Cloud Screening Research Articles

Dimensional accuracy inspection of steel joint elements with bolt holes assisted by point cloud data

Abstract During production and manufacturing, box joint structures with bolt holes are prone to dimensional accuracy errors due to systematic failures in production devices or inadequate control of welding deformation. In order to address the limitations of low efficiency and high subjectivity associated with traditional manual inspection, a dimensional accuracy inspection technique for the box joint structure assisted by point cloud data is proposed. A section size inspection method is built that uses the normal vector relationship between structural surfaces to establish the coordinate datum for point cloud model. Then, dimensional transformation and plane geometric features are utilized to calculate feature point coordinates and section size of the joint structure. Numerical simulation is conducted to investigate the performance of the section size inspection method. The simulation results indicate that when the density of the collected point cloud for the box joint structure is maintained above 0.01/mm², the error of section size inspection can be kept below 0.5 mm. A bolt hole accuracy inspection method is developed, which establishes a multi-stage candidate point screening process for the bolt hole recognition, outlier filtering, and point cloud screening. The advance of the method lies in its ability to quickly recognize bolt holes and eliminate interference from outliers and mixed pixel points. Comparative tests demonstrate that, under the recommended parameter configuration, the recognition efficiency of the bolt hole inspection method is five times higher than that of the classical Angle Criterion method. Moreover, the filtering precision of this method exceeds 98%, and the calculation error is only 3.5% of the specified tolerance. A laboratory experiment is performed to further validate the proposed technique from a structural perspective.

Improved cloud screening of OceanSat-3 OCM-3 satellite imagery using machine learning algorithm

Improved cloud screening of OceanSat-3 OCM-3 satellite imagery using machine learning algorithm

The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.

Trends from 30-Year Observations of Downward Solar Irradiance in Thessaloniki, Greece

The shortwave downward solar irradiance (SDR) is an important factor that drives climate processes and energy production and can affect all living organisms. Observations of SDR at different locations around the world with different environmental characteristics have been used to investigate its long-term variability and trends at different time scales. Periods of positive trends are referred to as brightening periods and of negative trends as dimming periods. In this study we have used 30 years of pyranometer data in Thessaloniki, Greece, to investigate the variability of SDR under three types of sky conditions (clear-, cloudy- and all-sky). The clear-sky data were identified by applying a cloud screening algorithm. We have found a positive trend of 0.38%/year for all-sky, ∼0.1%/year for clear-sky, and 0.41%/year for cloudy conditions. The consistency of these trends, their seasonal variability, and the effect of the solar zenith angle have also been investigated. Under all three sky categories, the SDR trend is stronger in winter, with 0.7, 0.4, and 0.76%/year, respectively, for all-, clear-, and cloudy-sky conditions. The next larger seasonal trends are in autumn—0.42 and 0.19%/year, for all and cloudy skies, respectively. The rest of the seasonal trends are significant smaller, close to zero, with a negative values in summer, for clear and cloudy skies. The SDR trend is increasing with increasing solar zenith angle, except under cloudy skies, where the trend is highly variable and close to zero. Finally, we discuss shorter-term variations in SDR anomalies by examining the patterns of the cumulative sums of monthly anomalies from the climatological mean, both before and after removing the long-term trend.

Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm

A U-Net algorithm was used to retrieve surface pressure and wind speed over the ocean within tropical cyclones (TCs) and their neighboring areas using NOAA-20 Advanced Technology Microwave Sounder (ATMS) reprocessed Sensor Data Record (SDR) brightness temperatures (TBs) and geolocation information. For TC locations, International Best Track Archive for Climate Stewardship (IBTrACS) data have been used over the North Atlantic Ocean and West Pacific Ocean between 2018 and 2021. The European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) surface pressure and wind speed were employed as reference labels. Preliminary results demonstrated that the visualizations for wind speed and pressure matched the prediction and ERA5 location. The residual biases and standard deviations between the predicted and reference labels were about 0.15 m/s and 1.95 m/s, respectively, for wind speed and 0.48 hPa and 2.67 hPa, respectively, for surface pressure, after applying cloud screening for each ATMS pixel. This indicates that the U-Net model is effective for surface wind speed and surface pressure estimates over general ocean conditions.

Background noise model of spaceborne photon-counting lidars over oceans and aerosol optical depth retrieval from ICESat-2 noise data

Background noise model of spaceborne photon-counting lidars over oceans and aerosol optical depth retrieval from ICESat-2 noise data

Improving atmospheric CO2 retrieval based on the collaborative use of Greenhouse gases Monitoring Instrument and Directional Polarimetric Camera sensors on Chinese hyperspectral satellite GF5-02

ABSTRACT The Greenhouse Gas Monitoring Instrument (GMI) onboard the Chinese hyperspectral satellite GF5–02 can provide abundant observations of global atmospheric CO2, which plays an important role in climate research. CO2 retrieval precision is the key to determining the application value of the GMI. To reduce the influence of atmospheric scattering on retrieval, we combined the Directional Polarimetric Camera (DPC) data on the same satellite to improve the anti-interference ability of GMI CO2 retrieval and ensure its retrieval precision. To realize the reliability and feasibility of the collaborative use of the GMI and DPC, this paper designs the pointing registration method of the GMI based on coastline observations, the spatial resolution matching method and the collaborative cloud screening method of the GMI and DPC observations. Combined with the DPC, which supplied the spectral data and aerosol product, the retrieval ability of the coupled bidirectional reflectance distribution function CO2 retrieval (CBCR) method developed for GMI CO2 retrieval was improved, with the retrieval efficiency of CO2 products increasing by 27%, and the CO2 retrieval precision increasing from 3.3 ppm to 2.7 ppm. Moreover, collaborative use not only guaranteed the GMI’s ability to detect global and area CO2 concentration distribution characteristics, such as significant concentration differences between the Northern and Southern Hemispheres in winter and high CO2 concentrations in urban agglomeration areas caused by human activities, but also extended the GMI’s potential for monitoring anomalous events, such as the Tonga volcanic eruption.

The (mis)identification of high-latitude dust events using remote sensing methods in the Yukon, Canada: a sub-daily variability analysis

Abstract. The observation and quantification of mineral dust fluxes from high-latitude sources remains difficult due to a known paucity of year-round in situ observations and known limitations of satellite remote sensing data (e.g. cloud cover and dust detection). Here we explore the chronology of dust emissions at a known and instrumented high-latitude dust source: Lhù'ààn Mân (Kluane Lake) in Yukon, Canada. At this location we use oblique time-lapse (RC) cameras as a baseline for analysis of aerosol retrievals from in situ metrological data, AERONET, and co-incident MODIS MAIAC to (i) investigate the daily to annual chronology of dust emissions recorded by these instrumental and remote sensing methods (at timescales ranging from minutes to years) and (ii) use data intercomparisons to comment on the principal factors that control the detection of dust in each case. Lhù'ààn Mân is a prolific mineral dust source; on 24 May 2018 the RC captured dust in motion throughout the entire day, with the longest dust-free period lasting only 30 min. When compared with time series of RC data, optimized AERONET data only manage an overall 26 % detection rate for events (sub-day) but 100 % detection rate for dust event days (DEDs) when dust was within the field of view. In this instance, RC and remote sensing data were able to suggest that the low event detection rate was attributed to fundamental variations in dust advection trajectory, dust plume height, and inherent restrictions in sun angle at high latitudes. Working with a time series of optimized aerosol optical depth (AOD) data (covering 2018/2019), we were able to investigate the gross impacts of data quality (DQ) choice on DED detection at the month or year scale. Relative to ground observations, AERONET's DQ2.0 cloud-screening algorithm may remove as much as 97 % of known dust events (3 % detection). Finally, when undertaking an AOD comparison for DED and non-DED retrievals, we find that cloud screening of MODIS/AERONET lead to a combined low sample of co-incident dust events and weak correlations between retrievals. Our results quantify and explain the extent of under-representation of dust in both ground and space remote sensing methods; this is a factor that impacts on the effective calibration and validation of global climate and dust models.

Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation

Abstract. Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

Cloud Screening Method in Complex Background Areas Containing Snow and Ice Based on Landsat 9 Images.

The first step in the application of Landsat 9 imagery is cloud screening, and the International Satellite Cloud Climatology Project (ISCCP) has made cloud screening an important part of the World Climate Research Program. The accurate identification of clouds in remote sensing satellite images containing snow and ice on the subsurface has been a challenging task in the cloud screening process. It is imperative to fully exploit the characteristic heterogeneous information of the cloud and snow subsurface, to solve the problem of cloud–snow confusion in the snow and ice environment, and to carry out research on cloud screening technology without interference from the snow and ice subsurface. In view of this, this paper will systematically carry out research on cloud screening methods in snow and ice environments. In this paper, we propose the building of a cloud screening algorithm that takes into account the difficulty of the fact that snow and ice on the subsurface can easily interfere with cloud recognition, and the influence of an empirical threshold or statistical threshold that makes its application less effective, and then establish a dynamic threshold cloud screening algorithm that is suitable for snow and ice environments. The research results will provide new ideas and perspectives to solve the problem of surface-type interference that most of the existing cloud screening algorithms contend with.

High Throughput Calculation and Screening of High Strength and High Conductivity Cu-Cr-Sn Copper Alloy

In this paper, based on the first principles, the exchange correlation functional (GGA) and modified functional (PBE) are used for the calculation of Cu-Cr-Sn high strength and high conductivity copper alloy. The cloud high-throughput cloud computing and screening platform such as Matcloud and cluster expansion method are mainly used. Starting from the most basic components of micro atoms, electrons, ions and energy bands, the high-throughput crystal structure modeling and screening optimization algorithm are adopted, The electronic structure, electromigration and diffusivity of the matrix and the second phase of the material were studied. The theoretical criteria for the collaborative optimization and control of the key performance parameters of the matrix and the characteristic microstructure were established. The characteristic microstructure and material component sequences with excellent mechanical, electrical and thermodynamic properties were predicted and screened.

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

Towards a novel approach for Sentinel-3 synergistic OLCI/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation

Towards a novel approach for Sentinel-3 synergistic OLCI/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation

Analysis of Aerosol Optical Depth from Sun Photometer at Shouxian, China

We use two cloud screening methods—the clustering method and the multiplet method—to process the measurements of a sun photometer from March 2020 to April 2021 in Shouxian. The aerosol optical depth (AOD) and Angström parameters α and β are retrieved; variation characteristics and single scattering albedo are studied. The results show that: (1) The fitting coefficient of AOD retrieved by the two methods is 0.921, and the changing trend is consistent. The clustering method has fewer effective data points and days, reducing the overall average of AOD by 0.0542 (500 nm). (2) Diurnal variation of AOD can be divided into flat type, convex type, and concave type. Concave type and convex type occurred the most frequently, whereas flat type the least. (3) During observation, the overall average of AOD is 0.48, which is relatively high. Among them, AOD had a winter maximum (0.70), autumn and spring next (0.54 and 0.40), and a summer minimum (0.26). The variation trend of AOD and β is highly consistent, and the monthly mean of α is between 0.69 and 1.61, concerning mainly continental and urban aerosols. (4) Compared with others, the single scattering albedo in Shouxian is higher, reflecting strong scattering and weak aerosol absorption.

Quantification of Enhancement in Atmospheric CO2 Background Due to Indian Biospheric Fluxes and Fossil Fuel Emissions

AbstractRegional carbon emissions impact global atmospheric carbon dioxide (CO2) background concentrations. This study quantified the enhancement in the atmospheric CO2 mole fractions due to biospheric and fossil fuel fluxes from India. Sensitivity experiments using model simulations were conducted, allowing CO2 enhancement due to biospheric and fossil fuel fluxes from India to diffuse into the global atmospheric background. The areal extent of column‐averaged enhancement of 0.2 ppm and above due to Indian fluxes are spread over a larger area covering the Indian subcontinent, neighboring Asian regions, and the north Indian Ocean in all four seasons. The boundary layer CO2 enhancement due to biospheric fluxes (fossil fuel fluxes) have a maximum range of −2.6 to +1.4 ppm (1.8–2.0 ppm) most time of the year. At higher altitude, the amplitudes of enhancement are reduced from ±1.8 to ±0.6 ppm as we go up from 850 to 500 hPa due to diffusion by prevailing atmospheric dynamics and convection. With the information of the areal extent of >0.2 ppm CO2 enhancement due to Indian fluxes, we have evaluated the representativeness of satellite observations (GOSAT and OCO‐2) in capturing those enhancements. Both the satellite coverage show a similar number of observations (0.1 per day) during all seasons except for June to August, wherein the cloud screening eliminates almost all the satellite data over the region. Within this areal extent, the satellite XCO2 shows average anomalies of nearly ±2.0 ppm; it is a valuable piece of information because it is well above the retrieval uncertainty, yet capturing the potential enhancement due to fluxes from India. The study implies that the regions of enhancement greater than 0.2 ppm can be considered locations for surface observations representing Indian fluxes. Similarly, the region with enhancement greater than one ppm could be covered by satellites/airborne observations to discern enhancement in the atmospheric CO2 mole fractions due to Indian fluxes.

Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record.

Reducing cloud contamination in aerosol optical depth (AOD) measurements

Abstract. We propose a new cloud screening method for sun photometry that is designed to effectively filter thin clouds. Our method is based on a k-nearest-neighbour algorithm instead of scanning time series of aerosol optical depth. Using 10 years of data from a precision filter radiometer in Innsbruck, we compare our new method and the currently employed screening technique. We exemplify the performance of the two routines in different cloud conditions. While both algorithms agree on the classification of a data point as clear or cloudy in a majority of the cases, the new routine is found to be more effective in flagging thin clouds. We conclude that this simple method can serve as a valid alternative for cloud detection, and we discuss the generalizability to other observation sites.

Comment on “Comparison of Cloud Cover Detection Algorithms on Sentinel-2 Images of the Amazon Tropical Forest”

In their recent study, Sanchez et al. compared various cloud detection methods applied to Sentinel-2, specifically on images acquired over the Amazonian region, known for its frequent cloud cover. Comparison of cloud screening methods for optical satellite images is a complex task, which must take several parameters into account, such as the definition of a cloud, which can differ according to the methods, the different coding of the cloud and shadow masks, the possible dilation of masks, and also the way the method must be used to perform in nominal conditions. We found that the otherwise serious and useful comparison of cloud masks by Sanchez et al. is not fair to the real performances of MAJA cloud detection, for two reasons: (i) two thirds of the images used in the comparison were acquired before the launch of Sentinel-2B satellite, when the revisit of the Sentinel-2 mission was 20 days instead of five days for the nominal conditions of the mission, and (ii) there is an error in the understanding of how MAJA cloud masks are coded which also probably artificially degraded the results of MAJA as compared to the other methods.

Theoretical Uncertainty Analysis of Satellite Retrieved Aerosol Optical Depth Associated with Surface Albedo and Aerosol Optical Properties

Deriving aerosol optical depth (AOD) from space-borne observations is still challenging due to uncertainties associated with sensor calibration drift, cloud screening, aerosol type classification, and surface reflectance characterization. As an initial step to understanding the physical processes impacting these uncertainties in satellite AOD retrievals, this study outlines a theoretical approach to estimate biases in the satellite aerosol retrieval algorithm affected by surface albedo and prescribed aerosol optical properties using a simplified radiative transfer model with a traditional error propagation approach. We expand the critical surface reflectance concept to obtain the critical surface albedo (CSA), critical single scattering albedo (CSSA), and critical asymmetry parameter (CAP). The top-of-atmosphere (TOA) reflectance is not sensitive to significant variability in aerosol loading (AOD) at the critical value; thus, the AOD cannot be determined. Results show that 5% bias in surface albedo (A), single scattering albedo (SSA), or asymmetry parameter (g) lead to large retrieved AOD errors, especially high under conditions when A, SSA, or g are close to their critical values. The results can be useful for future research related to improvements of satellite aerosol retrieval algorithms and provide a preliminary framework to analytically quantify AOD uncertainties from satellite retrievals.

Adapting the Dark Target Algorithm to Advanced MERSI Sensor on the FengYun-3-D Satellite: Retrieval and Validation of Aerosol Optical Depth Over Land

Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> ) and near-infrared ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.13~\mu \text{m}$ </tex-math></inline-formula> ) bands in surface reflectance estimation and the green band ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.55~\mu \text{m}$ </tex-math></inline-formula> ) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.834, which is close to the MODIS EE% of 70.59% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.