Quantification and mitigation of the airborne limb imaging FTIR GLORIA instrument effects and uncertainties

The Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) is an infrared imaging FTS spectrometer with a 2-D infrared detector operated on two high flying research aircrafts. It has flown on eight campaigns and measured along more than 300 000 km of flight track. This paper details our instrument calibration and characterization efforts, which in particular leverage almost exclusively in-flight data. First, we present the framework of our new calibration scheme, which uses information from all three available calibration measurements (two blackbodies and upward pointing deep space measurements). Part of this scheme is a new correction algorithm correcting the erratically changing non-linearity of a subset of detector pixels and the identification of remaining bad pixels. Using this new calibration, we derive a 1-σ bound of 1 % on the instrumental gain error and a bound of 30 nW cm−2 sr−1 cm on the instrumental offset error. We show how we can examine the noise and spectral accuracy for all measured atmospheric spectra and derive a spectral accuracy of 5 ppm, on average. All these errors are compliant with the initial instrument requirements. We also discuss, for the first time, the pointing system of the GLORIA instrument. Combining laboratory calibration efforts with the measurement of astronomical bodies during the flight, we can derive a pointing accuracy of 0.032°, which corresponds to one detector pixel. The paper concludes with a brief study on how these newly characterised instrumental parameters affect temperature and ozone retrievals. We find that, first, the pointing uncertainty and, second, the instrumental gain uncertainty introduce the largest error in the result.

A Spectral Collocation Technique for Riesz Fractional Chen-Lee-Liu Equation

This paper discusses the study of optical solitons that are modeled by Riesz fractional Chen-Lee-Liu model, one of the versions of the famous nonlinear Schrödinger equation. This model is solved by the assistance of consecutive spectral collocation technique with two independent approaches. The first is the approach of the spatial variable, while the other is the approach of the temporal variable. It is concluded that the method of the current paper is far more efficient and credible for the proposed problem. Numerical results illustrate the performance efficiency of the algorithm. The results also point out that the scheme can lead to spectral accuracy of the studied model.

Double Ghost Convolution Attention Mechanism Network: A Framework for Hyperspectral Reconstruction of a Single RGB Image

Current research on the reconstruction of hyperspectral images from RGB images using deep learning mainly focuses on learning complex mappings through deeper and wider convolutional neural networks (CNNs). However, the reconstruction accuracy of the hyperspectral image is not high and among other issues the model for generating these images takes up too much storage space. In this study, we propose the double ghost convolution attention mechanism network (DGCAMN) framework for the reconstruction of a single RGB image to improve the accuracy of spectral reconstruction and reduce the storage occupied by the model. The proposed DGCAMN consists of a double ghost residual attention block (DGRAB) module and optimal nonlocal block (ONB). DGRAB module uses GhostNet and PRELU activation functions to reduce the calculation parameters of the data and reduce the storage size of the generative model. At the same time, the proposed double output feature Convolutional Block Attention Module (DOFCBAM) is used to capture the texture details on the feature map to maximize the content of the reconstructed hyperspectral image. In the proposed ONB, the Argmax activation function is used to obtain the region with the most abundant feature information and maximize the most useful feature parameters. This helps to improve the accuracy of spectral reconstruction. These contributions enable the DGCAMN framework to achieve the highest spectral accuracy with minimal storage consumption. The proposed method has been applied to the NTIRE 2020 dataset. Experimental results show that the proposed DGCAMN method outperforms the spectral accuracy reconstructed by advanced deep learning methods and greatly reduces storage consumption.

On the Pseudo-Spectral Method and Spectral Accuracy

There are numerical accuracy problems related to the implementation of sharp internal interfaces in pseudo-spectral and finite-difference schemes. It is common practice to classify numerical errors due to the implementation of interfaces as being to some order in a Taylor expansion. An alternative approach is to classify these errors as being to some order in a Fourier expansion.The pseudo-spectral method does not provide spectral accuracy in inhomogeneous media. The numerical errorsfor the upper half of the frequency/wavenumber spectra of the propagating fields are not related to theimplementation of the derivative operators but to aliasing effects coming from the multiplicationof static material-parameter fields with the dynamic, propagating, fields. The pseudo-spectral methodcan only provide half-spectral accuracy. The same type of spatial aliasing errors are present also forfinite-difference schemes. High-order finite differences can provide the same accuracy as the pseudo-spectral method if the staggered finite-difference derivative operators have a negligible errorat four grid points per shortest wavelength and above. Smoothing of the material-parameter field leads to additional reduction in the error-free bandwidthof the propagating fields. Assuming that there is a maximum wavenumber up to which the spectrumof the smoothed model coincide with the implementation using a properly bandlimited Heaviside step function, then there exists a local critical wavenumber for the propagating field equal toone half of the maximum wavenumber for the smoothed model. Harmonic averaging of material-parameter fields also results in wavenumber spectra where there is a maximum wavenumber above whichthe wavenumber spectrum deviates from an implementation with a bandlimited Heaviside step function.The same one-half rule is applicable also in this case.

Physically Plausible Spectral Reconstruction

Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent methods—with the very best algorithms using deep learning—can already solve this problem with good spectral accuracy. However, the recovered spectra are physically incorrect in that they do not induce the RGBs from which they are recovered. Moreover, if the exposure of the RGB image changes then the recovery performance often degrades significantly—i.e., most contemporary methods only work for a fixed exposure. In this paper, we develop a physically accurate recovery method: the spectra we recover provably induce the same RGBs. Key to our approach is the idea that the set of spectra that integrate to the same RGB can be expressed as the sum of a unique fundamental metamer (spanned by the camera’s spectral sensitivities and linearly related to the RGB) and a linear combination of a vector space of metameric blacks (orthogonal to the spectral sensitivities). Physically plausible spectral recovery resorts to finding a spectrum that adheres to the fundamental metamer plus metameric black decomposition. To further ensure spectral recovery that is robust to changes in exposure, we incorporate exposure changes in the training stage of the developed method. In experiments we evaluate how well the methods recover spectra and predict the actual RGBs and RGBs under different viewing conditions (changing illuminations and/or cameras). The results show that our method generally improves the state-of-the-art spectral recovery (with more stabilized performance when exposure varies) and provides zero colorimetric error. Moreover, our method significantly improves the color fidelity under different viewing conditions, with up to a 60% reduction in some cases.

High-Resolution Mass Spectrometry for In Vivo Proteome Dynamics using Heavy Water Metabolic Labeling

Cellular proteins are continuously degraded and synthesized. The turnover of proteins is essential to many cellular functions. Combined with metabolic labeling using stable isotopes, LC–MS estimates proteome dynamics in high-throughput and on a large scale. Modern mass spectrometers allow a range of instrumental settings to optimize experimental output for specific research goals. One such setting which affects the results for dynamic proteome studies is the mass resolution. The resolution is vital for distinguishing target species from co-eluting contaminants with close mass-to-charge ratios. However, for estimations of proteome dynamics from metabolic labeling with stable isotopes, the spectral accuracy is highly important. Studies examining the effects of increased mass resolutions (in modern mass spectrometers) on the proteome turnover output and accuracy have been lacking. Here, we use a publicly available heavy water labeling and mass spectral data sets of murine serum proteome (acquired on Orbitrap Fusion and Agilent 6530 QToF) to analyze the effect of mass resolution of the Orbitrap mass analyzer on the proteome dynamics estimation. Increased mass resolution affected the spectral accuracy and the number acquired tandem mass spectra.

SI-Traceability and Measurement Uncertainty of the Atmospheric Infrared Sounder Version 5 Level 1B Radiances

The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched on 4 May 2002. The AIRS is designed to measure atmospheric temperature and water vapor profiles and has demonstrated exceptional radiometric and spectral accuracy and stability in orbit. The International System of Units (SI)-traceability of the derived radiances is achieved by transferring the calibration from the Large Area Blackbody (LABB) with SI traceable temperature sensors, to the On-Board Calibrator (OBC) blackbody during preflight testing. The AIRS views the OBC blackbody and four full aperture space views every scan. A recent analysis of pre-flight and on-board data has improved our understanding of the measurement uncertainty of the Version 5 AIRS L1B radiance product. For temperatures greater than 260 K, the measurement uncertainty is better than 250 mK 1-sigma for most channels. SI-traceability and quantification of the radiometric measurement uncertainty is critical to reducing biases in reanalysis products and radiative transfer models (RTMs) that use AIRS data, as well as establishing the suitability of AIRS as a benchmark for radiances established in the early 2000s.

Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling

In the digital printing process, reliable colour reproduction is commonly achieved by printer characterisation, which defines the correspondence between the input device control values and the output colour information. The cellular Yule–Nielsen spectral Neugebauer model, together with its variants, is widely adopted in this topic because of its superb colorimetric and spectral accuracy. However, it seems that current studies have neglected an inconspicuous defect in such models when characterising printers equipped with black ink. That is, the cellular structure of these models overemphasises the sampling for dark-tone colours, and thus leads to relatively large errors in light tones. In this paper, taking a CMYK printer as an example, a simple and effective solution is proposed with no need of extra sampling. With the aid of a newly built cellular spectral Neugebauer model for the embedded CMY printer, this approach optimises the printer characterisation for light tones, slightly improves the precision for middle tones while it maintains the accuracy for dark tones. The performance of the proposed method was evaluated with regard to three different kinds of substrates and the experimental results validated its improvement in spectral printer characterisation.