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  • Author or Editor: Julien Delanoë x
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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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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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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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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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Andrew J. Heymsfield, Alain Protat, Dominique Bouniol, Richard T. Austin, Robin J. Hogan, Julien Delanoë, Hajime Okamoto, Kaori Sato, Gerd-Jan van Zadelhoff, David P. Donovan, and Zhien Wang

Abstract

Vertical profiles of ice water content (IWC) can now be derived globally from spaceborne cloud satellite radar (CloudSat) data. Integrating these data with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data may further increase accuracy. Evaluations of the accuracy of IWC retrieved from radar alone and together with other measurements are now essential. A forward model employing aircraft Lagrangian spiral descents through mid- and low-latitude ice clouds is used to estimate profiles of what a lidar and conventional and Doppler radar would sense. Radar reflectivity Ze and Doppler fall speed at multiple wavelengths and extinction in visible wavelengths were derived from particle size distributions and shape data, constrained by IWC that were measured directly in most instances. These data were provided to eight teams that together cover 10 retrieval methods. Almost 3400 vertically distributed points from 19 clouds were used. Approximate cloud optical depths ranged from below 1 to more than 50. The teams returned retrieval IWC profiles that were evaluated in seven different ways to identify the amount and sources of errors. The mean (median) ratio of the retrieved-to-measured IWC was 1.15 (1.03) ± 0.66 for all teams, 1.08 (1.00) ± 0.60 for those employing a lidar–radar approach, and 1.27 (1.12) ± 0.78 for the standard CloudSat radar–visible optical depth algorithm for Ze > −28 dBZe. The ratios for the groups employing the lidar–radar approach and the radar–visible optical depth algorithm may be lower by as much as 25% because of uncertainties in the extinction in small ice particles provided to the groups. Retrievals from future spaceborne radar using reflectivity–Doppler fall speeds show considerable promise. A lidar–radar approach, as applied to measurements from CALIPSO and CloudSat, is useful only in a narrow range of ice water paths (IWP) (40 < IWP < 100 g m−2). Because of the use of the Rayleigh approximation at high reflectivities in some of the algorithms and differences in the way nonspherical particles and Mie effects are considered, IWC retrievals in regions of radar reflectivity at 94 GHz exceeding about 5 dBZe are subject to uncertainties of ±50%.

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Dominique Bouniol, Alain Protat, Julien Delanoë, Jacques Pelon, Jean-Marcel Piriou, François Bouyssel, Adrian M. Tompkins, Damian R. Wilson, Yohann Morille, Martial Haeffelin, Ewan J. O’Connor, Robin J. Hogan, Anthony J. Illingworth, David P. Donovan, and Henk-Klein Baltink

Abstract

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud parameterization.

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