Cloud Optical Thickness Research Articles

Glaciation of liquid clouds, snowfall, and reduced cloud cover at industrial aerosol hot spots.

The ability of anthropogenic aerosols to freeze supercooled cloud droplets remains debated. In this work, we present observational evidence for the glaciation of supercooled liquid-water clouds at industrial aerosol hot spots at temperatures between -10° and -24°C. Compared with the nearby liquid-water clouds, shortwave reflectance was reduced by 14% and longwave radiance was increased by 4% in the glaciation-affected regions. There was an 8% reduction in cloud cover and an 18% reduction in cloud optical thickness. Additionally, daily glaciation-induced snowfall accumulations reached 15 millimeters. Glaciation events downwind of industrial aerosol hot spots indicate that anthropogenic aerosols likely serve as ice-nucleating particles. However, rare glaciation events downwind of nuclear power plants indicate that factors other than aerosol emissions may also play a role in the observed glaciation events.

Testing Cloud Adjustment Hypotheses for the Maintenance of Earth's Hemispheric Albedo Symmetry With Natural Experiments

AbstractEarth's Northern and Southern Hemispheres reflect essentially equal amounts of sunlight. How—and whether—this hemispheric albedo symmetry is maintained remains a mystery. We decompose Earth's hemispheric albedo symmetry into components associated with the surface, clear‐sky atmosphere, and different cloud types as defined by cloud effective pressure and optical thickness. Climatologically, greater reflection by the surface, aerosols, and high clouds in the Northern Hemisphere is balanced by greater low and mid‐level cloudiness in the Southern Hemisphere. Both hemispheres have darkened at similar rates over the past two decades; whether the darkening from more rapidly declining aerosol in the Northern Hemisphere is causing a departure from all‐sky symmetry remains uncertain. Natural experiments including long‐term trends, sea ice loss, and volcanic eruptions provide strong evidence against the hypothesis that extratropical low clouds compensate changing clear‐sky asymmetries on annual‐to‐decadal timescales but some evidence that tropical high cloud shifts may do so.

Cloud classification through machine learning and global horizontal irradiance data analysis

AbstractCloud observations and characterization are crucial owing to their influence on energy balance, climate, and weather. Their particular effects on radiation vary depending on different cloud parameters, such as cloud base or top height, water content, and cloud optical thickness, all of them closely related to the specific cloud type. Cloud classification therefore becomes a crucial task in meteorology, although it remains challenging for weather services worldwide owing to the intensive associated labor and cost. In this study we introduce a new low‐cost method for automating cloud classification based on a combination of ground‐based global horizontal irradiance (GHI) measurements, a clear‐sky model, and machine learning. Based on the hypothesis that different cloud types have their own GHI signatures, we trained different supervised learning algorithms using GHI data manually labeled by meteorological observers from time‐synchronized all‐sky images. Multiple time windows were extracted from each GHI series, with eight features defined in each case to characterize the sequence. The best outcome was achieved using an XGBoost model on features extracted on time windows of 33 min, obtaining an accuracy of 0.88 and a Cohen's kappa of 0.84 in a held‐out test set. The development presented in this study has the ability to provide low‐cost cloud classification from ground‐based observations, which is a challenge for weather services worldwide owing to intensive labor and cost.

Vertical Profile Analysis of Cloud Feedbacks

AbstractCloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called “vertical profile analysis of cloud feedback,” which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method.

Satellite measurement data-based assessment of spatiotemporal characteristics of ultraviolet index (UVI) over the state of Johor, Malaysia

The study examines the spatiotemporal and statistical characteristics of the daily ultraviolet index (UVI) over the Johor state of Malaysia. The datasets utilized in this study are the Ozone Monitoring Instrument (OMI)/Aura satellite’s daily UVI observations between October 2004 and March 2023. The innovative trend analysis (ITA) method is employed to identify the statistical trend of daily UVI. Basic statistics of daily, monthly, and seasonal UVI are also carried out to better characterize UVI in the study area. The daily UVI data collected for the analysis exhibit wide variability, with more than 80% of the daily UVI data falling above UVI 8, indicating the severity of UV radiation (UVR) reaching the study area. The monthly and seasonal UVI also display highly varying characteristics, as observed in the daily data. The pixel-based analysis of ITA results indicates a significant increasing and decreasing trend of daily UVI in the region, with values varying between − 9.42 × 10−6 and 3.79 × 10−5. However, for the study area as a whole, UVI shows a significant increasing trend of 4.50 × 10−5. The influence of parameters such as cloud optical thickness (COT), solar zenith angle (SZA), and O3 column on the strength of UVR reaching the study area is also investigated. Based on the results, the study area is characterized by low thickness, broken clouds, and sometimes cloudless conditions. Overall, the results show that UVI in the study area is increasing, and therefore, proper health guidelines and direct UVR exposure regulations need to be implemented to reduce serious health risks associated with UVR exposure.Graphical

Evaluation of the Aggregation Efficiency Modeling at Colder Atmospheric Temperatures in Comparison to Satellite Observations

Abstract Aggregation efficiency in the upper troposphere is highly uncertain because of the lack of laboratory experiments and aircraft measurements, especially at atmospheric temperatures below −30°C. Aggregation is physically broken down into collision and sticking. In this study, theory-based parameterizations for the collision efficiency and sticking efficiency are newly implemented into a double-moment bulk cloud microphysics scheme. Satellite observations of the global ice cloud distribution are used to evaluate the aggregation efficiency modeling. Sensitivity experiments of 9-day global simulations using a high-resolution climate model show that the use of collision efficiency parameterization causes a slight increase in the cloud ice amount above the freezing level over the tropics to midlatitudes and that the use of our new sticking efficiency parameterization causes a significant increase in the cloud ice amount and a slight decrease in the snow amount particularly in the upper troposphere over the tropics. The increase/decrease in the cloud ice/snow amount in the upper troposphere over the tropics is consistent with the vertical profile of radar echoes. Moreover, the ice fraction of the cloud optical thickness is still underestimated worldwide. Finally, the cloud radiative forcing increases over the tropics to reduce the bias in the radiation budget. These results indicate that our new aggregation efficiency modeling reasonably functions even at atmospheric temperatures below −30°C; however, further improvements in the ice cloud modeling are needed. Significance Statement Long-standing biases in the radiative budget in climate models indicate the existence of a missing mechanism to realistically represent the ice cloud growth in the upper troposphere. This study focuses on aggregation efficiency, which has been assumed to be a tuning parameter to optimize the global radiative budget. Therefore, this study employs a theory-based parameterization to calculate the aggregation efficiency. According to the parameterization, aggregation efficiency in high clouds varies by the growth stage of the individual ice particles. As a result, small ice crystals are likely to grow more slowly, and the lifetime of cirrus clouds is prolonged to enhance cloud radiative forcing, particularly over the tropics. These results are promising for reducing the biases observed in climate models.

Effects of increasing spatial resolution on the spatial information content and accuracy of downward surface shortwave radiation

Downward Surface Shortwave Radiation (DSSR) plays a crucial role in ecological, hydrological, and solar photovoltaic studies. The estimation of DSSR at higher spatial resolutions is increasingly popular. However, the impact of increasing spatial resolution on spatial information content and accuracy of DSSR estimates is not well understood. Addressing this knowledge gap, an all-sky DSSR dataset at varying spatial resolutions (0.8° to 0.01°) in China was estimated using MODIS top-of-atmosphere reflectance data. Subsequently, the spatial heterogeneity and accuracy of the DSSR dataset as well as its driving factors were analyzed. The results indicate that the magnitude of the increase in spatial heterogeneity varies across different regions as the spatial resolution increases. On the edge of the Qinghai-Tibet Plateau, the spatial heterogeneity experiences the most significant increase. To quantify the factors driving the spatial heterogeneity of DSSR, the Correlation Coefficients (CC) were calculated between the Coefficient Of Variation (COV) of DSSR and the COVs of relevant factors. In cloudy-sky conditions, the CC between the COV of DSSR and cloud optical thickness ranged from 0.54 to 0.62, whereas the CC between DSSR COV and Column Water Vapor (CWV) COV was between 0.72 and 0.78. Under clear-sky conditions, the COV of CWV, aerosol optical depth, and PM2.5 showed more significant correlations with DSSR COV compared to altitude COV. Validation using ground-based measurements revealed that the accuracy of DSSR decreased at coarser spatial resolutions due to the presence of mixed pixel samples. Moreover, it was demonstrated that a spatial resolution of 0.05° offered the highest cost-effectiveness when estimating DSSR. These insights significantly advance our understanding of the role of spatial resolutions in DSSR estimation and are instrumental in guiding the selection of optimal resolutions for such studies.

Precipitation and cloud Parameters's characteristics of stratus in summer in Ningxia, eastern of northwest China

Stratus is the main artificial seeding target for weather modification in Ningxia. Meteorological satellites can detect macro and microcosmic physical parameters of large-scale stratus. Researching precipitation and cloud parameter characteristics of stratus can provide scientific and technological support for understanding the development and evolution of stratus to identify seeding conditions and effects. Eight typical stratus precipitation events of the summers of 2016–2021 of Ningxia were selected, using the nine point average method, that matched characteristic cloud parameters retrieved by FY-2G satellite with precipitation classification data of 25 national meteorological observation stations in Ningxia. These stratus precipitation characteristics were correlated with cloud parameters. The results indicate that precipitation decreased rapidly from the southeast to the northwest of Ningxia. In the synoptic background of precipitation, the cloud top height, super-cooled layer thickness, cloud optical thickness, cloud particle effective radius, and cloud liquid water content were all larger than those in the synoptic background of no precipitation. The spatial distribution feature manifests as gradually increased from southeast to northwest. The cloud top temperature and cloud blackbody temperature were smaller than those in the synoptic background of no precipitation. The cloud top temperature was near the cloud “seeding temperature window”, and the depth of the super-cooled layer was thick. This was in line with the “Seeder-Feeder” structure characteristic of cold stratus.

CloudRoots-Amazon22: Integrating Clouds with Photosynthesis by Crossing Scales

Abstract How are rain forest photosynthesis and turbulent fluxes influenced by clouds? To what extent are clouds affected by local processes driven by rain forest energy, water, and carbon fluxes? These interrelated questions were the main drivers of the intensive field experiment CloudRoots-Amazon22 which took place at the Amazon Tall Tower Observatory (ATTO)/Campina supersites in the Amazon rain forest during the dry season, in August 2022. CloudRoots-Amazon22 collected observational data to derive cause–effect relationships between processes occurring at the leaf level up to canopy scales in relation to the diurnal evolution of the clear-to-cloudy transition. First, we studied the impact of cloud and canopy radiation perturbations on the subdiurnal variability of stomatal conductance. Stoma opening is larger in the morning, modulated by the cloud optical thickness. Second, we combined 1-Hz frequency measurements of the stable isotopologues of carbon dioxide and water vapor with measurements of turbulence to determine carbon dioxide and water vapor sources and sinks within the canopy. Using scintillometer observations, we inferred 1-min sensible heat flux that responded within minutes to the cloud passages. Third, collocated profiles of state variables and greenhouse gases enabled us to determine the role of clouds in vertical transport. We then inferred, using canopy and upper-atmospheric observations and a parameterization, the cloud cover and cloud mass flux to establish causality between canopy and cloud processes. This shows the need for a comprehensive observational set to improve weather and climate model representations. Our findings contribute to advance our knowledge of the coupling between cloudy boundary layers and primary carbon productivity of the Amazon rain forest.

Global cloud optical depth daily variability based on DSCOVR/ EPIC observations

When investigating the potential effects of cloud changes on climate, the interday and intraday variabilities should be distinguished. Historically, studies have focused on the long-term cloud changes, and the intraday cycles of cloud properties have been mostly ignored partly owing to the limited availability of global datasets to study higher frequency variabilities. In this regard, DSCOVR’s vantage point at the Lagrange L1 point overcomes the temporal limitations of polar orbiters as well as the limited spatial views of geostationary satellites, allowing characterization of the daytime variability of cloud properties using a single sensor. In previous analyses, we used DSCOVR’s EPIC instrument to characterize the diurnal cycles of cloud height and cloud fraction; here, we expand on previous studies to additionally investigate the variability of cloud optical thickness. We observe a recurring diurnal pattern of cloud optical thicknesses for different latitudinal zones that reaches a maximum around noon regardless of the underlying surface. Once we separate the clouds based on their optical thickness into optically thin (0–3), intermediate (3–10), and thick (10–25), we find that these cloud classes follow different optical thickness diurnal cycles on the global scale. We further explore these differences by combining the evolution of cloud fraction and optical thickness of each group to obtain the diurnal evolution of cloud-fraction-weighted optical depths.

Correction of cloud optical thickness retrievals from nadir reflectances in the presence of 3D radiative effects. Part I: concept and tests on 3D RT simulations

3D effects cause substantial underestimation of cloud optical thickness (COT) in airborne and satellite retrievals based on 1D radiative transfer computations (such as in the case of widely used bispectral technique). For a single-layer isolated cloud we propose a simple linear correction of the retrieved COT with the renormalization factor dependent on the cloud’s aspect ratio (the ratio between vertical and horizontal dimensions of the cloud). This is an empirical assumption which we successfully test using synthetic 3D RT data. We introduce a heuristic “block model” of 3D radiative effects and show that the functional form of the renormalization factor is consistent with the process of radiation escape from cloud sides in an essentially 3D geometry. We also extend the block model to the case of single-layer broken cloud field with radiative interaction between the neighboring clouds. In this case the renormalization factor depends also on the distance between clouds.

Aerosol-induced closure of marine cloud cells: enhanced effects in the presence of precipitation

Abstract. The Weather Research Forecasting (WRF) version 4.3 model is configured within a Lagrangian framework to quantify the impact of aerosols on evolving cloud fields. Kilometer-scale simulations utilizing meteorological boundary conditions are based on 10 case study days offering diverse meteorology during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA). Measurements from aircraft, the ground-based Atmosphere Radiation Measurement (ARM) site at Graciosa Island in the Azores, and A-Train and geostationary satellites are utilized for validation, demonstrating good agreement with the WRF-simulated cloud and aerosol properties. Higher aerosol concentration leads to suppressed drizzle and increased cloud water content in all case study days. These changes lead to larger radiative cooling rates at cloud top, enhanced vertical velocity variance, and increased vertical and horizontal wind speed near the base of the lower-tropospheric inversion. As a result, marine cloud cell area expands, narrowing the gap between shallow clouds and increasing cloud optical thickness, liquid water content, and the top-of-atmosphere outgoing shortwave flux. While similar aerosol effects are observed in lightly to non-raining clouds, they tend to be smaller by comparison. These simulations show a relationship between cloud cell area expansion and the radiative adjustments caused by liquid water path and cloud fraction changes. The adjustments are positive and scale as 74 % and 51 %, respectively, relative to the Twomey effect. While higher-resolution large-eddy simulations may provide improved representation of cloud-top mixing processes, these results emphasize the importance of addressing mesoscale cloud-state transitions in the quantification of aerosol radiative forcing that cannot be attained from traditional climate models.

Statistical Downscaling of Remote Sensing Precipitation Estimates Using MODIS Cloud Properties Data over Northeastern Greece

The aim of this study is to spatially downscale the daily precipitation data from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG), utilizing cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Cloud optical thickness (COT), cloud effective radius (CER), and cloud water path (CWP) are used to statistically downscale IMERG precipitation estimates from 0.1 to 0.01° spatial resolution, using the Multivariate Linear Regression (MLR) and residual correction methods. The downscaled precipitation estimates were subsequently validated using in situ rain gauge measurements. The residual corrected IMERG downscaled precipitation estimates were found to be more accurate than the downscaled predicted precipitation without the implementation of the residual correction algorithm (up to 37%), with a respective decrease of the Root Mean Square Error (RMSE) (up to 75%), Normalized Root Mean Square Error (NRMSE) (up to 79%), and the Percent Bias (PB) (up to 98%). In addition, the final downscaled product after the MLR method implementation with residual correction was better correlated with the rain gauge observations than the initial IMERG product (up to 20%). Thus, the implementation of the MLR method in conjunction with the residual correction algorithm is an efficient tool for downscaling remote sensing products with a coarse spatial resolution.

On the retrieval of cloud optical thickness from spectral radiances - A sensitivity study with high albedo surfaces

Measurements of spectral zenith radiance in the 320–950 nm wavelength range have been carried out since 2021 at the Thule High Arctic Atmospheric Observatory (THAAO, https://www.thuleatmos-it.it/, 76.5° N, 68.8° W, 225 m a.s.l) located in Pituffik, northern Greenland. This study evaluates whether such observations could provide, in principle, scientifically meaningful cloud optical depth (τ) estimates, what would be the limitations, which are the necessary auxiliary measurements, and which radiance wavelengths would be better suited for the goal. Although clouds play a critical role in the Arctic, a climatology of τ in high albedo conditions is particularly difficult to obtain. THAAO might have the instrument capabilities to provide such a long-term dataset. We use a radiative transfer package to simulate visible spectra with different cloud and surface conditions, assuming homogeneous overcast sky and liquid water clouds of fixed geometrical thickness. Simulations are run to reproduce typical conditions encountered at THAAO when measurements are carried out. We find that the assumption of a broadband albedo instead of a spectrally-resolved one is the source of the largest uncertainties. Tests on size and phase of cloud particles showed that a 50% uncertainty in reff leads to a ∼10% error in τ, and that a 10% contamination of ice crystals in a low-level liquid water cloud leads to an error in τ estimates of less than 5%. All the tests showed that the most critical τ range is between the thin and thick cloud regimes (τ ∼ 7–15), where the retrievals can be less reliable. Otherwise, tests suggest that in the environmental conditions that characterize late spring and summer at THAAO, and given the observatory measurements capabilities, estimates of τ for low-level clouds could be accurately retrieved both in high and low surface albedo conditions by means of zenith radiance measurements in the UV-Vis-NIR range.

FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm

FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm

Optimized strategies of cloud droplet distribution retrieval using satellite multi-directional polarimetric optical measurements: information content approach

Multi-directional polarized optical sensors are increasingly vital in passive remote sensing, deepening our understanding of global cloud properties. Nevertheless, uncertainty lingers on how these observations can contribute to our knowledge of cloud diversity. The variability in cloud PSD (Particle Size Distribution) significantly influences a wide array of cloud characteristics, while unidentified factors in RT (Radiative Transfer) may introduce errors into the cloud PSD retrieval algorithm. Therefore, establishing unified evaluation criteria for both optical device configuration and inversion methods is crucial. Our study, based on Bayesian theory and RT, assesses the information content of both cloud effective radius and effective variance retrieval, along with the key factors affecting their retrieval in multi-directional polarized observations, using the calculation of DFS (Degree of Freedom for Signals).We consider the process of solar incidence, cloud scattering, and sensor reception, and discuss the impact of various sensor configurations, cloud characteristics, and other components on the retrieval of cloud PSD. Correspondingly, we observed a 48% improvement in the information content of cloud PSD with the incorporation of multi-directional polarized measurements in the rainbow region. Cloud droplet concentration significantly influences inversion, but its PSD does not cause monotonic linear interference on information content. The blending of particle mixtures with different PSD has a significant negative impact on DFS. In cases where the AOD (Aerosol Optical Depth) is less than 0.5 and the COT (Cloud Optical Thickness) exceeds 5, the influence of aerosol and surface contributions on inversion can be neglected. Our findings would serve as a foundation for future instrument design improvements and enhancements to retrieval algorithms.

COMPARATIVE ANALYSIS OF CLOT-ADJUSTED AHI-8/9 AND FY-4A SOLAR RADIATION DATA FOR SOLAR PV POWER POTENTIAL ASSESSMENT IN THE PHILIPPINES

Abstract. The theoretical solar photovoltaic (PV) power (PPV) potential in the Philippines has been estimated to range from 1.87 to 3.19 kWh/kWp daily and 681.80 to 1,162.58 kWh/kWp yearly using the shortwave radiation (SWR) product derived from the Advanced Himawari Imager-8 and 9 (AHI-8/9) satellite data. However, solar radiation measurements, especially for satellite-derived data, must be accurate when used to estimate the maximum solar energy available for solar PV power production. The objective of this study is to evaluate the satellite-derived measurements of AHI-8/9 SWR and Fengyun 4A surface solar irradiance (SSI) data products in the estimation of theoretical PPV output in the Philippines. Moreover, selected actual solar PV installation sites located across the Philippines, such as in the cities of Baguio, Cebu, and Tagum, are included in the analysis PV power production data. Both SWR and SSI products were adjusted using the cloud optical thickness (CLOT) product for cloud effects and consequently, obtain the maximum solar PV power potential. In this study, SWR and SSI products were analyzed to determine which factors may have contributed to the differences in the estimated values. The in-situ PV power production data was compared to the two satellite-based solar radiation data to assess the degree of correlation between the two. Diurnal trends for representative areas in Luzon, Visayas and Mindanao show that for all sites, the SWR data agrees with the actual PV production data better than the SSI data. Results show that the range of CLOT-adjusted solar irradiance values are higher when using SSI than SWR. It was also observed that CLOT reduces the solar radiation by an average of 19.36%, 16.08% and 10.54% during the cool dry season (December to January), rainy season (June to November) and hot dry season (March to May), respectively. In future studies, the adjusted solar radiation (R') data may be used instead of the raw AHI8 SWR or FY-4A SSI to estimate the theoretical maximum solar PV power potential (PPV). Geomorphological and meteorological factors can also be considered in the estimation of effective PPV.

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

AbstractThe surface temperature‐mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low‐ and mid‐level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm‐track clouds to thinner oceanic low‐level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid‐ and low‐level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

Creating and Leveraging a Synthetic Dataset of Cloud Optical Thickness Measures for Cloud Detection in MSI

Cloud formations often obscure optical satellite-based monitoring of the Earth’s surface, thus limiting Earth observation (EO) activities such as land cover mapping, ocean color analysis, and cropland monitoring. The integration of machine learning (ML) methods within the remote sensing domain has significantly improved performance for a wide range of EO tasks, including cloud detection and filtering, but there is still much room for improvement. A key bottleneck is that ML methods typically depend on large amounts of annotated data for training, which are often difficult to come by in EO contexts. This is especially true when it comes to cloud optical thickness (COT) estimation. A reliable estimation of COT enables more fine-grained and application-dependent control compared to using pre-specified cloud categories, as is common practice. To alleviate the COT data scarcity problem, in this work, we propose a novel synthetic dataset for COT estimation, which we subsequently leverage for obtaining reliable and versatile cloud masks on real data. In our dataset, top-of-atmosphere radiances have been simulated for 12 of the spectral bands of the Multispectral Imagery (MSI) sensor onboard Sentinel-2 platforms. These data points have been simulated under consideration of different cloud types, COTs, and ground surface and atmospheric profiles. Extensive experimentation of training several ML models to predict COT from the measured reflectivity of the spectral bands demonstrates the usefulness of our proposed dataset. In particular, by thresholding COT estimates from our ML models, we show on two satellite image datasets (one that is publicly available, and one which we have collected and annotated) that reliable cloud masks can be obtained. The synthetic data, the newly collected real dataset, code and models have been made publicly available.

Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products

Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is the first mission that will provide measurements from active profiling, passive imaging and a broadband radiometer from a single satellite platform. The passive multi-spectral imager (MSI) features four solar and three thermal infrared channels, and has a swath of 150 km and a spatial pixel resolution of 500 m. The MSI observations will provide across-track information on clouds and aerosol to extend the active profiling information into the swath. In this paper, we present the algorithm used for retrieving the cloud optical and physical products (M-COP), specifically cloud optical thickness, effective radius and top height. The algorithm is based on the solar and terrestrial MSI channels within an optimal estimation framework. This framework enables full error propagation given by the uncertainties in measurements and a priori information. The MSI cloud algorithm has been successfully exercised on different imagers and on synthetically generated MSI observations.