Cloud Screening Algorithm Research Articles

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.

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.

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.

On the Misclassification of Dust as Cloud at an AERONET Site in the Sonoran Desert

Abstract Here we present retrievals of aerosol optical depth τ from an Aerosol Robotic Network (AERONET) station in the southeastern corner of California, an area where dust storms are frequent. By combining AERONET data with collocated ceilometer measurements, camera imagery, and satellite data, we show that during significant dust outbreaks the AERONET cloud-screening algorithm oftentimes classifies dusty measurements as cloud contaminated, thus removing them from the aerosol record. During dust storms we estimate that approximately 85% of all dusty retrievals of τ and more than 95% of retrievals when τ > 0.1 are rejected, resulting in a factor-of-2 reduction in dust-storm averaged τ. We document the specific components in the screening algorithm responsible for the misclassification. We find that a major reason for the loss of these dusty measurements is the high temporal variability in τ during the passage of dust storms over the site, which itself is related to the proximity of the site to the locations of emission. We describe a method to recover these dusty measurements that is based on collocated ceilometer measurements. These results suggest that AERONET sites that are located close to dust source regions may require ancillary measurements to aid in the identification of dust. Significance Statement In this study we demonstrate that, during dust storms, measurements made with a sun photometer at an AERONET site in the western Sonoran Desert are frequently classified as cloud contaminated by the network’s processing algorithm. We identify the various algorithmic tests that result in the misclassification and discuss the physical reasons why dust typically fails those tests. We then present a method to restore these data that utilizes measurements from a collocated ceilometer. This work highlights the challenges, and one solution, to operating an AERONET site in a region that is close to the sources of airborne dust.

OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors

We introduce a new platform, Ocean Color - Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART), for analysis of data obtained by satellite ocean color sensors. OC-SMART is a multi-sensor data analysis platform which supports heritage, current, and possible future multi-spectral and hyper-spectral sensors from US, EU, Korea, Japan, and China, including SeaWiFS, Aqua/MODIS, SNPP/VIIRS, ISS/HICO, Landsat8/OLI, DSCOVR/EPIC, Sentinel-2/MSI, Sentinel-3/OLCI, COMS/GOCI, GCOM-C/SGLI and FengYun-3D/MERSI2. The products provided by OC-SMART include spectral normalized remote sensing reflectances (Rrs values), chlorophyll_a (CHL) concentrations, and spectral in-water inherent optical properties (IOPs) including absorption coefficients due to phytoplankton (aph), absorption coefficients due to detritus and Gelbstoff (adg) and backscattering coefficients due to particulates (bbp). Spectral aerosol optical depths (AODs) and cloud mask results are also provided by OC-SMART.The goal of OC-SMART is to improve the quality of global ocean color products retrieved from satellite sensors, especially under complex environmental conditions, such as coastal/inland turbid water areas and heavy aerosol loadings. Therefore, the atmospheric correction (AC) and ocean IOP algorithms in OC-SMART are driven by extensive radiative transfer (RT) simulations in conjunction with powerful machine learning techniques.To simulate top of the atmosphere (TOA) radiances, we solve the radiative transfer equation pertinent for the coupled atmosphere-ocean system. For each sensor, we have created about 13 million RT simulations and comprehensive training datasets to support the development of the machine learning AC and in-water IOP algorithms. The results, as demonstrated in this paper, are very promising. Not only does OC-SMART improve the quality of the retrieved water products, it also resolves the negative water-leaving radiance problem that has plagued heritage AC algorithms. The comprehensive training datasets created using multiple atmosphere, aerosol, and ocean IOP models ensure global and generic applicability of OC-SMART.The use of machine learning algorithms makes OC-SMART roughly 10 times faster than NASA's SeaDAS platform. OC-SMART also includes an advanced cloud screening algorithm and is resilient to the contamination by weak to moderate sunglint and cloud edges. It is therefore capable of recovering large amounts of data that are discarded by other algorithms (such as those implemented in NASA's SeaDAS package), especially in coastal areas. OC-SMART is currently available as a standalone Python package or as a plugin that can be installed in ESA's Sentinel Application Platform (SNAP).

Daytime and nighttime aerosol optical depth implementation in CÆLIS

Abstract. The University of Valladolid (UVa, Spain) has managed a calibration center of the AErosol RObotic NETwork (AERONET) since 2006. The CÆLIS software tool, developed by UVa, was created to manage the data generated by AERONET photometers for calibration, quality control and data processing purposes. This paper exploits the potential of this tool in order to obtain products like the aerosol optical depth (AOD) and Ångström exponent (AE), which are of high interest for atmospheric and climate studies, as well as to enhance the quality control of the instruments and data managed by CÆLIS. The AOD and cloud screening algorithms implemented in CÆLIS, both based on AERONET version 3, are described in detail. The obtained products are compared with the AERONET database. In general, the differences in daytime AOD between CÆLIS and AERONET are far below the expected uncertainty of the instrument, ranging in mean differences between -1.3×10-4 at 870 nm and 6.2×10-4 at 380 nm. The standard deviations of the differences range from 2.8×10-4 at 675 nm to 8.1×10-4 at 340 nm. The AOD and AE at nighttime calculated by CÆLIS from Moon observations are also presented, showing good continuity between day and nighttime for different locations, aerosol loads and Moon phase angles. Regarding cloud screening, around 99.9 % of the observations classified as cloud-free by CÆLIS are also assumed cloud-free by AERONET; this percentage is similar for the cases considered cloud-contaminated by both databases. The obtained results point out the capability of CÆLIS as a processing system. The AOD algorithm provides the opportunity to use this tool with other instrument types and to retrieve other aerosol products in the future.

Influence of cloud, fog, and high relative humidity during pollution transport events in South Korea: Aerosol properties and PM2.5 variability

Abstract This investigation examines aerosol dynamics during major fine mode aerosol transboundary pollution events in South Korea primarily during the KORUS-AQ campaign from May 1 – June 10, 2016, particularly when cloud fraction was high and/or fog was present to quantify the change in aerosol characteristics due to near-cloud or fog interaction. We analyze the new AERONET Version 3 data that have significant changes to cloud screening algorithms, allowing many more fine-mode observations in the near vicinity of clouds or fog. Case studies for detailed investigation include May 25–26, 2016 when cloud fraction was high over much of the peninsula, associated with a weak frontal passage and advection of pollution from China. These cloud-influenced Chinese transport dates also had the highest aerosol optical depth (AOD), surface PM2.5 concentrations and fine mode particle sizes of the entire campaign. Another likewise cloud/high relative humidity (RH) case is June 9 and 10, 2016 when fog was present over the Yellow Sea that appears to have affected aerosol properties well downwind over the Korean peninsula. In comparison we also investigated aerosol properties on air stagnation days with very low cloud cover and relatively low RH (May 17 & 18, 2016), when local Korean emissions dominated. Aerosol volume size distributions show marked differences between the transport days (with high RH and cloud influences) and the local pollution stagnation days, with total column-integrated particle fine mode volume being an order of magnitude greater on the pollution transport dates. The PM2.5 over central Seoul were significantly greater than for coastal sites on the transboundary transport days yet not on stagnation days, suggesting additional particle formation from gaseous urban emissions in cloud/fog droplets and/or in the high RH humidified aerosol environment. Many days had KORUS-AQ research aircraft flights that provided observations of aerosol absorption, particle chemistry and vertical profiles of extinction. AERONET retrievals and aircraft in situ measurements both showed high single scattering albedo (weak absorption) on the cloudy or cloud influenced days, plus aircraft profile in situ measurements showed large AOD enhancements (versus dried aerosol) at ambient relative humidity (RH) on the pollution transport days, consistent with the significantly larger fine mode particle radii and weak absorption.

Development of a cloud-screening algorithm for direct and diffuse AODs from the Skyradiometer Network

We developed a cloud-screening algorithm for direct and diffuse aerosol optical depths (AODs) from the Skyradiometer Network (CSDD). Variabilities of direct AODs were checked not only to screen cloud-contaminated (cloudy) data effectively but also to make direct AODs available for aerosol study, as in the Aerosol Robotic Network (AERONET). Skyradiometer data were used from the Seoul National University (SNU) site for three years from 2012 to 2014. CSDD tested the spectral and temporal variabilities according to the Ångström exponent at the first stage, and the temporal smoothness in the form of the coefficient of variation at the second stage. The algorithm for CSDD was constructed to minimize the differences with optical properties of cloudy data (and clear-sky data as well) based on the cloud cover from the synoptic station. A number of cloudy data that was not screened by the previous algorithms was removed, and the absolute value of the bias total could be substantially lowered. The performances of algorithms for cloud detection were also examined using lidar observations at the study site in terms of accuracy, probability of detection (POD), and false detection rate. The statistics of cloud detection for CSDD were generally comparable to those of AERONET for direct data, and the POD for diffuse data was improved to the level of direct data.

Optimization of the OCO-2 Cloud Screening Algorithm and Evaluation against MODIS and TCCON Measurements over Land Surfaces in Europe and Japan

A method to tighten the cloud screening thresholds based on local conditions is used to provide more stringent schemes for Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms. Cloud screening strategies are essential to remove scenes with significant cloud and/or aerosol contamination from OCO-2 observations, which helps to save on the data processing cost and ensure high quality retrievals of the column-averaged CO2 dry air mole fraction (XCO2). Based on the radiance measurements in the 0.76 μm O2A band, 1.61 μm (weak), and 2.06 μm (strong) CO2 bands, the current combination of the A-Band Preprocessor (ABP) algorithm and Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) algorithm passes around 20%–25% of all soundings, which means that some contaminated scenes also pass the screening process. In this work, three independent pairs of threshold parameters used in the ABP and IDP algorithms are sufficiently tuned until the overall pass rate is close to the monthly clear-sky fraction from the MODIS cloud mask. The tightened thresholds are applied to observations over land surfaces in Europe and Japan in 2016. The results show improvement of agreement and positive predictive value compared to the collocated MODIS cloud mask, especially in summer and fall. In addition, analysis indicates that XCO2 retrievals with more stringent thresholds are in closer agreement with measurements from collocated Total Carbon Column Observing Network (TCCON) sites.

Assessing the impact of clouds on ground-based UV–visible total column ozone measurements in the high Arctic

Abstract. Zenith-Sky scattered light Differential Optical Absorption Spectroscopy (ZS-DOAS) has been used widely to retrieve total column ozone (TCO). ZS-DOAS measurements have the advantage of being less sensitive to clouds than direct-sun measurements. However, the presence of clouds still affects the quality of ZS-DOAS TCO. Clouds are thought to be the largest contributor to random uncertainty in ZS-DOAS TCO, but their impact on data quality still needs to be quantified. This study has two goals: (1) to investigate whether clouds have a significant impact on ZS-DOAS TCO, and (2) to develop a cloud-screening algorithm to improve ZS-DOAS measurements in the Arctic under cloudy conditions. To quantify the impact of weather, 8 years of measured and modelled TCO have been used, along with information about weather conditions at Eureka, Canada (80.05∘ N, 86.41∘ W). Relative to direct-sun TCO measurements by Brewer spectrophotometers and modelled TCO, a positive bias is found in ZS-DOAS TCO measured in cloudy weather, and a negative bias is found for clear conditions, with differences of up to 5 % between clear and cloudy conditions. A cloud-screening algorithm is developed for high latitudes using the colour index calculated from ZS-DOAS spectra. The quality of ZS-DOAS TCO datasets is assessed using a statistical uncertainty estimation model, which suggests a 3 %–4 % random uncertainty. The new cloud-screening algorithm reduces the random uncertainty by 0.6 %. If all measurements collected during cloudy conditions, as identified using the weather station observations, are removed, the random uncertainty is reduced by 1.3 %. This work demonstrates that clouds are a significant contributor to uncertainty in ZS-DOAS TCO and proposes a method that can be used to screen clouds in high-latitude spectra.

Implications of Pixel Quality Flags on the Observation Density of a Continental Landsat Archive

Pixel quality (PQ) products delivered with Analysis Ready Data (ARD) provide users with information about the conditions of the surface, atmosphere, and sensor at the time of acquisition. Knowing whether an observation was affected by clouds or sensor saturation is crucial when selecting data to include in automated analysis, as imperfect or erroneous observations are undesirable for most applications. There is, however, a certain rate of commission error in cloud detection, and saturation may not affect all spectral bands at a time, which can lead to suitable observations being excluded. This can have a substantial impact on the amount of data available for analysis. To understand how different surface types can affect cloud commission and saturation, we analyzed cloud and per-band saturation PQ flags for 31 years of Landsat data within Digital Earth Australia. Areas showing substantial reduction in observation density compared to their surroundings were investigated to characterize how specific surface types impact on the temporal density of observations deemed desirable. Using Fmask 3.2 by way of example, our approach demonstrates a method that can be applied to summarize the characteristics of cloud-screening algorithms and sensor saturation. Results indicate that cloud commission and sensor saturation rates show specific characteristics depending on the targets under observation. This potentially leads to an imbalance in data availability driven by surface type in a given study area. Based on our findings, the level of detail in PQ flags delivered with ARD is pivotal in maximizing the potential of EO data.

Observations of the Interaction and Transport of Fine Mode Aerosols with Cloud and/or Fog in Northeast Asia from Aerosol Robotic Network (AERONET) and Satellite Remote Sensing.

Analysis of sun photometer measured and satellite retrieved aerosol optical depth (AOD) data has shown that major aerosol pollution events with very high fine mode AOD (>1.0 in mid-visible) in the China/Korea/Japan region are often observed to be associated with significant cloud cover. This makes remote sensing of these events difficult even for high temporal resolution sun photometer measurements. Possible physical mechanisms for these events that have high AOD include a combination of aerosol humidification, cloud processing, and meteorological co-variation with atmospheric stability and convergence. The new development of Aerosol Robotic network (AERONET) Version 3 Level 2 AOD with improved cloud screening algorithms now allow for unprecedented ability to monitor these extreme fine mode pollution events. Further, the Spectral Deconvolution Algorithm (SDA) applied to Level 1 data (L1; no cloud screening) provides an even more comprehensive assessment of fine mode AOD than L2 in current and previous data versions. Studying the 2012 winter-summer period, comparisons of AERONET L1 SDA daily average fine mode AOD data showed that Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing of AOD often did not retrieve and/or identify some of the highest fine mode AOD events in this region. Also, compared to models that include data assimilation of satellite retrieved AOD, the L1 SDA fine mode AOD was significantly higher in magnitude, particularly for the highest AOD events that were often associated with significant cloudiness.

Intercomparison of CALIOP, MODIS, and AERONET aerosol optical depth over China during the past decade

ABSTRACTAerosol optical depth (AOD) data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were intercompared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as space-based Moderate Resolution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2015. This article aims to evaluate CALIOP daytime AOD using MODIS and AERONET AODs. Comparing the AOD between CALIOP and AERONET in different regions over China using quality control flags to screen the AOD data, we find that CALIOP AOD is generally lower than AERONET AOD especially at optical depths over 0.4 likely due to differences in the cloud screening algorithms and general retrieval uncertainty. Comparison between CALIOP AOD and MODIS AOD results show that the overall spatio-temporal distribution of CALIOP AOD and MODIS AOD is basically consistent. As for the spatial distribution, both data sets show several high-value regions and low-value regions in China. CALIOP is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data sets show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. A long-term linear trend analysis based on the domain averaged monthly mean CALIOP and MODIS AOD shows agreement among CALIOP and MODIS for the trends over the 10-year period in four regions examined. The trends in AOD derived from CALIOP and MODIS indicate a decline in aerosol loading in China since 2006. It is found from frequency comparison that CALIOP and MODIS AOD generally exhibit a degree of correlation over China. Statistical frequency analysis shows that CALIOP AOD frequency distribution shows a higher peak than MODIS AOD when AOD < 0.4. For the most part, mean MODIS AOD is higher than mean CALIOP AOD. Evaluation of CALIOP AOD retrievals provides the prospect for application of CALIOP data. The intercomparison suggests that CALIOP has systematically underestimated daytime AOD retrievals, especially deteriorating with increasing AOD, and therefore, CALIOP daytime AOD retrievals should be treated with some degree of caution when the AOD is over 0.4.

Langley Calibration of Sunphotometer using Perez’s Clearness Index at Tropical Climate

In the tropics, Langley calibration is often complicated by abundant cloud cover. The lack of an objective and robust cloud screening algorithm in Langley calibration is often problematic, especially for tropical climate sites where short, thin cirrus clouds are regular and abundant. Errors in this case could be misleading and undetectable unless one scrutinizes the performance of the best fitted line on the Langley regression individually. In this work, we introduce a new method to improve the sun photometer calibration past the Langley uncertainty over a tropical climate. A total of 20 Langley plots were collected using a portable spectrometer over a mid-altitude (1,574 m a.s.l.) tropical site at Kinabalu Park, Sabah. Data collected were daily added to Langley plots, and the characteristics of each Langley plot were carefully examined. Our results show that a gradual evolution pattern of the calculated Perez index in a time-series was observable for a good Langley plot, but days with poor Langley data basically demonstrated the opposite behavior. Taking advantage of this fact, the possibly contaminated data points were filtered by calculating the Perez derivative of each distinct air mass until a negative value was obtained. Any points that exhibited a negative derivative were considered bad data and discarded from the Langley regression. The implementation was completely automated and objective, rendering qualitative observation no longer necessary. The improved Langley plot exhibits significant improvement in addressing higher values for correlation, R, and lower values for aerosol optical depth, τa. The proposed method is sensitive enough to identify the occurrence of very short and thin cirrus clouds and is particularly useful for sun-photometer calibration over a tropical climate.

Development of a Quality Control and Cloud-screening Algorithm for Sun Photometry Langley Calibration

Development of a Quality Control and Cloud-screening Algorithm for Sun Photometry Langley Calibration

The thin border between cloud and aerosol: Sensitivity of several ground based observation techniques

Cloud and aerosol are two manifestations of what it is essentially the same physical phenomenon: a suspension of particles in the air. The differences between the two come from the different composition (e.g., much higher amount of condensed water in particles constituting a cloud) and/or particle size, and also from the different number of such particles (10–10,000 particles per cubic centimeter depending on conditions). However, there exist situations in which the distinction is far from obvious, and even when broken or scattered clouds are present in the sky, the borders between cloud/not cloud are not always well defined, a transition area that has been coined as the “twilight zone”. The current paper presents a discussion on the definition of cloud and aerosol, the need for distinguishing or for considering the continuum between the two, and suggests a quantification of the importance and frequency of such ambiguous situations, founded on several ground-based observing techniques. Specifically, sensitivity analyses are applied on sky camera images and broadband and spectral radiometric measurements taken at Girona (Spain) and Boulder (Co, USA). Results indicate that, at these sites, in more than 5% of the daytime hours the sky may be considered cloudless (but containing aerosols) or cloudy (with some kind of optically thin clouds) depending on the observing system and the thresholds applied. Similarly, at least 10% of the time the extension of scattered or broken clouds into clear areas is problematic to establish, and depends on where the limit is put between cloud and aerosol. These findings are relevant to both technical approaches for cloud screening and sky cover categorization algorithms and radiative transfer studies, given the different effect of clouds and aerosols (and the different treatment in models) on the Earth's radiation balance.

Desarrollo de productos avanzados para la misión SEOSAT/Ingenio

&lt;p align="justify"&gt;SEOSAT/Ingenio is the future Spanish Earth Observation high spatial resolution mission in the optical domain. While Level 1 products, at-sensor geo-referenced radiances, are in an advanced phase of development under the framework of an industrial contractor, Level 2 products must be developed by the users. This fact limits the use of the satellite images only to the scientific community, restricting their use in other applications. The need to alleviate this limitation motivated this work, developed under the framework of a coordinate project, which aimed at offering a list of Level2 products to the Ingenio/SEOSAT user community. In this paper, we present the different methodologies developed to produce the proposed Level2 products, from surface reflectance at nominal sensor spatial resolution to images with higher spatial resolution or the possibility to create spatial and temporal mosaics. On the one side, for the surface reflectance product, we proposed an atmospheric correction algorithm based on using the spatial information, linked to a cloud screening algorithm and including morphological and topographic shadow corrections. On the other side, to enhance the image spatial resolution, we applied different fusion techniques using the multispectral and the panchromatic band, as well as some of the so-called “super-resolution” techniques. Finally, we provided different tools to develop spatial mosaics and temporal composites, directed to users interested on the exploitation of the Ingenio/ SEOSAT images.&lt;/p&gt;

A cloud detection scheme for the Chinese Carbon Dioxide Observation Satellite (TANSAT)

Cloud detection is an essential preprocessing step for retrieving carbon dioxide from satellite observations of reflected sunlight. During the pre-launch study of the Chinese Carbon Dioxide Observation Satellite (TANSAT), a cloud-screening scheme was presented for the Cloud and Aerosol Polarization Imager (CAPI), which only performs measurements in five channels located in the visible to near-infrared regions of the spectrum. The scheme for CAPI, based on previous cloudscreening algorithms, defines a method to regroup individual threshold tests for each pixel in a scene according to the derived clear confidence level. This scheme is proven to be more effective for sensors with few channels. The work relies upon the radiance data from the Visible and Infrared Radiometer (VIRR) onboard the Chinese FengYun-3A Polar-orbiting Meteorological Satellite (FY-3A), which uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements. The scheme has been applied to a number of the VIRR scenes over four target areas (desert, snow, ocean, forest) for all seasons. To assess the screening results, comparisons against the cloud-screening product from MODIS are made. The evaluation suggests that the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results. A seasonal analysis reveals that this scheme provides better performance during warmer seasons, except for observations over oceans, where results are much better in colder seasons.

Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data

Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O2 A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 µm (weak CO2 band) and 2.06 µm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set.To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of ≃ 20–25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations.No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

Estimating bias in the OCO-2 retrieval algorithm caused by 3-D radiation scattering from unresolved boundary layer clouds

Abstract. Due to the complexity of the multiple scattering problem for shortwave radiative transfer in Earth's atmosphere, operational physical retrieval algorithms commonly use a plane parallel radiative transfer model (RTM). This so-called one-dimensional (1-D) assumption allows practical retrieval algorithms to be implemented. In order to understand the impacts of this assumption for low altitude, unresolved clouds observed by OCO-2, the three-dimensional (3-D) radiative transfer model SHDOM is used to generate synthetic observations which are then processed by the operational retrieval algorithm based on a 1-D RTM. Simulations are performed over three realistic surface spectral albedos, corresponding to snow, vegetation, and bare soil. The results show that the existing cloud screening algorithm has difficulty identifying sub-field of view (FOV), unresolved clouds that fill less than half of the FOV. The unresolved clouds introduce a bias in the retrieved CO2 concentration, as quantified by the dry air mole fraction (XCO2). The biases are relatively small (less than 1 ppm) when the albedo at 2.1 μm is high, which is common over bare land surfaces. For cases with low 2.1 μm albedo, such as snow, the bias becomes much larger, up to 5 ppm. These results indicate that the XCO2 retrieval appears robust to 3-D scattering effects from unresolved low level clouds when the short wave infrared surface albedo is large, but for darker surfaces these clouds can introduce significant biases.