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Shannon Mason, Christian Jakob, Alain Protat, and Julien Delanoë
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Alain Protat, Surendra Rauniyar, Julien Delanoë, Emmanuel Fontaine, and Alfons Schwarzenboeck

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Attenuation of the W-band (95 GHz) radar signal by atmospheric ice particles has long been neglected in cloud microphysics studies. In this work, 95-GHz airborne multibeam cloud radar observations in tropical stratiform ice anvils are used to estimate vertical profiles of 95-GHz attenuation. Two techniques are developed and compared, using very different assumptions. The first technique examines statistical reflectivity differences between repeated aircraft passes through the same cloud mass at different altitudes. The second technique exploits reflectivity differences between two different pathlengths through the same cloud, using the multibeam capabilities of the cloud radar. Using the first technique, the two-way attenuation coefficient produced by stratiform ice particles ranges between 1 and 1.6 dB km−1 for reflectivities between 13 and 18 dBZ, with an expected increase of attenuation with reflectivity. Using the second technique, the multibeam results confirm these high attenuation coefficient values and expand the reflectivity range, with typical attenuation coefficient values of up to 3–4 dB km−1 for reflectivities of 20 dBZ. The potential impact of attenuation on precipitating-ice-cloud microphysics retrievals is quantified using vertical profiles of the mean and the 99th percentile of ice water content derived from noncorrected and attenuation-corrected reflectivities. A large impact is found on the 99th percentile of ice water content, which increases by 0.3–0.4 g m−3 up to 11-km height. Finally, T-matrix calculations of attenuation constrained by measured particle size distributions, ice crystal mass–size, and projected area–size relationships are found to largely underestimate cloud radar attenuation estimates.

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Shannon Mason, Christian Jakob, Alain Protat, and Julien Delanoë

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Clouds strongly affect the absorption and reflection of shortwave and longwave radiation in the atmosphere. A key bias in climate models is related to excess absorbed shortwave radiation in the high-latitude Southern Ocean. Model evaluation studies attribute these biases in part to midtopped clouds, and observations confirm significant midtopped clouds in the zone of interest. However, it is not yet clear what cloud properties can be attributed to the deficit in modeled clouds. Present approaches using observed cloud regimes do not sufficiently differentiate between potentially distinct types of midtopped clouds and their meteorological contexts.

This study presents a refined set of midtopped cloud subregimes for the high-latitude Southern Ocean, which are distinct in their dynamical and thermodynamic background states. Active satellite observations from CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) are used to study the macrophysical structure and microphysical properties of the new cloud regimes. The subgrid-scale variability of cloud structure and microphysics is quantified within the cloud regimes by identifying representative physical cloud profiles at high resolution from the radar–lidar (DARDAR) cloud classification mask.

The midtopped cloud subregimes distinguish between stratiform clouds under a high inversion and moderate subsidence; an optically thin cold-air advection cloud regime occurring under weak subsidence and including altostratus over low clouds; optically thick clouds with frequent deep structures under weak ascent and warm midlevel anomalies; and a midlevel convective cloud regime associated with strong ascent and warm advection. The new midtopped cloud regimes for the high-latitude Southern Ocean will provide a refined tool for model evaluation and the attribution of shortwave radiation biases to distinct cloud processes and properties.

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Thorwald H. M. Stein, Julien Delanoë, and Robin J. Hogan

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The A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar–lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function.

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Julien Delanoë, A. Protat, D. Bouniol, Andrew Heymsfield, Aaron Bansemer, and Philip Brown

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The paper describes an original method that is complementary to the radar–lidar algorithm method to characterize ice cloud properties. The method makes use of two measurements from a Doppler cloud radar (35 or 95 GHz), namely, the radar reflectivity and the Doppler velocity, to recover the effective radius of crystals, the terminal fall velocity of hydrometeors, the ice water content, and the visible extinction from which the optical depth can be estimated. This radar method relies on the concept of scaling the ice particle size distribution. An error analysis using an extensive in situ airborne microphysical database shows that the expected errors on ice water content and extinction are around 30%–40% and 60%, respectively, including both a calibration error and a bias on the terminal fall velocity of the particles, which all translate into errors in the retrieval of the density–diameter and area–diameter relationships. Comparisons with the radar–lidar method in areas sampled by the two instruments also demonstrate the accuracy of this new method for retrieval of the cloud properties, with a roughly unbiased estimate of all cloud properties with respect to the radar–lidar method. This method is being systematically applied to the cloud radar measurements collected over the three-instrumented sites of the European Cloudnet project to validate the representation of ice clouds in numerical weather prediction models and to build a cloud climatology.

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Anna E. Luebke, Julien Delanoë, Vincent Noel, Hélène Chepfer, and Bjorn Stevens
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Yann Blanchard, Jacques Pelon, Edwin W. Eloranta, Kenneth P. Moran, Julien Delanoë, and Geneviève Sèze

Abstract

Active remote sensing instruments such as lidar and radar allow one to accurately detect the presence of clouds and give information on their vertical structure and phase. To better address cloud radiative impact over the Arctic area, a combined analysis based on lidar and radar ground-based and A-Train satellite measurements was carried out to evaluate the efficiency of cloud detection, as well as cloud type and vertical distribution, over the Eureka station (80°N, 86°W) between June 2006 and May 2010. Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat data were first compared with independent ground-based cloud measurements. Seasonal and monthly trends from independent observations were found to be similar among all datasets except when compared with the weather station observations because of the large reported fraction of ice crystals suspended in the lower troposphere in winter. Further investigations focused on satellite observations that are collocated in space and time with ground-based data. Cloud fraction occurrences from ground-based instruments correlated well with both CALIPSO operational products and combined CALIPSOCloudSat retrievals, with a hit rate of 85%. The hit rate was only 77% for CloudSat products. The misdetections were mainly attributed to 1) undetected low-level clouds as a result of sensitivity loss and 2) missed clouds because of the distance between the satellite track and the station. The spaceborne lidar–radar synergy was found to be essential to have a complete picture of the cloud vertical profile down to 2 km. Errors are quantified and discussed.

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Claire Tinel, Jacques Testud, Jacques Pelon, Robin J. Hogan, Alain Protat, Julien Delanoë, and Dominique Bouniol

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Clouds are an important component of the earth’s climate system. A better description of their microphysical properties is needed to improve radiative transfer calculations. In the framework of the Earth, Clouds, Aerosols, and Radiation Explorer (EarthCARE) mission preparation, the radar–lidar (RALI) airborne system, developed at L’Institut Pierre Simon Laplace (France), can be used as an airborne demonstrator. This paper presents an original method that combines cloud radar (94–95 GHz) and lidar data to derive the radiative and microphysical properties of clouds. It combines the apparent backscatter reflectivity from the radar and the apparent backscatter coefficient from the lidar. The principle of this algorithm relies on the use of a relationship between the extinction coefficient and the radar specific attenuation, derived from airborne microphysical data and Mie scattering calculations. To solve radar and lidar equations in the cloud region where signals can be obtained from both instruments, the extinction coefficients at some reference range z 0 must be known. Because the algorithms are stable for inversion performed from range z 0 toward the emitter, z 0 is chosen at the farther cloud boundary as observed by the lidar. Then, making an assumption of a relationship between extinction coefficient and backscattering coefficient, the whole extinction coefficient, the apparent reflectivity, cloud physical parameters, the effective radius, and ice water content profiles are derived. This algorithm is applied to a blind test for downward-looking instruments where the original profiles are derived from in situ measurements. It is also applied to real lidar and radar data, obtained during the 1998 Cloud Lidar and Radar Experiment (CLARE’98) field project when a prototype airborne RALI system was flown pointing at nadir. The results from the synergetic algorithm agree reasonably well with the in situ measurements.

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Julien Delanoë, Alain Protat, Olivier Jourdan, Jacques Pelon, Mathieu Papazzoni, Régis Dupuy, Jean-Francois Gayet, and Caroline Jouan

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This study illustrates the high potential of RALI, the French airborne radar–lidar instrument, for studying cloud processes and evaluating satellite products when satellite overpasses are available. For an Arctic nimbostratus ice cloud collected on 1 April 2008 during the Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, of Climate, Chemistry, Aerosols, and Transport (POLARCAT) campaign, the capability of this synergistic instrument to retrieve cloud properties and to characterize the cloud phase at scales smaller than a kilometer, which is crucial for cloud process analysis, is demonstrated. A variational approach, which combines radar and lidar, is used to retrieve the ice-water content (IWC), extinction, and effective radius. The combination of radar and lidar is shown to provide better retrievals than do stand-alone methods and, in general, the radar overestimates and the lidar underestimates IWC. As the sampled ice cloud was simultaneously observed by CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellites, a new way to assess satellite cloud products by combining in situ and active remote sensing measurements is identified. It was then possible to compare RALI to three satellite ice cloud products: CloudSat, CALIPSO, and the Cloud-Aerosol-Water-Radiation Interactions (ICARE) center’s radar–lidar project (DARDAR).

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Julien Delanoë, Alain Protat, Jean-Paul Vinson, Williams Brett, Christophe Caudoux, Fabrice Bertrand, Jacques Parent du Chatelet, Ruben Hallali, Laurent Barthes, Martial Haeffelin, and Jean-Charles Dupont

Abstract

Doppler cloud radars are amazing tools to characterize cloud and fog properties and to improve their representation in models. However, commercially available cloud radars (35 and 95 GHz) are still very expensive, which hinders their widespread deployment. This study presents the development of a lower-cost semioperational 95-GHz Doppler cloud radar called the Bistatic Radar System for Atmospheric Studies (BASTA). To drastically reduce the cost of the instrument, a different approach is used compared to traditional pulsed radars: instead of transmitting a large amount of energy for a very short time period (as a pulse), a lower amount of energy is transmitted continuously. By using a specific signal processing technique, the radar can challenge expensive radars and provide high-quality measurements of cloud and fog. The latest version of the instrument has a sensitivity of about −50 dBZ at 1 km for 3-s integration and a vertical resolution of 25 m. The BASTA radar currently uses four successive modes for specific applications: the 12.5-m vertical resolution mode is dedicated to fog and low clouds, the 25-m mode is for liquid and ice midtropospheric clouds, and the 100- and 200-m modes are ideal for optically thin high-level ice clouds. The advantages of such a radar for calibration procedures and field operations are also highlighted. The radar comes with a set of products dedicated to cloud and fog studies. For instance, cloud mask, corrected Doppler velocity, and multimode products combining the high-sensitivity mode and high-resolution modes are provided.

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