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DOE OSTI2026

High-Resolution South American Wind Resource Data Downscaled with Generative Machine Learning Conditioned on Near-Surface Observations

High-resolution historical wind data was developed for the entirety of South America using the innovative Super-Resolution for Renewable Resource Data (sup3r) machine learning framework. The publicly available Sup3rWind South America dataset represents a significant advancement in wind resource data generation, leveraging generative machine learning conditioned on near-surface observations from the Meteorological Assimilation Data Ingest System (MADIS) to efficiently and accurately downscale coarse reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5). This approach produces fine-scale, spatially and temporally coherent wind and meteorological fields hundreds of times more computationally efficient than traditional numerical weather modeling methods, enabling access to high-fidelity wind information across both continental and offshore regions. Sup3rWind South America builds on the earlier Sup3rWind Ukraine dataset through improvements in model architecture and outputs conditioned on near-surface observation inputs. As with the Ukraine data release, this dataset includes wind speed, wind direction, temperature, relative humidity, and pressure at a horizontal resolution of ~2 km, representing a 15x spatial enhancement relative to the 31 km ERA5 grid. Wind speed and direction are provided at 5-minute resolution, a 12x temporal refinement compared to the hourly ERA5 data, while temperature, relative humidity, and pressure remain at hourly resolution. The data covers all years from 2005 to 2024. Before downscaling, ERA5 inputs were bias-corrected using long-term monthly means and a limited number of quality-controlled observations to align large-scale statistics with regional conditions. The resulting dataset is the first publicly available high-resolution timeseries wind record that provides full spatial coverage of South America. Model validation demonstrates strong agreement with observations across several statistical metrics, consistent with other state-of-the-art high-resolution wind resource datasets. The potential applications of Sup3rWind South America span renewable energy resource assessment, energy system modeling, and grid resilience analysis. The 20-year record and high spatial and temporal resolution support accurate estimation of long-term energy yield and the economic feasibility of potential wind development sites. Continuous coverage across both continental and offshore regions enables comprehensive site prospecting within exclusive economic zones. The 2 km, 5-minute resolution data provide the spatial and temporal variability required for power system simulation, operational planning, and regional risk assessments.

17 WIND ENERGY↗

DOE OSTI2026

Using 2.5D super-resolution to improve flaw detection in metal additive manufacturing parts

Industrial X-ray computed tomography (XCT) enables non-destructive inspection of additively manufactured (AM) parts, but high-resolution scanning requires long acquisition times and significant computational resources, limiting throughput in production environments. Super-resolution techniques can recover high-resolution information from low-resolution scans, but existing methods face a trade-off between 2D approaches that ignore inter-slice information and 3D methods that are computationally prohibitive for practical deployment. To address this trade-off, we propose a 2.5D deep learning-based super-resolution approach that uses seven neighbouring low-resolution slices to super-resolve the centre slice. This work evaluates the method on real XCT scans of steel AM parts, comparing reconstruction quality and flaw detection performance of 2D, 2.5D, and 3D ESRGAN-based super-resolution methods. Results demonstrate that 2.5D super-resolution significantly improves detection of small, process-induced flaws (e.g. porosity) compared to 2D methods, while avoiding the prohibitive computational burden of full 3D approaches. These findings provide initial evidence of 2.5D super-resolution as a practical, deployable solution for improving flaw detection in high-throughput industrial XCT inspection.

X-ray CT↗

DOE OSTI2015

Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI

Purpose: To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging. Methods: The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency, image, and wavelet domains. Experiments were carried out with thick-slice T2-weighted fast spin echo sequence on the Academic College of Radiology MRI phantom, where the reconstructed images were compared to a reference high-resolution scan using peak signal-to-noise ratio (PSNR), structural similarity image metric (SSIM), mutual information (MI), and the mean absolute error (MAE) of image intensity profiles. The application of super-resolution reconstruction was then examined in retrospective processing of clinical neuroimages of ten pediatric patients with tuberous sclerosis complex (TSC) to reduce through-plane partial voluming for improved 3D delineation and visualization of thin radial bands of white matter abnormalities. Results: Quantitative evaluation results show improvements in all evaluation metrics through super-resolution reconstruction in the frequency, image, and wavelet domains, with the highest values obtained from SRR in the image domain. The metric values for image-domain SRR versus the original axial, coronal, and sagittal images were PSNR = 32.26 vs 32.22, 32.16, 30.65; SSIM = 0.931 vs 0.922, 0.924, 0.918; MI = 0.871 vs 0.842, 0.844, 0.831; and MAE = 5.38 vs 7.34, 7.06, 6.19. All similarity metrics showed high correlations with expert ranking of image resolution with MI showing the highest correlation at 0.943. Qualitative assessment of the neuroimages of ten TSC patients through in-plane and out-of-plane visualization of structures showed the extent of partial voluming effect in a real clinical scenario and its reduction using SRR. Blinded expert evaluation of image resolution in resampled out-of-plane views consistently showed the superiority of SRR compared to original axial and coronal image acquisitions. Conclusions: Thick-slice 2D T2-weighted MRI scans are part of many routine clinical protocols due to their high signal-to-noise ratio, but are often severely affected by through-plane partial voluming effects. This study shows that while radiologic assessment is performed in 2D on thick-slice scans, super-resolution MRI reconstruction techniques can be used to fuse those scans to generate a high-resolution image with reduced partial voluming for improved postacquisition processing. Qualitative and quantitative evaluation showed the efficacy of all SRR techniques with the best results obtained from SRR in the image domain. The limitations of SRR techniques are uncertainties in modeling the slice profile, density compensation, quantization in resampling, and uncompensated motion between scans.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-EF-BRA-11: Feasibility of Super-Resolution Time-Resolved 4DMRI for Multi-Breath Volumetric Motion Simulation in Radiotherapy Planning

Purpose: To develop a novel super-resolution time-resolved 4DMRI technique to evaluate multi-breath, irregular and complex organ motion without respiratory surrogate for radiotherapy planning. Methods: The super-resolution time-resolved (TR) 4DMRI approach combines a series of low-resolution 3D cine MRI images acquired during free breathing (FB) with a high-resolution breath-hold (BH) 3DMRI via deformable image registration (DIR). Five volunteers participated in the study under an IRB-approved protocol. The 3D cine images with voxel size of 5×5×5 mm{sup 3} at two volumes per second (2Hz) were acquired coronally using a T1 fast field echo sequence, half-scan (0.8) acceleration, and SENSE (3) parallel imaging. Phase-encoding was set in the lateral direction to minimize motion artifacts. The BH image with voxel size of 2×2×2 mm{sup 3} was acquired using the same sequence within 10 seconds. A demons-based DIR program was employed to produce super-resolution 2Hz 4DMRI. Registration quality was visually assessed using difference images between TR 4DMRI and 3D cine and quantitatively assessed using average voxel correlation. The fidelity of the 3D cine images was assessed using a gel phantom and a 1D motion platform by comparing mobile and static images. Results: Owing to voxel intensity similarity using the same MRI scanning sequence, accurate DIR between FB and BH images is achieved. The voxel correlations between 3D cine and TR 4DMRI are greater than 0.92 in all cases and the difference images illustrate minimal residual error with little systematic patterns. The 3D cine images of the mobile gel phantom preserve object geometry with minimal scanning artifacts. Conclusion: The super-resolution time-resolved 4DMRI technique has been achieved via DIR, providing a potential solution for multi-breath motion assessment. Accurate DIR mapping has been achieved to map high-resolution BH images to low-resolution FB images, producing 2Hz volumetric high-resolution 4DMRI. Further validation and improvement are still required prior to clinical applications. This study is in part supported by the NIH (U54CA137788/U54CA132378).

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Resolution recovery for Compton camera using origin ensemble algorithm

Purpose: Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. Methods: To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Results: Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2–3 orders of magnitude per iteration. Conclusions: The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Complete resolution across the neodymium/samarium isotopic envelope with a liquid sampling‐atmospheric pressure glow discharge — Orbitrap mass spectrometer

Rationale Nd and Sm isotope ratios play an important role in geological dating and as nuclear forensic signatures; however, the overlap of the respective 144, 148, 150 Nd/Sm isobars requires prior separations to be performed before analysis on typical MS platforms. The work presented here overcomes these isobaric interferences using ultrahigh‐mass resolution to alleviate interference without prior chemical separations. Methods A liquid sampling‐atmospheric pressure glow discharge ion source was coupled to a standard, QExactive Focus Orbitrap mass spectrometer, providing a mass resolution of ~80 k. A Spectroswiss FTMS booster X2 data acquisition package was used to collect extended transients, providing much higher mass resolution; ~230 k and ~600 k are employed here for Nd and Sm isotopes. Results While the standard Orbitrap resolution is far greater than typical “atomic” MS platforms, it was insufficient to alleviate all isobars. The use of a resolution of ~230 k resulted in baseline separation across the entire isotopic envelope for both Nd and Sm. Isotope ratios obtained from Nd:Sm mixtures using high‐resolution were equivalent to those found for individual‐element solutions, while isotope ratios obtained at a resolution of ~80 k (standard for the OEM data system) showed large deviations. Conclusions Use of ultrahigh‐resolution is an attractive alternative to extensive chemical separations to alleviate severe isobaric interferences. Sufficient mass resolution greatly reduces/eliminates the need for sample manipulations (separations) before analysis while reducing costs and total analysis times.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Energy resolution and gain measurements in Argon-based gas mixtures: Exploring Ar:CF 4 for low energy measurements with TPCs

Time Projection Chambers (TPCs) are among the most advanced charged-particle detectors. Gas-filled TPCs have tracking capabilities that provide 3D-imaging of charged particles with a good energy resolution for spectroscopy. Different gas mixtures have different properties that determine the energy resolution as well as the spatial resolution. Therefore, optimization of operating conditions is required to simultaneously obtain adequate gain, energy resolution, spatial/track resolution, as well as higher drift velocities for high counting rates applications. Ar:CF 4 gas mixture has higher electron drift velocities and lower electron diffusion, which makes it an attractive candidate for TPC filling gas for low energy nuclear physics applications as compared to commonly used Ar:CH 4 and Ar:CO 2 gas mixtures, namely when tracking information is needed. However, other properties, including energy resolution and gain, remain largely unexplored in Ar:CF 4 especially at pressures and other operating conditions relevant for low-energy nuclear physics applications. Here, in this paper we report on gain and energy resolution measurements, using Gas Electron Multipliers (GEMs), in the less explored Ar:CF 4 mixture (Alfonsi et al., 2006), as well as in the more commonly used gas mixtures Ar:CH 4 and Ar:CO 2 . In addition to obtaining energy resolution and gain, we provide results from Garfield++ simulations for gain fluctuations, and their impact on energy resolution is discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2017

Resolution Versus Error for Computational Electron Microscopy

Images that are collected via scanning transmission electron microscopy (STEM) can be undersampled to avoid damage to the specimen while maintaining resolution [1, 2]. We have used BPFA to impute missing data and reduce noise [3]. The reconstruction is typically evaluated using the peak signal-to-noise ratio (PSNR). This measure is too conservative for STEM images and we propose that the Fourier ring correlation (FRC) is used instead to evaluate the reconstruction. We are not concerned with exact reconstruction of the truth image, and therefore PSNR is a conservative estimation of the quality of the reconstruction. Instead, we are concerned with the visual resolution of the image and whether atoms can be distinguished. We have evaluated the reconstruction of a simulated STEM image using the FRC and compared the results with the PSNR measurements. The FRC captures the resolution of the image and is not affected by a large MSE if the atom peaks are still distinguishable. The noisy and reconstructed images are shown in Figure 1. The simulated STEM image was sampled at 100%, 80%, 40%, and 20% of the original pixels to simulate an undersampled scan. The reconstruction was done using BPFA with a patch size of 10 x 10 and no overlapping patches. Not having overlapping patches produces inferior results but they are still acceptable. The dictionary size is 64 and 30 iterations were completed during each reconstruction. The 100% image was denoised instead of reconstructed. Poisson noise was applied to the simulated image with λ values of 500, 50, and 5 to simulate lower imaging dose. The original simulated STEM image was also included in our calculations and was generated using a dose of 1000. The simulated STEM image is 100 by 100 pixels and has essentially no high frequency components. The image reconstruction tends to smooth the data, also resulting in no high frequency components. This causes the FRC of the two images to be large at higher resolutions and may be misleading. For this reason, the BPFA has no overlap to avoid excessive smoothing. Moreover, the resolution of the simulated image is approximately 9.2 (1/nm), so we only look that far in the frequency domain when performing FRC. If the FRC curve does not crossover the threshold, a resolution value of 9.2 is used. We emphasize that our reported results are conservative. The FRC and PSNR values using the ground truth and the reconstructed images are shown in Tables 1 and 2. The left side show the metrics without using BPFA (missing pixels) and the right side show the metrics after using BPFA. When we did not use BPFA, the Fourier transform was estimated [4]. Some threshold curves have been studied [5], but they are derived for additive noise models. Since we have a Poisson noise model, we have used the more conservative threshold of 0.5 for our calculations. Ten images were used to construct each cell of tables in the form of the mean of the metric plus or minus its standard deviation. As expected, the PSNR dies off much quicker than the FRC values for the same image. For the 100% and 80% sampled versions of the truth image, the resolution only dies off when the dose is 5. However, the PSNR dies off rapidly as the dose is reduced. For the 1000, 500, and 50 dose images, the FRC is the maximum, or close, until we undersample at 20%. The PSNR for these values tapers down as we get into the bottom right hand corner of the table, even though the resolution remains high. Overall, we find that undersampled images can be reconstructed to acceptable resolution even when the dose per pixel is also reduced[6]. References: [1]A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41. [2]A Stevens, L Kovarik, P Abellan et al. Advanced Structural and Chemical Imaging 1(1), (2015), pp. 1. [3]M Zhou, H Chen, J Paisley et al. Image Processing, IEEE Transactions on 21(1), (2012), pp. 130. [4]V. Y. Liepin’sh. Automatic control and computer sciences 30(3), (1996), pp. 20.

Luzi, Lorenzo↗

DOE OSTI2022

Effects of Molecular Size on Resolution in Charge Detection Mass Spectrometry

Instrumental resolution of Fourier transform-charge detection mass spectrometry instruments with electrostatic ion trap detection of individual ions depends on the precision with which ion energy is determined. Energy can be selected using ion optic filters or from harmonic amplitude ratios (HARs) that provide Fellgett’s advantage and eliminate the necessity of ion transmission loss to improve resolution. Unlike the ion energy-filtering method, the resolution of the HAR method increases with charge (improved S/N) and thus with mass. An analysis of the HAR method with current instrumentation indicates that higher resolution can be obtained with the HAR method than the best resolution demonstrated for instruments with energy-selective optics for ions in the low MDa range and above. However, this gain is typically unrealized because the resolution obtainable with molecular systems in this mass range is limited by sample heterogeneity. Here, this phenomenon is illustrated with both tobacco mosaic virus (0.6–2.7 MDa) and AAV9 (3.7–4.7 MDa) samples where mass spectral resolution is limited by the sample, including salt adducts, and not by instrument resolution. Nevertheless, the ratio of full to empty AAV9 capsids and the included genome mass can be accurately obtained in a few minutes from 1× PBS buffer solution and an elution buffer containing 300+ mM nonvolatile content despite extensive adduction and lower resolution. Empty and full capsids adduct similarly indicating that salts encrust the complexes during late stages of droplet evaporation and that mass shifts can be calibrated in order to obtain accurate analyte masses even from highly salty solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Model Resolution Sensitivity of the Simulation of North Atlantic Oscillation Teleconnections to Precipitation Extremes

We evaluate a high-resolution (0.25°), four-member ensemble simulation of the global climate (1979–2005) with the U.S. Department of Energy's Energy Exascale Earth System Model v0.3—forced with observed ocean surface temperatures and sea ice extent—for its ability to represent the North Atlantic Oscillation (NAO) teleconnections to winter precipitation extremes over western Europe. As compared to the low-resolution model (1°), it simulates a stronger impact of NAO on daily precipitation extremes over the western slopes of mountain ranges over southwestern Norway, northwestern United Kingdom, and the Western Balkan states. Precipitation extremes and their linear relationship with NAO are quantified using the generalized extreme value distribution. NAO-dependent large-scale (stratiform) precipitation intensity strengthens in the high-resolution model on seasonal time scales and plays a dominant role during simulated daily precipitation extremes. Improvements in the high-resolution model over these varied-topography regions largely appear to be due to finer resolved scales of motion that amplify NAO-dependent seasonal vertical moisture fluxes and enhance stable condensation. However, the high-resolution model simulates a weaker than observed impact of NAO on extratropical cyclone activity and total precipitable water, generally underperforming the low-resolution model These effects possibly offset the impact of enhanced vertical moisture fluxes on NAO-dependent precipitation extremes in the high-resolution model in these regions. Over the southwestern Iberian peninsula, the high-resolution model underperforms the low-resolution model simulating weaker than observed impact of NAO on precipitation extremes. Finally, this appears to be due to the reduction in total precipitable water despite an increase in NAO-dependent extratropical activity there.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗

DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

The Resolution Sensitivity of Northern Hemisphere Blocking in Four 25-km Atmospheric Global Circulation Models

The aim of this study is to investigate if the representation of Northern Hemisphere blocking is sensitive to resolution in current-generation atmospheric global circulation models (AGCMs). An evaluation is thus conducted of how well atmospheric blocking is represented in four AGCMs whose horizontal resolution is increased from a grid spacing of more than 100 km to about 25 km. It is shown that Euro-Atlantic blocking is simulated overall more credibly at higher resolution (i.e., in better agreement with a 50-yr reference blocking climatology created from the reanalyses ERA-40 and ERA-Interim). The improvement seen with resolution depends on the season and to some extent on the model considered. Euro-Atlantic blocking is simulated more realistically at higher resolution in winter, spring, and autumn, and robustly so across the model ensemble. The improvement in spring is larger than that in winter and autumn. Summer blocking is found to be better simulated at higher resolution by one model only, with little change seen in the other three models. The representation of Pacific blocking is not found to systematically depend on resolution. Despite the improvements seen with resolution, the 25-km models still exhibit large biases in Euro-Atlantic blocking. For example, three of the four 25-km models underestimate winter northern European blocking frequency by about one-third. The resolution sensitivity and biases in the simulated blocking are shown to be in part associated with the mean-state biases in the models' midlatitude circulation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP–PRIMAVERA Multimodel Ensemble

A multimodel, multiresolution set of simulations over the period 1950–2014 using a common forcing protocol from CMIP6 HighResMIP have been completed by six modeling groups. Analysis of tropical cyclone performance using two different tracking algorithms suggests that enhanced resolution toward 25 km typically leads to more frequent and stronger tropical cyclones, together with improvements in spatial distribution and storm structure. Both of these factors reduce typical GCM biases seen at lower resolution. Using single ensemble members of each model, there is little evidence of systematic improvement in interannual variability in either storm frequency or accumulated cyclone energy as compared with observations when resolution is increased. Changes in the relationships between large-scale drivers of climate variability and tropical cyclone variability in the Atlantic Ocean are also not robust to model resolution. However, using a larger ensemble of simulations (of up to 14 members) with one model at different resolutions does show evidence of increased skill at higher resolution. The ensemble mean correlation of Atlantic interannual tropical cyclone variability increases from ~0.5 to ~0.65 when resolution increases from 250 to 100 km. In the northwestern Pacific Ocean the skill keeps increasing with 50-km resolution to 0.7. These calculations also suggest that more than six members are required to adequately distinguish the impact of resolution within the forced signal from the weather noise.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Simulation of ENSO Teleconnections to Precipitation Extremes over the United States in the High-Resolution Version of E3SM

Abstract We evaluate the simulated teleconnection of El Niño–Southern Oscillation (ENSO) to winter season precipitation extremes over the United States in a long (98 years) 1950 control high-resolution version (HR; 25-km nominal atmosphere model horizontal resolution) of the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model version 1 (E3SMv1). The model bias and spatial pattern of ENSO teleconnections to mean and extreme precipitation in HR overall are similar to the low-resolution model’s (LR; 110 km) historical simulation (four-member ensemble, 1925–59). However, over the southeastern United States (SE-U.S.), HR produces stronger El Niño–associated extremes, reducing LR’s model bias. Both LR and HR produce weaker than observed increase in storm track activity during El Niño events there, but HR improves the ENSO-associated variability of moisture transport over SE-U.S. During El Niño, stronger vertical velocities in HR produce stronger large-scale precipitation, causing larger latent heating of the troposphere that pulls in more moisture from the Gulf of Mexico into the SE-U.S. This positive feedback also contributes to the stronger mean and extreme precipitation response in HR. Over the Pacific Northwest, LR’s bias of stronger than observed La Niña associated extremes is amplified in HR. Both models simulate stronger than observed moisture transport from the Pacific Ocean into the region during La Niña years. The amplified HR bias there is due to stronger orographically driven vertical updrafts that create stronger large-scale precipitation, despite weaker La Niña–induced storm track activity. Significance Statement New high-resolution Earth system models (ESMs) solve mathematical equations of fluid flow at much smaller spatial scales than prevalent ESMs, and thus are prohibitively expensive to compute. However, they can be useful for simulating accurate details of regional climate extremes that are driven by naturally occurring climate oscillations like El Niño–Southern Oscillation (ENSO). Here, we evaluate the simulation of ENSO-driven precipitation extremes over the United States in the high-resolution version of the U.S. Department of Energy’s new Energy Exascale Earth System Model version 1. We find that the high-resolution model improves upon its low-resolution counterpart over the southeastern United States by producing a better transport of moisture into the region from the Gulf of Mexico during El Niño. Over the U.S. Pacific Northwest, the high-resolution model simulates the atmospheric flow in more detail over the complex mountainous terrain. However, it also brings in more moisture from the Pacific Ocean just like the low-resolution model. This causes it to produce precipitation extremes during La Niña years there that are stronger than that observed in the real world.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics

Flow depth and velocity are the most important hydrodynamic variables that govern various river functions, including water resources, navigation, sediment transport, and biogeochemical cycling. Existing high-resolution flow depth simulations rely on either computationally expensive river hydrodynamic models (RHMs) or data-driven models with formidable training costs, whereas data-driven modeling of flow velocity has rarely been explored. Here, using the hybrid Low-fidelity, Spatial analysis, and Gaussian process learning (LSG) model, we developed a downscaling approach to construct high-resolution flow depth and velocity from a two-dimensional (2-D) RHM simulation at coarse resolution. The LSG models were trained and tested in an urban watershed in Houston using two different hurricane-driven flood events. The high-resolution (as fine as 30 m resolution) and low-resolution (mostly 1000 m resolution) meshes include 664 724 and 14 536 grid cells, respectively. The results showed that through downscaling, the simulation errors were reduced to less than one-fourth and one-third of the errors of the low-resolution 2-D RHM for flow depth and velocity, respectively. Our analysis further revealed that the dominant uncertainty sources of the downscaled hydrodynamics are different, with flow velocity dominated by the dimensionality reduction error, which we reduced by using a regionalized training procedure. The downscaling approach achieves an 84-fold acceleration in computational time compared to the high-resolution 2-D RHM, making high-fidelity ensemble flood modeling feasible. More importantly, the developed method provides an opportunity to couple large-scale hydrodynamical processes with local physical, chemical, and biological processes in river models.

Tan, Zeli [Pacific Northwest National Laboratory (↗

NASA NTRS1985

Retinally stabilized differential resolution television display

A remote television viewing system employing an eye tracker is disclosed, wherein a small region of the image appears in high resolution, and the remainder of the image appears in low resolution. The eye tracker monitors the position of the viewer's line of sight. The eye tracker position data is transmitted to the remote television camera and control. Both the remote camera and television display are adapted to have selectable high-resolution and low resolution raster scan modes. The position data from the eye tracker is used to determine the point at which the high-resolution scan is to commence. The video data defining the observed image is encoded in a novel format, wherein in each data field, the data representing the position of the high resolution region of predetermined size appears first, followed by the high resolution zone video data and then the low-resolution region data. As the viewer's line of sight relative to the displayed image changes, the position of the high resolution region changes to track the viewer's line of sight.

Ruoff, C. F., Jr.↗

NASA NTRS1993

Performance of high resolution decoding with Multi-Anode Microchannel Array detectors

The Multi-Anode Microchannel Array (MAMA) is a microchannel plate based photon counting detector with applications in ground-based and space-based astronomy. The detector electronics decode the position of each photon event, and the decoding algorithm that associates a given event with the appropriate pixel is determined by the geometry of the anode array. The standard MAMA detector has a spatial resolution set by the anode array of 25 microns, but the MCP pore resolution exceeds this. The performance of a new algorithm that halves the pixel spacing and improves the pixel spatial resolution is described. The new algorithm does not degrade the pulse-pair resolution of the detector and does not require any modifications to the detector tube. Measurements of the detector's response demonstrate that high resolution decoding yields a 60 percent enhancement in spatial resolution. Measurements of the performance of the high resolution algorithm with a 14 micron MAMA detector are also described. The parameters that control high resolution performance are discussed. Results of the application of high resolution decoding to speckle interferometry are presented.

Kasle, David B.↗