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.

A likely important feature of the poorly understood aerosol‐cloud interactions over the Southern Ocean (SO) is the dominant role of sea spray aerosol, versus terrestrial aerosol. Ice nucleating particles (INPs), or particles required for heterogeneous ice nucleation, present over the SO have not been studied in several decades. In this study, boundary layer aerosol properties and immersion freezing INP number concentrations (nINPs) were measured during a ship campaign that occurred south of Australia (down to 53°S) in March–April 2016. Ocean surface chlorophyll a concentrations ranged from 0.11 to 1.77 mg/m3, and nINPs were a factor of 100 lower than historical surveys, ranging from 0.38 to 4.6 m−3 at −20 °C. The INP population included organic heat‐stable material, with contributions from heat‐labile material. Lower INP source potentials of SO seawater samples compared to Arctic seawater were consistent with lower ice nucleating site densities in this study compared to north Atlantic air masses.

Abstract. Ground-based remote sensing observatories have a crucial role to play in providing data to improve our understanding of atmospheric processes, to test the performance of atmospheric models, and to develop new methods for future space-borne observations. Institut Pierre Simon Laplace, a French research institute in environmental sciences, created the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA), an atmospheric observatory with these goals in mind. Today SIRTA, located 20 km south of Paris, operates a suite a state-of-the-art active and passive remote sensing instruments dedicated to routine monitoring of cloud and aerosol properties, and key atmospheric parameters. Detailed description of the state of the atmospheric column is progressively archived and made accessible to the scientific community. This paper describes the SIRTA infrastructure and database, and provides an overview of the scientific research associated with the observatory. Researchers using SIRTA data conduct research on atmospheric processes involving complex interactions between clouds, aerosols and radiative and dynamic processes in the atmospheric column. Atmospheric modellers working with SIRTA observations develop new methods to test their models and innovative analyses to improve parametric representations of sub-grid processes that must be accounted for in the model. SIRTA provides the means to develop data interpretation tools for future active remote sensing missions in space (e.g. CloudSatCorrespondence to: M. Haeffelin (martial.haeffelin@lmd.polytechnique.fr) and CALIPSO). SIRTA observation and research activities take place in networks of atmospheric observatories that allow scientists to access consistent data sets from diverse regions on the globe.

[1] 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 Earth's 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 Kristjánsson 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 Kristjánsson 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.

[1] This paper presents the implementation of a new version of the DARDAR (radar lidar) classification derived from CloudSat and CALIPSO data. The resulting target classification called DARDAR v2 is compared to the first version called DARDAR v1. Overall DARDAR v1 reports more cloud or rain pixels than DARDAR v2. In the low troposphere this is because v1 detects too many liquid cloud pixels, and in the higher troposphere this is because v2 is more restrictive in lidar detection than v1. Nevertheless, the spatial distribution of different types of hydrometeors show similar patterns in both classifications. The French airborne Radar-Lidar (RALI) platform carries a CloudSat/CALIPSO instrument configuration (lidar at a wavelength of 532 nm and a 95 GHz cloud radar) as well as an EarthCare instrument configuration (high spectral resolution lidar at 355 nm and a 95 GHz Doppler cloud radar). It therefore represents an ideal go-between for A-Train and EarthCare. The DARDAR v2 classification algorithm is adapted to RALI data for A-Train overpasses during dedicated airborne field experiments using the lidar at 532 nm and the radar Doppler measurements. The results from the RALI classification are compared with the DARDAR v2 classification to identify where the classification should still be interpreted with caution. Finally, the RALI classification algorithm with lidar at 532 nm is adapted to RALI with high spectral resolution lidar data at 355nm in preparation for EarthCare.

A radar wind profiler data set collected during the 2 year Department of Energy Atmospheric Radiation Measurement Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign is used to estimate convective cloud vertical velocity, area fraction, and mass flux profiles. Vertical velocity observations are presented using cumulative frequency histograms and weighted mean profiles to provide insights in a manner suitable for global climate model scale comparisons (spatial domains from 20 km to 60 km). Convective profile sensitivity to changes in environmental conditions and seasonal regime controls is also considered. Aggregate and ensemble average vertical velocity, convective area fraction, and mass flux profiles, as well as magnitudes and relative profile behaviors, are found consistent with previous studies. Updrafts and downdrafts increase in magnitude with height to midlevels (6 to 10 km), with updraft area also increasing with height. Updraft mass flux profiles similarly increase with height, showing a peak in magnitude near 8 km. Downdrafts are observed to be most frequent below the freezing level, with downdraft area monotonically decreasing with height. Updraft and downdraft profile behaviors are further stratified according to environmental controls. These results indicate stronger vertical velocity profile behaviors under higher convective available potential energy and lower low‐level moisture conditions. Sharp contrasts in convective area fraction and mass flux profiles are most pronounced when retrievals are segregated according to Amazonian wet and dry season conditions. During this deployment, wet season regimes favored higher domain mass flux profiles, attributed to more frequent convection that offsets weaker average convective cell vertical velocities.

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 Z e 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) Ϯ 0.66 for all teams, 1.08 (1.00) Ϯ 0.60 for those employing a lidar-radar approach, and 1.27 (1.12) Ϯ 0.78 for the standard CloudSat radar-visible optical depth algorithm for Z e Ͼ Ϫ28 dBZ e . 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 lidarradar approach, as applied to measurements from CALIPSO and CloudSat, is useful only in a narrow range of ice water paths (IWP) (40 Ͻ IWP Ͻ 100 g m Ϫ2 ). 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 dBZ e are subject to uncertainties of Ϯ50%.

This study presents a summary of the properties of deep convective updraft and downdraft cores over the central plains of the United States, accomplished using a novel and now-standard Atmospheric Radiation Measurement Program (ARM) scanning mode for a commercial wind-profiler system. A unique profiler-based hydrometeor fall-speed correction method modeled for the convective environment was adopted. Accuracy of the velocity retrievals from this effort is expected to be within 2 m s−1, with minimal bias and base core resolution expected near 1 km. Updraft cores are found to behave with height in reasonable agreement with aircraft observations of previous continental convection, including those of the Thunderstorm Project. Intense updraft cores with magnitudes exceeding 15 m s−1 are routinely observed. Downdraft cores are less frequently observed, with weaker magnitudes than updrafts. Weak, positive correlations are found between updraft intensity (maximum) and updraft diameter length (coefficient r to 0.5 aloft). Negligible correlations are observed for downdraft core lengths and intensity.