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1
Zhu, Xifang, Feng Wu, Tao Wu, and Chunyu Zhao. "Remote sensing imaging simulation and cloud removal." International Journal of Modern Physics B 31, no. 16-19 (2017): 1744079. http://dx.doi.org/10.1142/s0217979217440799.
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Cloud obstacles obscure ground information frequently during remote sensing imaging which leads to valuable information losses. Removing clouds from a single image becomes challenging since no reference images containing cloud-free regions are available. In order to study cloud removal technologies and evaluate their performances, a method to simulate evenly and unevenly distributed clouds was proposed by analyzing the physical model of remote sensing imaging. Dual tree complex wavelet transform (DTCWT) and its features were introduced briefly. According to the frequency relationships between clouds and ground objects in remote sensing images, a novel cloud removal algorithm was proposed. The algorithm divided the cloud-contaminated image into low-level high frequency sub-bands, high-level high frequency sub-bands and low frequency sub-band by DTCWT. Low-level high frequency sub-bands were filtered to enhance the ground object information by Laplacian sharpening. The other two types of sub-bands were processed to remove clouds by cloud cover coefficient weighting (CCCW). The experiments were implemented to process cloud disturbed images produced by the proposed simulation method. The results of cloud removal from remote sensing images were analyzed. It proved the proposed algorithm is greatly superior to algorithms based on traditional wavelet transform and dark channel prior.
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Burley, Jarred L., Steven T. Fiorino, Brannon J. Elmore, and Jaclyn E. Schmidt. "A Remote Sensing and Atmospheric Correction Method for Assessing Multispectral Radiative Transfer through Realistic Atmospheres and Clouds." Journal of Atmospheric and Oceanic Technology 36, no. 2 (2019): 203–16. http://dx.doi.org/10.1175/jtech-d-18-0078.1.
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Abstract The ability to quickly and accurately model actual atmospheric conditions is essential to remote sensing analyses. Clouds present a particularly complex challenge, as they cover up to 70% of Earth’s surface, and their highly variable and diverse nature necessitates physics-based modeling. The Laser Environmental Effects Definition and Reference (LEEDR) is a verified and validated atmospheric propagation and radiative transfer code that creates physically realizable vertical and horizontal profiles of meteorological data. Coupled with numerical weather prediction (NWP) model output, LEEDR enables analysis, nowcasts, and forecasts for radiative effects expected for real-world scenarios. A recent development is the inclusion of the U.S. Air Force’s World-Wide Merged Cloud Analysis (WWMCA) cloud data in a new tool set that enables radiance calculations through clouds from UV to radio frequency (RF) wavelengths. This effort details the creation of near-real-time profiles of atmospheric and cloud conditions and the resulting radiative transfer analysis for virtually any wavelength(s) of interest. Calendar year 2015 data are analyzed to establish climatological limits for diffuse transmission in the 300–1300-nm band, and the impacts of various geometry, cloud microphysical, and atmospheric conditions are examined. The results show that 80% of diffuse band transmissions are estimated to fall between 0.248 and 0.889 under the assumptions of cloud homogeneity and maximum overlap and are sufficient for establishing diffuse transmission percentiles. The demonstrated capability provides an efficient way to extend optical wavelength cloud parameters across the spectrum for physics-based multiple-scattering effects modeling through cloudy and clear atmospheres, providing an improvement to atmospheric correction for remote sensing and cloud effects on system performance metrics.
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Strandgren, Johan, Jennifer Fricker, and Luca Bugliaro. "Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI." Atmospheric Measurement Techniques 10, no. 11 (2017): 4317–39. http://dx.doi.org/10.5194/amt-10-4317-2017.
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Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate large-scale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary. In this paper the CiPS (Cirrus Properties from SEVIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m−2. For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.
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Sinclair, Kenneth, Bastiaan van Diedenhoven, Brian Cairns, John Yorks, Andrzej Wasilewski, and Matthew McGill. "Remote sensing of multiple cloud layer heights using multi-angular measurements." Atmospheric Measurement Techniques 10, no. 6 (2017): 2361–75. http://dx.doi.org/10.5194/amt-10-2361-2017.
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Abstract. Cloud top height (CTH) affects the radiative properties of clouds. Improved CTH observations will allow for improved parameterizations in large-scale models and accurate information on CTH is also important when studying variations in freezing point and cloud microphysics. NASA's airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. For the determination of CTH, a set of consecutive nadir reflectances is selected and the cross correlations between this set and collocated sets at other viewing angles are calculated for a range of assumed cloud top heights, yielding a correlation profile. Under the assumption that cloud reflectances are isotropic, local peaks in the correlation profile indicate cloud layers. This technique can be applied to every RSP footprint and we demonstrate that detection of multiple peaks in the correlation profile allows retrieval of heights of multiple cloud layers within single RSP footprints. This paper provides an in-depth description of the architecture and performance of the RSP's CTH retrieval technique using data obtained during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. RSP-retrieved cloud heights are evaluated using collocated data from the Cloud Physics Lidar (CPL). The method's accuracy associated with the magnitude of correlation, optical thickness, cloud thickness and cloud height are explored. The technique is applied to measurements at a wavelength of 670 and 1880 nm and their combination. The 1880 nm band is virtually insensitive to the lower troposphere due to strong water vapor absorption. It is found that each band is well suitable for retrieving heights of cloud layers with optical thicknesses above about 0.1 and that RSP cloud layer height retrievals more accurately correspond to CPL cloud middle than cloud top. It is also found that the 1880 nm band yields the most accurate results for clouds at middle and high altitudes (4.0 to 17 km), while the 670 nm band is most accurate at low and middle altitudes (1.0–13.0 km). The dual band performs best over the broadest range and is suitable for accurately retrieving cloud layer heights between 1.0 and 16.0 km. Generally, the accuracy of the retrieved cloud top heights increases with increasing correlation value. Improved accuracy is achieved by using customized filtering techniques for each band with the most significant improvements occurring in the primary layer retrievals. RSP is able to measure a primary layer CTH with a median error of about 0.5 km when compared to CPL. For multilayered scenes, the second and third layer heights are determined median errors of about 1.5 and 2.0–2.5 km, respectively.
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Wang, Zheng, Jun Du, Junshi Xia, et al. "An Effective Method for Detecting Clouds in GaoFen-4 Images of Coastal Zones." Remote Sensing 12, no. 18 (2020): 3003. http://dx.doi.org/10.3390/rs12183003.
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Cloud-cover information is important for a wide range of scientific studies, such as the studies on water supply, climate change, earth energy budget, etc. In remote sensing, correct detection of clouds plays a crucial role in deriving the physical properties associated with clouds that exert a significant impact on the radiation budget of planet earth. Although the traditional cloud detection methods have generally performed well, these methods were usually developed specifically for particular sensors in a particular region with a particular underlying surface (e.g., land, water, vegetation, and man-made objects). Coastal regions are known to have a variety of underlying surfaces, which represent a major challenge in cloud detection. Therefore, there is an urgent requirement for developing a cloud detection method that could be applied to a variety of sensors, situations, and underlying surfaces. In the present study, a cloud detection method based on spatial and spectral uniformity of clouds was developed. In addition to having a spatially uniform texture, a spectrally approximate value was also present between the blue and green bands of the cloud region. The blue and green channel data appeared more uniform over the cloudy region, i.e., the entropy of the cloudy region was lower than that of the cloud-free region. On the basis of this difference in entropy, it would be possible to categorize the satellite images into cloud region images and cloud-free region images. Furthermore, the performance of the proposed method was validated by applying it to the data from various sensors across the coastal zone of the South China Sea. The experimental results demonstrated that compared to the existing operational algorithms, EN-clustering exhibited higher accuracy and scalability, and also performed robustly regardless of the spatial resolution of the different satellite images. It is concluded that the EN-clustering algorithm proposed in the present study is applicable to different sensors, different underlying surfaces, and different regions, with the support of NDSI and NDBI indices to remove the interference information from snow, ice, and man-made objects.
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Zamora, Lauren M., Ralph A. Kahn, Sabine Eckhardt, et al. "Aerosol indirect effects on the nighttime Arctic Ocean surface from thin, predominantly liquid clouds." Atmospheric Chemistry and Physics 17, no. 12 (2017): 7311–32. http://dx.doi.org/10.5194/acp-17-7311-2017.
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Abstract. Aerosol indirect effects have potentially large impacts on the Arctic Ocean surface energy budget, but model estimates of regional-scale aerosol indirect effects are highly uncertain and poorly validated by observations. Here we demonstrate a new way to quantitatively estimate aerosol indirect effects on a regional scale from remote sensing observations. In this study, we focus on nighttime, optically thin, predominantly liquid clouds. The method is based on differences in cloud physical and microphysical characteristics in carefully selected clean, average, and aerosol-impacted conditions. The cloud subset of focus covers just ∼ 5 % of cloudy Arctic Ocean regions, warming the Arctic Ocean surface by ∼ 1–1.4 W m−2 regionally during polar night. However, within this cloud subset, aerosol and cloud conditions can be determined with high confidence using CALIPSO and CloudSat data and model output. This cloud subset is generally susceptible to aerosols, with a polar nighttime estimated maximum regionally integrated indirect cooling effect of ∼ −0.11 W m−2 at the Arctic sea ice surface (∼ 8 % of the clean background cloud effect), excluding cloud fraction changes. Aerosol presence is related to reduced precipitation, cloud thickness, and radar reflectivity, and in some cases, an increased likelihood of cloud presence in the liquid phase. These observations are inconsistent with a glaciation indirect effect and are consistent with either a deactivation effect or less-efficient secondary ice formation related to smaller liquid cloud droplets. However, this cloud subset shows large differences in surface and meteorological forcing in shallow and higher-altitude clouds and between sea ice and open-ocean regions. For example, optically thin, predominantly liquid clouds are much more likely to overlay another cloud over the open ocean, which may reduce aerosol indirect effects on the surface. Also, shallow clouds over open ocean do not appear to respond to aerosols as strongly as clouds over stratified sea ice environments, indicating a larger influence of meteorological forcing over aerosol microphysics in these types of clouds over the rapidly changing Arctic Ocean.
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Sorooshian, Armin, Bruce Anderson, Susanne E. Bauer, et al. "Aerosol–Cloud–Meteorology Interaction Airborne Field Investigations: Using Lessons Learned from the U.S. West Coast in the Design of ACTIVATE off the U.S. East Coast." Bulletin of the American Meteorological Society 100, no. 8 (2019): 1511–28. http://dx.doi.org/10.1175/bams-d-18-0100.1.
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AbstractWe report on a multiyear set of airborne field campaigns (2005–16) off the California coast to examine aerosols, clouds, and meteorology, and how lessons learned tie into the upcoming NASA Earth Venture Suborbital (EVS-3) campaign: Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE; 2019–23). The largest uncertainty in estimating global anthropogenic radiative forcing is associated with the interactions of aerosol particles with clouds, which stems from the variability of cloud systems and the multiple feedbacks that affect and hamper efforts to ascribe changes in cloud properties to aerosol perturbations. While past campaigns have been limited in flight hours and the ability to fly in and around clouds, efforts sponsored by the Office of Naval Research have resulted in 113 single aircraft flights (>500 flight hours) in a fixed region with warm marine boundary layer clouds. All flights used nearly the same payload of instruments on a Twin Otter to fly below, in, and above clouds, producing an unprecedented dataset. We provide here i) an overview of statistics of aerosol, cloud, and meteorological conditions encountered in those campaigns and ii) quantification of model-relevant metrics associated with aerosol–cloud interactions leveraging the high data volume and statistics. Based on lessons learned from those flights, we describe the pragmatic innovation in sampling strategy (dual-aircraft approach with combined in situ and remote sensing) that will be used in ACTIVATE to generate a dataset that can advance scientific understanding and improve physical parameterizations for Earth system and weather forecasting models, and for assessing next-generation remote sensing retrieval algorithms.
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Yao, Ziqiang, Jinlu Jia, and Yurong Qian. "MCNet: Multi-Scale Feature Extraction and Content-Aware Reassembly Cloud Detection Model for Remote Sensing Images." Symmetry 13, no. 1 (2020): 28. http://dx.doi.org/10.3390/sym13010028.
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Cloud detection plays a vital role in remote sensing data preprocessing. Traditional cloud detection algorithms have difficulties in feature extraction and thus produce a poor detection result when processing remote sensing images with uneven cloud distribution and complex surface background. To achieve better detection results, a cloud detection method with multi-scale feature extraction and content-aware reassembly network (MCNet) is proposed. Using pyramid convolution and channel attention mechanisms to enhance the model’s feature extraction capability, MCNet can fully extract the spatial information and channel information of clouds in an image. The content-aware reassembly is used to ensure that sampling on the network can recover enough in-depth semantic information and improve the model cloud detection effect. The experimental results show that the proposed MCNet model has achieved good detection results in cloud detection tasks.
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Mace, Gerald G., Stephanie Houser, Sally Benson, Stephen A. Klein, and Qilong Min. "Critical Evaluation of the ISCCP Simulator Using Ground-Based Remote Sensing Data." Journal of Climate 24, no. 6 (2011): 1598–612. http://dx.doi.org/10.1175/2010jcli3517.1.
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Abstract Given the known shortcomings in representing clouds in global climate models (GCMs), comparisons with observations are critical. The International Satellite Cloud Climatology Project (ISCCP) diagnostic products provide global descriptions of cloud-top pressure and column optical depth that extend over multiple decades. Given the characteristics of the ISCCP product, the model output must be converted into what the ISCCP algorithm would diagnose from an atmospheric column with similar physical characteristics. This study evaluates one component of this so-called ISCCP simulator by comparing ISCCP results with simulated ISCCP diagnostics that are derived from data collected at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Climate Research Facility. It is shown that if a model were to simulate the cloud radiative profile with the same accuracy as can be derived from the ARM data, the likelihood of that occurrence being classified with similar cloud-top pressure and optical depth as ISCCP would range from 30% to 70% depending on optical depth. The ISCCP simulator improved the agreement of cloud-top pressure between ground-based remote sensors and satellite observations, and we find only minor discrepancies because of the parameterization of cloud-top pressure in the ISCCP simulator. The differences seem to be primarily due to discrepancies between satellite and ground-based sensors in the visible optical depth. The source of the optical depth bias appears to be due to subpixel cloud field variability in the retrieval of optical depths from satellite sensors. These comparisons suggest that caution should be applied to comparisons between models and ISCCP observations until the differences in visible optical depths are fully understood. The simultaneous use of ground-based and satellite retrievals in the evaluation of model clouds is encouraged.
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Chiriaco, M., H. Chepfer, P. Minnis, et al. "Comparison of CALIPSO-Like, LaRC, and MODIS Retrievals of Ice-Cloud Properties over SIRTA in France and Florida during CRYSTAL-FACE." Journal of Applied Meteorology and Climatology 46, no. 3 (2007): 249–72. http://dx.doi.org/10.1175/jam2435.1.
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Abstract This study compares cirrus-cloud properties and, in particular, particle effective radius retrieved by a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-like method with two similar methods using Moderate-Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS), and Geostationary Operational Environmental Satellite imagery. The CALIPSO-like method uses lidar measurements coupled with the split-window technique that uses the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands to infer the microphysical properties of cirrus clouds. The two other methods, using passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (using 20 spectral bands from visible to infrared, referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the Clouds and the Earth’s Radiant Energy System (CERES) team at Langley Research Center (LaRC) in support of CERES algorithms (using 0.65-, 3.75-, 10.8-, and 12.05-μm bands); the two algorithms will be referred to as the MOD06 and LaRC methods, respectively. The three techniques are compared at two different latitudes. The midlatitude ice-clouds study uses 16 days of observations at the Palaiseau ground-based site in France [Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA)], including a ground-based 532-nm lidar and the MODIS overpasses on the Terra platform. The tropical ice-clouds study uses 14 different flight legs of observations collected in Florida during the intensive field experiment known as the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE), including the airborne cloud-physics lidar and the MAS. The comparison of the three methods gives consistent results for the particle effective radius and the optical thickness but discrepancies in cloud detection and altitudes. The study confirms the value of an active remote sensing method (CALIPSO like) for the study of subvisible ice clouds, in both the midlatitudes and Tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds, because of their particular microphysical properties.
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Lu, Ming, Feng Li, Bangcheng Zhan, et al. "An Improved Cloud Detection Method for GF-4 Imagery." Remote Sensing 12, no. 9 (2020): 1525. http://dx.doi.org/10.3390/rs12091525.
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Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.
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Wang, Yuan, Xiaojian Zheng, Xiquan Dong, et al. "Impacts of long-range transport of aerosols on marine-boundary-layer clouds in the eastern North Atlantic." Atmospheric Chemistry and Physics 20, no. 23 (2020): 14741–55. http://dx.doi.org/10.5194/acp-20-14741-2020.
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Abstract. Vertical profiles of aerosols are inadequately observed and poorly represented in climate models, contributing to the current large uncertainty associated with aerosol–cloud interactions. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) aircraft field campaign near the Azores islands provided ample observations of vertical distributions of aerosol and cloud properties. Here we utilize the in situ aircraft measurements from the ACE-ENA and ground-based remote-sensing data along with an aerosol-aware Weather Research and Forecast (WRF) model to characterize the aerosols due to long-range transport over a remote region and to assess their possible influence on marine-boundary-layer (MBL) clouds. The vertical profiles of aerosol and cloud properties measured via aircraft during the ACE-ENA campaign provide detailed information revealing the physical contact between transported aerosols and MBL clouds. The European Centre for Medium-Range Weather Forecasts Copernicus Atmosphere Monitoring Service (ECMWF-CAMS) aerosol reanalysis data can reproduce the key features of aerosol vertical profiles in the remote region. The cloud-resolving WRF sensitivity experiments with distinctive aerosol profiles suggest that the transported aerosols and MBL cloud interactions (ACIs) require not only aerosol plumes to get close to the marine-boundary-layer top but also large cloud top height variations. Based on those criteria, the observations show that the occurrence of ACIs involving the transport of aerosol over the eastern North Atlantic (ENA) is about 62 % in summer. For the case with noticeable long-range-transport aerosol effects on MBL clouds, the susceptibilities of droplet effective radius and liquid water content are −0.11 and +0.14, respectively. When varying by a similar magnitude, aerosols originating from the boundary layer exert larger microphysical influence on MBL clouds than those entrained from the free troposphere.
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Yang, Jie, Siwei Li, Feiyue Mao, et al. "Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds." Remote Sensing 12, no. 14 (2020): 2252. http://dx.doi.org/10.3390/rs12142252.
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Previous studies have shown that it is feasible to retrieve multiple cloud properties simultaneously based on the space-borne hyperspectral observation in the oxygen A-band, such as cloud optical depth, cloud-top height, and cloud geometrical thickness. However, hyperspectral remote sensing is time-consuming if based on the precise radiative transfer solution that counts multiple scatterings of light. To speed up the radiation transfer solution in cloud scenarios for nadir space-borne observations, we developed a physical parameterization of hyperspectral reflectance in the oxygen A-band for single-layer water clouds. The parameterization takes into account the influences of cloud droplet forward-scattering and nonlinear oxygen absorption on hyperspectral reflectance, which are improvements over the previous studies. The performance of the parameterization is estimated through comparison with DISORT (Discrete Ordinates Radiative Transfer Program Multi-Layered Plane-Parallel Medium) on the cases with solar zenith angle θ, the cloud optical depth τc, and the single-scattering albedo ω in the range of 0 ≤ θ ≤ 75, 5 ≤ τc ≤ 50, 0.5 ≤ ω ≤ 1. The relative error of the cloud reflectance is within 5% for most cases, even for clouds with optical depths around five or at strong absorption wavelengths. We integrate the parameterization with a slit function and a simplified atmosphere to evaluate its performance in simulating the observed cloud reflection at the top of the atmosphere by OCO-2 (Orbiting Carbon Observatory-2). To better visualize the possible errors from the new parameterization, gas molecular scattering, aerosol scattering, and reflection from the underlying surface are ignored. The relative error of the out-of-band radiance is less than 4% and the relative error of the intra-band radiance ratio is less than 4%. The radiance ratio is the ratio of simulated observations with and without in-cloud absorption and is used to assess the accuracy of the parameterization in quantifying the in-cloud absorption. The parameterization is a preparation for rapid hyperspectral remote sensing in the oxygen A-band. It would help to improve retrieval efficiency and provide cloud geometric thickness products.
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Wendisch, Manfred, Andreas Macke, André Ehrlich, et al. "The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multiplatform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification." Bulletin of the American Meteorological Society 100, no. 5 (2019): 841–71. http://dx.doi.org/10.1175/bams-d-18-0072.1.
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AbstractClouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, and surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) aircraft campaign and the Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and Aerosol (PASCAL) ice breaker expedition. Both observational studies were performed in the framework of the German Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC)3 project. They took place in the vicinity of Svalbard, Norway, in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft; the icebreaker Research Vessel (R/V) Polarstern; an ice floe camp including an instrumented tethered balloon; and the permanent ground-based measurement station at Ny-Ålesund, Svalbard, were employed to observe Arctic low- and mid-level mixed-phase clouds and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above the R/V Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.
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Mace, Gerald G., Yuying Zhang, Steven Platnick, Michael D. King, Patrick Minnis, and Ping Yang. "Evaluation of Cirrus Cloud Properties Derived from MODIS Data Using Cloud Properties Derived from Ground-Based Observations Collected at the ARM SGP Site." Journal of Applied Meteorology and Climatology 44, no. 2 (2005): 221–40. http://dx.doi.org/10.1175/jam2193.1.
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Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra satellite has been collecting global data since March 2000 and the one on the Aqua satellite since June 2002. In this paper, cirrus cloud properties derived from ground-based remote sensing data are compared with similar cloud properties derived from MODIS data on Terra. To improve the space–time correlation between the satellite and ground-based observations, data from a wind profiler are used to define the cloud advective streamline along which the comparisons are made. In this paper, approximately two dozen cases of cirrus are examined and a statistical approach to the comparison that relaxes the requirement that clouds occur over the ground-based instruments during the overpass instant is explored. The statistical comparison includes 168 cloudy MODIS overpasses of the Southern Great Plains (SGP) region and approximately 300 h of ground-based cirrus observations. The physical and radiative properties of cloud layers are derived from MODIS data separately by the MODIS Atmospheres Team and the Clouds and the Earth’s Radiant Energy System (CERES) Science Team using multiwavelength reflected solar and emitted thermal radiation measurements. Using two ground-based cloud property retrieval algorithms and the two MODIS algorithms, a positive correlation in the effective particle size, the optical thickness, the ice water path, and the cloud-top pressure between the various methods is shown, although sometimes there are significant biases. Classifying the clouds by optical thickness, it is demonstrated that the regionally averaged cloud properties derived from MODIS are similar to those diagnosed from the ground. Because of a conservative approach toward identifying thin cirrus pixels over this region, the area-averaged cloud properties derived from the MODIS Atmospheres MOD06 product tend to be biased slightly toward the optically thicker pixels. This bias tendency has implications for model validation and parameterization development applied to thin cirrus retrieved over SGP-like land surfaces. A persistent bias is also found in the derived cloud tops of thin cirrus with both satellite algorithms reporting cloud top several hundred meters less than that reported by the cloud radar. Overall, however, it is concluded that the MODIS retrieval algorithms characterize with reasonable accuracy the properties of thin cirrus over this region.
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Du, Jing, Zuning Jiang, Shangfeng Huang, et al. "Point Cloud Semantic Segmentation Network Based on Multi-Scale Feature Fusion." Sensors 21, no. 5 (2021): 1625. http://dx.doi.org/10.3390/s21051625.
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The semantic segmentation of small objects in point clouds is currently one of the most demanding tasks in photogrammetry and remote sensing applications. Multi-resolution feature extraction and fusion can significantly enhance the ability of object classification and segmentation, so it is widely used in the image field. For this motivation, we propose a point cloud semantic segmentation network based on multi-scale feature fusion (MSSCN) to aggregate the feature of a point cloud with different densities and improve the performance of semantic segmentation. In our method, random downsampling is first applied to obtain point clouds of different densities. A Spatial Aggregation Net (SAN) is then employed as the backbone network to extract local features from these point clouds, followed by concatenation of the extracted feature descriptors at different scales. Finally, a loss function is used to combine the different semantic information from point clouds of different densities for network optimization. Experiments were conducted on the S3DIS and ScanNet datasets, and our MSSCN achieved accuracies of 89.80% and 86.3%, respectively, on these datasets. Our method showed better performance than the recent methods PointNet, PointNet++, PointCNN, PointSIFT, and SAN.
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Painemal, D., and P. Zuidema. "The first aerosol indirect effect quantified through airborne remote sensing during VOCALS-REx." Atmospheric Chemistry and Physics 13, no. 2 (2013): 917–31. http://dx.doi.org/10.5194/acp-13-917-2013.
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Abstract. The first aerosol indirect effect (1AIE) is investigated using a combination of in situ and remotely-sensed aircraft (NCAR C-130) observations acquired during VOCALS-REx over the southeast Pacific stratocumulus cloud regime. Satellite analyses have previously identified a high albedo susceptibitility to changes in cloud microphysics and aerosols over this region. The 1AIE was broken down into the product of two independently-estimated terms: the cloud aerosol interaction metric ACIτ =dlnτ/dlnNa|LWP , and the relative albedo (A) susceptibility SR-τ =dA/3dlnτ|LWP, with τ and Na denoting retrieved cloud optical thickness and in situ aerosol concentration respectively and calculated for fixed intervals of liquid water path (LWP). ACIτ was estimated by combining in situ Na sampled below the cloud, with τ and LWP derived from, respectively, simultaneous upward-looking broadband irradiance and narrow field-of-view millimeter-wave radiometer measurements, collected at 1 Hz during four eight-hour daytime flights by the C-130 aircraft. ACIτ values were typically large, close to the physical upper limit (0.33), with a modest increase with LWP. The high ACIτ values slightly exceed values reported from many previous in situ airborne studies in pristine marine stratocumulus and reflect the imposition of a LWP constraint and simultaneity of aerosol and cloud measurements. SR-τ increased with LWP and τ, reached a maximum SR-τ (0.086) for LWP (τ) of 58 g m−2 (~14), and decreased slightly thereafter. The 1AIE thus increased with LWP and is comparable to a radiative forcing of −3.2– −3.8 W m−2 for a 10% increase in Na, exceeding previously-reported global-range values. The aircraft-derived values are consistent with satellite estimates derived from instantaneous, collocated Clouds and the Earth's Radiant Energy System (CERES) albedo and MOderate resolution Imaging Spectroradiometer (MODIS)-retrieved droplet number concentrations at 50 km resolution. The consistency of the airborne and satellite estimates, despite their independent approaches, differences in observational scales, and retrieval assumptions, is hypothesized to reflect the ideal remote sensing conditions for these homogeneous clouds. We recommend the southeast Pacific for regional model assessments of the first aerosol indirect effect on this basis. This airborne remotely-sensed approach towards quantifying 1AIE should in theory be more robust than in situ calculations because of increased sampling. However, although the technique does not explicitly depend on a remotely-derived cloud droplet number concentration (Nd), the at-times unrealistically-high Nd values suggest more emphasis on accurate airborne radiometric measurements is needed to refine this approach.
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Miller, Daniel J., Michal Segal-Rozenhaimer, Kirk Knobelspiesse, et al. "Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm." Atmospheric Measurement Techniques 13, no. 6 (2020): 3447–70. http://dx.doi.org/10.5194/amt-13-3447-2020.
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Abstract. In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re: R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.
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Zhuang, Yin, Baogui Qi, He Chen, Fukun Bi, Lianlin Li, and Yizhuang Xie. "Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images." Sensors 18, no. 11 (2018): 3799. http://dx.doi.org/10.3390/s18113799.
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Due to strong ocean waves, broken clouds, and extensive cloud cover interferences, ocean ship detection performs poorly when using optical remote sensing images. In addition, it is a challenge to detect small ships on medium resolution optical remote sensing that cover a large area. In this paper, in order to balance the requirements of real-time processing and high accuracy detection, we proposed a novel ship detection framework based on locally oriented scene complexity analysis. First, the proposed method can separate a full image into two types of local scenes (i.e., simple or complex local scenes). Next, simple local scenes would utilize the fast saliency model (FSM) to rapidly complete candidate extraction, and for complex local scenes, the ship feature clustering model (SFCM) will be applied to achieve refined detection against severe background interferences. The FSM considers a fusion enhancement image as an input of the pulse response analysis in the frequency domain to achieve rapid ship detection in simple local scenes. Next, the SFCM builds the descriptive model of the ship feature clustering algorithm to ensure the detection performance on complex local scenes. Extensive experiments on SPOT-5 and GF-2 ocean optical remote sensing images show that the proposed ship detection framework has better performance than the state-of-the-art methods, and it addresses the tricky problem of real-time ocean ship detection under strong waves, broken clouds, extensive cloud cover, and ship fleet interferences. Finally, the proposed ocean ship detection framework is demonstrated on an onboard processing hardware.
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Sourdeval, Odran, Edward Gryspeerdt, Martina Krämer, et al. "Ice crystal number concentration estimates from lidar–radar satellite remote sensing – Part 1: Method and evaluation." Atmospheric Chemistry and Physics 18, no. 19 (2018): 14327–50. http://dx.doi.org/10.5194/acp-18-14327-2018.
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Abstract. The number concentration of cloud particles is a key quantity for understanding aerosol–cloud interactions and describing clouds in climate and numerical weather prediction models. In contrast with recent advances for liquid clouds, few observational constraints exist regarding the ice crystal number concentration (Ni). This study investigates how combined lidar–radar measurements can be used to provide satellite estimates of Ni, using a methodology that constrains moments of a parameterized particle size distribution (PSD). The operational liDAR–raDAR (DARDAR) product serves as an existing base for this method, which focuses on ice clouds with temperatures Tc<-30 ∘C. Theoretical considerations demonstrate the capability for accurate retrievals of Ni, apart from a possible bias in the concentration in small crystals when Tc≳−50 ∘C, due to the assumption of a monomodal PSD shape in the current method. This is verified via a comparison of satellite estimates to coincident in situ measurements, which additionally demonstrates the sufficient sensitivity of lidar–radar observations to Ni. Following these results, satellite estimates of Ni are evaluated in the context of a case study and a preliminary climatological analysis based on 10 years of global data. Despite a lack of other large-scale references, this evaluation shows a reasonable physical consistency in Ni spatial distribution patterns. Notably, increases in Ni are found towards cold temperatures and, more significantly, in the presence of strong updrafts, such as those related to convective or orographic uplifts. Further evaluation and improvement of this method are necessary, although these results already constitute a first encouraging step towards large-scale observational constraints for Ni. Part 2 of this series uses this new dataset to examine the controls on Ni.
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Wang, Chisheng, Junzhuo Ke, Wenqun Xiu, Kai Ye, and Qingquan Li. "Emergency Response Using Volunteered Passenger Aircraft Remote Sensing Data: A Case Study on Flood Damage Mapping." Sensors 19, no. 19 (2019): 4163. http://dx.doi.org/10.3390/s19194163.
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Current satellite remote sensing data still have some inevitable defects, such as a low observing frequency, high cost and dense cloud cover, which limit the rapid response to ground changes and many potential applications. However, passenger aircraft may be an alternative remote sensing platform in emergency response due to the high revisit rate, dense coverage and low cost. This paper introduces a volunteered passenger aircraft remote sensing method (VPARS) for emergency response. It uses the images captured by the passenger volunteers during flight. Based on computer vision algorithms and geocoding procedures, these images can be processed into a mosaic orthoimage for rapid ground disaster mapping. Notable, due to the relatively low flight latitude, small clouds can be easily removed by stacking multi-angle tilt images in the VPARS method. A case study on the 2019 Guangdong flood monitoring validates these advantages. The frequent aircraft revisit time, intensive flight coverage, multi-angle images and low cost of the VPARS make it a potential way to complement traditional remote sensing methods in emergency response.
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Davis, Anthony B., Alexander Marshak, Robert F. Cahalan, and Warren J. Wiscombe. "Interactions: Solar and Laser Beams in Stratus Clouds, Fractals & Multifractals in Climate & Remote-Sensing Studies." Fractals 05, supp02 (1997): 129–66. http://dx.doi.org/10.1142/s0218348x97000875.
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Recent research on cloud structure and cloud-radiation interaction at NASA's Goddard Space Flight Center is presented as a show case of interdisciplinary work where fractals and multifractals play a central role. Focus has been primarily on stratocumulus because of their first-order effect on the Earth's energy balance (hence the global climate) due to their unusual horizontal extension and persistence. These cloud layers have quasi-flat upper/lower boundaries and appear to be quite uniform but are highly variable inside. The general strategy has been: utilization of spatial statistics of in situ and remotely sensed data pertaining to cloud structure to constrain stochastic cloud models used in turn for radiative transfer simulations where artificial radiation fields are generated; these fields are compared to actual measurements. and so on, until a degree of closure is achieved. The major trends have been: i) computation and understanding of cloudradiative properties from the large scales of interest to Global Climate Models (over 102 km) down to the smallest observable scales (less than 10 m); ii) from predicting the outcome of "ideal" measurements to those of "real" ones with limited accuracy, sampling and averaging; iii) from passive to active remote-sensing methods; and iv) shifting from standard to wavelet-based analysis/modeling techniques. In terms of potential for impact on geophysical research at large, the most important contributions are: a) criteria for and measures of nonstationarity and intermittency in scale-invariant data; b) so-called "bounded" multifractal cascade models having a continuously variable degree of nonstationarity: c) a parameterization of the bulk effect of fractal variability on large-scale planetary albedo; and d) the basic scaling theory of radiative "smoothing" that explains non-trivially related multiple scattering phenomena in both solar- and lidar-based remote sensing. The last item also suggests new methods of observing clouds and new ways of processing cloud radiance data to retrieve physical cloud properties.
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Petras, V., A. Petrasova, J. Jeziorska, and H. Mitasova. "PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 945–52. http://dx.doi.org/10.5194/isprsarchives-xli-b7-945-2016.
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Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.
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Petras, V., A. Petrasova, J. Jeziorska, and H. Mitasova. "PROCESSING UAV AND LIDAR POINT CLOUDS IN GRASS GIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 945–52. http://dx.doi.org/10.5194/isprs-archives-xli-b7-945-2016.
Full textAbstract:
Today’s methods of acquiring Earth surface data, namely lidar and unmanned aerial vehicle (UAV) imagery, non-selectively collect or generate large amounts of points. Point clouds from different sources vary in their properties such as number of returns, density, or quality. We present a set of tools with applications for different types of points clouds obtained by a lidar scanner, structure from motion technique (SfM), and a low-cost 3D scanner. To take advantage of the vertical structure of multiple return lidar point clouds, we demonstrate tools to process them using 3D raster techniques which allow, for example, the development of custom vegetation classification methods. Dense point clouds obtained from UAV imagery, often containing redundant points, can be decimated using various techniques before further processing. We implemented and compared several decimation techniques in regard to their performance and the final digital surface model (DSM). Finally, we will describe the processing of a point cloud from a low-cost 3D scanner, namely Microsoft Kinect, and its application for interaction with physical models. All the presented tools are open source and integrated in GRASS GIS, a multi-purpose open source GIS with remote sensing capabilities. The tools integrate with other open source projects, specifically Point Data Abstraction Library (PDAL), Point Cloud Library (PCL), and OpenKinect libfreenect2 library to benefit from the open source point cloud ecosystem. The implementation in GRASS GIS ensures long term maintenance and reproducibility by the scientific community but also by the original authors themselves.
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Lindner, Bernhard Lee, and Ronald G. Isaacs. "Remote sensing of clouds by multispectral sensors." Applied Optics 32, no. 15 (1993): 2744. http://dx.doi.org/10.1364/ao.32.002744.
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Painemal, D., and P. Zuidema. "The first aerosol indirect effect quantified through airborne remote sensing during VOCALS-REx." Atmospheric Chemistry and Physics Discussions 12, no. 9 (2012): 25441–85. http://dx.doi.org/10.5194/acpd-12-25441-2012.
Full textAbstract:
Abstract. The first aerosol indirect effect (1AIE) is investigated using a combination of in situ and remotely-sensed aircraft (NCAR C-130) observations acquired during VOCALS-REx over the Southeast Pacific stratocumulus cloud regime. Satellite analyses have previously identified a high albedo susceptibitility to changes in cloud microphysics and aerosols over this region. The 1AIE was broken down into the product of two independently-estimated terms: the cloud aerosol interaction metric ACIτ =dln τ/dln Na|LWP, and the relative albedo (A) susceptibility SR-τ = dA/3dln τ|LWP, with τ and Na denoting retrieved cloud optical thickness and in-situ aerosol concentration, respectively and calculated for fixed intervals of liquid water path (LWP). ACIτ was estimated by combining in-situ Na sampled below the cloud, with τ and LWP derived from, respectively, simultaneous upward-looking broadband irradiance and narrow field-of-view millimeter-wave radiometer measurements, collected at 1 Hz during four eight-hour daytime flights by the C-130 aircraft. ACIτ values were typically large, close to the physical upper limit (0.33), increasing with LWP. The high ACIτ values were in agreement with other in-situ airborne studies in pristine marine stratocumulus and reflect the imposition of a LWP constraint and simultaneity of aerosol and cloud measurements. SR-τ increased with LWP and τ, reached a maximum SR-τ (0.086) for LWP (τ) of 58 g m−2 (13–14), decreasing slightly thereafter. The net first aerosol indirect effect thus increased over the LWP range of 30–80 g m−2. These values were consistent with satellite estimates derived from instantaneous, collocated CERES albedo and MODIS-retrieved droplet number concentrations at 50 km resolution. The consistency of the airborne and satellite estimates (for airborne remotely sensed Nd < 1100 cm−3), despite their independent approaches, differences in observational scales, and retrieval assumptions, is hypothesized to reflect the robust remote sensing conditions for these homogeneous clouds. We recommend the Southeast Pacific for a regional assessment of the first aerosol indirect effect in climate models on this basis.
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Sun, Yingwei, Jiancheng Luo, Tianjun Wu, et al. "Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data." Sensors 19, no. 19 (2019): 4227. http://dx.doi.org/10.3390/s19194227.
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Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.
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Gutleben, Manuel, Silke Groß, and Martin Wirth. "Cloud macro-physical properties in Saharan-dust-laden and dust-free North Atlantic trade wind regimes: a lidar case study." Atmospheric Chemistry and Physics 19, no. 16 (2019): 10659–73. http://dx.doi.org/10.5194/acp-19-10659-2019.
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Abstract. The Next-generation Aircraft Remote-Sensing for Validation Studies (NARVAL) aimed at providing a better understanding of shallow marine trade wind clouds and their interplay with long-range-transported elevated Saharan dust layers over the subtropical North Atlantic Ocean. Two airborne campaigns were conducted – the first one in December 2013 (winter) and the second one in August 2016, the latter one during the peak season of transatlantic Saharan dust transport (summer). In this study airborne lidar measurements in the vicinity of Barbados performed during both campaigns are used to investigate possible differences between shallow marine cloud macro-physical properties in dust-free regions and regions comprising elevated Saharan dust layers as well as between different seasons. The cloud top height distribution derived in dust-laden regions differs from the one derived in dust-free regions and indicates that there are less and shallower clouds in the dust-laden than in dust-free trades. Additionally, a clear shift of the distribution to higher altitudes is observed in the dust-free winter season, compared to the summer season. While during the summer season most cloud tops are observed in heights ranging from 0.5 to 1.0 km, most cloud tops in winter season are detected between 2.0 and 2.5 km. Moreover, it is found that regions comprising elevated Saharan dust layers show a larger fraction of small clouds and larger cloud-free regions, compared to dust-free regions. The cloud fraction in the dust-laden summer trades is only 14 % compared to a fraction of 31 % and 37 % in dust-free trades and the winter season. Dropsonde measurements show that long-range-transported Saharan dust layers come along with two additional inversions which counteract convective development, stabilize the stratification and may lead to a decrease in convection in those areas. Moreover, a decreasing trend of cloud fractions and cloud top heights with increasing dust layer vertical extent as well as aerosol optical depth is found.
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Yang, Jie, Xinchang Zhang, and Yun Huang. "Graph Attention Feature Fusion Network for ALS Point Cloud Classification." Sensors 21, no. 18 (2021): 6193. http://dx.doi.org/10.3390/s21186193.
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Classification is a fundamental task for airborne laser scanning (ALS) point cloud processing and applications. This task is challenging due to outdoor scenes with high complexity and point clouds with irregular distribution. Many existing methods based on deep learning techniques have drawbacks, such as complex pre/post-processing steps, an expensive sampling cost, and a limited receptive field size. In this paper, we propose a graph attention feature fusion network (GAFFNet) that can achieve a satisfactory classification performance by capturing wider contextual information of the ALS point cloud. Based on the graph attention mechanism, we first design a neighborhood feature fusion unit and an extended neighborhood feature fusion block, which effectively increases the receptive field for each point. On this basis, we further design a neural network based on encoder–decoder architecture to obtain the semantic features of point clouds at different levels, allowing us to achieve a more accurate classification. We evaluate the performance of our method on a publicly available ALS point cloud dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The experimental results show that our method can effectively distinguish nine types of ground objects. We achieve more satisfactory results on different evaluation metrics when compared with the results obtained via other approaches.
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Hirsch, E., E. Agassi, and I. Koren. "Determination of optical and microphysical properties of thin warm clouds using ground based hyper-spectral analysis." Atmospheric Measurement Techniques 5, no. 4 (2012): 851–71. http://dx.doi.org/10.5194/amt-5-851-2012.
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Abstract. Clouds play a critical role in the Earth's radiative budget as they modulate the atmosphere by reflecting shortwave solar radiation and absorbing long wave IR radiation emitted by the Earth's surface. Although extensively studied for decades, cloud modelling in global circulation models is far from adequate, mostly due to insufficient spatial resolution of the circulation models. In addition, measurements of cloud properties still need improvement, since the vast majority of remote sensing techniques are focused in relatively large, thick clouds. In this study, we utilize ground based hyperspectral measurements and analysis to explore very thin water clouds. These clouds are characterized by liquid water path (LWP) that spans from as high as ~50g m−2 and down to 65 mg m−2 with a minimum of about 0.01 visible optical depth. The retrieval methodology relies on three elements: a detailed radiative transfer calculations in the longwave IR regime, signal enhancement by subtraction of a clear sky reference, and spectral matching method which exploits fine spectral differences between water droplets of different radii. A detailed description of the theoretical basis for the retrieval technique is provided along with a comprehensive discussion regarding its limitations. The proposed methodology was validated in a controlled experiment where artificial clouds were sprayed and their effective radii were both measured and retrieved simultaneously. This methodology can be used in several ways: (1) the frequency and optical properties of very thin water clouds can be studied more precisely in order to evaluate their total radiative forcing on the Earth's radiation budget. (2) The unique optical properties of the inter-region between clouds (clouds' "twilight zone") can be studied in order to more rigorously understanding of the governing physical processes which dominate this region. (3) Since the optical thickness of a developed cloud gradually decreases towards its edges, the proposed methodology can be used to study the spatial microphysical behaviour of these edges. (4) A spatial-temporal analysis can be used to study mixing processes in clouds' entrainment zone.
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Hirsch, E., E. Agassi, and I. Koren. "Determination of optical and microphysical properties of thin warm clouds using ground based hyper-spectral analysis." Atmospheric Measurement Techniques Discussions 4, no. 6 (2011): 7277–335. http://dx.doi.org/10.5194/amtd-4-7277-2011.
Full textAbstract:
Abstract. Clouds play a critical role in the Earth's radiative budget as they modulate the atmosphere by reflecting shortwave solar radiation and absorbing long wave IR radiation emitted by the Earth's surface. Although extensively studied for decades, cloud modelling in global circulation models is far from adequate, mostly due to insufficient spatial resolution of the circulation models. In addition, measurements of cloud properties still need improvement, since the vast majority of remote sensing techniques are focused in relatively large, thick clouds. In this study, we utilize ground based hyperspectral measurements and analysis to explore very thin water clouds. These clouds are characterized by liquid water path (LWP) that spans from as high as ~50 g m−2 and down to 65 mg m−2 with a minimum of about 0.01 visible optical depth. The retrieval methodology relies on three elements: a detailed radiative transfer calculations in the longwave IR regime, signal enhancement by subtraction of a clear sky reference, and spectral matching method which exploits fine spectral differences between water droplets of different radii. A detailed description of the theoretical basis for the retrieval technique is provided along with a comprehensive discussion regarding its limitations. The proposed methodology was validated in a controlled experiment where artificial clouds were sprayed and their effective radii were both measured and retrieved simultaneously. This methodology can be used in several ways: (1) the frequency and optical properties of very thin water clouds can be studied more precisely in order to evaluate their total radiative forcing on the Earth's radiation budget. (2) The unique optical properties of the inter-region between clouds (clouds' "twilight zone") can be studied in order to more rigorously understanding of the governing physical processes which dominate this region. (3) Since the optical thickness of a developed cloud gradually decreases towards its edges, the proposed methodology can be used to study the spatial microphysical behaviour of these edges. (4) A spatial-temporal analysis can be used to study mixing processes in clouds' entrainment zone.
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Lee, Kuan-Yi, and Chao-Hung Lin. "CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 289–93. http://dx.doi.org/10.5194/isprsarchives-xli-b7-289-2016.
Full textAbstract:
Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. &lt;br&gt;&lt;br&gt; The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.
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Lee, Kuan-Yi, and Chao-Hung Lin. "CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 289–93. http://dx.doi.org/10.5194/isprs-archives-xli-b7-289-2016.
Full textAbstract:
Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. <br><br> The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.
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Sedlar, Joseph, Laura D. Riihimaki, Kathleen Lantz, and David D. Turner. "Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products." Journal of Applied Meteorology and Climatology 60, no. 4 (2021): 477–91. http://dx.doi.org/10.1175/jamc-d-20-0153.1.
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AbstractVarious methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.
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Pincus, Robert, Chris W. Fairall, Adriana Bailey, et al. "Observations from the NOAA P-3 aircraft during ATOMIC." Earth System Science Data 13, no. 7 (2021): 3281–96. http://dx.doi.org/10.5194/essd-13-3281-2021.
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Abstract. The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC), part of the larger experiment known as Elucidating the Role of Clouds-Circulation Coupling in Climate (EUREC4A), was held in the western Atlantic during the period 17 January–11 February 2020. This paper describes observations made during ATOMIC by the US National Oceanic and Atmospheric Administration's (NOAA) Lockheed WP-3D Orion research aircraft based on the island of Barbados. The aircraft obtained 95 h of observations over 11 flights, many of which were coordinated with the NOAA research ship R/V Ronald H. Brown and autonomous platforms deployed from the ship. Each flight contained a mixture of sampling strategies including high-altitude circles with frequent dropsonde deployment to characterize the large-scale environment, slow descents and ascents to measure the distribution of water vapor and its isotopic composition, stacked legs aimed at sampling the microphysical and thermodynamic state of the boundary layer, and offset straight flight legs for observing clouds and the ocean surface with remote sensing instruments and the thermal structure of the ocean with in situ sensors dropped from the plane. The characteristics of the in situ observations, expendable devices, and remote sensing instrumentation are described, as is the processing used in deriving estimates of physical quantities. Data archived at the National Center for Environmental Information include flight-level data such as aircraft navigation and basic thermodynamic information (NOAA Aircraft Operations Center and NOAA Physical Sciences Laboratory, 2020, https://doi.org/10.25921/7jf5-wv54); high-accuracy measurements of water vapor concentration from an isotope analyzer (National Center for Atmospheric Research, 2020, https://doi.org/10.25921/c5yx-7w29); in situ observations of aerosol, cloud, and precipitation size distributions (Leandro and Chuang, 2020, https://doi.org/10.25921/vwvq-5015); profiles of seawater temperature made with Airborne eXpendable BathyThermographs (AXBTs; NOAA Physical Sciences Laboratory, 2020a, https://doi.org/10.25921/pe39-sx75); radar reflectivity, Doppler velocity, and spectrum width from a nadir-looking W-band radar (NOAA Physical Sciences Laboratory, 2020c, https://doi.org/10.25921/n1hc-dc30); estimates of cloud presence, the cloud-top location, and the cloud-top radar reflectivity and temperature, along with estimates of 10 m wind speed obtained from remote sensing instruments operating in the microwave and thermal infrared spectral regions (NOAA Physical Sciences Laboratory, 2020b, https://doi.org/10.25921/x9q5-9745); and ocean surface wave characteristics from a Wide Swath Radar Altimeter (Prosensing, Inc., 2020, https://doi.org/10.25921/qm06-qx04). Data are provided as netCDF files following Climate and Forecast conventions.
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Kölling, Tobias, Tobias Zinner, and Bernhard Mayer. "Aircraft-based stereographic reconstruction of 3-D cloud geometry." Atmospheric Measurement Techniques 12, no. 2 (2019): 1155–66. http://dx.doi.org/10.5194/amt-12-1155-2019.
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Abstract. This work describes a method to retrieve the location and geometry of clouds using RGB images from a video camera on an aircraft and data from the aircraft's navigation system. Opposed to ordinary stereo methods for which two cameras with fixed relative position at a certain distance are used to match images taken at the exact same moment, this method uses only a single camera and the aircraft's movement to provide the needed parallax. Advantages of this approach include a relatively simple installation on a (research) aircraft and the possibility to use different image offsets that are even larger than the size of the aircraft. Detrimental effects are the evolution of observed clouds during the time offset between two images as well as the background wind. However we will show that some wind information can also be recovered and subsequently used for the physics-based filtering of outliers. Our method allows the derivation of cloud top geometry which can be used, e.g., to provide location and distance information for other passive cloud remote sensing products. In addition it can also improve retrieval methods by providing cloud geometry information useful for the correction of 3-D illumination effects. We show that this method works as intended through comparison to data from a simultaneously operated lidar system. The stereo method provides lower heights than the lidar method; the median difference is 126 m. This behavior is expected as the lidar method has a lower detection limit (leading to greater cloud top heights for the downward view), while the stereo method also retrieves data points on cloud sides and lower cloud layers (leading to lower cloud heights). Systematic errors across the measurement swath are less than 50 m.
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Kokhanovsky, A. A. "On remote sensing of optically thick ice clouds." Optical Engineering 45, no. 4 (2006): 046201. http://dx.doi.org/10.1117/1.2190927.
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Zhou, Siyan, Yanlei Li, Fubo Zhang, Longyong Chen, and Xiangxi Bu. "Automatic Regularization of TomoSAR Point Clouds for Buildings Using Neural Networks." Sensors 19, no. 17 (2019): 3748. http://dx.doi.org/10.3390/s19173748.
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Tomographic SAR (TomoSAR) is a remote sensing technique that extends the conventional two-dimensional (2-D) synthetic aperture radar (SAR) imaging principle to three-dimensional (3-D) imaging. It produces 3-D point clouds with unavoidable noise that seriously deteriorates the quality of 3-D imaging and the reconstruction of buildings over urban areas. However, existing methods for TomoSAR point cloud processing notably rely on data segmentation, which influences the processing efficiency and denoising performance to a large extent. Inspired by regression analysis, in this paper, we propose an automatic method using neural networks to regularize the 3-D building structures from TomoSAR point clouds. By changing the point heights, the surface points of a building are refined. The method has commendable performance on smoothening the building surface, and keeps a precise preservation of the building structure. Due to the regression mechanism, the method works in a high automation level, which avoids data segmentation and complex parameter adjustment. The experimental results demonstrate the effectiveness of our method to denoise and regularize TomoSAR point clouds for urban buildings.
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Formenti, Paola, Barbara D’Anna, Cyrille Flamant, et al. "The Aerosols, Radiation and Clouds in Southern Africa Field Campaign in Namibia: Overview, Illustrative Observations, and Way Forward." Bulletin of the American Meteorological Society 100, no. 7 (2019): 1277–98. http://dx.doi.org/10.1175/bams-d-17-0278.1.
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AbstractThe Aerosol, Radiation and Clouds in southern Africa (AEROCLO-sA) project investigates the role of aerosols on the regional climate of southern Africa. This is a unique environment where natural and anthropogenic aerosols and a semipermanent and widespread stratocumulus (Sc) cloud deck are found. The project aims to understand the dynamical, chemical, and radiative processes involved in aerosol–cloud–radiation interactions over land and ocean and under various meteorological conditions. The AEROCLO-sA field campaign was conducted in August and September of 2017 over Namibia. An aircraft equipped with active and passive remote sensors and aerosol in situ probes performed a total of 30 research flight hours. In parallel, a ground-based mobile station with state-of-the-art in situ aerosol probes and remote sensing instrumentation was implemented over coastal Namibia, and complemented by ground-based and balloonborne observations of the dynamical, thermodynamical, and physical properties of the lower troposphere. The focus laid on mineral dust emitted from salty pans and ephemeral riverbeds in northern Namibia, the advection of biomass-burning aerosol plumes from Angola subsequently transported over the Atlantic Ocean, and aerosols in the marine boundary layer at the ocean–atmosphere interface. This article presents an overview of the AEROCLO-sA field campaign with results from the airborne and surface measurements. These observations provide new knowledge of the interactions of aerosols and radiation in cloudy and clear skies in connection with the atmospheric dynamics over southern Africa. They will foster new advanced climate simulations and enhance the capability of spaceborne sensors, ultimately allowing a better prediction of future climate and weather in southern Africa.
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Rusli, Stephanie P., David P. Donovan, and Herman W. J. Russchenberg. "Simultaneous and synergistic profiling of cloud and drizzle properties using ground-based observations." Atmospheric Measurement Techniques 10, no. 12 (2017): 4777–803. http://dx.doi.org/10.5194/amt-10-4777-2017.
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Abstract. Despite the importance of radar reflectivity (Z) measurements in the retrieval of liquid water cloud properties, it remains nontrivial to interpret Z due to the possible presence of drizzle droplets within the clouds. So far, there has been no published work that utilizes Z to identify the presence of drizzle above the cloud base in an optimized and a physically consistent manner. In this work, we develop a retrieval technique that exploits the synergy of different remote sensing systems to carry out this task and to subsequently profile the microphysical properties of the cloud and drizzle in a unified framework. This is accomplished by using ground-based measurements of Z, lidar attenuated backscatter below as well as above the cloud base, and microwave brightness temperatures. Fast physical forward models coupled to cloud and drizzle structure parameterization are used in an optimal-estimation-type framework in order to retrieve the best estimate for the cloud and drizzle property profiles. The cloud retrieval is first evaluated using synthetic signals generated from large-eddy simulation (LES) output to verify the forward models used in the retrieval procedure and the vertical parameterization of the liquid water content (LWC). From this exercise it is found that, on average, the cloud properties can be retrieved within 5 % of the mean truth. The full cloud–drizzle retrieval method is then applied to a selected ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) campaign dataset collected in Cabauw, the Netherlands. An assessment of the retrieval products is performed using three independent methods from the literature; each was specifically developed to retrieve only the cloud properties, the drizzle properties below the cloud base, or the drizzle fraction within the cloud. One-to-one comparisons, taking into account the uncertainties or limitations of each retrieval, show that our results are consistent with what is derived using the three independent methods.
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Mitrescu, Cristian, Tristan L’Ecuyer, John Haynes, Steven Miller, and Joseph Turk. "CloudSat Precipitation Profiling Algorithm—Model Description." Journal of Applied Meteorology and Climatology 49, no. 5 (2010): 991–1003. http://dx.doi.org/10.1175/2009jamc2181.1.
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Abstract Identifying and quantifying the intensity of light precipitation at global scales is still a difficult problem for most of the remote sensing algorithms in use today. The variety of techniques and algorithms employed for such a task yields a rather wide spectrum of possible values for a given precipitation event, further hampering the understanding of cloud processes within the climate. The ability of CloudSat’s millimeter-wavelength Cloud Profiling Radar (CPR) to profile not only cloud particles but also light precipitation brings some hope to the above problems. Introduced as version zero, the present work uses basic concepts of detection and retrieval of light precipitation using spaceborne radars. Based on physical principles of remote sensing, the radar model relies on the description of clouds and rain particles in terms of a drop size distribution function. Use of a numerical model temperature and humidity profile ensures the coexistence of mixed phases otherwise undetected by the CPR. It also provides grounds for evaluating atmospheric attenuation, important at this frequency. Related to the total attenuation, the surface response is used as an additional constraint in the retrieval algorithm. Practical application of the profiling algorithm includes a 1-yr preliminary analysis of global rainfall incidence and intensity. These results underscore once more the role of CloudSat rainfall products for improving and enhancing current estimates of global light rainfall, mostly at higher latitudes, with the goal of understanding its role in the global energy and water cycle.
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Yu, Ruixuan, and Jian Sun. "Learning Polynomial-Based Separable Convolution for 3D Point Cloud Analysis." Sensors 21, no. 12 (2021): 4211. http://dx.doi.org/10.3390/s21124211.
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Shape classification and segmentation of point cloud data are two of the most demanding tasks in photogrammetry and remote sensing applications, which aim to recognize object categories or point labels. Point convolution is an essential operation when designing a network on point clouds for these tasks, which helps to explore 3D local points for feature learning. In this paper, we propose a novel point convolution (PSConv) using separable weights learned with polynomials for 3D point cloud analysis. Specifically, we generalize the traditional convolution defined on the regular data to a 3D point cloud by learning the point convolution kernels based on the polynomials of transformed local point coordinates. We further propose a separable assumption on the convolution kernels to reduce the parameter size and computational cost for our point convolution. Using this novel point convolution, a hierarchical network (PSNet) defined on the point cloud is proposed for 3D shape analysis tasks such as 3D shape classification and segmentation. Experiments are conducted on standard datasets, including synthetic and real scanned ones, and our PSNet achieves state-of-the-art accuracies for shape classification, as well as competitive results for shape segmentation compared with previous methods.
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Stevenson, J. A., S. C. Millington, F. M. Beckett, G. T. Swindles, and T. Thordarson. "Big grains go far: reconciling tephrochronology with atmospheric measurements of volcanic ash." Atmospheric Measurement Techniques Discussions 8, no. 1 (2015): 65–120. http://dx.doi.org/10.5194/amtd-8-65-2015.
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Abstract. There is a large discrepancy between the size of volcanic ash particles measured from deposits on the ground (known as cryptotephra; 20–125 μm in length) and those reported by satellite remote sensing (effective radii of 0.5–9 μm; 95% of particles < 17 μm diameter). We use results from the fields of tephrochronology (a dating technique based on volcanic ash layers), dispersion modelling and satellite remote sensing in an attempt to understand from where it arises. We show that Icelandic cryptotephras deposited in NW Europe have lognormal particle size distributions (PSDs) with median lengths of 20–70 μm (geometric standard deviation: 1.40–1.66; 95th percentile length: 42–126 microns). This is consistent with semi-quantitative grainsize range estimates from the literature. Using measured fall velocities of ash particles, a release height typical of moderate Icelandic eruptions (10 km) and a wind speed typical for NW Europe (10 m s−1), we find that an ash cloud can transport particles < 80 μm diameter up to 850 km in 24 h, so that even moderately sized Icelandic eruptions can deposit cryptotephra on mainland Europe. The proportion of cryptotephra in airborne clouds is unknown. We used simulated satellite data of dispersion-model-derived ash clouds to investigate the effect of PSD on satellite retrievals and show that as the median radius of the input PSD increases, fewer ash-containing pixels are correctly identified. Where retrievals are made of simulated clouds with mass median radii larger than ~ 10 μm, the mean retrieved reff plateaus at around 9 μm. This is a systematic bias in the retrieval algorithm that would cause the grainsize of distal clouds containing significant cryptotephra to be underestimated. This cannot explain discrepancies in coarser proximal clouds, however, which may be because the complex physics of scattering by highly irregularly-shaped grains is inadequately represented by assuming that particles are dense spheres.
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Davis, C. P., K. F. Evans, S. A. Buehler, D. L. Wu, and H. C. Pumphrey. "3-D polarised simulations of space-borne passive mm/sub-mm midlatitude cirrus observations: a case study." Atmospheric Chemistry and Physics 7, no. 15 (2007): 4149–58. http://dx.doi.org/10.5194/acp-7-4149-2007.
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Abstract. Global observations of ice clouds are needed to improve our understanding of their impact on earth's radiation balance and the water-cycle. Passive mm/sub-mm has some advantages compared to other space-borne cloud-ice remote sensing techniques. The physics of scattering makes forward radiative transfer modelling for such instruments challenging. This paper demonstrates the ability of a recently developed RT code, ARTS-MC, to accurately simulate observations of this type for a variety of viewing geometries corresponding to operational (AMSU-B, EOS-MLS) and proposed (CIWSIR) instruments. ARTS-MC employs an adjoint Monte-Carlo method, makes proper account of polarisation, and uses 3-D spherical geometry. The actual field of view characteristics for each instrument are also accounted for. A 3-D midlatitude cirrus scenario is used, which is derived from Chilbolton cloud radar data and a stochastic method for generating 3-D ice water content fields. These demonstration simulations clearly demonstrate the beamfilling effect, significant polarisation effects for non-spherical particles, and also a beamfilling effect with regard to polarisation.
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Ziolkowski, Patryk, Jakub Szulwic, and Mikolaj Miskiewicz. "Deformation Analysis of a Composite Bridge during Proof Loading Using Point Cloud Processing." Sensors 18, no. 12 (2018): 4332. http://dx.doi.org/10.3390/s18124332.
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Remote sensing in structural diagnostics has recently been gaining attention. These techniques allow the creation of three-dimensional projections of the measured objects, and are relatively easy to use. One of the most popular branches of remote sensing is terrestrial laser scanning. Laser scanners are fast and efficient, gathering up to one million points per second. However, the weakness of terrestrial laser scanning is the troublesome processing of point clouds. Currently, many studies deal with the subject of point cloud processing in various areas, but it seems that there are not many clear procedures that we can use in practice, which indicates that point cloud processing is one of the biggest challenges of this issue. To tackle that challenge we propose a general framework for studying the structural deformations of bridges. We performed an advanced object shape analysis of a composite foot-bridge, which is subject to spatial deformations during the proof loading process. The added value of this work is the comprehensive procedure for bridge evaluation, and adaptation of the spheres translation method procedure for use in bridge engineering. The aforementioned method is accurate for the study of structural element deformation under monotonic load. The study also includes a comparative analysis between results from the spheres translation method, a total station, and a deflectometer. The results are characterized by a high degree of convergence and reveal the highly complex state of deformation more clearly than can be concluded from other measurement methods, proving that laser scanning is a good method for examining bridge structures with several competitive advantages over mainstream measurement methods.
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Pavolonis, Michael J. "Advances in Extracting Cloud Composition Information from Spaceborne Infrared Radiances—A Robust Alternative to Brightness Temperatures. Part I: Theory." Journal of Applied Meteorology and Climatology 49, no. 9 (2010): 1992–2012. http://dx.doi.org/10.1175/2010jamc2433.1.
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Abstract Infrared measurements can be used to obtain quantitative information on cloud microphysics, including cloud composition (ice, liquid water, ash, dust, etc.), with the advantage that the measurements are independent of solar zenith angle. As such, infrared brightness temperatures (BT) and brightness temperature differences (BTD) have been used extensively in quantitative remote sensing applications for inferring cloud composition. In this study it is shown that BTDs are fundamentally limited and that a more physically based infrared approach can lead to significant increases in sensitivity to cloud microphysics, especially for optically thin clouds. In lieu of BTDs, a derived radiative parameter β, which is directly related to particle size, habit, and composition, is used. Although the concept of effective absorption optical depth ratios β has been around since the mid-1980s, this is the first study to explore the use of β for inferring cloud composition in the total absence of cloud vertical boundary information. The results showed that, even in the absence of cloud vertical boundary information, one could significantly increase the sensitivity to cloud microphysics by converting the measured radiances to effective emissivity and constructing effective absorption optical depth ratios from a pair of spectral emissivities in the 8–12-μm “window.” This increase in sensitivity to cloud microphysics is relative to BTDs constructed from the same spectral pairs. In this article, the focus is on describing the physical concepts (which can be applied to narrowband or hyperspectral infrared measurements) used in constructing the β data space.
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List, Roland. "Weather Modification—a Scenario for the Future." Bulletin of the American Meteorological Society 85, no. 1 (2004): 51–64. http://dx.doi.org/10.1175/bams-85-1-51.
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The ever-increasing severe economic damage imposed on national and world wide economies by severe weather, the need for sufficient and safe water resources for an increasing world population, and the threat of adverse climate change led to this critical assessment of the state-of-the-art of weather modification (WM) and to a proposal of a road map for the future. Special attention is given to rain enhancement because it is further developed than snowpack augmentation, hail suppression, tornado and hurricane modification, and other weather-related disaster control ideas. The question of what makes a rain enhancement experiment acceptable to the scientific community is answered by the World Meteorological Organization's (WMO) criteria, which address statistical evaluation, the measurement of rain, the understanding of nature's precipitation processes with the underlying physics and dynamics of clouds and cloud systems, and the transferability of experiment design. These criteria are no longer specific enough or satisfactory and will have to be reconsidered. An actual WM experiment also involves a variety of techniques and technologies, aspects that need to be complemented by numerical modeling of clouds and cloud responses to seeding. Modeling also allows assessment of the extra-area effects, that is, detrimental effects of precipitation on adjacent areas. Assimilation models may be giving better estimates of the rain at the ground because they can integrate restricted information from radar and rain gauges with mesoscale meteorological and remote sensing, as well as hydrological, data. However, massive improvements in computer capacity are required to handle these problems. Weather modification has been progressing very slowly in the past because of the enormity of the problem and the fact that the precipitation process is far from being understood. Considering that rain increases are attempted within a range of 10%–20%, the lack of knowledge at corresponding accuracy is particularly evident in the fields of cloud physics, cloud and cloud systems dynamics, weather forecasting, numerical modeling, and measuring technology. Benefits of new intensive studies of precipitation processes will not be limited to WM; they are also vital to improving weather forecasting and climate change modeling. There is one additional aspect of WM; WM can also be used to test newly developed precipitation physics and models by studying if the clouds react to seeding in the predicted manner. This article is a wake-up call to put more intellectual and financial resources into the exploration and modification of the precipitation processes in all their forms. All these points lead to the suggestion of an outline of a national precipitation research and weather modification program.
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Redemann, Jens, Robert Wood, Paquita Zuidema, et al. "An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the southeast Atlantic basin." Atmospheric Chemistry and Physics 21, no. 3 (2021): 1507–63. http://dx.doi.org/10.5194/acp-21-1507-2021.
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Abstract. Southern Africa produces almost a third of the Earth's biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three intensive observation periods designed to study key atmospheric processes that determine the climate impacts of these aerosols. During the Southern Hemisphere winter and spring (June–October), aerosol particles reaching 3–5 km in altitude are transported westward over the southeast Atlantic, where they interact with one of the largest subtropical stratocumulus (Sc) cloud decks in the world. The representation of these interactions in climate models remains highly uncertain in part due to a scarcity of observational constraints on aerosol and cloud properties, as well as due to the parameterized treatment of physical processes. Three ORACLES deployments by the NASA P-3 aircraft in September 2016, August 2017, and October 2018 (totaling ∼350 science flight hours), augmented by the deployment of the NASA ER-2 aircraft for remote sensing in September 2016 (totaling ∼100 science flight hours), were intended to help fill this observational gap. ORACLES focuses on three fundamental science themes centered on the climate effects of African BB aerosols: (a) direct aerosol radiative effects, (b) effects of aerosol absorption on atmospheric circulation and clouds, and (c) aerosol–cloud microphysical interactions. This paper summarizes the ORACLES science objectives, describes the project implementation, provides an overview of the flights and measurements in each deployment, and highlights the integrative modeling efforts from cloud to global scales to address science objectives. Significant new findings on the vertical structure of BB aerosol physical and chemical properties, chemical aging, cloud condensation nuclei, rain and precipitation statistics, and aerosol indirect effects are emphasized, but their detailed descriptions are the subject of separate publications. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project and the dataset it produced.
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Podhorský, Dušan, and Peter Guba. "History of remote-sensing methods in meteorology, cloud physics and nowcasting in Slovakia over the period 1965–1990." Contributions to Geophysics and Geodesy 44, no. 1 (2014): 79–94. http://dx.doi.org/10.2478/congeo-2014-0005.
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Abstract A brief overview of building the radar and satellite meteorology in Slovakia over the period 1965-1990 and application of dispatching locators of PAR, SRE and RSR types for studying the evolution of convective cells is given. Further, the conception and implementation of a meteorological radar network in Slovakia, the algorithms for recognition of clouds and phenomena related to the parameters of radioecho are reviewed. The development of a new laser radar (LIDAR) and the application of a prototype meteorological radar with the TESLA RM-3 controlled polarizer are described.
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Ghosh, S., S. Osborne, and M. H. Smith. "On the importance of cumulus penetration on the microphysical and optical properties of stratocumulus clouds." Atmospheric Chemistry and Physics 5, no. 3 (2005): 755–65. http://dx.doi.org/10.5194/acp-5-755-2005.
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Abstract. Owing to their extensive spatial coverage, stratocumulus clouds play a crucial role in the radiation budget of the earth. Climate models need an accurate characterisation of stratocumulus in order to provide an accurate forecast. However, remote sensing as well as in-situ observations reveal that on several occasions, cumulus clouds present below the stratocumulus, often have a significant impact on the main stratocumulus microphysical properties. This was observed during the ACE-2 (Aerosol Characterisation Experiment-2) campaign designed to study the impact of polluted continental air on stratocumulus formation. In this paper we used a detailed micro-physical chemical parcel model to quantify the extent of this cumulus-stratocumuls coupling. In addition, we made extensive use of microphysical observations from the C-130 aircraft that was operated during ACE-2. For the ACE-2 case studies considered in this paper, our analysis revealed that the chemical, microphysical and optical characteristics of the main stratocumulus cloud deck had significant contributions from cumulus clouds that often penetrated the stratocumulus deck. The amount of fine mode ionic species, the average droplet number concentrations, the effective radii and the optical depths during the flight A562 (when cumulus clouds interacted with the main stratocumulus) were estimated and model runs that included this effect yielded microphysical and optical properties which compared more favourably with the observations than the runs which did not. This study highlights the importance of including these cumulus effects in stratocumulus related modelling studies.