2 H–OH–(H 2 O) 2 intracluster proton-transfer reaction. These systems thus provide particularly clear examples where the network shape controls the potential energy landscape that governs water network-mediated, intracluster proton transfer. The CH stretching bands of the CH 3 NO 2 – ·(H 2 O) n=1–6 clusters also exhibit strong solvatochromic shifts, but in this case, they smoothly blue-shift with increasing hydration with no discontinuity at n = 6. Furthermore, this behavior is analyzed in the context of the solute-ion polarizability response and partial charge transfer to the water networks.

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

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Lastly, overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of the SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species where databases are incomplete. Here, the relationship extraction (RE) performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools were compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected-networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist in-between the two other approaches, with variable performances. Finally, the selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Nonideality in Silicone Network Formation via Solvent Swelling and 1 H Double-Quantum NMR

The versatile crosslinking chemistry of poly(dimethylsiloxane) (PDMS) based materials affords a large research space in which polymers with widely varying elastomeric properties may be synthesized. Parameters such as chain length, crosslink density, crosslink functionality, filler content, and chain chemistry can all be modified to produce materials with specific physical and mechanical properties. Commercial polysiloxane based, ‘silicone’ elastomers are generally intractable, which makes the precise characterization of their networks problematic. We report here the application of equilibrium solvent uptake analysis and 1 H double-quantum nuclear magnetic resonance ( 1 H DQ NMR) spectroscopy to determine the network topology of end-linked PDMS networks with non-ideal network topology. Despite their structural complexity, we can quantify both the classical and non-classical contributions to network structure using 1 H DQ NMR which are in reasonable agreement with solvent uptake data. These findings serve as the foundation for future investigations of even more complex commercial silicones using 1 H DQ NMR.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Methodology for Phased Development of a Hypothetical Pipeline Network for CO 2 Transport during Carbon Capture, Utilization, and Storage

If implemented on a commercial scale, carbon capture, utilization, and storage (CCUS) has the potential to significantly reduce carbon dioxide (CO 2 ) emissions. Moving the CO 2 from the point sources to the geologic storage locations will likely require a pipeline network. The Plains CO 2 Reduction (PCOR) Partnership developed a four-step methodology that can be used to estimate the length, cost, and time frame of a hypothetical pipeline network that would be built in phases. The methodology was tested during a case study in which a hypothetical phased pipeline network was estimated for the PCOR Partnership region. The hypothetical pipeline network consisted of trunk lines roughly 10 780 km in total length that could provide an overall CO 2 reduction for the region of about 555.6 Mtonnes of CO 2 /year by 2050. Furthermore, the results also indicate that an extensive pipeline network may not be required to transport to storage locations the quantity of CO 2 required to meet the emission reduction targets for the PCOR Partnership region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Structural Coupling Throughout the Active Site Hydrogen Bond Networks of Ketosteroid Isomerase and Photoactive Yellow Protein

Hydrogen bonds are fundamental to biological systems and are regularly found in networks implicated in folding, molecular recognition, catalysis, and allostery. Given their ubiquity, we asked the fundamental questions of whether, and to what extent, hydrogen bonds within networks are structurally coupled. To address these questions, we turned to three protein systems, two variants of ketosteroid isomerase and one of photoactive yellow protein. We perturbed their hydrogen bond networks via a combination of site-directed mutagenesis and unnatural amino acid substitution, and we used 1 H NMR and high-resolution X-ray crystallography to determine the effects of these perturbations on the lengths of the two oxyanion hole hydrogen bonds that are donated to negatively charged transition state analogs. Perturbations that lengthened or shortened one of the oxyanion hole hydrogen bonds had the opposite effect on the other. The oxyanion hole hydrogen bonds were also affected by distal hydrogen bonds in the network, with smaller perturbations for more remote hydrogen bonds. Across 19 measurements in three systems, the length change in one oxyanion hole hydrogen bond was propagated to the other, by a factor of –0.30 ± 0.03. This common effect suggests that hydrogen bond coupling is minimally influenced by the remaining protein scaffold. The observed coupling is reproduced by molecular mechanics and quantum mechanics/molecular mechanics (QM/MM) calculations for changes to a proximal oxyanion hole hydrogen bond. However, effects from distal hydrogen bonds are reproduced only by QM/MM, suggesting the importance of polarization in hydrogen bond coupling. These results deepen our understanding of hydrogen bonds and their networks, providing strong evidence for long-range coupling and for the extent of this coupling. In conclusion, we provide a broadly predictive quantitative relationship that can be applied to and can be further tested in new systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Double-Gyroid Network Morphology in Tapered Diblock Copolymers

We report the formation of a double-gyroid network morphology in normal-tapered poly(isoprene-b-isoprene/styrene-b-styrene) [P(I-IS-S)] and inverse-tapered poly(isoprene-b-styrene/isoprene-b-styrene) [P(I-SI-S)] diblock copolymers. Our tapered diblock copolymers with overall poly(styrene) volume fractions of 0.65 (normal-tapered) and 0.67 (inverse-tapered), and tapered regions comprising 30 vol % of the total polymer, were shown to self-assemble into the double-gyroid network morphology through a combination of small-angle X-ray scattering (SAXS) and transmission electron microscopy (TEM). The block copolymers were synthesized by anionic polymerization, where the tapered region between the pure poly(isoprene) and poly(styrene) blocks was generated using a semibatch feed with programmed syringe pumps. The overall composition of these tapered copolymers lies within the expected network-forming region for conventional poly(isoprene-b-styrene) [P(I-S)] diblock copolymers. Dynamic mechanical analysis (DMA) clearly demonstrated that the order-disorder transition temperatures (T{sub ODT}'s) of the network-forming tapered block copolymers were depressed when compared to the T{sub ODT} of their nontapered counterpart, with the P(I-SI-S) showing the greater drop in T{sub ODT}. These results indicate that it is possible to manipulate the copolymer composition profile between blocks in a diblock copolymer, allowing significant control over the T{sub ODT}, while maintaining the ability to form complex network structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. Herein we introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach.

54 ENVIRONMENTAL SCIENCES↗