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1
Koren, I., L. Oreopoulos, G. Feingold, L. A. Remer, and O. Altaratz. "How small is a small cloud?" Atmospheric Chemistry and Physics Discussions 8, no. 2 (March 28, 2008): 6379–407. http://dx.doi.org/10.5194/acpd-8-6379-2008.
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Abstract. The interplay between clouds and aerosols and their contribution to the radiation budget is one of the largest uncertainties of climate change. Most work to date has separated cloudy and cloud-free areas in order to evaluate the individual radiative forcing of aerosols, clouds, and aerosol effects on clouds. Here we examine the size distribution and the optical properties of small, sparse cumulus clouds and the associated optical properties of what is considered a cloud-free atmosphere within the cloud field. We show that any separation between clouds and cloud free atmosphere will incur errors in the calculated radiative forcing. The nature of small cumulus cloud size distributions suggests that at any resolution, a significant fraction of the clouds are missed, and their optical properties are relegated to the apparent cloud-free optical properties. At the same time, the cloudy portion incorporates significant contribution from non-cloudy pixels. We show that the largest contribution to the total cloud reflectance comes from the smallest clouds and that the spatial resolution changes the apparent energy flux of a broken cloudy scene. When changing the resolution from 30 m to 1 km (Landsat to MODIS) the average "cloud-free" reflectance at 1.65 μm increases more than 25%, the cloud reflectance decreases by half, and the cloud coverage doubles, resulting in an important impact on climate forcing estimations. The apparent aerosol forcing is on the order of 0.5 to 1 Wm−2 per cloud field.
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Koren, I., L. Oreopoulos, G. Feingold, L. A. Remer, and O. Altaratz. "How small is a small cloud?" Atmospheric Chemistry and Physics 8, no. 14 (July 21, 2008): 3855–64. http://dx.doi.org/10.5194/acp-8-3855-2008.
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Abstract. The interplay between clouds and aerosols and their contribution to the radiation budget is one of the largest uncertainties of climate change. Most work to date has separated cloudy and cloud-free areas in order to evaluate the individual radiative forcing of aerosols, clouds, and aerosol effects on clouds. Here we examine the size distribution and the optical properties of small, sparse cumulus clouds and the associated optical properties of what is considered a cloud-free atmosphere within the cloud field. We show that any separation between clouds and cloud free atmosphere will incur errors in the calculated radiative forcing. The nature of small cumulus cloud size distributions suggests that at any resolution, a significant fraction of the clouds are missed, and their optical properties are relegated to the apparent cloud-free optical properties. At the same time, the cloudy portion incorporates significant contribution from non-cloudy pixels. We show that the largest contribution to the total cloud reflectance comes from the smallest clouds and that the spatial resolution changes the apparent energy flux of a broken cloudy scene. When changing the resolution from 30 m to 1 km (Landsat to MODIS) the average "cloud-free" reflectance at 1.65 μm increases from 0.0095 to 0.0115 (>20%), the cloud reflectance decreases from 0.13 to 0.066 (~50%), and the cloud coverage doubles, resulting in an important impact on climate forcing estimations. The apparent aerosol forcing is on the order of 0.5 to 1 Wm−2 per cloud field.
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Schulte, Richard M., Matthew D. Lebsock, and John M. Haynes. "What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations." Atmospheric Measurement Techniques 16, no. 14 (July 25, 2023): 3531–46. http://dx.doi.org/10.5194/amt-16-3531-2023.
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Abstract. Single-layer nonprecipitating warm clouds are integral to Earth's climate, and accurate estimates of cloud liquid water content for these clouds are critical for constraining cloud models and understanding climate feedbacks. As the only cloud-sensitive radar currently in space, CloudSat provides very important cloud-profiling capabilities. However, a significant fraction of clouds is missed by CloudSat because they are either too thin or too close to the Earth's surface. We find that the CloudSat Radar-Visible Optical Depth Cloud Water Content Product, 2B-CWC-RVOD, misses about 73 % of nonprecipitating liquid cloudy pixels and about 63 % of total nonprecipitating liquid cloud water content compared to coincident Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Those percentages increase to 84 % and 69 %, respectively, if MODIS “partly cloudy” pixels are included. We develop a method, based on adiabatic parcel theory but modified to account for the fact that observed clouds are often subadiabatic, to estimate profiles of cloud liquid water content based on MODIS observations of cloud-top effective radius and cloud optical depth combined with lidar observations of cloud-top height. We find that, for cloudy pixels that are detected by CloudSat, the resulting subadiabatic profiles of cloud water are similar to what is retrieved from CloudSat. For cloudy pixels that are not detected by CloudSat, the subadiabatic profiles can be used to supplement the CloudSat profiles, recovering much of the missing cloud water and generating realistic-looking merged profiles of cloud water. Adding this missing cloud water to the CWC-RVOD product increases the mean cloud liquid water path by 228 % for single-layer nonprecipitating warm clouds. This method will be included in a subsequent reprocessing of the 2B-CWC-RVOD algorithm.
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Li, J., Z. Wu, Z. Hu, Y. Zhang, and M. Molinier. "AUTOMATIC CLOUD DETECTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORKS IN REMOTE SENSING IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 885–92. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-885-2020.
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Abstract. Clouds in optical remote sensing images seriously affect the visibility of background pixels and greatly reduce the availability of images. It is necessary to detect clouds before processing images. In this paper, a novel cloud detection method based on attentive generative adversarial network (Auto-GAN) is proposed for cloud detection. Our main idea is to inject visual attention into the domain transformation to detect clouds automatically. First, we use a discriminator (D) to distinguish between cloudy and cloud free images. Then, a segmentation network is used to detect the difference between cloudy and cloud-free images (i.e. clouds). Last, a generator (G) is used to fill in the different regions in cloud image in order to confuse the discriminator. Auto-GAN only requires images and their labels (1 for a cloud-free image, 0 for a cloudy image) in the training phase which is more time-saving to acquire than existing methods based on CNNs that require pixel-level labels. Auto-GAN is applied to cloud detection in Sentinel-2A Level 1C imagery. The results indicate that Auto-GAN method performs well in cloud detection over different land surfaces.
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Coakley, James A., Michael A. Friedman, and William R. Tahnk. "Retrieval of Cloud Properties for Partly Cloudy Imager Pixels." Journal of Atmospheric and Oceanic Technology 22, no. 1 (January 1, 2005): 3–17. http://dx.doi.org/10.1175/jtech-1681.1.
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Abstract Retrievals of cloud properties from satellite imagery often invoke the assumption that the fields of view are overcast when cloud-contaminated, even though a significant fraction are only partially cloud-covered. The overcast assumption leads to biases in the retrieved cloud properties: cloud amounts and droplet effective radii are typically overestimated, while visible optical depths, cloud altitudes, cloud liquid water amounts, and column droplet number concentrations are typically underestimated. In order to estimate these biases, a retrieval scheme was developed to obtain the properties of clouds for partially covered imager fields of view. The partly cloudy pixel retrieval scheme is applicable to single-layered cloud systems and invokes the assumption that clouds that only partially cover a field of view are at the same altitude as nearby clouds from the same layer that completely cover imager pixels. The properties of the retrieval are illustrated through its application to 2-km Visible and Infrared Scanner (VIRS) data from the Tropical Rainfall Measuring Mission (TRMM) for a marine stratocumulus scene. The scene was chosen because the cloud properties are typical of such systems based on an analysis of VIRS data for February and March 1998. Comparisons of properties for clouds in partly cloudy pixels and those for clouds in nearby overcast pixels reveal that the optical depths and droplet effective radii are generally smaller for the clouds in the partly cloudy pixels. In addition, for pixel-scale cloud fractions between 0.2 and 0.8, optical depth, droplet effective radius, and column droplet number concentration decrease slowly with decreasing cloud cover fraction. The changes are only about 20%–30%, while cloud cover fraction changes by 80%. For comparison, changes in optical depth and column number concentration retrieved using a threshold method decrease by 80%–90%. As long as the cloud cover in partly cloudy pixels is greater than about 0.1, uncertainties in the estimates of the cloud altitudes and of the radiances for the cloud-free portions of the fields of view give rise to uncertainties in the retrieved cloud properties that are comparable to the uncertainties in the properties retrieved for overcast pixels.
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Mieslinger, Theresa, Bjorn Stevens, Tobias Kölling, Manfred Brath, Martin Wirth, and Stefan A. Buehler. "Optically thin clouds in the trades." Atmospheric Chemistry and Physics 22, no. 10 (May 30, 2022): 6879–98. http://dx.doi.org/10.5194/acp-22-6879-2022.
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Abstract. We develop a new method to describe the total cloud cover including optically thin clouds in trade wind cumulus cloud fields. Climate models and large eddy simulations commonly underestimate the cloud cover, while estimates from observations largely disagree on the cloud cover in the trades. Currently, trade wind clouds significantly contribute to the uncertainty in climate sensitivity estimates derived from model perturbation studies. To simulate clouds well, especially how they change in a future climate, we have to know how cloudy it is. In this study we develop a method to quantify the cloud cover from a cloud-free perspective. Using well-known radiative transfer relations we retrieve the cloud-free contribution in high-resolution satellite observations of trade cumulus cloud fields during EUREC4A. Knowing the cloud-free part, we can investigate the remaining cloud-related contributions consisting of areas detected by common cloud-masking algorithms and undetected areas related to optically thin clouds. We find that the cloud-mask cloud cover underestimates the total cloud cover by 33 %. Aircraft lidar measurements support our findings by showing a high abundance of optically thin clouds during EUREC4A. Mixing the undetected optically thin clouds into the cloud-free signal can cause an underestimation of the cloud radiative effect of up to −7.5 %. We further discuss possible artificial correlations in aerosol–cloud cover interaction studies that might arise from undetected optically thin low clouds. Our analysis suggests that the known underestimation of trade wind cloud cover and simultaneous overestimation of cloud brightness in models are even higher than assumed so far.
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Lu, Shiming, Mingjun He, Shuangyan He, Shuo He, Yunhe Pan, Wenbin Yin, and Peiliang Li. "An Improved Cloud Masking Method for GOCI Data over Turbid Coastal Waters." Remote Sensing 13, no. 14 (July 10, 2021): 2722. http://dx.doi.org/10.3390/rs13142722.
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Clouds severely hinder the radiative transmission of visible light; thus, correctly masking cloudy and non-cloudy pixels is a preliminary step in processing ocean color remote sensing data. However, cloud masking over turbid waters is prone to misjudgment, leading to loss of non-cloudy pixel data. This research proposes an improved cloud masking method over turbid water to classify cloudy and non-cloudy pixels based on spectral variability of Rayleigh-corrected reflectance acquired by the Geostationary Ocean Color Imager (GOCI). Compared with other existing cloud masking methods, we demonstrated that this improved method can identify the spatial positions and shapes of clouds more realistically, and more accurate pixels of turbid waters were retained. This improved method can be effectively applied in typical turbid coastal waters. It has potential to be used in cloud masking procedures of spaceborne ocean color sensors without short-wave infrared bands.
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Sun, J., H. Leighton, M. K. Yau, and P. Ariya. "Numerical evidence for cloud droplet nucleation at the cloud-environment interface." Atmospheric Chemistry and Physics Discussions 12, no. 7 (July 18, 2012): 17723–42. http://dx.doi.org/10.5194/acpd-12-17723-2012.
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Abstract. Cumulus clouds have long been recognized as being the results of ascending moist air from below the cloud base. Cloud droplet nucleation is understood to take place near the cloud base and inside accelerating rising cloudy air. Here we describe circumstances under which cloud droplet nucleation takes place at the interface of ascending cloudy air and clear air. Evaporation is normally expected to occur at this interface. However, continuity of moving air requires cloud-free air above the boundary of rising cloudy air to move upwards in response to the gradient force of perturbation pressure. We used a one and half dimensional non-hydrostatic cloud model and the Weather Research and Forecast model to investigate the impacts of this force on the evolution of cloud spectra. Our study shows that expansion and cooling of ascending moist air above the cloud top causes it to become supersaturated with condensation rather than evaporation occurring at the interface. We also confirm that Eulerain models can describe the cloud droplet activation and prohibit spurious activation at this interface. The continuous feeding of newly activated cloud droplets at the cloud summit may accelerate warm rain formation.
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Sun, J., H. Leighton, M. K. Yau, and P. Ariya. "Numerical evidence for cloud droplet nucleation at the cloud-environment interface." Atmospheric Chemistry and Physics 12, no. 24 (December 21, 2012): 12155–64. http://dx.doi.org/10.5194/acp-12-12155-2012.
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Abstract. Cumulus clouds have long been recognized as being the results of ascending moist air from below the cloud base. Cloud droplet nucleation is understood to take place near the cloud base and inside accelerating rising cloudy air. Here we describe circumstances under which cloud droplet nucleation takes place at the interface of ascending cloudy air and clear air. Evaporation is normally expected to occur at this interface. However, continuity of moving air requires cloud-free air above the boundary of rising cloudy air to move upwards in response to the gradient force of perturbation pressure. We used a one and half dimensional non-hydrostatic cloud model and the Weather Research and Forecast model to investigate the impacts of this force on the evolution of cloud spectra. Our study shows that expansion and cooling of ascending moist air above the cloud top causes it to become supersaturated with condensation rather than evaporation occurring at the interface. We also confirm that Eulerian models can describe the cloud droplet activation and prohibit spurious activation at this interface. The continuous feeding of newly activated cloud droplets at the cloud summit may accelerate warm rain formation.
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Massons, J., D. Domingo, and J. Lorente. "Seasonal cycle of cloud cover analyzed using Meteosat images." Annales Geophysicae 16, no. 3 (March 31, 1998): 331–41. http://dx.doi.org/10.1007/s00585-998-0331-3.
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Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat
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Stubenrauch, C. J., S. Cros, A. Guignard, and N. Lamquin. "A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat." Atmospheric Chemistry and Physics Discussions 10, no. 3 (March 30, 2010): 8247–96. http://dx.doi.org/10.5194/acpd-10-8247-2010.
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Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height as well as to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated as about 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover (0 or 0.3). 40% of all clouds are high clouds, and about 44% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thickness optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.
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Stubenrauch, C. J., S. Cros, A. Guignard, and N. Lamquin. "A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat." Atmospheric Chemistry and Physics 10, no. 15 (August 6, 2010): 7197–214. http://dx.doi.org/10.5194/acp-10-7197-2010.
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Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height. In addition, they permit to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated from 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover from 0 to 0.3. 42% of all clouds are high clouds, and about 42% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thickness optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.
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Hutchison, Keith D., Barbara D. Iisager, Thomas J. Kopp, and John M. Jackson. "Distinguishing Aerosols from Clouds in Global, Multispectral Satellite Data with Automated Cloud Classification Algorithms." Journal of Atmospheric and Oceanic Technology 25, no. 4 (April 1, 2008): 501–18. http://dx.doi.org/10.1175/2007jtecha1004.1.
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Abstract A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.
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Kim, Hye-Sil, Bryan A. Baum, and Yong-Sang Choi. "Use of spectral cloud emissivities and their related uncertainties to infer ice cloud boundaries: methodology and assessment using CALIPSO cloud products." Atmospheric Measurement Techniques 12, no. 9 (September 19, 2019): 5039–54. http://dx.doi.org/10.5194/amt-12-5039-2019.
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Abstract. Satellite-imager-based operational cloud property retrievals generally assume that a cloudy pixel can be treated as being plane-parallel with horizontally homogeneous properties. This assumption can lead to high uncertainties in cloud heights, particularly for the case of optically thin, but geometrically thick, clouds composed of ice particles. This study demonstrates that ice cloud emissivity uncertainties can be used to provide a reasonable range of ice cloud layer boundaries, i.e., the minimum to maximum heights. Here ice cloud emissivity uncertainties are obtained for three IR channels centered at 11, 12, and 13.3 µm. The range of cloud emissivities is used to infer a range of ice cloud temperature and heights, rather than a single value per pixel as provided by operational cloud retrievals. Our methodology is tested using MODIS observations over the western North Pacific Ocean during August 2015. We estimate minimum–maximum heights for three cloud regimes, i.e., single-layered optically thin ice clouds, single-layered optically thick ice clouds, and multilayered clouds. Our results are assessed through comparison with CALIOP version 4 cloud products for a total of 11873 pixels. The cloud boundary heights for single-layered optically thin clouds show good agreement with those from CALIOP; biases for maximum (minimum) heights versus the cloud-top (base) heights of CALIOP are 0.13 km (−1.01 km). For optically thick and multilayered clouds, the biases of the estimated cloud heights from the cloud top or cloud base become larger (0.30/−1.71 km, 1.41/−4.64 km). The vertically resolved boundaries for ice clouds can contribute new information for data assimilation efforts for weather prediction and radiation budget studies. Our method is applicable to measurements provided by most geostationary weather satellites including the GK-2A advanced multichannel infrared imager.
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Wang, P., P. Stammes, R. van der A, G. Pinardi, and M. van Roozendael. "FRESCO+: an improved O<sub>2</sub> A-band cloud retrieval algorithm for tropospheric trace gas retrievals." Atmospheric Chemistry and Physics Discussions 8, no. 3 (May 27, 2008): 9697–729. http://dx.doi.org/10.5194/acpd-8-9697-2008.
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Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm has been used to retrieve cloud information from measurements of the O2 A-band around 760 nm by GOME, SCIAMACHY and GOME-2. The cloud parameters retrieved by FRESCO are the effective cloud fraction and cloud pressure, which are used for cloud correction in the retrieval of trace gases like O3 and NO2. To improve the cloud pressure retrieval for partly cloudy scenes, single Rayleigh scattering has been included in an improved version of the algorithm, called FRESCO+. We compared FRESCO+ and FRESCO effective cloud fractions and cloud pressures using simulated spectra and one month of GOME measured spectra. As expected, FRESCO+ gives more reliable cloud pressures over partly cloudy pixels. Simulations and comparisons with ground-based radar/lidar measurements of clouds shows that the FRESCO+ cloud pressure is about the optical midlevel of the cloud. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher than the FRESCO cloud pressure, while the FRESCO+ effective cloud fraction is about 0.01 larger. The effect of FRESCO+ cloud parameters on O3 and NO2 vertical column densities (VCD) is studied using SCIAMACHY data and ground-based DOAS measurements. We find that the FRESCO+ algorithm has a significant effect on tropospheric NO2 retrievals but a minor effect on total O3 retrievals. The retrieved SCIAMACHY tropospheric NO2 VCDs using FRESCO+ cloud parameters (v1.1) are lower than the tropospheric NO2 VCDs which used FRESCO cloud parameters (v1.04), in particular over heavily polluted areas with low clouds. The difference between SCIAMACHY tropospheric NO2 VCDs v1.1 and ground-based MAXDOAS measurements performed in Cabauw, The Netherlands, during the DANDELIONS campaign is about −2.12×1014 molec cm−2.
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Wang, P., P. Stammes, R. van der A, G. Pinardi, and M. van Roozendael. "FRESCO+: an improved O<sub>2</sub> A-band cloud retrieval algorithm for tropospheric trace gas retrievals." Atmospheric Chemistry and Physics 8, no. 21 (November 14, 2008): 6565–76. http://dx.doi.org/10.5194/acp-8-6565-2008.
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Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm has been used to retrieve cloud information from measurements of the O2 A-band around 760 nm by GOME, SCIAMACHY and GOME-2. The cloud parameters retrieved by FRESCO are the effective cloud fraction and cloud pressure, which are used for cloud correction in the retrieval of trace gases like O3 and NO2. To improve the cloud pressure retrieval for partly cloudy scenes, single Rayleigh scattering has been included in an improved version of the algorithm, called FRESCO+. We compared FRESCO+ and FRESCO effective cloud fractions and cloud pressures using simulated spectra and one month of GOME measured spectra. As expected, FRESCO+ gives more reliable cloud pressures over partly cloudy pixels. Simulations and comparisons with ground-based radar/lidar measurements of clouds show that the FRESCO+ cloud pressure is about the optical midlevel of the cloud. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher than the FRESCO cloud pressure, while the FRESCO+ effective cloud fraction is about 0.01 larger. The effect of FRESCO+ cloud parameters on O3 and NO2 vertical column density (VCD) retrievals is studied using SCIAMACHY data and ground-based DOAS measurements. We find that the FRESCO+ algorithm has a significant effect on tropospheric NO2 retrievals but a minor effect on total O3 retrievals. The retrieved SCIAMACHY tropospheric NO2 VCDs using FRESCO+ cloud parameters (v1.1) are lower than the tropospheric NO2VCDs which used FRESCO cloud parameters (v1.04), in particular over heavily polluted areas with low clouds. The difference between SCIAMACHY tropospheric NO2 VCDs v1.1 and ground-based MAXDOAS measurements performed in Cabauw, The Netherlands, during the DANDELIONS campaign is about −2.12×1014molec cm−2.
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Harshvardhan, Guang Guo, Robert N. Green, Zheng Qu, and Takashi Y. Nakajima. "Remotely Sensed Microphysical and Thermodynamic Properties of Nonuniform Water Cloud Fields." Journal of the Atmospheric Sciences 61, no. 21 (November 1, 2004): 2574–87. http://dx.doi.org/10.1175/jas3301.1.
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Abstract Visible and near-infrared reflected radiances have been used to estimate the cloud optical depth and effective radius of cloud-filled global area coverage (GAC) pixels from the Advanced Very High Resolution Radiometer (AVHRR) for two cases in the North Atlantic Ocean. One is representative of clouds having low concentrations of cloud condensation nuclei (CCN), while the other is an example of maritime clouds forming in continental air, in this case, intruding from Europe around a cutoff low pressure system. It is shown that an estimate of the cloud drop concentration can be obtained from remotely sensed cloud radiative properties and standard meteorological analyses. These concentrations show very clearly the influence of enhanced CCN on cloud microphysics. However, conclusions regarding the indirect radiative effect of aerosol on cloud must wait for the development of a framework for analyzing changes in cloud liquid water path (LWP). It is shown that estimates of LWP are greatly influenced by the scheme that is used to identify cloudy pixels at the AVHRR GAC resolution. Application of a very strict thermal channel spatial coherence criterion for identifying cloud-filled pixels yields mean LWP estimates for cloudy pixels alone that are 40%–75% higher than mean LWP estimates for the much larger sample of possibly cloudy pixels identified by a reflectance threshold criterion.
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Sanchez, Adriana, Nicole M. Hughes, and William K. Smith. "Importance of natural cloud regimes to ecophysiology in the alpine species, Caltha leptosepala and Arnica parryi, Snowy Range Mountains, southeast Wyoming, USA." Functional Plant Biology 42, no. 2 (2015): 186. http://dx.doi.org/10.1071/fp14096.
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The south-central Rocky Mountains, USA, are characterised by a dry, continental mesoclimate with typical convective cloud formation during the afternoon. Little is known about the specific influence of such predictable cloud patterns on the microclimate and ecophysiology of associated species. During the summer of 2012, days with afternoon clouds were most common (50% of all days) compared with completely clear (24%) or cloudy days (6.5%). In two representative alpine species, Caltha leptosepala DC. and Arnica parryi A. Gray, fully overcast days reduced mean daily photosynthesis (A) by nearly 50% relative to fully clear days. Mean afternoon A was significantly lower on fully cloudy days relative to days with afternoon clouds only or no clouds in both species. Notably, A did not differ during afternoon cloud days relative to clear afternoons. Afternoon clouds significantly reduced transpiration (E) in C. leptosepala relative to clear days, and both species showed mean reductions in plant water stress (i.e. higher Ψ), though this difference was not significant. Water use efficiency (WUE) (A/E) decreased from morning to afternoon, especially on cloudy days, and the presence of clouds had a positive effect on the light reactions of photosynthesis based on fluorescence measurements (Fv′/Fm′), in both species. Cloudy days were characterised by higher Fv/Fm than afternoon clouds and clear days during both the morning and the afternoon (especially for A. parryi) and recovery to near pre-dawn values for cloudy and afternoon cloud day types, but not clear days. Overall, similar ecophysiological advantages of this typical afternoon cloud pattern was apparent in both species, although their spatial microsite differences related to winter snow accumulation may also play an important role.
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Xia, Shuang, Alberto Mestas-Nuñez, Hongjie Xie, Jiakui Tang, and Rolando Vega. "Characterizing Variability of Solar Irradiance in San Antonio, Texas Using Satellite Observations of Cloudiness." Remote Sensing 10, no. 12 (December 12, 2018): 2016. http://dx.doi.org/10.3390/rs10122016.
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Since the main attenuation of solar irradiance reaching the earth’s surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites.
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Hayes, J. "Clout of the cloud (cloud computing)." Engineering & Technology 4, no. 6 (April 11, 2009): 60–61. http://dx.doi.org/10.1049/et.2009.0611.
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Balmes, Kelly, and Qiang Fu. "An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars." Atmosphere 9, no. 11 (November 14, 2018): 445. http://dx.doi.org/10.3390/atmos9110445.
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Optically very thin ice clouds from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and ground-based Raman lidars (RL) at the atmospheric radiation measurement (ARM) sites of the Southern Great Plains (SGP) and Tropical Western Pacific (TWP) are analyzed. The optically very thin ice clouds, with ice cloud column optical depths below 0.01, are about 23% of the transparent ice-cloudy profiles from the RL, compared to 4–7% from CALIPSO. The majority (66–76%) of optically very thin ice clouds from the RLs are found to be adjacent to ice clouds with ice cloud column optical depths greater than 0.01. The temporal structure of RL-observed optically very thin ice clouds indicates a clear sky–cloud continuum. Global cloudiness estimates from CALIPSO observations leveraged with high-sensitivity RL observations suggest that CALIPSO may underestimate the global cloud fraction when considering optically very thin ice clouds.
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Utrillas, María Pilar, María José Marín, Víctor Estellés, Carlos Marcos, María Dolores Freile, José Luis Gómez-Amo, and José Antonio Martínez-Lozano. "Comparison of Cloud Amounts Retrieved with Three Automatic Methods and Visual Observations." Atmosphere 13, no. 6 (June 9, 2022): 937. http://dx.doi.org/10.3390/atmos13060937.
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Four methods have been used for the estimation of the total cloud amount and cloud amount for low clouds: visual observations, the Long method applied on pyranometer measurements, the Automatic Partial Cloud Amount Detection Algorithm (APCADA) method applied on pyrgeometers measurements, and ceilometer measurements of the cloud base height. Records from meteorological observers indicate that clear days (0–1 octa) represent the most frequent cloud amount for low clouds. In contrast, the total cloud amount is more aleatory. Results obtained from the Long method show maximum frequency in the extreme cloud amount values. The APCADA method also indicates the predominance of cloudless skies. The ceilometer method shows a predominance of completely clear skies, but the completely cloudy (8 octas) is the second most frequent case. Automatic methods report more cloudless and overcast skies than the observer. Automatic methods agree with the visual method or differ in ±1 octa for 60–76% cases for low cloud amount and for 56–63% cases for total cloud amount. In general, low cloud amount agrees more with observer measurements than total cloud amount and the automatic methods underestimated total cloud amount observer values possibly due to the difficulty in monitoring high clouds.
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Chen, Xidong, Liangyun Liu, Yuan Gao, Xiao Zhang, and Shuai Xie. "A Novel Classification Extension-Based Cloud Detection Method for Medium-Resolution Optical Images." Remote Sensing 12, no. 15 (July 23, 2020): 2365. http://dx.doi.org/10.3390/rs12152365.
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Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.
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Chang, Fu-Lung, and James A. Coakley. "Relationships between Marine Stratus Cloud Optical Depth and Temperature: Inferences from AVHRR Observations." Journal of Climate 20, no. 10 (May 15, 2007): 2022–36. http://dx.doi.org/10.1175/jcli4115.1.
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Abstract Studies using International Satellite Cloud Climatology Project (ISCCP) data have reported decreases in cloud optical depth with increasing temperature, thereby suggesting a positive feedback in cloud optical depth as climate warms. The negative cloud optical depth and temperature relationships are questioned because ISCCP employs threshold assumptions to identify cloudy pixels that have included partly cloudy pixels. This study applies the spatial coherence technique to one month of Advanced Very High Resolution Radiometer (AVHRR) data over the Pacific Ocean to differentiate overcast pixels from the partly cloudy pixels and to reexamine the cloud optical depth–temperature relationships. For low-level marine stratus clouds studied here, retrievals from partly cloudy pixels showed 30%–50% smaller optical depths, 1°–4°C higher cloud temperatures, and slightly larger droplet effective radii, when they were compared to retrievals from the overcast pixels. Despite these biases, retrievals for the overcast and partly cloudy pixels show similar negative cloud optical depth–temperature relationships and their magnitudes agree with the ISCCP results for the midlatitude and subtropical regions. There were slightly negative droplet effective radius–temperature relationships, and considerable positive cloud liquid water content–temperature relationships indicated by aircraft measurements. However, cloud thickness decreases appear to be the main reason why cloud optical depth decreases with increasing temperature. Overall, cloud thickness thinning may explain why similar negative cloud optical depth–temperature relationships are found in both overcast and partly cloudy pixels. In addition, comparing the cloud-top temperature to the air temperature at 740 hPa indicates that cloud-top height generally rises with warming. This suggests that the cloud thinning is mainly due to the ascending of cloud base. The results presented in this study are confined to the midlatitude and subtropical Pacific and may not be applicable to the Tropics or other regions.
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Yang, S., and X. Zou. "Temperature Profiles and Lapse Rate Climatology in Altostratus and Nimbostratus Clouds Derived from GPS RO Data." Journal of Climate 26, no. 16 (August 6, 2013): 6000–6014. http://dx.doi.org/10.1175/jcli-d-12-00646.1.
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Abstract Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) refractivity profiles in altostratus and nimbostratus clouds from 2007 to 2010 are first identified based on collocated CloudSat data. Vertical temperature profiles in these clouds are then retrieved from cloudy refractivity profiles. Contributions of cloud liquid water content and ice water content are also included in the retrieval algorithm. The temperature profiles and their lapse rates are compared with those from a standard GPS RO wet retrieval without including cloud effects. On average, the temperatures from cloudy retrieval are about 0.5–1.0 K warmer than the GPS RO wet retrieval, except for the altitudes near the nimbostratus base. The differences of temperature between the two methods are largest in summer and smallest in winter. The lapse rate in altostratus clouds is around 6.5°–7.5°C km−1 and does not vary greatly with height. On the contrary, the lapse rate increases significantly with height in nimbostratus clouds, from about 2.5°–3.5°C km−1 near the cloud base to about 5.0°–6.0°C km−1 at cloud center and 6.5°–7.5°C km−1 below the cloud top. Seasonal variability of lapse rate derived from the cloudy retrieval is larger than that derived from the wet retrieval. The lapse rate within clouds is smaller in summer and larger in winter. The mean lapse rate decreases with temperature in all seasons.
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Ahlgrimm, Maike, David A. Randall, and Martin Köhler. "Evaluating Cloud Frequency of Occurrence and Cloud-Top Height Using Spaceborne Lidar Observations." Monthly Weather Review 137, no. 12 (December 1, 2009): 4225–37. http://dx.doi.org/10.1175/2009mwr2937.1.
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Abstract A strategy for model evaluation using spaceborne lidar observations is presented. Observations from the Geoscience Laser Altimeter System are recast onto the model grid to assess the ability of two versions of the Integrated Forecasting System to model marine stratocumulus clouds. The two model versions differ primarily in their treatment of clear and cloudy boundary layers. For each grid column, a representative cloud fraction and cloud-top height are derived from the observations, as well as from the model. By applying the same threshold criteria for cloud fraction and cloud-top height independently to model and observations, samples containing marine stratocumulus clouds can be identified. The frequency of occurrence, cloud fraction, and cloud-top height distributions for all samples thus identified are compared. The evaluation shows improvements in the frequency of occurrence and cloud-top height of marine stratocumulus, though modeled cloud tops remain lower than observed. Additional runs reveal a sensitivity to the strength of the environmental mixing that occurs during the test parcel ascent of the boundary layer parameterization. With a more aggressive parcel, the modeled clouds agree even better with observations.
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Sedlar, Joseph. "Implications of Limited Liquid Water Path on Static Mixing within Arctic Low-Level Clouds." Journal of Applied Meteorology and Climatology 53, no. 12 (December 2014): 2775–89. http://dx.doi.org/10.1175/jamc-d-14-0065.1.
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AbstractObservations of cloud properties and thermodynamics from two Arctic locations, Barrow, Alaska, and Surface Heat Budget of the Arctic (SHEBA), are examined. A comparison of in-cloud thermodynamic mixing characteristics for low-level, single-layer clouds from nearly a decade of data at Barrow and one full annual cycle over the sea ice at SHEBA is performed. These cloud types occur relatively frequently, evident in 27%–30% of all cloudy cases. To understand the role of liquid water path (LWP), or lack thereof, on static in-cloud mixing, cloud layers are separated into optically thin and optically thick LWP subclasses. Clouds with larger LWPs tend to have a deeper in-cloud mixed layer relative to optically thinner clouds. However, both cloud LWP subclasses are frequently characterized by an in-cloud stable layer above the mixed layer top. The depth of the stable layer generally correlates with an increased temperature gradient across the layer. This layer often contains a specific humidity inversion, but it is more frequently present when cloud LWP is optically thinner (LWP < 50 g m−2). It is suggested that horizontal thermodynamic advection plays a key role modifying the vertical extent of in-cloud mixing and likewise the depth of in-cloud stable layers. Furthermore, longwave atmospheric opacity above the cloud top is generally enhanced during cases with optically thinner clouds. Thermodynamic advection, cloud condensate distribution within the stable layer, and enhanced atmospheric radiation above the cloud are found to introduce a thermodynamic–radiative feedback that potentially modifies the extent of LWP and subsequent in-cloud mixing.
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Xu, Wenjing, and Daren Lyu. "Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau." Remote Sensing 13, no. 8 (April 7, 2021): 1418. http://dx.doi.org/10.3390/rs13081418.
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The Tibetan Plateau (TP) has profound thermal and dynamic influences on the atmospheric circulation, energy, and water cycles of the climate system, which make the clouds over the TP the forefront of atmospheric and climate science. However, the highest altitude and most complex terrain of the TP make the retrieval of cloud properties challenging. In order to understand the performance and limitations of cloud retrievals over the TP derived from the state-of-the-art Advanced Geosynchronous Radiation Imager (AGRI) onboard the new generation of Chinese Geostationary (GEO) meteorological satellites Fengyun-4 (FY-4), a three-month comparison was conducted between FY-4A/AGRI and the Moderate Resolution Imaging Spectroradiometer (MODIS) for both cloud detection and cloud top height (CTH) pixel-level retrievals. For cloud detection, the AGRI and MODIS cloud mask retrievals showed a fractional agreement of 0.93 for cloudy conditions and 0.73 for clear scenes. AGRI tended to miss lower CTH clouds due to the lack of thermal contrast between the clouds and the surface of the TP. For cloud top height retrievals, the comparison showed that on average, AGRI underestimated the CTH relative to MODIS by 1.366 ± 2.235 km, and their differences presented a trend of increasing with height.
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Gielen, C., M. Van Roozendael, F. Hendrick, G. Pinardi, T. Vlemmix, V. De Bock, H. De Backer, et al. "A simple and versatile cloud-screening method for MAX-DOAS retrievals." Atmospheric Measurement Techniques 7, no. 10 (October 13, 2014): 3509–27. http://dx.doi.org/10.5194/amt-7-3509-2014.
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Abstract. We present a cloud-screening method based on differential optical absorption spectroscopy (DOAS) measurements, more specifically using intensity measurements and O4 differential slant-column densities (DSCDs). Using the colour index (CI), i.e. the ratio of the radiance at two wavelengths, we define different sky conditions including clear, thin clouds/polluted, fully-cloudy, and heavily polluted. We also flag the presence of broken and scattered clouds. The O4 absorption is a good tracer for cloud-induced light-path changes and is used to detect clouds and discriminate between instances of high aerosol optical depth (AOD) and high cloud optical depth (COD). We apply our cloud screening to MAX-DOAS (multi-axis DOAS) retrievals at three different sites with different typical meteorological conditions, more specifically suburban Beijing (39.75° N, 116.96° E), Brussels (50.78° N, 4.35° E) and Jungfraujoch (46.55° N, 7.98° E). We find that our cloud screening performs well characterizing the different sky conditions. The flags based on the colour index are able to detect changes in visibility due to aerosols and/or (scattered) clouds. The O4-based multiple-scattering flag is able to detect optically thick clouds, and is needed to correctly identify clouds for sites with extreme aerosol pollution. Removing data taken under cloudy conditions results in a better agreement, in both correlation and slope, between the MAX-DOAS AOD retrievals and measurements from other co-located instruments.
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Gielen, C., M. Van Roozendael, F. Hendrick, G. Pinardi, T. Vlemmix, V. De Bock, H. De Backer, et al. "A simple and versatile cloud-screening method for MAX-DOAS retrievals." Atmospheric Measurement Techniques Discussions 7, no. 6 (June 12, 2014): 5883–920. http://dx.doi.org/10.5194/amtd-7-5883-2014.
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Abstract. We present a cloud-screening method based on differential optical absorption spectroscopy (DOAS) measurements, more specifically using zenith sky spectra and O4 differential slant-column densities (DSCDs). Using the colour index (CI), i.e. the ratio of the radiance at two wavelengths, we define different sky conditions including clear, thin clouds/polluted, fully-cloudy, and heavily polluted. We also flag the presence of broken and scattered clouds. The O4 absorption is a good tracer for cloud-induced light-path changes and is used to detect clouds and discriminate between instances of high aerosol optical depth (AOD) and high cloud optical depth (COD). We apply our cloud screening to MAX-DOAS (multi-axis DOAS) retrievals at three different sites with different typical meteorological conditions, more specifically suburban Beijing (39.75° N, 116.96° E), Brussels (50.78° N, 4.35° E) and Jungfraujoch (46.55° N, 7.98° E). We find that our cloud screening performs well characterizing the different sky conditions. The flags based on the colour index are able to detect changes in visibility due to aerosols and/or (scattered) clouds. The O4-based multiple-scattering flag is able to detect optically thick clouds, and is needed to correctly identify clouds for sites with extreme aerosol pollution. Removing data taken under cloudy conditions results in a better agreement, in both correlation and slope, between the AOD retrievals and measurements from other co-located instruments.
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Luffarelli, Marta, Yves Govaerts, and Lucio Franceschini. "Aerosol Optical Thickness Retrieval in Presence of Cloud: Application to S3A/SLSTR Observations." Atmosphere 13, no. 5 (April 26, 2022): 691. http://dx.doi.org/10.3390/atmos13050691.
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The Combined Inversion of Surface and AeRosols (CISAR) algorithm for the joint retrieval of surface and aerosol single scattering properties has been further developed in order to extend the retrieval to clouds and overcome the need for an external cloud mask. Pixels located in the transition zone between pure cloud and pure aerosol are often discarded by both aerosol and cloud algorithms, despite being essential for studying aerosol–cloud interactions, which still represent the largest source of uncertainty in climate predictions. The proposed approach aims at filling this gap and deepening the understanding of aerosol properties in cloudy environments. The new CISAR version is applied to Sentinel-3A/SLSTR observations and evaluated against different satellite products and ground measurements. The spatial coverage is greatly improved with respect to algorithms processing only pixels flagged as clear sky by the SLSTR cloud mask. The continuous retrieval of aerosol properties without any safety zone around clouds opens new possibilities for studying aerosol properties in cloudy environments.
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Luffarelli, Marta, Yves Govaerts, and Lucio Franceschini. "Aerosol Optical Thickness Retrieval in Presence of Cloud: Application to S3A/SLSTR Observations." Atmosphere 13, no. 5 (April 26, 2022): 691. http://dx.doi.org/10.3390/atmos13050691.
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The Combined Inversion of Surface and AeRosols (CISAR) algorithm for the joint retrieval of surface and aerosol single scattering properties has been further developed in order to extend the retrieval to clouds and overcome the need for an external cloud mask. Pixels located in the transition zone between pure cloud and pure aerosol are often discarded by both aerosol and cloud algorithms, despite being essential for studying aerosol–cloud interactions, which still represent the largest source of uncertainty in climate predictions. The proposed approach aims at filling this gap and deepening the understanding of aerosol properties in cloudy environments. The new CISAR version is applied to Sentinel-3A/SLSTR observations and evaluated against different satellite products and ground measurements. The spatial coverage is greatly improved with respect to algorithms processing only pixels flagged as clear sky by the SLSTR cloud mask. The continuous retrieval of aerosol properties without any safety zone around clouds opens new possibilities for studying aerosol properties in cloudy environments.
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Wang, P., O. N. E. Tuinder, L. G. Tilstra, M. de Graaf, and P. Stammes. "Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events." Atmospheric Chemistry and Physics 12, no. 19 (October 4, 2012): 9057–77. http://dx.doi.org/10.5194/acp-12-9057-2012.
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Abstract. Cloud and aerosol information is needed in trace gas retrievals from satellite measurements. The Fast REtrieval Scheme for Clouds from the Oxygen A band (FRESCO) cloud algorithm employs reflectance spectra of the O2 A band around 760 nm to derive cloud pressure and effective cloud fraction. In general, clouds contribute more to the O2 A band reflectance than aerosols. Therefore, the FRESCO algorithm does not correct for aerosol effects in the retrievals and attributes the retrieved cloud information entirely to the presence of clouds, and not to aerosols. For events with high aerosol loading, aerosols may have a dominant effect, especially for almost cloud free scenes. We have analysed FRESCO cloud data and Absorbing Aerosol Index (AAI) data from the Global Ozone Monitoring Experiment (GOME-2) instrument on the Metop-A satellite for events with typical absorbing aerosol types, such as volcanic ash, desert dust and smoke. We find that the FRESCO effective cloud fractions are correlated with the AAI data for these absorbing aerosol events and that the FRESCO cloud pressure contains information on aerosol layer pressure. For cloud free scenes, the derived FRESCO cloud pressure is close to the aerosol layer pressure, especially for optically thick aerosol layers. For cloudy scenes, if the strongly absorbing aerosols are located above the clouds, then the retrieved FRESCO cloud pressure may represent the height of the aerosol layer rather than the height of the clouds. Combining FRESCO and AAI data, an estimate for the aerosol layer pressure can be given.
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Dinh, Tra, and Stephan Fueglistaler. "Cirrus, Transport, and Mixing in the Tropical Upper Troposphere." Journal of the Atmospheric Sciences 71, no. 4 (March 27, 2014): 1339–52. http://dx.doi.org/10.1175/jas-d-13-0147.1.
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Abstract The impact of cloud radiative heating on transport time scales from the tropical upper troposphere to the stratosphere is studied in two-dimensional numerical simulations. Clouds are idealized as sources of radiative heating and are stochastically distributed in space and time. A spatial probability function constrains clouds to occur in only part of the domain to depict heterogeneously distributed clouds in the atmosphere. The transport time from the lower to upper boundaries (age of air) is evaluated with trajectories. The spectra of age of air obtained in the simulations are bimodal, with the first mode composed of trajectories that remain in the cloudy part of the domain during their passages from the lower to upper boundaries, and the second mode composed of the remaining trajectories that visit the cloud-free regions. For the first group of trajectories only, the mean age scales inversely with the time-mean radiative heating in cloudy air, and the one-dimensional advection–diffusion equation provides an adequate model for transport. However, the exchange between the cloudy and cloud-free regions renders the mean age over all trajectories (including those that visit the cloud-free region) much longer than the time expected if all air parcels remain in cloudy air. In addition, the overall mean age is not inversely proportional to the time-mean heating rate in cloudy air. Sensitivity calculations further show that the sizes, durations, and amplitudes of the individual clouds are also important to the transport time. The results show that the frequently used decomposition of radiative heating into clear-sky and cloud radiative heating may give incorrect interpretations regarding the time scale of transport into the stratosphere.
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Cho, Hyoun-Myoung, Shaima L. Nasiri, and Ping Yang. "Application of CALIOP Measurements to the Evaluation of Cloud Phase Derived from MODIS Infrared Channels." Journal of Applied Meteorology and Climatology 48, no. 10 (October 1, 2009): 2169–80. http://dx.doi.org/10.1175/2009jamc2238.1.
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Abstract In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) infrared-based cloud thermodynamic phase retrievals are evaluated using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals for the 6 months from January to June of 2008. The CALIOP 5-km cloud-layer product provides information on cloud opacity, cloud-top height, midlayer cloud temperature, and cloud thermodynamic phase. Comparisons are made between MODIS IR phase and CALIOP observations for single-layer clouds (54% of the cloudy CALIOP scenes) and for the top layer of the CALIOP scenes. Both CALIOP and MODIS retrieve larger fractions of water clouds in the single-layer cases than in the top-layer cases, demonstrating that focusing on only single-layer clouds may introduce a water-cloud bias. Of the single-layer clouds, 60% are transparent and 40% are opaque (defined by the lack of a CALIOP ground return). MODIS tends to classify single-layer clouds with midlayer temperatures below −40°C as ice; around −30°C nearly equally as ice, mixed, and unknown; between −28° and −15°C as mixed; and above 0°C as water. Ninety-five percent of the single-layer CALIOP clouds not detected by MODIS are transparent. Approximately ⅓ of transparent single-layer clouds with temperatures below −30°C are not detected by MODIS and close to another ⅓ are classified as ice, with the rest assigned as water, mixed, or unknown. CALIOP classes nearly all of these transparent cold clouds as ice.
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Tjernström, Michael, Joseph Sedlar, and Matthew D. Shupe. "How Well Do Regional Climate Models Reproduce Radiation and Clouds in the Arctic? An Evaluation of ARCMIP Simulations." Journal of Applied Meteorology and Climatology 47, no. 9 (September 1, 2008): 2405–22. http://dx.doi.org/10.1175/2008jamc1845.1.
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Abstract Downwelling radiation in six regional models from the Arctic Regional Climate Model Intercomparison (ARCMIP) project is systematically biased negative in comparison with observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, although the correlations with observations are relatively good. In this paper, links between model errors and the representation of clouds in these models are investigated. Although some modeled cloud properties, such as the cloud water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties with observations is poor. The vertical distribution of cloud water is distinctly different among the different models; some common features also appear. Most models underestimate the presence of high clouds, and, although the observed preference for low clouds in the Arctic is present in most of the models, the modeled low clouds are too thin and are displaced downward. Practically all models show a preference to locate the lowest cloud base at the lowest model grid point. In some models this happens also to be where the observations show the highest occurrence of the lowest cloud base; it is not possible to determine if this result is just a coincidence. Different factors contribute to model surface radiation errors. For longwave radiation in summer, a negative bias is present both for cloudy and clear conditions, and intermodel differences are smaller when clouds are present. There is a clear relationship between errors in cloud-base temperature and radiation errors. In winter, in contrast, clear-sky cases are modeled reasonably well, but cloudy cases show a very large intermodel scatter with a significant bias in all models. This bias likely results from a complete failure in all of the models to retain liquid water in cold winter clouds. All models overestimate the cloud attenuation of summer solar radiation for thin and intermediate clouds, and some models maintain this behavior also for thick clouds.
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Wind, Galina, Steven Platnick, Michael D. King, Paul A. Hubanks, Michael J. Pavolonis, Andrew K. Heidinger, Ping Yang, and Bryan A. Baum. "Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band." Journal of Applied Meteorology and Climatology 49, no. 11 (November 1, 2010): 2315–33. http://dx.doi.org/10.1175/2010jamc2364.1.
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Abstract Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System (EOS) Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that present difficulties for retrieving cloud effective radius using single-layer plane-parallel cloud models. The algorithm uses the MODIS 0.94-μm water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94-μm methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases.
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Wang, P., O. N. E. Tuinder, L. G. Tilstra, and P. Stammes. "Interpretation of FRESCO cloud retrievals in case of absorbing aerosol events." Atmospheric Chemistry and Physics Discussions 11, no. 12 (December 12, 2011): 32685–721. http://dx.doi.org/10.5194/acpd-11-32685-2011.
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Abstract. Cloud and aerosol information is needed in trace gas retrievals from satellite measurements. The Fast REtrieval Scheme for Clouds from the Oxygen A band (FRESCO) cloud algorithm employs reflectance spectra of the O2 A band around 760 nm to derive cloud pressure and effective cloud fraction. In general, clouds contribute more to the O2 A band reflectance than aerosols. Therefore, the FRESCO algorithm does not correct for aerosol effects in the retrievals and attributes the retrieved cloud information entirely to the presence of clouds, and not to aerosols. For events with high aerosol loading, aerosols may have a dominant effect, especially for almost cloud-free scenes. We have analysed FRESCO cloud data and Absorbing Aerosol Index (AAI) data from the Global Ozone Monitoring Experiment (GOME-2) instrument on the Metop-A satellite for events with typical absorbing aerosol types, such as volcanic ash, desert dust and smoke. We find that the FRESCO effective cloud fractions are correlated with the AAI data for these absorbing aerosol events and that the FRESCO cloud pressures contain information on aerosol layer pressure. For cloud-free scenes, the derived FRESCO cloud pressures are close to those of the aerosol layer for optically thick aerosols. For cloudy scenes, if the strongly absorbing aerosols are located above the clouds, then the retrieved FRESCO cloud pressures may represent the height of the aerosol layer rather than the height of the clouds. Combining FRESCO cloud data and AAI, an estimate for the aerosol layer pressure can be given, which can be beneficial for aviation safety and operations in case of e.g. volcanic ash plumes.
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Yue, Zhiguo, Daniel Rosenfeld, Guihua Liu, Jin Dai, Xing Yu, Yannian Zhu, Eyal Hashimshoni, Xiaohong Xu, Ying Hui, and Oliver Lauer. "Automated Mapping of Convective Clouds (AMCC) Thermodynamical, Microphysical, and CCN Properties from SNPP/VIIRS Satellite Data." Journal of Applied Meteorology and Climatology 58, no. 4 (April 2019): 887–902. http://dx.doi.org/10.1175/jamc-d-18-0144.1.
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AbstractThe advent of the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi NPP (SNPP) satellite made it possible to retrieve a new class of convective cloud properties and the aerosols that they ingest. An automated mapping system of retrieval of some properties of convective cloud fields over large areas at the scale of satellite coverage was developed and is presented here. The system is named Automated Mapping of Convective Clouds (AMCC). The input is level-1 VIIRS data and meteorological gridded data. AMCC identifies the cloudy pixels of convective elements; retrieves for each pixel its temperature T and cloud drop effective radius re; calculates cloud-base temperature Tb based on the warmest cloudy pixels; calculates cloud-base height Hb and pressure Pb based on Tb and meteorological data; calculates cloud-base updraft Wb based on Hb; calculates cloud-base adiabatic cloud drop concentrations Nd,a based on the T–re relationship, Tb, and Pb; calculates cloud-base maximum vapor supersaturation S based on Nd,a and Wb; and defines Nd,a/1.3 as the cloud condensation nuclei (CCN) concentration NCCN at that S. The results are gridded 36 km × 36 km data points at nadir, which are sufficiently large to capture the properties of a field of convective clouds and also sufficiently small to capture aerosol and dynamic perturbations at this scale, such as urban and land-use features. The results of AMCC are instrumental in observing spatial covariability in clouds and CCN properties and for obtaining insights from such observations for natural and man-made causes. AMCC-generated maps are also useful for applications from numerical weather forecasting to climate models.
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Lin, L., X. Zou, R. Anthes, and Y.-H. Kuo. "COSMIC GPS Radio Occultation Temperature Profiles in Clouds." Monthly Weather Review 138, no. 4 (April 1, 2010): 1104–18. http://dx.doi.org/10.1175/2009mwr2986.1.
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Abstract Thermodynamic states in clouds are closely related to physical processes such as phase changes of water and longwave and shortwave radiation. Global Positioning System (GPS) radio occultation (RO) data are not affected by clouds and have high vertical resolution, making them ideally suited to cloud profiling on a global basis. By comparing the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO refractivity data with those of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis and ECMWF analysis for soundings in clouds and clear air separately, a systematic bias of opposite sign was found between large-scale global analyses and the GPS RO observations under cloudy and clear-sky conditions. As a modification to the standard GPS RO wet temperature retrieval that does not distinguish between cloudy- and clear-sky conditions, a new cloudy retrieval algorithm is proposed to incorporate the knowledge that in-cloud specific humidity (which affects the GPS refractivities) should be close to saturation. To implement this new algorithm, a linear regression model for a sounding-dependent relative humidity parameter α is first developed based on a high correlation between relative humidity and ice water content. In the absence of ice water content information, α takes an empirical value of 85%. The in-cloud temperature profile is then retrieved from GPS RO data modeled by a weighted sum of refractivities with and without the assumption of saturation. Compared to the standard wet retrieval, the cloudy temperature retrieval is consistently warmer within clouds by ∼2 K and slightly colder near the cloud top (∼1 K) and cloud base (1.5 K), leading to a more rapid increase of the lapse rate with height in the upper half of the cloud, from a nearly constant moist lapse rate below and at the cloud middle (∼6°C km−1) to a value of 7.7°C km−1, which must be closer to the dry lapse rate than the standard wet retrieval.
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Marchant, Benjamin, Steven Platnick, Kerry Meyer, G. Thomas Arnold, and Jérôme Riedi. "MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP." Atmospheric Measurement Techniques 9, no. 4 (April 11, 2016): 1587–99. http://dx.doi.org/10.5194/amt-9-1587-2016.
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Abstract. Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.
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Marchant, B., S. Platnick, K. Meyer, G. T. Arnold, and J. Riedi. "MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP." Atmospheric Measurement Techniques Discussions 8, no. 11 (November 16, 2015): 11893–924. http://dx.doi.org/10.5194/amtd-8-11893-2015.
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Abstract. Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate-Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.
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Tompkins, Adrian M., and Francesca Di Giuseppe. "An Interpretation of Cloud Overlap Statistics." Journal of the Atmospheric Sciences 72, no. 8 (August 1, 2015): 2877–89. http://dx.doi.org/10.1175/jas-d-14-0278.1.
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Abstract Observational studies have shown that the vertical overlap of cloudy layers separated by clear sky can exceed that of the random overlap assumption, suggesting a tendency toward minimum overlap. In addition, the rate of decorrelation of vertically continuous clouds with increasing layer separation is sensitive to the horizontal scale of the cloud scenes used. The authors give a heuristic argument that these phenomena result from data truncation, where overcast or single cloud layers are removed from the analysis. This occurs more frequently as the cloud sampling scale falls progressively below the typical cloud system scale. The postulate is supported by sampling artificial cyclic and subsequently more realistic fractal cloud scenes at various length scales. The fractal clouds indicate that the degree of minimal overlap diagnosed in previous studies for discontinuous clouds could result from sampling randomly overlapped clouds at spatial scales that are 30%–80% of the cloud system scale. Removing scenes with cloud cover exceeding 50% from the analysis reduces the impact of data truncation, with discontinuous clouds not minimally overlapped and the decorrelation of continuous clouds less sensitive to the sampling scale. Using CloudSat–CALIPSO data, a decorrelation length scale of approximately 4.0 km is found. In light of these results, the previously documented dependence of overlap decorrelation length scale on latitude is not entirely a physical phenomenon but can be reinterpreted as resulting from sampling cloud systems that increase significantly in size from the tropics to midlatitudes using a fixed sampling scale.
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Dong, Xiquan, Baike Xi, and Patrick Minnis. "A Climatology of Midlatitude Continental Clouds from the ARM SGP Central Facility. Part II: Cloud Fraction and Surface Radiative Forcing." Journal of Climate 19, no. 9 (May 1, 2006): 1765–83. http://dx.doi.org/10.1175/jcli3710.1.
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Abstract Data collected at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (SCF) are analyzed to determine the monthly and hourly variations of cloud fraction and radiative forcing between January 1997 and December 2002. Cloud fractions are estimated for total cloud cover and for single-layered low (0–3 km), middle (3–6 km), and high clouds (>6 km) using ARM SCF ground-based paired lidar–radar measurements. Shortwave (SW) and longwave (LW) fluxes are derived from up- and down-looking standard precision spectral pyranometers and precision infrared radiometer measurements with uncertainties of ∼10 W m−2. The annual averages of total and single-layered low-, middle-, and high-cloud fractions are 0.49, 0.11, 0.03, and 0.17, respectively. Both total- and low-cloud amounts peak during January and February and reach a minimum during July and August; high clouds occur more frequently than other types of clouds with a peak in summer. The average annual downwelling surface SW fluxes for total and low clouds (151 and 138 W m−2, respectively) are less than those under middle and high clouds (188 and 201 W m−2, respectively), but the downwelling LW fluxes (349 and 356 W m−2) underneath total and low clouds are greater than those from middle and high clouds (337 and 333 W m−2). Low clouds produce the largest LW warming (55 W m−2) and SW cooling (−91 W m−2) effects with maximum and minimum absolute values in spring and summer, respectively. High clouds have the smallest LW warming (17 W m−2) and SW cooling (−37 W m−2) effects at the surface. All-sky SW cloud radiative forcing (CRF) decreases and LW CRF increases with increasing cloud fraction with mean slopes of −0.984 and 0.616 W m−2 %−1, respectively. Over the entire diurnal cycle, clouds deplete the amount of surface insolation more than they add to the downwelling LW flux. The calculated CRFs do not appear to be significantly affected by uncertainties in data sampling and clear-sky screening. Traditionally, cloud radiative forcing includes not only the radiative impact of the hydrometeors, but also the changes in the environment. Taken together over the ARM SCF, changes in humidity and surface albedo between clear and cloudy conditions offset ∼20% of the NET radiative forcing caused by the cloud hydrometeors alone. Variations in water vapor, on average, account for 10% and 83% of the SW and LW CRFs, respectively, in total cloud cover conditions. The error analysis further reveals that the cloud hydrometeors dominate the SW CRF, while water vapor changes are most important for LW flux changes in cloudy skies. Similar studies over other locales are encouraged where water and surface albedo changes from clear to cloudy conditions may be much different than observed over the ARM SCF.
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Bugliaro, L., T. Zinner, C. Keil, B. Mayer, R. Hollmann, M. Reuter, and W. Thomas. "Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI." Atmospheric Chemistry and Physics Discussions 10, no. 9 (September 21, 2010): 21931–88. http://dx.doi.org/10.5194/acpd-10-21931-2010.
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Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due to the large differences in scale and observation geometry between the satellite footprint and the independent ground based or airborne observations. Here we illustrate and demonstrate an alternative approach: starting from the output of the COSMO-EU weather model of the German Weather Service realistic three-dimensional cloud structures at a spatial scale of 2.33 km are produced by statistical downscaling and microphysical properties are associated to them. The resulting data sets are used as input to the one-dimensional radiative transfer model libRadtran to simulate radiance observations for all eleven low resolution channels of MET-8/SEVIRI. At this point, both cloud properties and satellite radiances are known such that cloud property retrieval results can be tested and tuned against the objective input "truth". As an example, we validate a cloud property retrieval of the Institute of Atmospheric Physics of DLR and that of EUMETSAT's Climate Monitoring Science Application Facility CMSAF. Cloud detection and cloud phase assignment perform well. By both retrievals 88% of the pixels are correctly classified as clear or cloudy. The DLR algorithm assigns the correct thermodynamic phase to 95% of the cloudy pixels and the CMSAF retrieval to 79%. Cloud top temperature is slightly overestimated by the DLR code (+3.1 K mean difference with a standard deviation of 10.6 K) and underestimated by the CMSAF code (−16.4 K with a standard deviation of 37.3 K). Both retrievals account reasonably well for the distribution of optical thickness for both water and ice clouds, with a tendency to underestimation for the DLR and to overestimation for the CMSAF algorithm. Cloud effective radii are most difficult to evaluate and not always the algorithms are able to produce realistic values. The CMSAF cloud water path, which is a combination of the last two quantities, is particularly accurate for ice clouds, while water clouds are overestimated, mainly because of the effective radius overestimation for water clouds.
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Romps, David M., and Andrew M. Vogelmann. "Methods for Estimating 2D Cloud Size Distributions from 1D Observations." Journal of the Atmospheric Sciences 74, no. 10 (October 1, 2017): 3405–17. http://dx.doi.org/10.1175/jas-d-17-0105.1.
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Abstract The two-dimensional (2D) size distribution of clouds in the horizontal plane plays a central role in the calculation of cloud cover, cloud radiative forcing, convective entrainment rates, and the likelihood of precipitation. Here, a simple method is proposed for calculating the area-weighted mean cloud size and for approximating the 2D size distribution from the 1D cloud-chord lengths measured by aircraft and vertically pointing lidar and radar. This simple method (which is exact for square clouds) compares favorably against the inverse Abel transform (which is exact for circular clouds) in the context of theoretical size distributions. Both methods also perform well when used to predict the size distribution of real clouds from a Landsat scene. When applied to a large number of Landsat scenes, the simple method is able to accurately estimate the mean cloud size. As a demonstration, the methods are applied to aircraft measurements of shallow cumuli during the Routine ARM Aerial Facility (AAF) Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) campaign, which then allow for an estimate of the true area-weighted mean cloud size.
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Grabowski, Wojciech W. "Representation of Turbulent Mixing and Buoyancy Reversal in Bulk Cloud Models." Journal of the Atmospheric Sciences 64, no. 10 (October 1, 2007): 3666–80. http://dx.doi.org/10.1175/jas4047.1.
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Abstract This paper discusses the representation of subgrid-scale turbulent mixing in bulk models of warm (ice free) clouds, which assume instantaneous adjustment to grid-scale saturation. This is a reasonable assumption for condensation of water vapor because supersaturations inside clouds are typically small (∼0.1% or smaller), except near cloud bases where about an order of magnitude larger supersaturations are anticipated. For the cloud evaporation, however, instantaneous adjustment to grid-scale saturation is questionable, especially when evaporation occurs as a result of turbulent mixing between a cloud and its unsaturated environment. This is because turbulent mixing between initially separated volumes of cloudy and cloud-free environmental air proceeds through a gradual filamentation of these volumes, with progressively increasing evaporation of cloud water during the approach to final homogenization. A relatively simple model of this chain of events is included in a bulk model of moist nonprecipitating thermodynamics. The model delays adjustment to saturation for cloud evaporation following the turbulent mixing until the volume can be assumed homogeneous. An additional prognostic variable, the width of a cloudy filament, is added to represent the progress of turbulent mixing and the approach to homogenization. Theoretical developments are illustrated by idealized 2D simulations of moist thermals rising from rest and realistic large-eddy simulations of a cloud field.
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Zuidema, P., B. Baker, Y. Han, J. Intrieri, J. Key, P. Lawson, S. Matrosov, M. Shupe, R. Stone, and T. Uttal. "An Arctic Springtime Mixed-Phase Cloudy Boundary Layer Observed during SHEBA." Journal of the Atmospheric Sciences 62, no. 1 (January 1, 2005): 160–76. http://dx.doi.org/10.1175/jas-3368.1.
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Abstract The microphysical characteristics, radiative impact, and life cycle of a long-lived, surface-based mixed-layer, mixed-phase cloud with an average temperature of approximately −20°C are presented and discussed. The cloud was observed during the Surface Heat Budget of the Arctic experiment (SHEBA) from 1 to 10 May 1998. Vertically resolved properties of the liquid and ice phases are retrieved using surface-based remote sensors, utilize the adiabatic assumption for the liquid component, and are aided by and validated with aircraft measurements from 4 and 7 May. The cloud radar ice microphysical retrievals, originally developed for all-ice clouds, compare well with aircraft measurements despite the presence of much greater liquid water contents than ice water contents. The retrieved time-mean liquid cloud optical depth of 10.1 ± 7.8 far surpasses the mean ice cloud optical depth of 0.2, so that the liquid phase is primarily responsible for the cloud’s radiative (flux) impact. The ice phase, in turn, regulates the overall cloud optical depth through two mechanisms: sedimentation from a thin upper ice cloud, and a local ice production mechanism with a time scale of a few hours, thought to reflect a preferred freezing of the larger liquid drops. The liquid water paths replenish within half a day or less after their uptake by ice, attesting to strong water vapor fluxes. Deeper boundary layer depths and higher cloud optical depths coincide with large-scale rising motion at 850 hPa, but the synoptic activity is also associated with upper-level ice clouds. Interestingly, the local ice formation mechanism appears to be more active when the large-scale subsidence rate implies increased cloud-top entrainment. Strong cloud-top radiative cooling rates promote cloud longevity when the cloud is optically thick. The radiative impact of the cloud upon the surface is significant: a time-mean positive net cloud forcing of 41 W m−2 with a diurnal amplitude of ∼20 W m−2. This is primarily because a high surface reflectance (0.86) reduces the solar cooling influence. The net cloud forcing is primarily sensitive to cloud optical depth for the low-optical-depth cloudy columns and to the surface reflectance for the high-optical-depth cloudy columns. Any projected increase in the springtime cloud optical depth at this location (76°N, 165°W) is not expected to significantly alter the surface radiation budget, because clouds were almost always present, and almost 60% of the cloudy columns had optical depths >6.
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Di Natale, Gianluca, Giovanni Bianchini, Massimo Del Guasta, Marco Ridolfi, Tiziano Maestri, William Cossich, Davide Magurno, and Luca Palchetti. "Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy." Remote Sensing 12, no. 21 (October 31, 2020): 3574. http://dx.doi.org/10.3390/rs12213574.
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Optical and microphysical cloud properties are retrieved from measurements acquired in 2013 and 2014 at the Concordia base station in the Antarctic Plateau. Two sensors are used synergistically: a Fourier transform spectroradiometer named REFIR-PAD (Radiation Explorer in Far Infrared-Prototype for Applications and Developments) and a backscattering-depolarization LiDAR. First, in order to identify the cloudy scenes and assess the cloud thermodynamic phase, the REFIR-PAD spectral radiances are ingested by a machine learning algorithm called Cloud Identification and Classification (CIC). For each of the identified cloudy scenes, the nearest (in time) LiDAR backscattering profile is processed by the Polar Threshold (PT) algorithm that allows derivation of the cloud top and bottom heights. Subsequently, using the CIC and PT results as external constraints, the Simultaneous Atmospheric and Clouds Retrieval (SACR) code is applied to the REFIR-PAD spectral radiances. SACR simultaneously retrieves cloud optical depth and effective dimensions and atmospheric vertical profiles of water vapor and temperature. The analysis determines an average effective diameter of 28 μm with an optical depth of 0.76 for the ice clouds. Water clouds are only detected during the austral Summer, and the retrieved properties provide an average droplet diameter of 9 μm and average optical depth equal to four. The estimated retrieval error is about 1% for the ice crystal/droplet size and 2% for the cloud optical depth. The sensitivity of the retrieved parameters to the assumed crystal shape is also assessed. New parametrizations of the optical depth and the longwave downwelling forcing for Antarctic ice and water clouds, as a function of the ice/liquid water path, are presented. The longwave downwelling flux, computed from the top of the atmosphere to the surface, ranges between 70 and 220 W/m2. The estimated cloud longwave forcing at the surface is (31 ± 7) W/m2 and (29 ± 6) W/m2 for ice clouds and (64 ± 12) and (62 ± 11) W/m2 for water clouds, in 2013 and 2014, respectively. The total average cloud forcing for the two years investigated is (46 ± 9) W/m2.
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Lima, Prijith, Sesha Sai, Rao, Niranjan, and Ramana. "Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D." Remote Sensing 11, no. 23 (November 27, 2019): 2811. http://dx.doi.org/10.3390/rs11232811.
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Investigation of cloud top temperature (CTT) and its diurnal variation is highly reliant on high spatial and temporal resolution satellite data, which is lacking over the Indian region. An algorithm has been developed for detection of clouds and retrieval of CTT from the geostationary satellite INSAT-3D. These retrievals are validated (inter-compared) with collocated in-situ (satellite) measurements with specific intent to generate climate-quality data. The cloud detection algorithm employs nine different tests, in accordance with solar illumination, satellite angle and surface type conditions to generate pixel-resolution cloud mask. Validation of cloud mask with cloud-aerosol lidar with orthogonal polarization (CALIOP) shows that probability of detection (POD) of cloudy (clear) sky is 81% (85%), with 83% hit rate. The algorithm is also implemented on similar channels of moderate resolution imaging spectroradiometer (MODIS), which provides 88% (83%) POD of cloudy (clear) sky, with 86% hit rate. CTT retrieval is done at the pixel level, for all cloud pixels, by employing appropriate methods for various types of clouds. Comparison of CTT with radiosonde and cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) shows mean absolute error less than 3%. The study also examines sensitivity of retrieved CTT to the cloud classification scheme and retrieval criteria. Validation results and their close agreements with those of similar satellites demonstrate the reliability of the retrieved product for climate studies.