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1. Resolution Dependence of Atmosphere–Ocean Interactions and Water Mass Transformation in the North Atlantic

   https://doi.org/10.1029/2021JC018102  

   Oldenburg, Dylan; Wills, Robert C. J.; Armour, Kyle C.; Thompson, LuAnne (April 2022, Journal of Geophysical Research: Oceans)

   Abstract Water mass transformation (WMT) in the North Atlantic plays a key role in driving the Atlantic Meridional Overturning Circulation (AMOC) and its variability. Here, we analyze subpolar North Atlantic WMT in high‐ and low‐resolution versions of the Community Earth System Model version 1 (CESM1) and investigate whether differences in resolution and climatological WMT impact low‐frequency AMOC variability and the atmospheric response to this variability. We find that high‐resolution simulations reproduce the WMT found in a reanalysis‐forced high‐resolution ocean simulation more accurately than low‐resolution simulations. We also find that the low‐resolution simulations, including one forced with the same atmospheric reanalysis data, have larger biases in surface heat fluxes, sea‐surface temperatures, and salinities compared to the high‐resolution simulations. Despite these major climatological differences, the mechanisms of low‐frequency AMOC variability are similar in the high‐ and low‐resolution versions of CESM1. The Labrador Sea WMT plays a major role in driving AMOC variability, and a similar North Atlantic Oscillation‐like sea‐level pressure pattern leads AMOC changes. However, the high‐resolution simulation shows a pronounced atmospheric response to the AMOC variability not found in the low‐resolution version. The consistent role of Labrador Sea WMT in low‐frequency AMOC variability across high‐ and low‐resolution coupled simulations, including a simulation which accurately reproduces the WMT found in an atmospheric‐reanalysis‐forced high‐resolution ocean simulation, suggests that the mechanisms may be similar in nature. 

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2. A Generative Super‐Resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution

   https://doi.org/10.1029/2024JH000375  

   Lockwood, Joseph_W; Gori, Avantika; Gentine, Pierre (November 2024, Journal of Geophysical Research: Machine Learning and Computation)

   Abstract Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks. 

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3. A Pixel Level Scaled Fusion Model to Provide High Spatial-Spectral Resolution for Satellite Images Using LSTM Networks

   https://doi.org/10.1109/WHISPERS.2019.8921269  

   Theran, Carlos A.; Alvarez, Michael A.; Arzuaga, Emmanuel; Sierra, Heidy (December 2019, 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS))

   Pixel-level fusion of satellite images coming from multiple sensors allows for an improvement in the quality of the acquired data both spatially and spectrally. In particular, multispectral and hyperspectral images have been fused to generate images with a high spatial and spectral resolution. In literature, there are several approaches for this task, nonetheless, those techniques still present a loss of relevant spatial information during the fusion process. This work presents a multi scale deep learning model to fuse multispectral and hyperspectral data, each with high-spatial-and-low-spectral resolution (HSaLS) and low-spatial-and-high-spectral resolution (LSaHS) respectively. As a result of the fusion scheme, a high-spatial-and-spectral resolution image (HSaHS) can be obtained. In order of accomplishing this result, we have developed a new scalable high spatial resolution process in which the model learns how to transition from low spatial resolution to an intermediate spatial resolution level and finally to the high spatial-spectral resolution image. This step-by-step process reduces significantly the loss of spatial information. The results of our approach show better performance in terms of both the structural similarity index and the signal to noise ratio. 

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4. Mixed-state electron ptychography enables sub-angstrom resolution imaging with picometer precision at low dose

   https://doi.org/10.1038/s41467-020-16688-6  

   Chen, Zhen; Odstrcil, Michal; Jiang, Yi; Han, Yimo; Chiu, Ming-Hui; Li, Lain-Jong; Muller, David A. (June 2020, Nature Communications)

   Abstract Both high resolution and high precision are required to quantitatively determine the atomic structure of complex nanostructured materials. However, for conventional imaging methods in scanning transmission electron microscopy (STEM), atomic resolution with picometer precision cannot usually be achieved for weakly-scattering samples or radiation-sensitive materials, such as 2D materials. Here, we demonstrate low-dose, sub-angstrom resolution imaging with picometer precision using mixed-state electron ptychography. We show that correctly accounting for the partial coherence of the electron beam is a prerequisite for high-quality structural reconstructions due to the intrinsic partial coherence of the electron beam. The mixed-state reconstruction gains importance especially when simultaneously pursuing high resolution, high precision and large field-of-view imaging. Compared with conventional atomic-resolution STEM imaging techniques, the mixed-state ptychographic approach simultaneously provides a four-times-faster acquisition, with double the information limit at the same dose, or up to a fifty-fold reduction in dose at the same resolution. 

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5. STSRNet: Deep Joint Space-Time Super-Resolution for Vector Field Visualization

   Yifei An, Han-Wei Shen (November 2021, IEEE computer graphics and applications)

   We propose STSRNet, a joint space-time super-resolution deep learning based model for time-varying vector field data. Our method is designed to reconstruct high temporal resolution (HTR) and high spatial resolution (HSR) vector fields sequence from the corresponding low-resolution key frames. For large scale simulations, only data from a subset of time steps with reduced spatial resolution can be stored for post-hoc analysis. In this paper, we leverage a deep learning model to capture the non-linear complex changes of vector field data with a two-stage architecture: the first stage deforms a pair of low spatial resolution (LSR) key frames forward and backward to generate the intermediate LSR frames, and the second stage performs spatial super-resolution to output the high-resolution sequence. Our method is scalable and can handle different data sets. We demonstrate the effectiveness of our framework with several data sets through quantitative and qualitative evaluations. 

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