The CO Human Emissions project has generated realistic high-resolution 9 km global simulations for atmospheric carbon tracers referred to as nature runs to foster carbon-cycle research applications with current and planned satellite missions, as well as the surge of in situ observations. Realistic atmospheric CO, CH and CO fields can provide a reference for assessing the impact of proposed designs of new satellites and in situ networks and to study atmospheric variability of the tracers modulated by the weather. The simulations spanning 2015 are based on the Copernicus Atmosphere Monitoring Service forecasts at the European Centre for Medium Range Weather Forecasts, with improvements in various model components and input data such as anthropogenic emissions, in preparation of a CO Monitoring and Verification Support system. The relative contribution of different emissions and natural fluxes towards observed atmospheric variability is diagnosed by additional tagged tracers in the simulations. The evaluation of such high-resolution model simulations can be used to identify model deficiencies and guide further model improvements.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9001646PMC
http://dx.doi.org/10.1038/s41597-022-01228-2DOI Listing

Publication Analysis

Top Keywords

realistic high-resolution
8
atmospheric variability
8
global nature
4
nature data
4
data realistic
4
high-resolution carbon
4
carbon weather
4
weather year
4
year paris
4
paris agreement
4

Similar Publications

Mechanisms of anion permeation within ion channels and nanopores remain poorly understood. Recent cryo-electron microscopy structures of the human bestrophin 1 Cl channel (hBest1) provide an opportunity to evaluate ion interactions predicted by molecular dynamics (MD) simulations against experimental observations. Here, we implement the fully polarizable force field AMOEBA in MD simulations on different conformations of hBest1.

View Article and Find Full Text PDF

A fast BEM (boundary element method) based approach is developed to solve an EEG/MEG forward problem for a modern high-resolution head model. The method utilizes a charge-based BEM accelerated by the fast multipole method (BEM-FMM) with an adaptive mesh pre-refinement method (called b-refinement) close to the singular dipole source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models within 90 seconds after initial model assembly using a regular workstation.

View Article and Find Full Text PDF

Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks.

Comput Biol Med

December 2024

Department of Computer Science, University of Toronto, 40 St George St., Toronto, M5S 2E4, ON, Canada; Neurosciences & Mental Health Research Program, The Hospital for Sick Children, 686 Bay St., Toronto, M5G 0A4, ON, Canada; Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, 170 Elizabeth St., Toronto, M5G 1H3, ON, Canada; Institute of Medical Science, University of Toronto, 1 King's College Circle, Toronto, M5S 1A8, ON, Canada; Department of Medical Imaging, University of Toronto, 263 McCaul St., Toronto, M5T 1W7, ON, Canada; Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, M5S 3G8, ON, Canada. Electronic address:

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes.

View Article and Find Full Text PDF

Fast and customizable image formation model for optical coherence tomography.

Biomed Opt Express

December 2024

Department of Medical Physics and Biomedical Engineering, University College London, Malet Place, Gower Street, London WC1E 6BT, UK.

Optical coherence tomography (OCT) is a technique that performs high-resolution, three-dimensional, imaging of semi-transparent scattering biological tissues. Models of OCT image formation are needed for applications such as aiding image interpretation and validating OCT signal processing techniques. Existing image formation models generally trade off between model realism and computation time.

View Article and Find Full Text PDF

A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients.

Med Phys

December 2024

Radiotherapy-Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

Background: High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these images require long acquisition times, so routinely acquired follow-up images after treatment often consist of 2D low-resolution (LR) images (with thick slices in multiple planes).

Purpose: In this work, we present a super-resolution convolutional neural network, based on previous single-image MRI super-resolution work, that can reconstruct a HR image from 2D LR slices in multiple planes in order to facilitate the extraction of structural biomarkers from routine scans.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!