Spectral Bins Research Articles

Multi-spectral radiation thermometry of space point targets based on spectral image pixel binning

The temperature characteristics of space point targets are essential indicators of their operational status and performance. To address the issue of significant temperature measurement errors in space point targets caused by low temperatures and a low imaging signal-to-noise ratio (SNR), we propose a mathematical model for multi-spectral radiation thermometry, derived from the principles of dual-band radiation thermometry. Furthermore, a multi-spectral image pixel binning method is introduced to enhance the SNR and minimize measurement errors. The experimental results indicate that the proposed multi-spectral radiation thermometry outperforms dual-band radiation thermometry. After merging 2 to 20 pixels, multi-spectral radiation thermometry in the 3.75–4.1 and 4.3–4.62 µm bands demonstrates an enhanced SNR and reduced temperature measurement errors. For a 378.15 K blackbody, the relative errors decrease from 1.52% and 2.19% to 0.26% and 0.74%, respectively, after merging six and eight pixels in the two different bands, compared to unmerged images. This method provides a valuable reference for developing techniques to enhance the SNR and improve temperature measurement accuracy for space point targets.

Chemometric Classification of Motor Oils Using 1H NMR Spectroscopy With Simultaneous Phase and Baseline Optimization

ABSTRACTHere, we demonstrate mid‐field 1H NMR spectroscopy combined with chemometrics to be powerful in the classification and authentication of motor oils (MOs). The 1H NMR data were processed with a new algorithm for simultaneous phase and baseline correction, which, for crowded spectra such as those of the refinery products, allowed for more accurate estimation of phase parameters than other literature approaches tested. A principal component analysis (PCA) model based on the unbinned CH3 fingerprint region (0.6–1.0 ppm) enabled the differentiation of hydrocracked and poly‐α‐olefin‐based MOs and was effective in resolving mixtures of these base stocks with conventional base oils. PCA analysis of the 1.0‐ to 1.14‐ppm region enabled the detection of poly (isobutylene) additive and was useful for differentiating between single‐grade and multigrade MOs. Non‐equidistantly binned 1H NMR data were used to detect the addition of esters and to establish discriminant models for classifying MOs by viscosity grade and by major categories of synthetic, semisynthetic, and mineral oils. The performances of four classifiers (linear discriminant analysis [LDA], quadratic discriminant analysis [QDA], naïve Bayes classifier [NBC], and support vector machine [SVM]) with and without PCA dimensionality reduction were compared. In both tasks, SVM showed the best efficiency, with average error rates of ~2.3% and 8.15% for predicting major MO categories and viscosity grades, respectively. The potential to merge spectra collected from different NMR instruments is discussed for models based on spectral binning. It is also shown that small errors in phase parameters are not detrimental to binning‐based PCA models.

The diagnostic potential of urine in paediatric patients undergoing initial treatment for tuberculous meningitis

Tuberculous meningitis (TBM)—the extrapulmonary form of tuberculosis, is the most severe complication associated with tuberculosis, particularly in infants and children. The gold standard for the diagnosis of TBM requires cerebrospinal fluid (CSF) through lumbar puncture—an invasive sample collection method, and currently available CSF assays are often not sufficient for a definitive TBM diagnosis. Urine is metabolite-rich and relatively unexplored in terms of its potential to diagnose neuroinfectious diseases. We used an untargeted proton magnetic resonance (1H-NMR) metabolomics approach to compare the urine from 32 patients with TBM (stratified into stages 1, 2 and 3) against that from 39 controls in a South African paediatric cohort. Significant spectral bins had to satisfy three of our four strict cut-off quantitative statistical criteria. Five significant biological metabolites were identified—1-methylnicotinamide, 3-hydroxyisovaleric acid, 5-aminolevulinic acid, N-acetylglutamine and methanol—which had no correlation with medication metabolites. ROC analysis revealed that methanol lacked diagnostic sensitivity, but the other four metabolites showed good diagnostic potential. Furthermore, we compared mild (stage 1) TBM and severe (stages 2 and 3) TBM, and our multivariate metabolic model could successfully classify severe but not mild TBM. Our results show that urine can potentially be used to diagnose severe TBM.

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification.

Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions presents challenges of noisy data from low X-ray yields and mixed signals from phases that overlap along the electron beam trajectory. Here, we introduce a novel method, non-negative matrix factorization based pan-sharpening (PSNMF), to address these limitations. Leveraging the Poisson nature of EDX spectral noise and binning operations, PSNMF retrieves high-quality phase spectral and spatial signatures via consecutive factorizations. After validating PSNMF with synthetic data sets of different noise levels, we illustrate its effectiveness on two distinct experimental cases: a nanomineralogical lamella, and supported catalytic nanoparticles. Not only does PSNMF obtain accurate phase signatures, but data sets reconstructed from the outputs have demonstrably lower noise and better fidelity than from the benchmark denoising method of principle component analysis.

First atmospheric aerosol-monitoring results from the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia

Abstract. Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of −0.225 km and 55.29 % (71.70 %) of data points falling within ±1 km (1.5 km).

Calibration of MAJIS (Moons And Jupiter Imaging Spectrometer). III. Spectral calibration.

The Moons And Jupiter Imaging Spectrometer (MAJIS) is the visible and near-infrared imaging spectrometer onboard the European Space Agency (ESA)'s Jupiter Icy Moons Explorer mission. Before its integration into the spacecraft, the instrument undergoes an extensive ground calibration to establish its baseline performances. This process prepares the imaging spectrometer for flight operations by characterizing the behavior of the instrument under various operative conditions and uncovering instrumental distortions that may depend on instrumental commands. Two steps of the on-ground calibration campaigns were held at the instrument level to produce the data. Additional in-flight measurements have recently been obtained after launch during the Near-Earth Commissioning Phase. In this article, we present the analyses of these datasets, focusing on the characterization of the spectral performances. First, we describe and analyze the spectral calibration datasets obtained using both monochromatic sources and polychromatic sources coupled with solid and gas samples. Then, we derive the spectral sampling and the spectral response function over the entire field of view. These spectral characteristics are quantified for various operational parameters of MAJIS, such as temperature and spectral binning. The derived on-ground performances are then compared with in-flight measurements obtained after launch and presented in the framework of the MAJIS performance requirements.

1H-NMR-Based Chemometric Analysis of Echinacea Species to Predict Effectors of Myeloid Progenitor Stimulation

Echinacea, a herbaceous, perennial flowering plant from the Compositae (Asteraceae) family, exhibits stimulating effects on myeloid progenitors (CFU-GMs) in rat bone marrow, as demonstrated in our previous study using a 75% (v v−1) ethanol extract of aerial parts. Expanding on this work, we have investigated eleven different Echinacea samples that belong to three species for their myeloid progenitor-stimulating activity. Simultaneously, we employed 1H-NMR spectroscopy (400 MHz, 0.02–10.02 ppm) and chemometric analysis to predict constituents responsible for activity. Female Sprague–Dawley rats received oral doses of ethanol extracts (0–200 mg extract dry weight kg−1 body weight) of Echinacea for seven days. Bone marrow cells were then cultured with CFU-GM growth factors. Extracts showing a statistically significant (p < 0.05) increase in CFU-GM, compared to the control, were considered active. Significant CFU-GM increases were observed in rats treated with seven Echinacea samples, ranging from 39% to 91% higher than the control, while four samples were inactive. All five Echinacea purpurea samples showed myeloid progenitor-stimulating activity, while one sample each of Echinacea angustifolia and Echinacea pallida also exhibited the same activity. By applying orthogonal partial least squares discriminant analysis (OPLS-DA) to the 1H-NMR spectra, we identified specific spectral bins (0.70–1.98 ppm aliphatic and 6.38–7.76 ppm aromatic) correlating with myelopoiesis stimulation. These findings highlight the potential of chemometric analysis using 1H-NMR spectroscopy to infer the chemical classes that could be responsible for the bioactive properties of complex herbal mixtures, like Echinacea.

Spectral approximation scheme for a hybrid, spin-density Kohn–Sham density-functional theory in an external (nonuniform) magnetic field and a collinear exchange-correlation energy

AbstractWe provide a mathematical justification of a spectral approximation scheme known as spectral binning for the Kohn–Sham spin density-functional theory in the presence of an external (nonuniform) magnetic field and a collinear exchange-correlation energy term. We use an extended density-only formulation for modeling the magnetic system. No current densities enter the description in this formulation, but the particle density is split into different spin components. By restricting the exchange-correlation energy functional to be of a collinear LSDA form, we prove a series of results which enable us to mathematically justify the spectral binning scheme using the method of Gamma-convergence, in conjunction with auxiliary steps involving recasting the electrostatic potentials, justifying the spectral approximation by making a spectral decomposition of the Hamiltonian and “linearizing" the latter Hamiltonian.

Doppler Shifted Auroral Hydrogen Lyman‐Alpha Spectra Observed by DMSP/SSUSI

AbstractProton auroras, when viewed from space, are subjected to Doppler red shifts. Reports on space‐based observations of Doppler shift of H Lyman‐alpha (Ly‐α) spectral line associated with proton precipitation are still scarce. This study, as a first attempt, extracts Doppler shifted H Ly‐α spectral profiles resulting from proton precipitation from data acquired with the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) on board the Defense Meteorological Satellite Program (DMSP)/F16 spacecraft. SSUSI consists of a scanning imaging spectrograph, which covers the far ultraviolet spectrum from ∼1,120 to 1,850 Å, with 160 spectral bins and a spectral resolution of ∼20 Å. We show redshift Ly‐α spectra observed by SSUSI when its field of view traverses the auroral ovals. The inferred proton energy and energy flux are in close agreement with the DMSP SSJ in situ particle measurements. This study demonstrates the potential capability of SSUSI in monitoring the energetics of proton precipitation.

Estimation of Apple Leaf Nitrogen Concentration Using Hyperspectral Imaging-Based Wavelength Selection and Machine Learning

In apple cultivation, the total nitrogen content is an important indicator of plant growth, fruit quality, and yield. Timely monitoring of growth becomes imperative, since an imbalance, either in deficiency or excess nitrogen, can result in physiological disorders, adversely impacting both the quantity and quality of fruit. Leaf nitrogen content can be determined using simple chlorophyll meters or destructive testing; however, these methods are time-consuming. However, by employing spectral imaging technology, it is possible to swiftly predict leaf nitrogen content. This study estimated the total nitrogen content in apple trees via hyperspectral imaging and machine learning-based regression analysis (partial least-squares regression (PLSR), support vector regression (SVR), and eXtreme gradient boosting regression (XGBoost). Additionally, to reduce computational costs and improve reproducibility, spectral binning was divided into three stages (4, 8, and 16 bins), and models were compared with a 2-binning estimation model. The analysis focused on green, red, red edge, and near-infrared (NIR) spectra, with 5–10 selected wavelengths, and the SVR-based prediction model showed a similar or greater performance to that of the full spectrum. At 4- and 8-binning, the selected wavelengths were similar to those at 2-binning, maintaining similar prediction model performance. However, at 16 bp, the performance of the prediction model decreased owing to spectral data loss, leading to a significant reduction in wavelengths for nitrogen content estimation. These results can support informed nitrogen fertilization decisions, enabling precise, real-time monitoring of nitrogen content for enhanced plant growth, fruit quality, and yield in apple trees. Additionally, the selected wavelengths can be considered in the development of new types of multispectral sensors.

Multiplet-Assisted Peak Alignment for 1H NMR-Based Metabolomics.

NMR-based metabolomics aims at recovering biological information by comparing spectral data from samples of biological interest and appropriate controls. Any statistical analysis performed on the data matrix relies on the proper peak alignment to produce meaningful results. Through the last decades, several peak alignment algorithms have been proposed, as well as alternatives like spectral binning or strategies for annotation and quantification, the latter depending on reference databases. Most of the alignment algorithms, mainly based on segmentation of the spectra, present limitations for regions with peak overlap or cases of frequency order exchange. Here, we present our multiplet-assisted peak alignment algorithm, a new methodology that consists of aligning peaks by matching multiplet profiles of f1 traces from J-resolved spectra. A correspondence matrix with the linked f1 traces is built, and multivariate data analysis can be performed on it to obtain useful information from the data, overcoming the issues of peak overlap and frequency crossovers. Statistical total correlation spectroscopy can be applied on the matrix as well, toward a better identification of molecules of interest. The results can be queried on one-dimensional (1D) 1H databases or can be directly coupled to our previously published Chemical Shift Multiplet Database.

Sharing calibration information among laser-induced breakdown spectroscopy instruments using spectral line binning and calibration transfer

The ability of a publicly accessible preexisting laser-induced breakdown spectroscopy (LIBS) database to train multivariate models to predict rock and mineral compositions from other laboratories or from remotely collected spectra is evaluated using LIBS spectra collected on >2500 unique geological targets using three different instruments and a range of collection protocols. Datasets collected under increasingly disparate conditions are utilized, including a single instrument with different resolution settings; two different instruments with very similar ablation and collection optics; and a benchtop instrument and portable instrument with different collection protocols, resolutions, and plasma conditions. Cross-calibration among datasets is performed for a range of scenarios designed to test the efficacy of post-processing techniques.Major element predictions are most accurate when instrument parameters match among training and test spectra. Use of a piecewise direct standardization-partial least squares (PDS-PLS) calibration transfer algorithm reduces major element prediction uncertainties when the resolution of training and test spectra do not match. Even when training and test spectra are collected on different instruments, reasonable predictions can be derived by binning peak areas prior to training calibration models. Finally, incorporating a large, preexisting database into a smaller dataset collected under test conditions has the potential to greatly improve the reliability of predicted compositions. With careful consideration to match plasma conditions and utilize post-processing, existing large-scale LIBS calibration databases like the one used in this study can be used to boost LIBS accuracy and expand the potential of LIBS as a widely-applicable quantitative geochemical tool.

Spectral Effects on the Energy Harvesting Efficiency of Two‐ and Four‐Terminal Tandem Photovoltaics

In this work, the effect of a varying spectral irradiance and top cell bandgap on the energy harvesting efficiency of two‐terminal (2T) and four‐terminal (4T) perovskite//silicon tandem solar cells under outdoor operating conditions is investigated. For the comparison, an optoelectronic model employing a 1 year outdoor data set for a 4T mechanical stacked gallium arsenide (GaAs) on crystalline silicon (Si) tandem device is first validated. Then, the verified model is used to simulate perovskite//silicon tandem devices with a varying perovskite top cell bandgap for a location in Golden, Colorado, USA. A spectral binning method to efficiently reduce and improve the visualization of the 1 min‐resolved environmental data while maintaining the simulation accuracy is introduced. The findings reveal that, for a device that is current matched under standard testing conditions, the annual spectral deviation reduces the energy harvesting efficiency by only 2%rel. When additional realistic losses for the 4T are taken into account, 2T devices are shown to have an energy‐harvesting efficiency that is at parity or higher. Deviations in the top cell bandgap are more than 0.1 eV from current matching result in a reduced energy harvesting efficiency of more than 5%rel for the 2T tandem device.

Raman scattering in the Earth’s atmosphere, Part II: Radiative transfer modeling for remote sensing applications

Raman scattering in the Earth’s atmosphere is caused predominantly by its most abundant molecular components, N2 and O2. After the computation of the optical properties that govern the spectral and angular redistribution of light due to various inelastic scattering events, viz. rotational Raman scattering (RRS), vibrational Raman scattering (VRS), and rovibrational Raman scattering (RVRS), covered in Part I of this series, the next challenge in the simulation of inelastic scattering in the Earth’s atmosphere is to carry out radiative transfer (RT) computations across several wavelengths simultaneously.In this part of our work, we provide the RT formulation for fully polarized simulations of inelastic scattering using the matrix-operator-method-based RT model vSmartMOM. The formalism is optimized for easy use with GPUs, allowing an unprecedented speedup of accurate multi-wavelength RT computations of inelastic scattering using the full Stokes-vector, thus allowing its operational use without coarse spectral binning (Rozanov and Vountas, 2014), or single scattering approximations (Sioris and Evans, 1999) at longer wavelengths.After comparing our model against the current state-of-the-art, we demonstrate the use of vSmartMOM to simulate Raman lidar measurements, the Ring effect, the ghosting of Fraunhofer lines due to vibrational Raman scattering and spectral corrections due to inelastic scattering in the O2 A-band in the Earth’s atmosphere. We use our model (1.) to validate the convention of neglecting the contribution of VRS and RVRS, and (2.) to quantify the speed and accuracy of the single scattering approximation in the O2 A-band.

Rational biodiesel production by screening different long-term stored plant oils via nuclear magnetic resonance spectroscopic measurement

Screening the raw materials for biodiesel production has normally concentrated on the triglyceride (TAG) content and fatty acid profile without considering the oxidation stability, a crucial restriction factor for commercial life of biodiesel. The traditional screening methods had complex procedures by characterizing the raw material’ and the prepared biodiesel’ properties, which was not applicable for large sets of raw materials. These factors propelled the development of a simple and efficient screening method for potential biodiesel feedstock. Herein, we compared two kinds of spectroscopic measurements, the three-dimensional fluorescence (3D-FL) and high field nuclear magnetic resonance (NMR), for screening special and differential raw materials by investigating 19 plant oil species with one-year storage for biodiesel production. Among these samples, 13 kinds of plant oils contained TAGs, and the other 6 were free of TAGs. The results showed that 3D-FL measurements improperly assigned citronella oil as a TAG-containing plant oil and determined limited information about their chemical composition, suggesting unsatisfactory screening performance. In contrast, 1H NMR measurements screened 3 kinds of differential oils out of those 19 plant oil species, including coconut oil (Cocos nucifera L.), cotton seed oil (Gossypium barbadense L.), palm oil (Elaeis guineensis). Hierarchical clustering analysis (HCA) and partial least square discriminant analysis (PLS-DA) presented that the statistically significant NMR spectral bins for their differential compositions were signals at 3.6 ppm (–OCH3), 2.31 ppm (–OCH2–COOH), 4.29 ppm (TAG skeleton), and 5.27 ppm (TAG skeleton), representing the oxidation products, unsaturated fatty acids, and TAGs. Finally, the converted methyl and ethyl esters (biodiesel) from those three differential plant oil samples were subjected to Rancimat tests. They showed quite different oxidation stabilities from Rancimat tests, which was relevant to the composition differences in the NMR analysis. Our results suggested that the rapid, simple, and reliable NMR measurement combined with HCA and PLS-DA can serve as an efficient tool for potential biodiesel feedstock screening.

P07.12.A DIAGNOSTIC POTENTIAL OF INFRARED SPECTROSCOPY IN MENINGIOMA PATIENTS

Abstract BACKGROUND Vibrational spectroscopy probes molecular vibrations in the tissue and can provide optical diagnosis of brain tumors. Here, we investigated its potential for the identification of aggressive meningioma. MATERIAL AND METHODS Tissue samples were obtained during routine surgery from 113 meningioma patients (WHO 1 n= 33; WHO 2 n=39; WHO 3 n=41). Infrared (IR) spectroscopic imaging was performed at three positions (170 x 170 µm, 16x16 = 256 spectra) on frozen sections in reference to HE histology. Spectra were labeled according to the clinical diagnosis of the patient. Baseline correction, area normalization and fourfold spectral binning was performed. Spectra displaying artifacts were identified by visual inspection and were excluded from further analysis. The data was split into test and training set. 42825 spectra of 57 specimens (training set) were utilized to develop an algorithm using linear discriminant analysis to identify aggressive WHO 3 meningioma. RESULTS The developed algorithm assigned 88.6% of the 40626 spectra of the independent test set to the correct class. Based on these RESULTS , the class assignment for each measurement position was calculated as mean of class assignment of the respective spectra: All WHO 1 (16/16) samples, 16/19 WHO 2 and 17/21 WHO 3 meningioma were correctly assigned at all three positions. One sample of meningioma WHO 3 was misclassified at all measurement positions. However, in case of six patients inconclusive results were obtained for the samples as the measurement positions were assigned to different classes. Comparison of IR spectral signatures revealed increased bands at 1114, 1242, 1336 and 1450 cm-1 and decreased bands at 1544 and 1651 and 1734 cm-1 in meningioma WHO 3. This suggests increased phospholipids and decreased proteins as main difference to meningioma WHO 1 and 2 and might form the basis for diagnostic exploitation of IR spectral information. CONCLUSION IR spectroscopy qualifies as reliable tool for the identification of aggressive meningioma. Based on the classification results of this study, translational strategies may be deduced. The data suggests, that analysis of the tumor needs to be performed at multiple sites to obtain reliable clinical information. In case of conflicting results at different measurement positions, the tissue should be evaluated as unratable. As infrared spectroscopy can be applied in situ using fiber based systems, it might allow intraoperative meningioma grading and tumor delineation in future translational settings.

Computational refocusing in phase-unstable polarization-sensitive optical coherence tomography.

We present computational refocusing in polarization-sensitive optical coherence tomography (PS-OCT) to improve spatial resolution in the calculated polarimetric parameters and extend the depth-of-field in phase-unstable, fiber-based PS-OCT systems. To achieve this, we successfully adapted short A-line range phase-stability adaptive optics (SHARP), a computational aberration correction technique compatible with phase-unstable systems, into a Stokes-based PS-OCT system with inter-A-line polarization modulation. Together with the spectral binning technique to mitigate system-induced chromatic polarization effects, we show that computational refocusing improves image quality in tissue polarimetry of swine eye anterior segment ex vivo with PS-OCT. The benefits, drawbacks, and potential applications of computational refocusing in anterior segment imaging are discussed.

Exoplanet spectroscopy with JWST NIRISS: diagnostics and case studies

ABSTRACT The JWST is ushering in a new era in remote sensing of exoplanetary atmospheres. Atmospheric retrievals of exoplanets can be highly sensitive to high-precision JWST data. It is, therefore, imperative to characterize the instruments and noise sources using early observations to enable robust characterization of exoplanetary atmospheres using JWST-quality spectra. This work is a step in that direction, focusing on the Near Infrared Imager and Slitless Spectrograph (NIRISS) Single Object Slitless Spectroscopy (SOSS) instrument mode, with a wavelength coverage of 0.6–2.8 $\mu$m and R ∼ 700. Using a custom-built pipeline, JExoRES, we investigate key diagnostics of NIRISS SOSS with observations of two giant exoplanets, WASP-39 b and WASP-96 b, as case studies. We conduct a detailed evaluation of the different aspects of the data reduction and analysis, including sources of contamination, 1/f noise, and system properties such as limb darkening. The slitless nature of NIRISS SOSS makes it susceptible to contamination due to background sources. We present a method to model and correct for dispersed field stars that can significantly improve the accuracy of the observed spectra. In doing so, we also report an empirically determined throughput function for the instrument. We find significant correlated noise in the derived spectra, which may be attributed to 1/f noise, and discuss its implications for spectral binning. We quantify the covariance matrix that would enable the consideration of correlated noise in atmospheric retrievals. Finally, we conduct a comparative assessment of NIRISS SOSS spectra of WASP-39 b reported using different pipelines and highlight important lessons for exoplanet spectroscopy with JWST NIRISS.

Introducing Temporal Correlation in Rainfall and Wind Prediction From Underwater Noise

While in the past the prediction of wind and rainfall from underwater noise was performed using empirical equations fed with very few spectral bins and fitted to the data, it has recently been shown that regression performed using supervised machine learning techniques can benefit from the simultaneous use of all spectral bins, at the cost of increased complexity. However, both empirical equations and machine learning regressors perform the prediction using only the acoustic information collected at the time when one wants to know the wind speed or the rainfall intensity. At most, averages are made between spectra measured at subsequent times (spectral compounding) or between predictions obtained at subsequent times (prediction compounding). In this article, it is proposed to exploit the temporal correlation inherent in the phenomena being predicted, as has already been done in methods that forecast wind and rainfall from their values (and sometimes those of other meteorological quantities) in the recent past. A special architecture of recurrent neural networks, the long short-term memory, is used along with a data set composed of about 16 months of underwater noise measurements (acquired every 10 min, simultaneously with wind and rain measurements above the sea surface) to demonstrate that the introduction of temporal correlation brings significant advantages, improving the accuracy and reducing the problems met in the widely adopted memoryless prediction performed by random forest regression. Working with samples acquired at 10-min intervals, the best performance is obtained by including three noise spectra for wind prediction and six spectra for rainfall prediction.

Assessment of SWAN and WAVEWATCH-III models regarding the directional wave spectra estimates based on Eastern Black Sea measurements

The spectral wave models are often calibrated or validated based on bulk wave data derived from spectral information such as the significant wave height (Hs) and mean period (Tm). However, the precision in the hindcasted spectral data was not evaluated in many seas, such as the Black Sea, due to the lack of spectral wave measurements. As part of this study, a spectral wave measuring buoy was installed near the cape Small Utrish to collect the spectral measurement data. The study objective is to assess the SWAN and WAVEWATCH-III (WWIII) models regarding the directional wave spectra estimates based on measurements conducted in the eastern Black Sea. For this purpose, the estimated spectral density distribution and spectral mean direction in terms of spectral frequency bins are assessed for both SWAN and WWIII outputs. The biases in variance densities are firstly calculated for both models at each frequency based on 14 710 spectral measurements, as well as the biases in the annual and seasonal averages. In addition, the correlation coefficients and root mean squared errors at each frequency are calculated and discussed. The accuracies in the mean direction at each spectral shape over the whole measurement period are secondly evaluated. Thirdly, the annual and seasonal averaged two-dimensional wave spectrums are discussed compared to measurements. The performances of both models are lastly verified based on bulk data measured at 8 locations on different coasts of the Black Sea. The results show that SWAN output fits better the average variance density shape at all frequency ranges f > 0.08Hz. However, it slightly overestimates the variance density in the 0.13–0.17 Hz range. In contrast to the SWAN model, WWIII data show only better correspondence in the 0.13–0.17 Hz frequency range but underestimate the lower frequency energies and overestimate higher frequency ranges. The SWAN model is therefore recommended for the spectral density estimate at seasonal and annual scales.