Search Results (30)

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth’s surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4–2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes. Full article

Glaucoma remains the primary cause of long-term blindness. While diabetes mellitus (DM) and hypertension (HTN) are known to influence glaucoma, other factors such as age and sex may be involved. In this retrospective study, we aimed to investigate the associations between age, sex, DM, HTN, and glaucoma risk. We employed optical coherence tomography (OCT) conducted using a 200 × 200-pixel optic cube (Cirrus HD OCT 6000, version 10.0; Carl Zeiss Meditec, Dublin, CA, USA). Effects obscured by low-test signals were disregarded. Data were amassed from 1337 patients. Among them, 218 and 402 patients had DM and HTN, respectively, with 133 (10%) exhibiting both. A sex-based comparison revealed slightly greater retinal nerve fiber layer (RNFL) and ganglion cell–inner plexiform layer (GCIPL) thickness in females. Patients without DM and HTN were predominantly in their 50 s and 60 s, whereas DM and HTN were most prevalent in those in their 60 s and 70 s. Both RNFL and GCIPL thicknesses decreased with advancing age in most patients. The study revealed that older individuals were more prone to glaucoma than younger individuals, with a higher incidence among patients with DM and HTN and reduced RNFL and GCIPL thicknesses. Furthermore, early detection before advancing age could furnish valuable preventive insights. Full article

Cloud detection is critical in remote sensing image processing, and convolutional neural networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily focus on extracting local features, which can be challenging for cloud detection due to the variability in the size, shape, and boundaries of clouds. To address this limitation, we propose a hybrid Swin transformer–CNN cloud detection (STCCD) network that combines the strengths of both architectures. The STCCD network employs a novel dual-stream encoder that integrates Swin transformer and CNN blocks. Swin transformers can capture global context features more effectively than traditional CNNs, while CNNs excel at extracting local features. The two streams are fused via a fusion coupling module (FCM) to produce a richer representation of the input image. To further enhance the network’s ability in extracting cloud features, we incorporate a feature fusion module based on the attention mechanism (FFMAM) and an aggregation multiscale feature module (AMSFM). The FFMAM selectively merges global and local features based on their importance, while the AMSFM aggregates feature maps from different spatial scales to obtain a more comprehensive representation of the cloud mask. We evaluated the STCCD network on three challenging cloud detection datasets (GF1-WHU, SPARCS, and AIR-CD), as well as the L8-Biome dataset to assess its generalization capability. The results show that the STCCD network outperformed other state-of-the-art methods on all datasets. Notably, the STCCD model, trained on only four bands (visible and near-infrared) of the GF1-WHU dataset, outperformed the official Landsat-8 Fmask algorithm in the L8-Biome dataset, which uses additional bands (shortwave infrared, cirrus, and thermal). Full article

Aviation turbulence is a major concern for flight safety. Detecting and nowcasting upper-level turbulence is usually associated with known sources of turbulence, such as convective clouds and transverse cirrus bands. However, in extended clear-air conditions where no optical indicators are present, this can be challenging for both aviation forecasters and pilots. This study aims to evaluate heavy–severe aviation scale turbulence events over 20.000 ft, by utilizing satellite data from MSG SEVIRI radiometer and in situ turbulence reports from en-route aircraft flights over Europe. We analyze 92 heavy–severe turbulence events during November 2009. The results could give an estimate of possible turbulence detection to pilots and aviation forecasters to identify and avoid upper-level turbulence, increasing flight safety. Full article

Infrared small target detection (ISTD) plays a significant role in earth observation infrared systems. However, some high reflection areas have a grayscale similar to the target, which will cause a false alarm in the earth observation infrared system. For the sake of raising the detection accuracy, we proposed a cirrus detection measure based on low-rank sparse decomposition as a supplementary method. To better detect cirrus that may be sparsely insufficient in a single frame image, the method treats the cirrus sequence image with time continuity as a tensor, then uses the visual saliency of the image to divide the image into a cirrus region and a cirrus-free region. Considering that the classical tensor rank surrogate cannot approximate the tensor rank very well, we used a non-convex tensor rank surrogate based on the Laplace function for the spatial-temporal tensor (Lap-NRSSTT) to surrogate the tensor rank. In an effort to compute the proposed model, we used a high-efficiency optimization approach on the basis of alternating the direction method of multipliers (ADMM). Finally, final detection results were obtained by the reconstructed cirrus images with a set threshold segmentation. Results indicate that the proposed scheme achieves better detection capabilities and higher accuracy than other measures based on optimization in some complex scenarios. Full article

The cloud thermodynamic phase is a crucial parameter to understand the Earth’s radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at −3 ${}^{\circ }$C, followed by that for MP at −13 ${}^{\circ }$C. Between 0 and −27 ${}^{\circ }$C, the occurrence of SC and MP dominates IC, while below −27 ${}^{\circ }$C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites. Full article

The VIIRS instrument (Visible Infrared Imaging Radiometer Suite) on board the SNPP (Suomi National Polar-orbiting Partnership) satellite contains 11 narrow channels (M1–M11) in the 0.4–2.5 μm solar spectral region. The M9 channel is specifically designed for detecting thin cirrus clouds. It is centered at 1.378 μm with a width of 15 nm, which is located within a strong atmospheric water vapor band absorption region. In comparison with the corresponding MODIS Channel 26, the VIIRS M9 channel is narrower and more sensitive for cirrus detections. Because the radiances of the M9 channel over cirrus pixels are subjected to absorption by atmospheric water vapor molecules above and within the cirrus clouds, the water vapor absorption effect needs to be properly taken into consideration when using the M9 channel for quantitative removal of cirrus effects in other VIIRS channels in the 0.4–2.5 μm spectral range. In this article, we describe in detail an empirical technique for the retrieval of cirrus reflectances in the visible and near-IR (VNIR, 0.4–1.0 μm), where ice particles within cirrus clouds have negligible absorption effects, and in shortwave IR (SWIR, 1.0–2.5 μm) where ice particles’ absorption effects are observed. The descriptions include all elements leading to the development of the operational VIIRS cirrus reflectance algorithm, the journal literature backing up the approach, theoretical descriptions of the algorithm’s physics and mathematical background, and sample retrieval results from the VIIRS data. The SNPP VIIRS cirrus reflectance data products from 1 March 2012 to the present are available from a NASA data center. Full article

Cloud properties are essential for the Clouds and the Earth’s Radiant Energy System (CERES) Project, enabling accurate interpretation of measured broadband radiances, providing a means to understand global cloud-radiation interactions, and constituting an important climate record. Producing consistent cloud retrievals across multiple platforms is critical for generating a multidecadal cloud and radiation record. Techniques used by CERES for retrievals from measurements by the MODerate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua platforms are adapted for the application to radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership to continue the CERES record beyond the MODIS era. The algorithm adjustments account for spectral and channel differences, use revised reflectance models, and set new thresholds for detecting thin cirrus clouds at night. Cloud amounts from VIIRS are less than their MODIS counterparts by 0.016 during the day and 0.026 at night, but trend consistently over the 2012–2020 period. The VIIRS mean liquid water cloud fraction differs by ~0.01 from the MODIS amount. The average cloud heights from VIIRS differ from the MODIS heights by less than 0.2 km, except the VIIRS daytime ice cloud heights, which are 0.4 km higher. The mean VIIRS nonpolar optical depths are 17% (1%) larger (smaller) than those from MODIS for liquid (ice) clouds. The VIIRS cloud hydrometeor sizes are generally smaller than their MODIS counterparts. Discrepancies between the MODIS and VIIRS properties stem from spectral and spatial resolution differences, new tests at night, calibration inconsistencies, and new reflectance models. Many of those differences will be addressed in future editions. Full article

Backgrounds and Objectives: To analyze the influence of multiple anti-VEGF intravitreal injections for exudative age-related macular degeneration on the thickness of peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GC + IPL) using spectral domain optical coherence tomography (SD-OCT). Materials and Methods: A prospective interventional study of consecutive patients treated with intravitreal bevacizumab (IVB) was performed. Average and sectorial values of RNFL and GC + IPL thickness were recorded using Cirrus SD-OCT at 0, 6, 12, and 24 months. Patients suffering from any ocular disease that could affect RNFL or GC + IPL thickness were excluded. Results: A total of 135 patients (70 women and 65 men, aged 65 ± 15 years) were included. The average number of injections per patient was 12.4 ± 2.4. Average RNFL and GC + IPL thickness prior to the first injection (87.6 ± 12.2 and 47.2 ± 15.5 respectively), and after 24-month follow-up (86.2 ± 12.6 and 46.7 ± 11.9 respectively) did not differ significantly (p > 0.05). There was a significant decrease in GC2, GC5 segments, and minimum GC + IPL thickness. Conclusion: Repeated anti-VEGF treatment did not cause significant changes in the thickness of RNFL and GC + IPL layers over a period of 24 months. The detected decrease in GC2 and GC5 sectors, as well as in minimum GC + IPL thickness, could be a sign of ganglion cell damage induced by the treatment or could occur during the natural course of the disease. Full article

The current study describes the development and assessment of innovative, machine learning (ML)-based approaches for automated detection and pixel-accurate measurements of regions with geographic atrophy (GA) in late-stage age-related macular degeneration (AMD) using optical coherence tomography systems. 900 OCT volumes, 100266 B-scans, and en face OCT images from 341 non-exudative AMD patients with or without GA were included in this study from both Cirrus (Zeiss) and Spectralis (Heidelberg) OCT systems. B-scan and en face level ground truth GA masks were created on OCT B-scan where the segmented ellipsoid zone (EZ) line, retinal pigment epithelium (RPE) line, and bruchs membrane (BM) line overlapped. Two deep learning-based approaches, B-scan level and en face level, were trained. The OCT B-scan model had detection accuracy of 91% and GA area measurement accuracy of 94%. The en face OCT model had detection accuracy of 82% and GA area measurement accuracy of 96% with primary target of hypertransmission on en face OCT. Accuracy was good for both devices tested (92–97%). Automated lesion size stratification for CAM cRORA definition of 250um minimum lesion size was feasible. High-performance models for automatic detection and segmentation of GA area were achieved using OCT systems and deep learning. The automatic measurements showed high correlation with the ground truth. The en face model excelled at identification of hypertransmission defects. The models performance generalized well across device types tested. Future development will include integration of both models to enhance feature detection across GA lesions as well as isolating hypertransmission defects without GA for pre-GA biomarker extraction. Full article

The study of cloud types is critical for understanding atmospheric motions and climate predictions; for example, accurately classified cloud products help improve meteorological predicting accuracies. However, the current satellite cloud classification methods generally analyze the threshold change in a single pixel and do not consider the relationship between the surrounding pixels. The classification development relies heavily on human recourses and does not fully utilize the data-driven advantages of computer models. Here, a new intelligent cloud classification method based on the U-Net network (CLP-CNN) is developed to obtain more accurate, higher frequency, and larger coverage cloud classification products. The experimental results show that the CLP-CNN network can complete a cloud classification task of 800 × 800 pixels in 0.9 s. The classification area covers most of China, and the classification task only needs to use the original L1-level data, which can meet the requirements of a real-time operation. With the Himawari-8 CLTYPE product and the CloudSat 2B-CLDCLASS product as the test comparison target, the CLP-CNN network results matched the Himawari-8 product highly by 76.8%. The probability of detection (POD) was greater than 0.709 for clear skies, deep-convection, and Cirrus–Stratus-type clouds. The probability of detection (POD) and accuracy are improved compared with other deep learning methods. Full article

Clouds cover more than two-thirds of the earth’s surface and play a dominant role in the energy and water cycle of our planet. Cirrus clouds are high-level clouds composed mostly of ice crystals and affect the earth’s radiation allocation mainly by absorbing outgoing longwave radiation and by reflecting solar radiation. This study presents the characterization of cirrus clouds observed on 10 and 11 April 2019 using the ground-based University of Zululand (UZ) light detection and ranging (lidar) for the first time. Dense cirrus clouds with an average thickness of ~1.5 km at a height range of 9.5–12 km on 10 and 11 April 2019 were observed by the UZ lidar. The UZ lidar observation on 10 April 2019 agreed with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observation. Full article

A polarized micropulse lidar (P-MPL) employing a pulsed laser at 532 nm was developed by the Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences). The optomechanical structure, technical parameters, detection principle, overlap factor calculation method, and inversion methods of the atmospheric boundary layer (ABL) depth and depolarization ratio (DR) were introduced. Continuous observations using the P-MPL were carried out at Xi’an Meteorological Bureau, and the observation data were analyzed. In this study, we gleaned much information on aerosols and clouds, including the temporal and spatial variation of aerosols and clouds, aerosol extinction coefficient, DR, and the structure of ABL were obtained by the P-MPL. The variation of aerosols and clouds before and after a short rainfall was analyzed by combining time-height-indication (THI) of range corrected signal (RCS) and DR was obtained by the P-MPL with profiles of potential temperature (PT) and relative humidity (RH) detected by GTS1 Digital Radiosonde. Then, the characteristics of tropopause cirrus cloud were discussed using the data of DR, PT, and RH. Finally, a haze process from January 1st to January 5th was studied by using aerosol extinction coefficients obtained by the P-MPL, PT, and RH profiles measured by GTS1 Digital Radiosonde and the time-varying of PM_2.5 and PM₁₀ observed by ambient air quality monitor. The source of the haze was simulated by using the NOAA HYSPLIT Trajectory Model. Full article

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >~0.4 and GEMS SSA values of <~0.95. Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy. Full article

This study utilizes an extremely high spatial resolution GOES-16 atmospheric motion vector (AMV) dataset processed at 15 min intervals in a modified version of our original dynamic initialization technique to analyze and forecast a rapid intensification (RI) event in Hurricane Irma (2017). The most important modifications are a more time-efficient dynamic initialization technique and adding a near-surface wind field adjustment as a low-level constraint on the distribution of deep convection relative to the translating center. With the new technique, the Coupled Ocean/Atmospheric Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model initial wind field at 12.86 km elevation quickly adjusts to the cirrus-level GOES-16 AMVs to better detect the Irma outflow magnitude and areal extent every 15 min, and predicts direct connections to adjacent synoptic circulations much better than a dynamic initialization with only lower-resolution hourly GOES-13 AMVs and also better than a cold-start COAMPS-TC initialization with a bogus vortex. Furthermore, only with the GOES-16 AMVs does the COAMPS-TC model accurately predict the timing of an intermediate 12 h constant-intensity period between two segments of the Irma RI. By comparison, HWRF model study of the Irma case that utilized the same GOES-16 AMV dataset predicted a continuous RI without the intermediate constant-intensity period, and predicted more limited outflow areal extents without strong direct connections with adjacent synoptic circulations. Full article