Dataset Information

Band-by-band spectral radiative kernels based on the ERA5 reanalysis.

ABSTRACT: Radiative kernel is a widely adopted method for diagnosing radiation variability and climate feedback. However, most of the existing radiative kernels are broadband flux kernels and lack the spectral information. Motivated by the growing interest in the spectral changes of the Earth radiation budget, we generate a new set of band-by-band radiative kernels based on the fifth generation European Center for Medium-Range Weather Forecasts (ERA5) reanalysis, which can be used for analyzing the spectrally decomposed changes in the top of atmosphere, surface and atmospheric radiation. The radiative sensitivity quantified by the ERA5 band-by-band kernel is compared to another spectral kernel and validated in a spectral radiation closure test. The use and benefits of the new ERA5 kernels are then demonstrated in an analysis of spectral feedbacks of an ensemble of global climate models (GCMs).

SUBMITTER: Huang H

PROVIDER: S-EPMC10894235 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Band-by-band spectral radiative kernels based on the ERA5 reanalysis.

Huang Han H Huang Yi Y Wei Qiang Q Hu Yongyun Y

Scientific data 20240224 1

Radiative kernel is a widely adopted method for diagnosing radiation variability and climate feedback. However, most of the existing radiative kernels are broadband flux kernels and lack the spectral information. Motivated by the growing interest in the spectral changes of the Earth radiation budget, we generate a new set of band-by-band radiative kernels based on the fifth generation European Center for Medium-Range Weather Forecasts (ERA5) reanalysis, which can be used for analyzing the spectr ...[more]

PMID: 38402264

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Project description:SignificanceThe identification of gastrointestinal bleeding holds significant importance in wireless capsule endoscopy examinations, primarily because bleeding is the most prevalent anomaly within the gastrointestinal tract. Moreover, gastrointestinal bleeding serves as a crucial indicator or manifestation of various other gastrointestinal disorders, including ulcers, polyps, tumors, and Crohn's disease. Gastrointestinal bleeding may be classified into two categories: active bleeding, which refers to the presence of continuing bleeding, and inactive bleeding, which can potentially manifest in any region of the gastrointestinal system. Currently, medical professionals diagnose gastrointestinal bleeding mostly by examining complete wireless capsule endoscopy images. This approach is known to be demanding in terms of labor and time.AimThis research used white-light images (WLIs) obtained from 100 patients using the PillCam™ SB 3 capsule endoscope to identify and label the areas of bleeding seen in the WLIs.ApproachA total of 152 photographs depicting bleeding and 182 images depicting non-bleeding were selected for analysis. In addition, hyperspectral imaging was used to transform WLI into hyperspectral images using spectral reconstruction through band selection. These images were then categorized into WLIs and hyperspectral images. The training set consisted of seven datasets, each including six spectra. These datasets were used to train the Visual Geometry Group-16 (VGG-16) model, which was developed using a convolutional neural network. Subsequently, the model was tested, and its diagnostic accuracy was assessed.ResultsThe accuracy rates for the respective measures are 83.1%, 65.8%, 66.2%, 72.2%, 73.7%, and 88%. The respective precision values are 78.5%, 47.5%, 30.6%, 59.5%, 77.7%, and 80.2%. The recall rates for the relevant data points are 83.3%, 67.9%, 86%, 74.2%, 68.6%, and 92.4%. The initial dataset comprises an image captured under white-light conditions, whereas the final dataset is the most refined spectral picture data.ConclusionsThe findings suggest that employing spectral imaging within the wavelength range of 405 to 415 nm can enhance the accuracy of detecting small intestinal bleeding.

Dataset Information

Band-by-band spectral radiative kernels based on the ERA5 reanalysis.

Publications

Band-by-band spectral radiative kernels based on the ERA5 reanalysis.

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