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

We have identified an error in the analysis related to Fig. 10 in Mason et al. (2014). The error affected how counts of cloud thermodynamic phase were normalized in preparation for the clustering algorithm, and therefore the resultant histograms were incorrect. Correcting the analysis led to slight changes in some resulting cloud structure profiles; an additional cluster was added to better distinguish between features. The error did not significantly alter the findings of the analysis nor those of the paper overall.

The corrected figure is reproduced below. The last sentence of the first paragraph of page 6200 should

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

Abstract

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

Abstract

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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Alain Protat, Surendra Rauniyar, Julien Delanoë, Emmanuel Fontaine, and Alfons Schwarzenboeck

Abstract

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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Anna E. Luebke, Julien Delanoë, Vincent Noel, Hélène Chepfer, and Bjorn Stevens

EARTHCARE-EECLAT JOINT WORKSHOP 2018

What: This was the first joint workshop between the German Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) community and the French "Expecting EarthCARE Learning from the A-Train" (EECLAT) community. The aim of the workshop was to share recent work related to remote sensing of clouds, aerosols, precipitation, and radiation and to strengthen cooperation in preparation for the upcoming EarthCARE satellite mission.

When: 29–31 January 2018

Where: Colmar, France

The German Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) community and the French Expecting EarthCARE Learning from the A-Train (EECLAT) community, both dedicated to the

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

Abstract

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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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

Abstract

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

Abstract

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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Marie Mazoyer, Didier Ricard, Gwendal Rivière, Julien Delanoë, Philippe Arbogast, Benoit Vié, Christine Lac, Quitterie Cazenave, and Jacques Pelon

Abstract

This study investigates diabatic processes along the warm conveyor belt (WCB) of a deep extratropical cyclone observed in the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX). The aim is to investigate the effect of two different microphysics schemes, the one-moment scheme ICE3 and the quasi two-moment scheme LIMA, on the WCB and the ridge building downstream. ICE3 and LIMA also differ in the processes of vapor deposition on hydrometeors in cold and mixed-phase clouds. Latent heating in ICE3 is found to be dominated by deposition on ice while the heating in LIMA is distributed among depositions on ice, snow, and graupel. ICE3 is the scheme leading to the largest number of WCB trajectories (30% more than LIMA) due to greater heating rates over larger areas. The consequence is that the size of the upper-level ridge grows more rapidly in ICE3 than LIMA, albeit with some exceptions in localized regions of the cyclonic branch of the WCB. A comparison with various observations (airborne remote sensing measurements, dropsondes, and satellite data) is then performed. Below the melting layer, the observed reflectivity is rather well reproduced by the model. Above the melting layer, in the middle of the troposphere, the reflectivity and retrieved ice water content are largely underestimated by both schemes while at upper levels, the ICE3 scheme performs much better than LIMA in agreement with a closer representation of the observed winds by ICE3. These results underline the strong sensitivity of upper-level dynamics to ice-related processes.

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