Search Results (68)

Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the RF_SI method was evaluated by using individual cases and time series observations. Results demonstrate that the RF_SI method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional SI and CLWP-based precipitation sounding algorithms, the accuracy and sounding rate of the RF_SI method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the RF_SI method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation. Full article

Retrieving LST from infrared spectral observations is challenging because it needs separation from emissivity in surface radiation emission, which is feasible only when the state of the surface–atmosphere system is known. Thanks to its high spectral resolution, the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard Metop polar-orbiting satellites is the only sensor that can simultaneously retrieve LST, the emissivity spectrum, and atmospheric composition. Still, it cannot penetrate thick cloud layers, making observations blind to surface emissions under cloudy conditions, with surface and atmospheric parameters being flagged as voids. The present paper aims to discuss a downscaling–fusion methodology to retrieve LST missing values on a spatial field retrieved from spatially scattered IASI observations to yield level 3, regularly gridded data, using as proxy data LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) flying on Meteosat Second Generation (MSG) platform, a geostationary instrument, and from the Advanced Very High-Resolution Radiometer (AVHRR) onboard Metop polar-orbiting satellites. We address this problem by using machine learning techniques, i.e., Gradient Boosting, Random Forest, Gaussian Process Regression, Neural Network, and Stacked Regression. We applied the methodology over the Po Valley region, a very heterogeneous area that allows addressing the trained models’ robustness. Overall, the methods significantly enhanced spatial sampling, keeping errors in terms of Root Mean Square Error (RMSE) and bias (Mean Absolute Error, MAE) very low. Although we demonstrate and assess the results primarily using IASI data, the paper is also intended for applications to the IASI follow-on, that is, IASI Next Generation (IASI-NG), and much more to the Infrared Sounder (IRS), which is planned to fly this year, 2025, on the Meteosat Third Generation platform (MTG). Full article

The COVID-19 epidemic has raised awareness of exactly how crucial it is to continuously observe issues and diagnose respiratory problems early. Although the respiratory system is the primary objective of the disease’s acute phase, subsequent complications of SARS-CoV-2 infection might trigger enduring respiratory problems and symptoms, according to new research. These signs and symptoms, which collectively inflict considerable strain on healthcare systems and people’s quality of life, comprise, but are not restricted to, congestion, shortage of breath, tightness in the chest, and a decrease in lung function. Wearable technology offers a promising remedy to this persistent issue by offering continuous respiratory parameter monitoring, facilitating the early control and intervention of post-COVID-19 issues with respiration. In an effort to enhance patient outcomes and reduce expenses related to healthcare, this paper examines the possibility of using wearable technology to provide remote surveillance and the early diagnosis of respiratory problems in individuals suffering from COVID-19. In this work, a fog computing-based cost-effective smart health monitoring device is proposed for infectious disease applications. Further, the proposed device consists of three different biosensor modules, namely a MAX90614 infrared temperature sensor, a MAX30100 pulse oximeter, and a microphone sensor. All these sensor modules are connected to a fog computing device, namely a Raspberry PI microcontroller. Also, three different sensor modules were integrated with the Raspberry PI microcontroller and individuals’ physiological parameters, such as oxygen saturation (SPO2), heartbeat rate, and cough sounds, were recorded by the computing device. Additionally, a convolutional neural network (CNN)-based deep learning algorithm was coded inside the Raspberry PI and was trained with normal and COVID-19 cough sounds from the KAGGLE database. This work appears to be of high clinical significance since the developed fog computing-based smart health monitoring device is capable of identifying the presence of infectious disease through individual physiological parameters. Full article

The present study aimed to enhance the efficiency and efficacy of the Al/Cu joint production process implemented by the company VEMID Ltd., Jagodina, Serbia, by attaining sound joints within a very short welding time. For this purpose, the present study aimed at investigating the accuracy and the quality of the continuous drive friction welding (CDFW) process, as well as the optimum combination of CDFW parameters with highest joint efficiency in terms of investigated properties. The accuracy was estimated through an analysis of temperature–time curves recorded during CDFW using an infrared camera. The quality was evaluated through an investigation of the properties of Al/Cu joints produced using different friction (66.7, 88.9, and 133.3 MPa) and forging (88.9, 222.2, and 355.6 MPa) pressures and a constant total welding time (4 s) and rotational speed (2100 rpm). Thermal imaging with an infrared camera demonstrated that the actual total welding time was 15% longer compared to the nominal value. This was attributed to the slow pressure response of the pneumatic brake system. The relative changes in the maximum surface temperature (T_MS) during the CDFW process corresponded to changes in welding pressures, indicating the potential of the thermal imaging method for monitoring and assessing this process. A preliminary investigation demonstrated that Al/Cu joints produced using welding pressures less than 88.9 MPa often displayed the presence of non-joined micro-regions at the Al/Cu interface and a significant thickness of interfacial Al₂Cu (up to 1 µm). However, when friction pressure was set at 66.7 MPa, an increase in the forging pressure to 222.2 MPa eliminated the presence of non-joined micro-regions and reduced the thickness of Al₂Cu to 0.5 µm on the average level. These Al/Cu joints achieved the highest joint efficiencies in terms of strength (100%) and ductility (61%). They exhibited an electrical conductivity higher than 92% of the theoretical value. A further increase in any welding pressure produced similar or deteriorated properties, accompanied by an increase in the consumption of raw materials and energy. Such turn of events was counterproductive to the original goal of increasing the efficiency and efficacy of the CDFW process. Full article

In this study, a novel method for retrieving atmospheric temperature profiles with tree-structured Parzen estimator (TPE) and multilayer perceptron (MLP) algorithms was proposed, using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) and ERA5 data. Firstly, by adding solar altitude angle, satellite zenith angle, 2m temperature, and surface temperature to the input layer of MLP, there is an improvement in retrieval accuracy. Secondly, TPE is effective in optimizing the hyper-parameters of MLP, and a set of optimized hyper-parameters is obtained through iterative optimization. Thirdly, comparing the retrieved temperature profiles with ERA5 data, we found that retrieval accuracy is influenced by detector, signal-to-noise ratio, terrain, solar altitude angle, satellite zenith angle, and the horizontal temperature gradient. The mean biases of the two adjacent detectors show significant differences, and the retrieval accuracy of the center detectors is greater than that of the north and south sides. The retrieval accuracy is relatively poor in areas with high terrain and large satellite zenith angle. There is a monthly variation in the retrieval accuracy due to the horizontal temperature gradient and signal-to-noise ratio and a significant diurnal variation due to solar altitude angle and signal-to-noise ratio. Compared to in situ sounding data, the mean biases vary from −0.56 K to 0.60 K, and the standard deviations vary from 1.26 K to 2.17 K. The analysis of factors influencing retrieval accuracy provides important insights into improving the ability to retrieve atmospheric temperatures from geostationary hyperspectral IR sounder observations for near real-time (NRT) applications. Full article

The variation in key parameters of the solar–terrestrial space during two consecutive auroral disturbances (the magnetic storm index, Dst index = −422 nT) that occurred during the 18–23 November 2003 period was analyzed in this paper, as well as the spatiotemporal characteristics of NO 5.3 μm radiation with an altitude around the location of 55°N 160°W. The altitude was divided into four regions (50–100 km, 100–150 km, 150–200 km, and 200–250 km), and it was found that the greatest amplification occurs at the altitude of 200–250 km. However, the radiance reached a maximum of 3.38 × 10⁻³ W/m²/sr at the altitude of 123 km during the aurora event, which was approximately 10 times higher than the usual value during “quiet periods”. Based on these findings, the spatiotemporal variations in NO 5.3 μm radiance within the range of latitude 51°S–83°N and longitude of 60°W–160°W were analyzed at 120 km, revealing an asymmetry between the northern and southern hemispheres during the recovery period. Additionally, the recovery was also influenced by the superposition of a second auroral event. The data used in this study were obtained from the OMNI database and the SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) infrared radiometer onboard the TIMED (Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics) satellite. Finally, the correlation of NO 5.3 μm radiance at 120 km with temperature, solar wind speed, auroral electrojet index (AE index), and Dst index were analyzed. It was found that only the Dst index had a good correlation with the radiance value. Furthermore, the correlation between the Dst index and radiance at different altitudes was also analyzed, and the highest correlation was found at 170 km. Full article

We investigated the ultrafast dynamics of a SrRuO₃/BiFeO₃ superlattice grown on a SrTiO₃ substrate using a near infrared pump–probe technique at various temperatures. The superlattice exhibits a ferromagnetic order inherited from the SrRuO₃ layer. The pump-induced changes in the reflectivity reveal periodic oscillations. We found that the oscillation frequency can be well explained by zone-folded acoustic phonon oscillations, whose dispersion depends on the sound velocity, density, and thickness within the supercell of each constituent layer. It is found that the observed oscillation frequency corresponds to the A₁ mode, which suggests that oscillations are excited due to pump-induced expansion of the SrRuO₃ layer that absorbs the pump photon. Temperature-dependent measurements reveal significant suppression of the oscillation amplitude in the ferromagnetic state. The suppressed amplitude is proportional to the square of the magnetization, M(T)². This phenomenon can be attributed to a strong magnetostriction effect of SrRuO₃ that suppresses lattice expansion upon pumping. Full article

Prior to assimilating hyperspectral infrared data on the FengYun (FY) satellite in the numerical weather prediction (NWP) system, it is necessary to identify the quality and bias characteristics of these data, especially as China’s first early-morning-orbit satellite data. Using the numerical model CMA-GFS (China Meteorological Administration Global Forecast System) and the observation of FY-3E HIRAS-II (Hyperspectral Infrared Atmospheric Sounder-II), the differences between observed and simulated brightness temperatures (O-Bs) are comprehensively analyzed, with a focus on evaluating the long-wave (LW) temperature sounding channels of HIRAS-II observation in the clear sky. The results show that the O-Bs in the LW channels are between ±1.0 K, except for the ${\mathrm{C}\mathrm{O}}_2$ absorption line peak at 667 ${\mathrm{c}\mathrm{m}}^{-1}$. Only a tiny variation in O-Bs, with relative consistency, could be observed during the day, the line of dawn and dusk, and night. The difference in the standard deviations of O-Bs in the three cases is less than 0.1 K. The O-Bs of two typical channels (channels 14 and 47) in the stratosphere have disturbances at individual times, whereas the O-Bs are much more stable in time series in the tropospheric channels. The O-Bs in different channels show the characteristics of changing with the latitude, but the bias and standard deviations of O-Bs during the ascending and descending stages are not much different, except for the bias of channel 47 in low latitude. The optimal ranking of Fields of View (FOVs) in assimilation is derived from a priori analysis of O-Bs. The results demonstrate that FOV4 and FOV5 are the best in a Field of Regard (FOR) compared to all LW channels of HIRAS-II in constructions of their O-Bs and magnitude of O-B standard deviations, and they can be used as the preferred FOVs for assimilation. While the O-Bs in FOV1 and FOV2 are slightly larger, the O-Bs’ characteristics also meet the assimilation requirements and can be used as assimilation FOVs in HIRAS-II LW channels after FOV4 and FOV5 lose their efficacy. Full article

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms. Full article

Objective: The goal of this study was to compare the pulp temperature increase during light curing of different composite thicknesses in deep class I cavities using two thermal sensing tools. Methodology: Round occlusal class I cavities with a remaining dentin thickness (RDT) of 1 mm from the cavity floor were performed on 15 extracted sound molars. Samples were divided into three groups (n = 5). In group A, cavities were restored using the Filtek Z350 XT conventional composite through the incremental filling technique, whereas group B cavities were restored using the Filtek Bulk-Fill flowable composite through the bulk-fill technique. Specimens of the Filtek Bulk-Fill flowable composite using the incremental filling technique were used to restore cavities in group C. An infrared thermal camera (IRT; Flir, Wilsonville, OR, USA) and K-type thermocouple (Perfect Prime TC0520, New York, NY, USA) were used to measure the heat generated during composite photo-polymerization. Results: There were no significant differences within the same group using either the thermocouple or IRT (p > 0.05). One-way ANOVA showed no significant differences between groups A and C (p > 0.05), whereas group B was significantly different from groups A and C with each sensing tool (p < 0.05). Conclusion: IRT and thermocouple heat readings were comparable. Based on the current findings, the bulk-fill technique resulted in the lowest heat generation among the groups. Therefore, increasing the light-curing time and the number of composite increments was directly correlated with increases in intra-pulpal temperature. Full article

The current investigation introduces and demonstrates a credible, economically sound system to remove agrochemical runoff using a vertical flow constructed wetland (VFCW). DuPont 1179 carbamate insecticide was applied as a simulating greenhouse crop production controller, which resulted in runoff loaded with DuPont 1179. A novel composite of constructed wetland from an alum sludge conjugate magnetite nanoparticle substrate was applied and supported with gravel as a filtration/adsorption bed in a vertical flow constructed wetland (VFCW) system. X-ray diffraction spectroscopy (XRD), scanning electron microscopy (SEM) augmented with energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR) were employed to characterize the suggested composite substrate. The experimental data showed VFCW to be significant in eliminating DuPont 1179. The isotherm time was explored at 300 min, which corresponded to complete insecticide removal (100%). The operational parameters were located at the natural pH (6.9) of the solution and room temperature (25 °C). The VFCW column was also investigated at various substrate concentrations ranging from 100% to 40% of the composite material supported by a gravel medium, and the existence of composite at a 75% concentration showed the highest yield. The experimental data verified that the adsorption followed the pseudo second-order adsorption kinetic model. Furthermore, according to the isotherm model results, the scheme followed the Langmuir isotherm model. Thus, the presented study is a promising indicator of the possibility of using alum sludge conjugate nanoparticles for the elimination of agrochemicals from wastewater. Full article

This study highlights hyperspectral infrared observations from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) collected as part of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) deployment on the icebreaker RV Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The ARM M-AERI directly measures the infrared radiance emission spectrum between 520 cm⁻¹ and 3000 cm⁻¹ (19.2–3.3 μm) at 0.5 cm⁻¹ spectral resolution. These ship-based observations provide a valuable set of radiance data for the modeling of snow/ice infrared emission as well as validation data for the assessment of satellite soundings. Remote sensing using hyperspectral infrared observations provides valuable information on sea surface properties (skin temperature and infrared emissivity), near-surface air temperature, and temperature lapse rate in the lowest kilometer. Comparison of the M-AERI observations with those from the DOE ARM meteorological tower and downlooking infrared thermometer are generally in good agreement with some notable differences. Operational satellite soundings from the NOAA-20 satellite were also assessed using ARM radiosondes launched from the RV Polarstern and measurements of the infrared snow surface emission from the M-AERI showing reasonable agreement. Full article

The atmospheric temperature profiles (ATPs) retrieved through the geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4A satellite (GIIRS/FY-4A) can effectively fill the gap of the scarce conventional sounding data in the Taklimakan Desert (TD), the second largest desert in the world, with an area of 330,000 square kilometers. In this study, we take the experimental radiosonde observations (RAOB) from one RAOB station in the hinterland of TD and seven conventional radiosondes in the oasis region around the desert as the true values and analyze the bias distribution characteristics of GIIRS/FY-4A ATPs with quality control (QC) flags 0 or 1 for this region. In addition, a bias comparison is made with GIIRS/FY-4A ATPs, and the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) ATPs. The results show that (1) Missing measurements in GIIRS/FY-4A ATPs are the most frequent in the near-surface layer, accounting for more than 80% of all the retrieved grid points. The averaged total proportion of GIIRS/FY-4A ATPs with QC marks 0 or 1 is about 33.06%. (2) The root mean square error (RMSE) of GIIRS/FY-4A ATPs is less than 3 K, smaller than that of ERA5 ATPs. The RMSE of ERA5 ATPs can exceed 10 K in the desert hinterland. The absolute mean biases of GIIRS/FY-4A ATPs and ERA5 ATPs are, respectively, smaller than 3 K and 2 K, the former being slightly larger. The correlation coefficients of GIIRS/FY-4A ATPs with ERA5 ATPs and RAOB ATPs are higher than 0.98 and 0.99, respectively, and the correlation between GIIRS/FY-4A ATPs and RAOB ATPs is inferior to the latter. (3) The overall atmospheric temperature retrieved by GIIRS/FY-4A is 0.08 K higher than the temperature of RAOB, on average, while the overall temperature from ERA5 is 0.13 K lower than that of RAOB, indicating that the temperature profile obtained by integrating GIIRS/FY-4A ATPs and ERA5 ATPs may be much closer to RAOB ATPs. (4) The probability density of the GIIRS/FY-4A ATP biases in the TD region generally follows the Gaussian distribution so that it can be effectively assimilated in the 3-D variational assimilation modules. The probability density distribution characteristics of the GIIRS/FY-4A ATP biases in the desert hinterland and oasis are not much different. However, due to the fusion analysis of the relatively rich multi-source conventional observation data from the oasis stations, the probability density of ERA5 ATPs biases at the oasis stations is nearer to Gaussian distribution than that of the GIIRS/FY-4A ATPs. In the desert hinterland, where conventional observation is not enough, the probability density distributions of the ATPs biases from ERA5 and GIIRS/FY-4A are alike. Therefore, the GIIRS FY4A can contribute to a more accurate estimation of ERA5 ATPs in the TD region. Full article

Accurate stratospheric temperature observations are crucial for weather forecasts and climate change studies. This paper discusses a precise measurement method for the stratospheric temperature profile using narrow bands with ultra-high spectral resolution from nadir-viewing satellites. First, the CO₂ absorption band around 15 μm is selected as the major sounding source by the calculation and analysis of the temperature Jacobian and the atmospheric molecular spectra. Next, the influence of spectral resolution, spectral range and instrumental noise on the sounding capability is analyzed, and the sounding feasibility of the single spectral band and multiple spectral bands is discussed under the condition that the spaceborne long-wave infrared space heterodyne spectrometer (SHS) is selected as suggested sounder onboard the satellite. Finally, the optimal joint-sounding scheme of narrow bands is proposed. The temperature retrieval and validation show that the joint-sounding of two discontinuous narrow bands can realize the high precision measurement of the stratospheric temperature profile for the given spectral resolution, spectral range, and instrumental noise. When the sounder adopts two narrow bands (the regions of 666.87–676.44 cm⁻¹ and 683.58–693.15 cm⁻¹) and a spectral resolution of 0.03 cm⁻¹, the retrieval accuracy (RMSE) is about 0.9 K over a pressure range of 200 to 0.7 hPa (11.5–50 km). This study will provide technical preparation for high-precision and low-cost satellite sounder design for stratospheric temperature observations. Full article

Observations from a hyperspectral infrared (IR) sounding interferometer such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) are crucial to numerical weather prediction (NWP). By measuring radiance at the top of the atmosphere using thousands of channels, these observations convey accurate atmospheric information to the initial condition through data assimilation (DA) schemes. The massive data volume has pushed the community to develop novel approaches to reduce the number of assimilated channels while retaining as much information content as possible. Thus, channel-selection schemes have become widely accepted in every NWP center. Two significant limitations of channel-selection schemes are (1) the deficiency in retaining the observational information content and (2) the higher cross-channel correlation in the observational error (R) matrix. This paper introduces a hyperspectral IR observation DA scheme in the principal component (PC) space. Four-month performance comparison case studies using the Weather Research and Forecasting model (WRF) as a forecast module between PC-score assimilation and the selected-channel assimilation experiment show that the PC-score assimilation scheme can reduce the initial condition’s root-mean-squared error for temperature and water vapor compared to the channel-selection scheme and thus improve the forecasting of precipitation and high-impact weather. Case studies using the Unified Forecast System Short-Range Weather (UFS-SRW) application as forecast module also indicate that the positive impact can be retained among different NWP models. Full article