Dominique Bouniol

Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations

Cloud fraction, liquid and ice water contents derived from long-term radar, lidar and microwave radiometer data are systematically compared to models to quantify and improve their performance.

Testing IWC retrieval methods using radar and ancillary measurements with in-situ data

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...

Statistical properties of the normalized ice particle size distribution

[i] Testud et al. (2001) have recently developed a formalism, known as the normalized particle size distribution (PSD), which consists in scaling the diameter and concentration axes in such a way that the normalized PSDs are independent of water content and mean volume-weighted diameter. In this paper we investigate the statistical properties of the normalized PSD for the particular case of ice clouds, which are known to play a crucial role in the Earths radiation balance. To do so, an extensive database of airborne in situ microphysical measurements has been constructed. A remarkable stability in shape of the normalized PSD is obtained. The impact of using a single analytical shape to represent all PSDs in the database is estimated through an error analysis on the instrumental (radar reflectivity and attenuation) and cloud (ice water content, effective radius, terminal fall velocity of ice crystals, visible extinction) properties. This resulted in a roughly unbiased estimate of the instrumental and cloud parameters, with small standard deviations ranging from 5 to 12%. This error is found to be roughly independent of the temperature range. This stability in shape and its single analytical approximation implies that two parameters are now sufficient to describe any normalized PSD in ice clouds: the intercept parameter N* 0 and the mean volume-weighted diameter D m . Statistical relationships (parameterizations) between N* 0 and D m have then been evaluated in order to reduce again the number of unknowns. It has been shown that a parameterization of N* 0 and D m by temperature could not be envisaged to retrieve the cloud parameters. Nevertheless, D m -T and mean maximum dimension diameter -T parameterizations have been derived and compared to the parameterization of Kristjansson et al. (2000) currently used to characterize particle size in climate models. The new parameterization generally produces larger particle sizes at any temperature than the Kristjansson et al. (2000) parameterization. These new parameterizations are believed to better represent particle size at global scale, owing to a better representativity of the in situ microphysical database used to derive it. We then evaluated the potential of a direct N* 0 -D m relationship. While the model parameterized by temperature produces strong errors on the cloud parameters, the N* 0 -D m model parameterized by radar reflectivity produces accurate cloud parameters (less than 3% bias and 16% standard deviation). This result implies that the cloud parameters can be estimated from the estimate of only one parameter of the normalized PSD (N* 0 or D m ) and a radar reflectivity measurement.

The retrieval of ice-cloud properties from cloud radar and lidar synergy

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 z0 must be known. Because the algorithms are stable for inversion performed from range z0 toward the emitter, z0 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.

Using Continuous Ground-Based Radar and Lidar Measurements for Evaluating the Representation of Clouds in Four Operational Models

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.

Diurnal and Seasonal Cycles of Cloud Occurrences, Types, and Radiative Impact over West Africa

AbstractThis study focuses on the occurrence and type of clouds observed in West Africa, a subject that has been neither much documented nor quantified. It takes advantage of data collected above Niamey, Niger, in 2006 with the Atmospheric Radiation Measurement (ARM) Mobile Facility. A survey of cloud characteristics inferred from ground measurements is presented with a focus on their seasonal evolution and diurnal cycle. Four types of clouds are distinguished: high-level clouds, deep convective clouds, shallow convective clouds, and midlevel clouds. A frequent occurrence of the latter clouds located at the top of the Saharan air layer is highlighted. High-level clouds are ubiquitous throughout the period whereas shallow convective clouds are mainly noticeable during the core of the monsoon. The diurnal cycle of each cloud category and its seasonal evolution are investigated. CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data are used to demonstrate that these f...

Absolute Calibration of 94/95-GHz Radars Using Rain

Abstract Absolute calibration of cloud radars is very difficult. A new method is proposed for 94/95-GHz radars that exploits the fact that at this frequency, the radar reflectivity factor of rain measured at a range of 250 m is approximately constant at 19 dBZ for rain rates between 3 and 10 mm h–1, due to the combined effects of extinction and non-Rayleigh scattering. The standard deviation of around 1.5 dB is due to natural variations in the number concentration of drops and is consistent with the variation predicted from theory, but averaging over a number of different rain events over a month or more should be sufficient to reduce the calibration error to less than 1 dB. A thin layer of rainwater on the radomes of the 94-GHz radar at Chilbolton, in southern England, was found to cause a two-way attenuation of between 9 and 14 dB, but it is shown here that the technique may be successfully implemented by operating the radar at a low elevation angle and employing a shelter to keep it dry. Most 94-GHz cl...

Independent Evaluation of the Ability of Spaceborne Radar and Lidar to Retrieve the Microphysical and Radiative Properties of Ice Clouds

Abstract The combination of radar and lidar in space offers the unique potential to retrieve vertical profiles of ice water content and particle size globally, and two algorithms developed recently claim to have overcome the principal difficulty with this approach—that of correcting the lidar signal for extinction. In this paper “blind tests” of these algorithms are carried out, using realistic 94-GHz radar and 355-nm lidar backscatter profiles simulated from aircraft-measured size spectra, and including the effects of molecular scattering, multiple scattering, and instrument noise. Radiation calculations are performed on the true and retrieved microphysical profiles to estimate the accuracy with which radiative flux profiles could be inferred remotely. It is found that the visible extinction profile can be retrieved independent of assumptions on the nature of the size distribution, the habit of the particles, the mean extinction-to-backscatter ratio, or errors in instrument calibration. Local errors in r...

Comparison of Airborne and Spaceborne 95-GHz Radar Reflectivities and Evaluation of Multiple Scattering Effects in Spaceborne Measurements

Abstract This paper provides an evaluation of the level 1 (reflectivity) CloudSat products by making use of coincident measurements collected by an airborne 95-GHz radar during the African Monsoon Multidisciplinary Analysis (AMMA) experiment that took place in summer 2006 over West Africa. In a first step the airborne radar calibration is assessed. Collocated measurements of the spaceborne and airborne radars within the ice anvil of a mesoscale convective system are then compared. Several aspects are interesting in this comparison: First, both instruments exhibit attenuation within the ice part of the convective system, which suggests either the presence of a significant amount of supercooled liquid water above the melting layer or the presence of wet and very dense ice. Second, from the differences in the observed reflectivity values, a multiple scattering enhancement of at least 2.5 dB in the CloudSat reflectivities at flight altitude is estimated. The main conclusion of this paper is that in such thick...

Le projet Rali: Combinaison d'un radar et d'un lidar pour l'étude des nuages faiblement précipitants

Le projet Rali (radar-lidar) developpe un nouveau moyen dobservation des nuages faiblement precipitants, qui repose sur la combinaison dun radar et dun lidar. On decrit ici les instruments utilises ou mis au point dans le cadre de Rali et les algorithmes de restitution des parametres des nuages, puis on presente quelques exemples des premiers resultats scientifiques obtenus. A lhorizon mars 2005, linstrument Rali sera integre dans les nouveaux avions de recherche francais et permettra a la communaute scientifique dacceder a des observations couplees de la dynamique, de la microphysique et des proprietes radiatives des nuages faiblement precipitants et des aerosols.