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Graeme L Stephens - One of the best experts on this subject based on the ideXlab platform.
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Improved Retrieval of Cloud Liquid Water from CloudSat and MODIS
Journal of Applied Meteorology and Climatology, 2016Co-Authors: Jussi Leinonen, Graeme L Stephens, Matthew Lebsock, Kentaroh SuzukiAbstract:AbstractA revised version of the CloudSat–MODIS cloud liquid water retrieval algorithm is presented. The new algorithm, which combines measurements of radar reflectivity and cloud optical depth, addresses issues discovered in the current CloudSat–MODIS cloud water content (CWC) product. This current product is shown to be underconstrained by observations and to be too dependent on prior information incorporated into the Bayesian optimal-estimation algorithm. The most significant change made to the algorithm in this study was decreasing the number of independent variables to allow the observations to constrain the retrieved values better. The retrieval was also reformulated for improved compliance with the mathematical assumptions of the optimal-estimation algorithm. To validate the accuracy of the revised algorithm, the path-integrated attenuation (PIA) of the CloudSat radar signal was computed from the algorithm results. These modeled values were compared with independent measurements of the PIA that wer...
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Exposing biases in retrieved low cloud properties from CloudSat: A guide for evaluating observations and climate data
Journal of Geophysical Research: Atmospheres, 2013Co-Authors: Matthew Christensen, Graeme L Stephens, Matthew LebsockAbstract:[1] This study provides an assessment of low cloud properties retrieved from CloudSat, MODIS (Moderate Resolution Imaging Spectroradiometer), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation with the goal of exposing biases that hinder meaningful comparisons with the simulated cloud properties in global climate models (GCMs). Being pertinent to GCM comparisons, CloudSat is the only satellite that can provide the vertical structure of cloud water and ice content from space. Biases in CloudSat low cloud properties are found to be tied to problems involving cloud detection and algorithm retrieval failures related to precipitation and strict cloud screening procedures. We show that MODIS and CloudSat cloud liquid water path (LWP) data agree when carefully screened for lack of precipitation but significantly depart in precipitating clouds due to rain water contamination of LWP in the CloudSat retrieval algorithm. The presence of drizzle and rain (occurring about 20% of the time) is associated with different mean LWP, mean particle sizes, and optical depths of all low clouds and therefore the radiative properties of the oceanic low clouds. Another more significant source of the LWP bias stems from the apparent lack of cloud detection. On average, the Cloud Profiling Radar misses clouds with adequate liquid and ice water retrievals as detected by MODIS in approximately 45% of warm clouds with the bulk of the bias occurring in clouds below 1 km in the so-called “ground clutter zone.” By incorporating additional sensors such as MODIS, the following results suggest that this LWP bias can be greatly reduced.
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The CloudSat Mission and the EOS Constellation: A New Dimension of Space-Based Observation of Clouds and Precipitation
2013Co-Authors: Graeme L Stephens, Gerald G Mace, Ewan J. O'connor, Deborah G. Vane, Ronald J. Boain, Kenneth Sassen, Zhien Wang, Anthony J. Illingworth, William B. Rossow, Stephen L. DurdenAbstract:CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004 and, once launched, CloudSat will orbit in formation as part of a constellation of satellites including NASA's Aqua and Aura satellites, a NASA-CNES lidar satellite (P-C) and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the P-C lidar footprint and the other measurements of the EOS constellation. The precision of this overlap creates a unique multi-satellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profile of cloud properties provided by CloudSat fills a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring the vertical profile of cloud properties requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with active and passive data from other sensors of the constellation. This paper describes the underpinning science, and gives an overview of the mission, and provides some idea of the expected products and anticipated application of these products. Notably, the CloudSat mission is expected to provide new knowledge about global cloudiness, stimulating new areas of research on clouds including data assimilation and cloud parameterization. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA/JPL, the Canadian Space Agency, Colorado State University, the US Air Force, and the US Department of Energy.
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comparison of regime sorted tropical cloud profiles observed by CloudSat with geos5 analyses and two general circulation model simulations
Journal of Geophysical Research, 2011Co-Authors: Jonathan H. Jiang, Graeme L Stephens, Deborah G. Vane, João Paulo Teixeira, Andrew Gettelman, Xianglei Huang, V. S. PerunAbstract:[1] CloudSat observed tropical liquid and ice water content (L/IWC, Version 4) profiles are compared with GEOS5 analyses, NCAR-CAM3, and GFDL-AM2 simulations. Both the analyses and free-running general circulation models (GCMs) underestimate IWC in the upper troposphere, with the simulated ice water paths (IWPs) being 22% (GEOS5), 9% (CAM3), and 54% (AM2) of the CloudSat retrieval. For liquid clouds, GEOS5 produces the closest match to CloudSat, with a distinct peak in LWC around 1.5–2 km. CAM3 and AM2 generate liquid clouds in a broad vertical layer in the lower and middle troposphere, resulting in slightly higher column-integrated liquid water path (LWP) than CloudSat, despite the fact that their LWC in the boundary layer is only 60%–70% of CloudSat. The data assimilation model and two GCMs produce substantial middle-level clouds, more than the CloudSat retrieval. We sort the cloud profiles by midtropospheric vertical velocity (ω500), sea surface temperature (SST), and lower tropospheric stability (LTS). The high clouds in the models are concentrated in large-scale ascending, warm SST, and low LTS regimes, consistent with the CloudSat observation. The CAM3 and AM2 model-simulated middle-level clouds are strongly correlated with ω500 but less clustered in the domains of SST and LTS. For low clouds, both CloudSat and GOES5 analyses show the preferential distribution of low clouds in regions of large-scale subsidence, relatively cold SST (SST 15 K), while CAM3 and AM2 low clouds are strongly controlled by SST and LTS and only weakly correlated with ω500. Exclusion of precipitating scenes would reduce the tropical mean CloudSat LWP and IWP by 73% and 48%, respectively, which does not fully explain the model-data discrepancies.
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Comparison of regime‐sorted tropical cloud profiles observed by CloudSat with GEOS5 analyses and two general circulation model simulations
Journal of Geophysical Research, 2011Co-Authors: Jonathan H. Jiang, Graeme L Stephens, Deborah G. Vane, João Paulo Teixeira, Andrew Gettelman, Xianglei Huang, V. S. PerunAbstract:[1] CloudSat observed tropical liquid and ice water content (L/IWC, Version 4) profiles are compared with GEOS5 analyses, NCAR-CAM3, and GFDL-AM2 simulations. Both the analyses and free-running general circulation models (GCMs) underestimate IWC in the upper troposphere, with the simulated ice water paths (IWPs) being 22% (GEOS5), 9% (CAM3), and 54% (AM2) of the CloudSat retrieval. For liquid clouds, GEOS5 produces the closest match to CloudSat, with a distinct peak in LWC around 1.5–2 km. CAM3 and AM2 generate liquid clouds in a broad vertical layer in the lower and middle troposphere, resulting in slightly higher column-integrated liquid water path (LWP) than CloudSat, despite the fact that their LWC in the boundary layer is only 60%–70% of CloudSat. The data assimilation model and two GCMs produce substantial middle-level clouds, more than the CloudSat retrieval. We sort the cloud profiles by midtropospheric vertical velocity (ω500), sea surface temperature (SST), and lower tropospheric stability (LTS). The high clouds in the models are concentrated in large-scale ascending, warm SST, and low LTS regimes, consistent with the CloudSat observation. The CAM3 and AM2 model-simulated middle-level clouds are strongly correlated with ω500 but less clustered in the domains of SST and LTS. For low clouds, both CloudSat and GOES5 analyses show the preferential distribution of low clouds in regions of large-scale subsidence, relatively cold SST (SST 15 K), while CAM3 and AM2 low clouds are strongly controlled by SST and LTS and only weakly correlated with ω500. Exclusion of precipitating scenes would reduce the tropical mean CloudSat LWP and IWP by 73% and 48%, respectively, which does not fully explain the model-data discrepancies.
Christophe Genthon - One of the best experts on this subject based on the ideXlab platform.
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evaluation of CloudSat snowfall rate profiles by a comparison with in situ micro rain radar observations in east antarctica
The Cryosphere, 2019Co-Authors: Florentin Lemonnier, Cyril Palerme, Chantal Claud, Christophe Genthon, Niels Souverijns, Alexis Berne, Jeanbaptiste Madeleine, Claudio Duranalarcon, Nicole Van Lipzig, Irina GorodetskayaAbstract:Abstract. The Antarctic continent is a vast desert and is the coldest and the most unknown area on Earth. It contains the Antarctic ice sheet, the largest continental water reservoir on Earth that could be affected by the current global warming, leading to sea level rise. The only significant supply of ice is through precipitation, which can be observed from the surface and from space. Remote-sensing observations of the coastal regions and the inner continent using CloudSat radar give an estimated rate of snowfall but with uncertainties twice as large as each single measured value, whereas climate models give a range from half to twice the space–time-averaged observations. The aim of this study is the evaluation of the vertical precipitation rate profiles of CloudSat radar by comparison with two surface-based micro-rain radars (MRRs), located at the coastal French Dumont d'Urville station and at the Belgian Princess Elisabeth station located in the Dronning Maud Land escarpment zone. This in turn leads to a better understanding and reassessment of CloudSat uncertainties. We compared a total of four precipitation events, two per station, when CloudSat overpassed within 10 km of the station and we compared these two different datasets at each vertical level. The correlation between both datasets is near-perfect, even though climatic and geographic conditions are different for the two stations. Using different CloudSat and MRR vertical levels, we obtain 10 km space-scale and short-timescale (a few seconds) CloudSat uncertainties from −13 % up to +22 %. This confirms the robustness of the CloudSat retrievals of snowfall over Antarctica above the blind zone and justifies further analyses of this dataset.
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How Does Ground Clutter Affect CloudSat Snowfall Retrievals Over Ice Sheets?
IEEE Geoscience and Remote Sensing Letters, 2019Co-Authors: Cyril Palerme, Chantal Claud, Norman B. Wood, Tristan L’ecuyer, Christophe GenthonAbstract:CloudSat has provided the first spaceborne snowfall observations in polar regions. Nevertheless, CloudSat retrievals may be affected by ground clutter even if the snowfall rate at the surface is estimated from the reflectivity measured at about 1200 m above land/ice surface. In this study, the impact of ground clutter contamination on CloudSat snowfall retrievals over the Antarctic and Greenland ice sheets is investigated. Our results suggest that ground clutter affects CloudSat snowfall observations over some areas, particularly over complex terrain such as mountain ranges and fjords. Over these areas, the snowfall rates deduced from CloudSat observations can be, therefore, significantly overestimated. This has implications when developing snowfall climatologies from CloudSat products.
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Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars
The Cryosphere, 2018Co-Authors: Niels Souverijns, Christophe Genthon, Alexandra Gossart, Stef Lhermitte, Irina Gorodetskaya, Jacopo Grazioli, Alexis Berne, Claudio Durán-alarcón, Brice Boudevillain, Claudio ScarchilliAbstract:Abstract. In situ observations of snowfall over the Antarctic Ice Sheet are scarce. Currently, continent-wide assessments of snowfall are limited to information from the Cloud Profiling Radar on board the CloudSat satellite, which has not been evaluated up to now. In this study, snowfall derived from CloudSat is evaluated using three ground-based vertically profiling 24 GHz precipitation radars (Micro Rain Radars: MRRs). Firstly, using the MRR long-term measurement records, an assessment of the uncertainty caused by the low temporal sampling rate of CloudSat (one revisit per 2.1 to 4.5 days) is performed. The 10–90th-percentile temporal sampling uncertainty in the snowfall climatology varies between 30 % and 40 % depending on the latitudinal location and revisit time of CloudSat. Secondly, an evaluation of the snowfall climatology indicates that the CloudSat product, derived at a resolution of 1 ∘ latitude by 2 ∘ longitude, is able to accurately represent the snowfall climatology at the three MRR sites (biases 15 %), outperforming ERA-Interim. For coarser and finer resolutions, the performance drops as a result of higher omission errors by CloudSat. Moreover, the CloudSat product does not perform well in simulating individual snowfall events. Since the difference between the MRRs and the CloudSat climatology are limited and the temporal uncertainty is lower than current Climate Model Intercomparison Project Phase 5 (CMIP5) snowfall variability, our results imply that the CloudSat product is valuable for climate model evaluation purposes.
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Evaluation of CloudSat snowfall rate profiles by a comparison with in-situ micro rain radars observations in East Antarctica
2018Co-Authors: Florentin Lemonnier, Cyril Palerme, Chantal Claud, Christophe Genthon, Niels Souverijns, Alexis Berne, Claudio Durán-alarcón, Jeanbaptiste Madeleine, Nicole Van Lipzig, Irina GorodetskayaAbstract:Abstract. The Antarctic continent is a vast desert, the coldest and the most unknown area on Earth. It contains the Antarctic ice sheet, the largest continental water reservoir on Earth that could be affected by the current global warming, leading to sea level rise. The only significant supply of ice is through precipitation, which can be observed from the surface and from space. Remote sensing observations of the coastal regions and the inner continent using CloudSat radar give an estimated rate of snowfall but with uncertainties twice as large as each single measured value, whereas climate models give a range from half to twice the time and spatial average observations. The aim of this study is the evaluation of the vertical precipitation rate profiles of CloudSat radar by comparison with two surface-based Micro-Rain Radars (MRR), located at the coastal French Dumont d'Urville station and at the Belgian Princess Elisabeth station, located in the Dronning Maud Land escarpment zone, respectively. This in turn leads to a better understanding and reassessment of CloudSat uncertainties. We compared a total of four precipitation events, two per station, when CloudSat overpassed within 10 km of the stations and we compared these two different data sets at each vertical level. The correlation between both datasets is near-perfect, even though climatic and geographic conditions are different for the stations. Using different CloudSat and MRR vertical levels, we obtain 10km-space and seconds-short-time CloudSat uncertainties from −24 % up to +21 %. This confirms the robustness of the CloudSat retrievals of snowfall over Antarctica above the blind zone and justifies further analyses of this dataset.
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Evaluation of the CloudSat surface snowfall product over Antarctica using ground-based precipitation radars
2018Co-Authors: Niels Souverijns, Christophe Genthon, Alexandra Gossart, Stef Lhermitte, Irina Gorodetskaya, Jacopo Grazioli, Alexis Berne, Claudio Durán-alarcón, Brice Boudevillain, Claudio ScarchilliAbstract:Abstract. In-situ observations of snowfall over the Antarctic Ice Sheet are scarce. Currently, continent-wide assessments of snowfall are limited to information from the Cloud Profiling Radar on board of CloudSat, which has not been evaluated up to now. In this study, snowfall derived from CloudSat is evaluated using three ground-based vertically profiling 24-GHz precipitation radars (Micro Rain Radars; MRRs). Firstly, using the MRRs long-term measurement records, an assessment of the uncertainty caused by the low temporal sampling rate of CloudSat (one revisit per 2.1 to 4.5 days) is performed. The 10–90th percentile temporal sampling uncertainty on the snowfall climatology varies between 30–40 % depending on the latitudinal location and revisit time of CloudSat. Secondly, an evaluation of the snowfall climatology indicates that the CloudSat product, derived at a resolution of 1° latitude by 2° longitude, is able to accurately represent the snowfall climatology at the three MRR sites (biases
D. P. Murtagh - One of the best experts on this subject based on the ideXlab platform.
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Comparison between early Odin-SMR, Aura MLS and CloudSat retrievals of cloud ice mass in the upper tropical troposphere
Atmospheric Chemistry and Physics, 2008Co-Authors: P. Eriksson, M. Ekström, B. Rydberg, R. T. Austin, D. P. MurtaghAbstract:Emerging microwave satellite techniques are expected to provide improved global measurements of cloud ice mass. CloudSat, Aura MLS and Odin-SMR fall into this category and early cloud ice retrievals from these instruments are compared. The comparison follows the SMR retrieval product and is made for partial ice water columns above 12 km. None of the retrievals shows a significant degree of false cloud detections, the ratio between local mean values from the instruments is fairly constant and a consistent view of the geographical distribution of cloud ice is obtained. However, important differences on the absolute levels exist, where the overall mean is 9.6, 4.2 and 3.7 g m?2 for CloudSat, SMR and MLS, respectively. Assumptions about the particle size distribution (PSD) are a consideration for all three instruments and constitute the dominating retrieval uncertainty for CloudSat. The mean for CloudSat when applying the same PSD as for MLS and SMR was estimated to 6.3 g m?2. A second main consideration for MLS and SMR are the effects caused by the poorer spatial resolution: a possible vertical misplacement of retrieved values and an impact of cloud inhomogeneities. The latter effect was found to be the dominating retrieval uncertainty for SMR, giving a possible mean value range of 2.3?8.9 g m?2. The comparison indicates a common retrieval accuracy in the order of 70%. Already this number should suffice for improved validations of cloud ice parametrisation schemes in atmospheric models, but a substantially better consistency between the datasets should be attainable through an increased understanding of main retrieval error sources.
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Comparison between the first Odin-SMR, Aura MLS and CloudSat retrievals of cloud ice mass in the upper tropical troposphere
Atmospheric Chemistry and Physics Discussions, 2007Co-Authors: P. Eriksson, M. Ekström, B. Rydberg, R. T. Austin, D. P. MurtaghAbstract:Emerging microwave satellite techniques are expected to provide improved global measurements of cloud ice mass. CloudSat, Aura MLS and Odin-SMR fall into this category and first cloud ice retrievals from these instruments are compared. The comparison is made for partial ice water columns above 12 km, following the SMR retrieval product. None of the instruments shows significant false cloud detections and a consistent view of the geographical distribution of cloud ice is obtained, but differences on the absolute levels exist. CloudSat gives the lowest values, with an overall mean of 2.12 g/m2. A comparable mean for MLS is 4.30 g/m2. This relatively high mean can be an indication of overestimation of the vertical altitude of cloud ice by the MLS retrievals. The vertical response of SMR has also some uncertainty, but this does not affect the comparison between MLS and CloudSat. SMR observations are sensitive to cloud inhomogeneities inside the footprint and some compensation is required. Results in good agreement with CloudSat, both in regard of the mean and probability density functions, are obtained for a weak compensation, while a simple characterisation of the effect indicates the need for stronger compensation. The SMR mean was found to be 1.89/2.62/4.10 g/m2 for no/selected/strongest compensation, respectively. Assumptions about the particle size distribution are a consideration for all three instruments, and constitute the dominating retrieval uncertainty for CloudSat. The comparison indicates a retrieval accuracy of about 40% (3.1±1.2 g/m2). This number is already very small compared to uncertainties of cloud ice parametrisation in atmospheric models, but can be decreased further through a better understanding of main retrieval error sources.
Deborah G. Vane - One of the best experts on this subject based on the ideXlab platform.
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The CloudSat Mission and the EOS Constellation: A New Dimension of Space-Based Observation of Clouds and Precipitation
2013Co-Authors: Graeme L Stephens, Gerald G Mace, Ewan J. O'connor, Deborah G. Vane, Ronald J. Boain, Kenneth Sassen, Zhien Wang, Anthony J. Illingworth, William B. Rossow, Stephen L. DurdenAbstract:CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004 and, once launched, CloudSat will orbit in formation as part of a constellation of satellites including NASA's Aqua and Aura satellites, a NASA-CNES lidar satellite (P-C) and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the P-C lidar footprint and the other measurements of the EOS constellation. The precision of this overlap creates a unique multi-satellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profile of cloud properties provided by CloudSat fills a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring the vertical profile of cloud properties requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with active and passive data from other sensors of the constellation. This paper describes the underpinning science, and gives an overview of the mission, and provides some idea of the expected products and anticipated application of these products. Notably, the CloudSat mission is expected to provide new knowledge about global cloudiness, stimulating new areas of research on clouds including data assimilation and cloud parameterization. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA/JPL, the Canadian Space Agency, Colorado State University, the US Air Force, and the US Department of Energy.
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comparison of regime sorted tropical cloud profiles observed by CloudSat with geos5 analyses and two general circulation model simulations
Journal of Geophysical Research, 2011Co-Authors: Jonathan H. Jiang, Graeme L Stephens, Deborah G. Vane, João Paulo Teixeira, Andrew Gettelman, Xianglei Huang, V. S. PerunAbstract:[1] CloudSat observed tropical liquid and ice water content (L/IWC, Version 4) profiles are compared with GEOS5 analyses, NCAR-CAM3, and GFDL-AM2 simulations. Both the analyses and free-running general circulation models (GCMs) underestimate IWC in the upper troposphere, with the simulated ice water paths (IWPs) being 22% (GEOS5), 9% (CAM3), and 54% (AM2) of the CloudSat retrieval. For liquid clouds, GEOS5 produces the closest match to CloudSat, with a distinct peak in LWC around 1.5–2 km. CAM3 and AM2 generate liquid clouds in a broad vertical layer in the lower and middle troposphere, resulting in slightly higher column-integrated liquid water path (LWP) than CloudSat, despite the fact that their LWC in the boundary layer is only 60%–70% of CloudSat. The data assimilation model and two GCMs produce substantial middle-level clouds, more than the CloudSat retrieval. We sort the cloud profiles by midtropospheric vertical velocity (ω500), sea surface temperature (SST), and lower tropospheric stability (LTS). The high clouds in the models are concentrated in large-scale ascending, warm SST, and low LTS regimes, consistent with the CloudSat observation. The CAM3 and AM2 model-simulated middle-level clouds are strongly correlated with ω500 but less clustered in the domains of SST and LTS. For low clouds, both CloudSat and GOES5 analyses show the preferential distribution of low clouds in regions of large-scale subsidence, relatively cold SST (SST 15 K), while CAM3 and AM2 low clouds are strongly controlled by SST and LTS and only weakly correlated with ω500. Exclusion of precipitating scenes would reduce the tropical mean CloudSat LWP and IWP by 73% and 48%, respectively, which does not fully explain the model-data discrepancies.
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Comparison of regime‐sorted tropical cloud profiles observed by CloudSat with GEOS5 analyses and two general circulation model simulations
Journal of Geophysical Research, 2011Co-Authors: Jonathan H. Jiang, Graeme L Stephens, Deborah G. Vane, João Paulo Teixeira, Andrew Gettelman, Xianglei Huang, V. S. PerunAbstract:[1] CloudSat observed tropical liquid and ice water content (L/IWC, Version 4) profiles are compared with GEOS5 analyses, NCAR-CAM3, and GFDL-AM2 simulations. Both the analyses and free-running general circulation models (GCMs) underestimate IWC in the upper troposphere, with the simulated ice water paths (IWPs) being 22% (GEOS5), 9% (CAM3), and 54% (AM2) of the CloudSat retrieval. For liquid clouds, GEOS5 produces the closest match to CloudSat, with a distinct peak in LWC around 1.5–2 km. CAM3 and AM2 generate liquid clouds in a broad vertical layer in the lower and middle troposphere, resulting in slightly higher column-integrated liquid water path (LWP) than CloudSat, despite the fact that their LWC in the boundary layer is only 60%–70% of CloudSat. The data assimilation model and two GCMs produce substantial middle-level clouds, more than the CloudSat retrieval. We sort the cloud profiles by midtropospheric vertical velocity (ω500), sea surface temperature (SST), and lower tropospheric stability (LTS). The high clouds in the models are concentrated in large-scale ascending, warm SST, and low LTS regimes, consistent with the CloudSat observation. The CAM3 and AM2 model-simulated middle-level clouds are strongly correlated with ω500 but less clustered in the domains of SST and LTS. For low clouds, both CloudSat and GOES5 analyses show the preferential distribution of low clouds in regions of large-scale subsidence, relatively cold SST (SST 15 K), while CAM3 and AM2 low clouds are strongly controlled by SST and LTS and only weakly correlated with ω500. Exclusion of precipitating scenes would reduce the tropical mean CloudSat LWP and IWP by 73% and 48%, respectively, which does not fully explain the model-data discrepancies.
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Radiative effects of upper tropospheric clouds observed by Aura MLS and CloudSat
Geophysical Research Letters, 2009Co-Authors: Jonathan H. Jiang, Graeme L Stephens, Deborah G. Vane, Nathaniel J. LiveseyAbstract:[1] The radiative effects of upper tropospheric (UT) clouds observed by CloudSat and Aura MLS during June-July-August 2008 are examined and contrasted. We find that the UT cloud occurrence frequency observed by MLS is more than CloudSat by 4–10% in the tropical average and by 40∼60% near the tropopause in the deep convective regions. The clouds detected by MLS but missed by CloudSat (denoted as TCC) typically have visible optical thickness less than 0.2. TCC produce a tropical-mean net warming of 3.5 W/m2 at the top-of-atmosphere and net cooling of 1.2 W/m2 at the surface. They induce a net radiative heating in the UT. Their heating rate at 200 hPa is ∼0.35 K/day in the tropical-mean and ∼0.8 K/day over South Asia, which is about 3–4 times the clear-sky radiative heating rate. Hence, they are potentially important in affecting the mass transport rates from the troposphere to the stratosphere.
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Evaluating CloudSat ice water content retrievals using a cloud‐resolving model: Sensitivities to frozen particle properties
Journal of Geophysical Research, 2008Co-Authors: Christopher P. Woods, Graeme L Stephens, Richard T. Austin, Duane E. Waliser, Deborah G. VaneAbstract:[1] The A-Train satellite constellation has dramatically increased the temporal and spatial coverage of atmospheric ice water content estimates. The new data are derived by retrieval algorithms designed to estimate atmospheric cloud ice water content from remotely sensed measurements. Such retrieval algorithms rely on simplifying assumptions regarding the characteristics of ice particles in the atmosphere. In this study, the sensitivities of CloudSat ice water content retrievals to frozen particle characteristics are tested by generating CloudSat-like retrievals from profiles of known ice water content. CloudSat actively measures vertical profiles of radar reflectivity in clouds with a 94-GHz cloud-profiling radar. Ice water content is retrieved in each cloudy profile at temperatures below 0°C. To assess the CloudSat radar-only ice water content retrieval algorithm (version 5.0 in Release 3 [R03] and version 5.1 in Release 4 [R04] of 2B-CWC-RO), we apply a 94-GHz reflectivity simulator to profiles of ice water content generated by a cloud-resolving numerical model and comprising various frozen particle species (ice, snow, and graupel). The CloudSat ice water content retrieval algorithm is applied to the profiles of simulated reflectivity, and the results are compared to the modeled profiles of known frozen water mass. The results from each version of the algorithm are shown to be sensitive to the characteristics of the frozen particle size distributions and particle densities. Tests of version 5.0 indicate that height varying information could improve retrievals. Despite the addition of a height varying component implemented in version 5.1, similar positive biases are indicated in the tests of each algorithm.
Stephen L. Durden - One of the best experts on this subject based on the ideXlab platform.
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The CloudSat Mission and the EOS Constellation: A New Dimension of Space-Based Observation of Clouds and Precipitation
2013Co-Authors: Graeme L Stephens, Gerald G Mace, Ewan J. O'connor, Deborah G. Vane, Ronald J. Boain, Kenneth Sassen, Zhien Wang, Anthony J. Illingworth, William B. Rossow, Stephen L. DurdenAbstract:CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004 and, once launched, CloudSat will orbit in formation as part of a constellation of satellites including NASA's Aqua and Aura satellites, a NASA-CNES lidar satellite (P-C) and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the P-C lidar footprint and the other measurements of the EOS constellation. The precision of this overlap creates a unique multi-satellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profile of cloud properties provided by CloudSat fills a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring the vertical profile of cloud properties requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with active and passive data from other sensors of the constellation. This paper describes the underpinning science, and gives an overview of the mission, and provides some idea of the expected products and anticipated application of these products. Notably, the CloudSat mission is expected to provide new knowledge about global cloudiness, stimulating new areas of research on clouds including data assimilation and cloud parameterization. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA/JPL, the Canadian Space Agency, Colorado State University, the US Air Force, and the US Department of Energy.
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CloudSat W-Band Radar Measurements of Surface Backscatter
IEEE Geoscience and Remote Sensing Letters, 2011Co-Authors: Stephen L. Durden, Simone Tanelli, G. DobrowalskiAbstract:The authors examine the characteristics of the W-band surface backscatter cross section using data from the 94-GHz cloud profiling radar on the CloudSat mission. These data from CloudSat represent the first global measurements of surface properties at 94 GHz. The authors use these data to investigate seasonal changes in surface backscatter over both land and ocean. The authors also make use of a limited set of off-nadir data to investigate behavior of W-band backscatter from the ocean versus wind speed for incidence angles up to 17°.
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Comparisons of global cloud ice from MLS, CloudSat, and correlative data sets
Journal of Geophysical Research, 2009Co-Authors: Richard T. Austin, Andrew J. Heymsfield, Stephen L. Durden, Jonathan H. Jiang, Min Deng, Alyn Lambert, Nathaniel J. Livesey, Greg M. McfarquharAbstract:[1] Aura Microwave Limb Sounder (MLS) version 2.2 (V2.2) and CloudSat R04 (release 4) ice water content (IWC) and partial-column ice water path (pIWP) measurements are analyzed and compared to other correlative data sets. The MLS IWC, representing an average over ∼300 × 7 × 4 km3 volume, is retrieved at 215–268 hPa with precision varying between 0.06 and 1 mg/m3. The MLS pIWP products, representing the partial columns over ∼100 × 7 km2 area with the bottom at ∼8, ∼6, and ∼11 km for 115, 240, and 640 GHz, have estimated precisions of 5, 1.5, and 0.8 g/m2, respectively. CloudSat, on the other hand, shows a minimum detectable sensitivity of −31 dBZ in the reflectivity measurement at 94 GHz. CloudSat IWC is an average over ∼1.8 × 1.4 × 0.5 km3 volume, and its precision varies from 0.4 mg/m3 at 8 km to 1.6 mg/m3 at 12 km. The estimated single-profile precision for CloudSat IWP is ∼9 g/m2. However, these measurements are associated with relatively large systematic error, mostly due to uncertainties in the retrieval assumptions about microphysics, which lead to relatively poor accuracy compared to measurement precision. To characterize systematic differences among various observations and those derived from models, we employ the normalized probability density function (pdf) in the comparisons. CloudSat IWC shows generally consistent slopes of pdf distribution with in situ observations, particularly at ∼12 km where the in situ data come mostly from long-leg flights. Despite similar IWC morphology found between MLS and CloudSat observations, CloudSat R04 IWC retrieval is higher compared to MLS, especially at 14–17 km where the MLS technique is not limited by sensitivity saturation. The MLS and CloudSat IWC pdf's agree well in the overlapped sensitivity range with relative difference 500 g/m2.
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CloudSat and A-Train Observations of Tropical Cyclones
The Open Atmospheric Science Journal, 2009Co-Authors: Stephen L. Durden, Simone Tanelli, Gregg DobrowalskiAbstract:The CloudSat 94-GHz Cloud Profiling Radar was designed to provide global information on the vertical structure of clouds. It was launched in April 2006, joining the A-Train of earth science satellites. Although primarily focused on clouds and climate, the CloudSat radar also provides a unique view of the vertical structure of clouds in tropical cyclones. The authors use data from CloudSat and other A-Train satellite constellation missions to examine tropical cyclone cloud properties. They consider several case studies and then examine cloud statistics based on seventeen tropical cyclone overpasses. In addition to the new qualitative view of cloud structure provided by CloudSat, the CloudSat and other ATrain products also contain quantitative estimates of cloud properties. Although the accuracy of these products in tropical cyclones is not validated by direct comparison, the authors do find reasonable agreement with previous in situ measurements.
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CloudSat mission performance and early science after the first year of operation
Journal of Geophysical Research, 2008Co-Authors: Graeme L Stephens, Gerald G Mace, Stephen L. Durden, Simone Tanelli, Philip T. Partain, D Vane, E Im, Mark J Rokey, Don Reinke, R. T. AustinAbstract:[1] This paper reports on the early mission performance of the radar and other major aspects of the CloudSat mission. The CloudSat cloud profiling radar (CPR) has been operating since 2 June 2006 and has proven to be remarkably stable since turn-on. A number of products have been developed using these space-borne radar data as principal inputs. Combined with other A-Train sensor data, these new observations offer unique, global views of the vertical structure of clouds and precipitation jointly. Approximately 11% of clouds detected over the global oceans produce precipitation that, in all likelihood, reaches the surface. Warm precipitating clouds are both wetter and composed of larger particles than nonprecipitating clouds. The frequency of precipitation increases significantly with increasing cloud depth, and the increased depth and water path of precipitating clouds leads to increased optical depths and substantially more sunlight reflected from precipitating clouds compared to than nonprecipitating warm clouds. The CloudSat observations also provide an authoritative estimate of global ice water paths. The observed ice water paths are larger than those predicted from most climate models. CloudSat observations also indicate that clouds radiatively heat the global mean atmospheric column (relative to clear skies) by about 10 Wm−2. Although this heating appears to be contributed almost equally by solar and infrared absorption, the latter contribution is shown to vary significantly with latitude being influenced by the predominant cloud structures of the different region in questions.