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Polar environments are among the fastest changing regions on the planet. It is a crucial time to make significant improvements in our understanding of how ocean and ice biogeochemical processes are linked with the atmosphere. This is especially true over Antarctica and the Southern Ocean where observations are severely limited and the environment is far from anthropogenic influences. In this commentary, we outline major gaps in our knowledge, emerging research priorities, and upcoming opportunities and needs. We then give an overview of the large-scale measurement campaigns planned across Antarctica and the Southern Ocean in the next 5 years that will address the key issues. Until we do this, climate models will likely continue to exhibit biases in the simulated energy balance over this delicate region. Addressing these issues will require an international and interdisciplinary approach which we hope to foster and facilitate with ongoing community activities and collaborations.

Three-dimensional wind retrievals from ground-based Doppler radars have played an important role in meteorological research and nowcasting over the past four decades. However, in recent years, the proliferation of open-source software and increased demands from applications such as convective parameterizations in numerical weather prediction models has led to a renewed interest in these analyses. In this study, we analyze how a major, yet often-overlooked, error source effects the quality of retrieved 3D wind fields. Namely, we investigate the effects of spatial interpolation, and show how the common practice of pre-gridding radial velocity data can degrade the accuracy of the results. Alternatively, we show that assimilating radar data directly at their observation locations improves the retrieval of important dynamic features such as the rear flank downdraft and mesocyclone within a simulated supercell, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components., Revised version submitted to JTECH. Includes new section with a real data case

Primary ice nucleation mechanisms are rarely sufficient for producing observed concentrations of ice crystals, meaning secondary ice production processes (SIP) must occur under some conditions. Data from the High Altitude Ice Crystals-High Ice Water Content (HAIC-HIWC) field campaigns and related mesoscale model simulations with a property-based microphysics scheme (P3) are used to show multiple different SIP processes must be considered to explain observed ice crystal number concentrations. In particular, a multi-modal gamma fitting routine is used to automatically determine the number of modes and fit parameters of each mode for measured size distributions. These are combined with measured total water contents (TWCs) to characterize how cloud microphysical properties vary with temperature, TWC, and convective and meteorological characteristics (e.g., strength, proximity to convection, surface characteristics, updraft velocities) to assess under what conditions SIP processes might act. Comparison against WRF simulations using existing cloud microphysical parameterization schemes show that the intensity and spatial extent of the observed airborne X-band reflectivity and measured size distributions are not well replicated. Sensitivity tests showed that without modeling a variety of SIP processes, total ice number concentrations were 2 to 3 orders of magnitude less than observed between -10C and -45C. Including only one of three SIP mechanisms separately (i.e., Hallett-Mossop mechanism, fragmentation during ice-ice collisions, and shattering of freezing droplets) cannot replicate observed concentrations, but including all three SIP processes can. An effect of SIP on convective invigoration is also exhibited. Implications for understanding SIP processes and for their representations in models are discussed., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Antarctic precipitation remains poorly characterised and understood, especially within the boundary layer. This is due in part to a still-limited amount of surface-based remote sensing observations. A suite of cloud and precipitation remote-sensing instruments including a W-band cloud radar and a K-band Micro Rain Radar (MRR) were used to characterise snowfall over Davis (69S, 78E). Surface snowfall events occurred when boundary layer wind speeds were weaker, temperatures were warmer, and relative humidity over ice higher, than when virga were present. The presence of virga is associated with Fohn conditions due to the location of Davis in the lee of an ice ridgeline. Dual wavelength ratio values from the summer indicate particle aggregation at temperatures of -14C to -10C, consistent with observations made elsewhere, including in the Arctic. In-cloud updrafts were stronger in summer than in winter at these temperatures. Larger downward velocities and the presence of super-cooled liquid layers suggest some rimed particles at warm temperatures above -10C during summer. Sublimation of snowfall mass aloft was 50\% between the accumulation peak at 1.2~km and 205~m altitude, which occurs within CloudSat's `blind zone'. An estimated lower bound of blowing snow fraction is 30%. Given the common prevailing winds and numerous ice ridgelines along much of the East Antarctic coastline, these Davis results can be used as a basis to further understand snowfall across the region., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

While biases in simulated Southern Ocean (SO) shortwave radiative fluxes have improved in CMIP6 compared to CMIP5 models, there are remaining concerns regarding SO mixed phase clouds due to their important role in the climate system. Recent modeling studies come to opposing conclusions as to whether ice nucleation (IN) has a strong impact on SO shortwave cloud forcing. This study aims to investigate these conflicting findings using modeling and observations.In this work, we have implemented a deterministic immersion freezing ice nucleating particle (INP) scheme that includes contributions from mineral dust and marine sources into the Community Earth System Model version 2 (CESM2). This deterministic INP scheme replicates SO INP observations in the marine boundary layer with important sensitivities to modeled dust aerosol aloft. Simulated top-of-atmosphere cloud properties were minimally influenced by changes to the INP scheme. We investigated contributions of simulated cloud microphysical processes to the mass and number budgets in SO cloud regimes in the CESM2. Cloud regimes are identified based on cloud optical depth and altitude from satellite observations and simulated clouds sampled using the CFMIP Observation Simulator Package (COSP). This cloud regime clustering analysis allows for an assessment of cloud aerosol-ice processes in specific cloud regimes in CESM2. Results from these model experiments and analyses reveal the extent to which INPs control ice formation in specific cloud regimes in CESM2 and will be discussed in the context of recent SO field campaign observations., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

In Australia, hailstorms present considerable public safety and economic risks, where they are considered the most damaging natural hazard in terms of annual insured losses. Despite these impacts, the current climatological distribution of hailfall across the continent is still comparatively poorly understood. This study aims to supplement previous national hail climatologies, such as those based on environmental proxies or satellite radiometer data, with more direct radar-based hail observations. The heterogeneous and incomplete nature of the Australian radar network complicates this task and prompts the introduction of some novel methodological elements. We introduce an empirical correction technique to account for hail reflectivity biases at C-band, derived by comparing overlapping C- and S-band observations. Furthermore, we demonstrate how object-based hail swath analysis may be used to produce resolution-invariant hail frequencies, and describe an interpolation method used to create a spatially continuous hail climatology. The Maximum Estimated Size of Hail (MESH) parameter is then applied to a mixture of over fifty operational radars in the Australian radar archive, resulting in the first nationwide, radar-based hail climatology. The spatiotemporal distribution of hailstorms is examined, including their physical characteristics, seasonal and diurnal frequency, and regional variations of such properties across the continent., Comment: Preprint submitted to Monthly Weather Review

17 USC 105 interim-entered record; under review. The article of record as published may be found at https://doi.org/10.5194/amt-14-53-2021 The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dualpolarization quantities was wavelength dependent for 1 < R < 10 mm h−1 . These rates primarily originate from stratiform clouds and weak convection (median drop diameters less than 1.5mm). The dual-polarization specific differential phase and differential reflectivity increase in usefulness for rainfall estimators in times with R > 10 mm h−1 . Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1 < R < 10mmh−1 (stratiform rain) and when R > 10 mm h−1 (deeper convective rain). Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under contract DE-AC02-06CH11357. This work has been supported by the Office of Biological and Environmental Research (OBER) of the U.S. Department of Energy (DOE) as part of the Climate Model Development and Validation activity. NOAA PSL contributes effort with funding from the Weather Program Office’s Precipitation Prediction Grand Challenge. The development of the Python ARM radar toolkit was funded by the ARM program part of the Office of Biological and Environmental Research (OBER) of the U.S. Department of Energy (DOE). The work from Monash University and the Bureau of Meteorology was partly supported by the U.S. Department of Energy Atmospheric Systems Research Program through the grant DE-SC0014063. BD contributions are supported by the U.S. Department of Energy Atmospheric Systems Research Program through the grant DE-SC0017977.

Traditional parameterizations of the interaction between convection and the environment have relied on an assumption that the slowly-varying large-scale environment is in statistical equilibrium with a large number of small and short-lived convective clouds. They fail to capture non-equilibrium transitions such as the diurnal cycle and theformation of meso-scale convective systems as well asobserved precipitation statisticsand extremes. Informed by analysis of radar observations, cloud-permitting model simulation, theory and machine learning, this work presents a new stochastic cloud population dynamics model for characterizing the interactions between convective and stratiform clouds,with the ultimate goal of informing the representation ofthese interactions in global climate models. 15 wet seasons of precipitating cloud observations by a C-band radar at Darwin, Australia are fed into a machine learning algorithm to obtain transition functions that close a set of coupled equation relating large-scale forcing, mass flux, the convective cell size distribution and the stratiform area. Under realistic large-scale forcing, the derived transition functions show that, on the one hand, interactions with stratiform clouds act to dampen the variability in the size and number of convective cells and therefore in the convective mass flux. On the other hand, for a given convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. The combination of these two factors gives rise to solutions with a number of convective cells embedded in a large stratiform area, reminiscent of mesoscale convective systems.

International audience

International audience; Mesoscale Convective Systems are complex cloud systems which are primarily the result of specific synoptic conditions associated with mesoscale instabilities leading to the development of cumulonimbus type clouds (Houze, 2004). These systems can last several hours and can affect human societies in various ways. In general, weather and climate models use simplistic schemes to describe ice hydrometeors' properties. However, MCS are complex cloud systems where the dynamic, radiative and precipitation processes depend on spatiotemporal location in the MCS (Houze, 2004). As a consequence, hydrometeor growth processes in MCS vary in space and time, thereby impacting shape and concentration of ice crystals and finally CWC. As a consequence, differences in the representation of ice properties in models (Li et al., 2007, 2005) lead to significant disagreements in the quantification of ice cloud effects on climate evolution (Intergovernmental Panel on Climate Change Fourth Assessment Report). An accurate estimation of the spatiotemporal CWC distribution is therefore a key parameter for evaluating and improving numerical weather prediction (Stephens et al., 2002). The main purpose of this study is to show ice microphysical properties of MCS observed in three different locations in the tropical atmosphere: West-African continent, Indian Ocean, and Northern Australia. An intercomparison study is performed in order to quantify how similar or different are the ice hydrometeors' properties in these three regions related to radar reflectivity factors and temperatures observed in respective MCS.

International audience; High ice water content (IWC) regions in Mesoscale Convective Systems (MCS) are a potential threat to commercial aviation as they are suspected to cause in-service engine power-loss events and air data probe malfunctions. To investigate this, the High Altitude Ice Crystals (HAIC) / High Ice Water Content (HIWC) projects set up a first field campaign in Darwin (Australia) in 2014. The airborne instrumentation was selected to provide the most accurate measurements of both the bulk total water content (TWC), using a specially developed isokinetic evaporator, and the individual ice crystals properties using particle imaging probes.This study focuses on determining the size ranges of ice crystals responsible for the mass in high IWC regions, defined here as cloud regions with IWC greater than 1.5gm-3. It is shown that for high IWC areas in most of the encountered MCS systems, median mass diameters (MMDs) of ice crystals range from 250 to 500μm and decrease with increasing TWC and decreasing temperature. At the same time, the mass contribution of the smallest crystals (below 100μm) remains generally low (below 15%).In contrast, data from two flight missions in a long-lasting quasi-stationary tropical storm reveal that high IWC values can also be associated with MMDs in the range 400-800 μm and peak values of up to 2mm. Ice crystal images suggest a major growth contribution by vapor deposition (columns, capped columns) even for such larger MMDs values.

This study presents the evaluation of a technique to estimate cloud condensed water content (CWC) in tropical convection from airborne cloud radar reflectivity factors at 94 GHz and in situ measurements of particle size distributions (PSDs) and aspect ratios of ice crystal populations. The approach is to calculate from each 5 s mean PSD and flight-level reflectivity the variability of all possible solutions of m(D) relationships fulfilling the condition that the simulated radar reflectivity factor (T-matrix method) matches the measured radar reflectivity factor. For the reflectivity simulations, ice crystals were approximated as oblate spheroids, without using a priori assumptions on the mass–size relationship of ice crystals. The CWC calculations demonstrate that individual CWC values are in the range ±32 % of the retrieved average CWC value over all CWC solutions for the chosen 5 s time intervals. In addition, during the airborne field campaign performed out of Darwin in 2014, as part of the international High Altitude Ice Crystals/High Ice Water Content (HAIC/HIWC) projects, CWCs were measured independently with the new IKP-2 (isokinetic evaporator probe) instrument along with simultaneous particle imagery and radar reflectivity. Retrieved CWCs from the T-matrix radar reflectivity simulations are on average 16 % higher than the direct CWCIKP measurements. The differences between the CWCIKP and averaged retrieved CWCs are found to be primarily a function of the total number concentration of ice crystals. Consequently, a correction term is applied (as a function of total number concentration) that significantly improves the retrieved CWC. After correction, the retrieved CWCs have a median relative error with respect to measured values of only −1 %. Uncertainties in the measurements of total concentration of hydrometeors are investigated in order to calculate their contribution to the relative error of calculated CWC with respect to measured CWCIKP. It is shown that an overestimation of the concentration by about +50 % increases the relative errors of retrieved CWCs by only +29 %, while possible shattering, which impacts only the concentration of small hydrometeors, increases the relative error by about +4 %. Moreover, all cloud events with encountered graupel particles were studied and compared to events without observed graupel particles. Overall, graupel particles seem to have the largest impact on high crystal number-concentration conditions and show relative errors in retrieved CWCs that are higher than for events without graupel particles.

International audience; This study explores the potential of simulating cloud radar reflectivity factors (RRF) at 94GHz from a combination of cloud particle 2D images from optical array probes and bulk condensed water content, simultaneously measured during the first aircraft field campaign of the High Altitude Ice Crystals (HAIC) / High Ice Water Content (HIWC) international project performed out of Darwin in 2014. Within these simulations, ice crystals are approximated by oblate spheroids without à priori assumptions on mass-size relationships of ice crystals. The method allows calculating time dependent ranges of radar reflectivity factors deduced for given size distributions of hydrometeors, averaged over 5 seconds. Ice hydrometeor size distributions and shapes (flattening parameter for oblate spheroids) are deduced from 2D images, and are subsequently used to constrain the ice particle density (thereby matching simulated and corresponding measured radar reflectivity factors). Finally, uncertainties in oblate spheroid approximations are studied in order to demonstrate some limitations of this method.

International audience; Mesoscale convective systems (MCS) can last several hours or more and affect human societies in different ways. In order to predict and identify hazardous weather (e. g. in view of aviation safety) linked to MCS, numerical weather forecast and active/passive remote sensing products are currently the most useful tools. A major obstacle to increasing the accuracy of those tools is the fact that cumulonimbus clouds in MCS are composed of liquid droplets and ice hydrometeors (ice crystals); the latter have complex shapes based on diffusional growth, aggregation, and riming, which complicates the interpretation of remote sensing products and increases the uncertainties in numerical weather forecast. In this context, the study presented here investigates the properties of ice hydrometeors in MCS as a function of temperature and horizontal distance from the convective core. For this purpose, ice hydrometeor images, bulk TWC, and simultaneously measured radar reflectivity factors from three airborne campaigns in tropical MCS have been used. The underlying scope of producing these results is to improve the retrieval of MCS microphysical properties from satellite products and also improve cloud parameterizations in numerical weather prediction models.

International audience; At least one hundred jet engine power loss events since the 1990s have been attributed to the phenomenon known as ice crystal icing (ICI). Ingestion of high concentrations of ice particles into aircraft engines is thought to cause these events, but it is clear that the use of current on-board weather radar systems alone is insufficient for detecting conditions that might cause ICI. Passive radiometers in geostationary orbit are valuable for monitoring systems that produce high ice water content (HIWC) and will play an important role in nowcasting, but are incapable of making vertically resolved measurements of ice particle concentration, i.e., ice water content (IWC). Combined radar, lidar, and in-situ measurements are essential for developing a skilled satellite-based HIWC nowcasting technique. The High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) field campaigns in Darwin, Australia, and Cayenne, French Guiana, have produced a valuable dataset of in-situ total water content (TWC) measurements with which to study conditions that produce HIWC. The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) was used to derive cloud physical and optical properties such cloud top height, temperature, optical depth, and ice water path from multi-spectral satellite imagery acquired throughout the HAIC-HIWC campaigns. These cloud properties were collocated with the in-situ TWC measurements in order to characterize cloud properties in the vicinity of HIWC. Additionally, a database of satellite-derived overshooting cloud top (OT) detections was used to identify TWC measurements in close proximity to convective cores likely producing large concentrations of ice crystals. Certain cloud properties show some sensitivity to increasing TWC and a multivariate probabilistic indicator of HIWC was developed from these datasets. This paper describes the algorithm development and demonstrates the HIWC indicator with imagery from the HAIC-HIWC campaigns. Vertically resolved IWC retrievals from active sensors such as the Cloud Profiling Radar (CPR) on CloudSat and the Doppler Radar System Airborne (RASTA) provide IWC profiles with which to validate and potentially enhance the satellite-based HIWC indicator.

International audience; Un radar pour étudier les nuages et le brouillard

International audience; Ground-based continuous observation of non-precipitating clouds and fog

International audience; 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 95GHz 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 355 nm in preparation for EarthCare.

International audience; Ground-based radar and lidar observations obtained at the Department of Energy's Atmospheric Radiation Measurement Program's Tropical Western Pacific site located in Darwin, Australia are used to retrieve ice cloud properties in anvil and cirrus clouds. Cloud microphysical properties derived from four different retrieval algorithms (two radar-lidar and two radar only algorithms) are compared by examining mean profiles and probability density functions of effective radius (Re), ice water content (IWC), visible extinction coefficient, ice number concentration, ice crystal fall speed, and vertical air velocity. Retrieval algorithm uncertainty is quantified using radiative flux closure exercises. The effect of uncertainty in retrieved quantities on the cloud radiative effect and radiative heating rates are presented. Our analysis shows that IWC compares well among algorithms, but Re shows significant discrepancies, which is attributed primarily to assumptions of particle shape. Uncertainty in Re and IWC translates into sometimes-large differences in cloud shortwave radiative effect (CRE) though the majority of cases have a CRE difference of roughly 10 W m-2 on average. These differences, which we believe are primarily driven by the uncertainty in Re, can cause up to 2 K/day difference in the radiative heating rates between algorithms.

The airborne 95GHz cloud radar RASTA will be operated on-board the Falcon 20 during HyMeX SOP1.1 (September/October 2012). In addition to the radar, state of the art in-situ microphysical probes, such as CDP, 2DS, CPI, PIP, and Robust probe will be deployed to characterize bulk and individual hydrometeor microphysics. Ice particles size distribution will be retrieved and total water content will be quantified from the measurements. The very novel six-beam configuration of RASTA will allow one to retrieve microphysical ice-cloud properties and 3D dynamics of clouds. The underlying idea is to combine radar and in-situ measurements to infer cloud processes that originate precipitation. The spatially limited detailed cloud description using in-situ measurements will be extended to the area covered by the radar. To illustrate the potential of this platform, we will present the instruments and some results from previous campaigns.

Accurate observations of cloud microphysical properties are needed for evaluating\ud and improving the representation of cloud processes in climate models and better estimate\ud of the Earth radiative budget. However, large differences are found in current cloud\ud products retrieved from ground-based remote sensing measurements using various retrieval\ud algorithms. Understanding the differences is an important step to address uncertainties\ud in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based\ud cloud retrievals using ARM remote sensing measurements is carried out. We place\ud emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus\ud of many current retrieval development efforts due to their radiative importance and\ud relatively simple structure. Large systematic discrepancies in cloud microphysical\ud properties are found in these two types of clouds among the nine cloud retrieval products,\ud particularly for the cloud liquid and ice particle effective radius. Note that the differences\ud among some retrieval products are even larger than the prescribed uncertainties reported by\ud the retrieval algorithm developers. It is shown that most of these large differences have\ud their roots in the retrieval theoretical bases, assumptions, as well as input and constraint\ud parameters. This study suggests the need to further validate current retrieval theories and\ud assumptions and even the development of new retrieval algorithms with more observations\ud under different cloud regimes.

International audience; A climatology of the thermodynamic phase of the clouds over the Southern Ocean (40-65S,100-160E) has been constructed with the A-Train merged data product DARDAR-MASK for the four-year period 2006-2009 during Austral winter and summer. Low-elevation clouds with little seasonal cycle dominate this climatology, with the cloud-tops commonly found at heights less than 1km. Such clouds are problematic for the DARDAR-MASK in that the Cloud Profiling Radar (CPR) of CloudSat is unable to distinguish returns from the lowest four bins (heights up to 720 - 960m), and the CALIOP lidar of CALIPSO may suffer from heavy extinction. The CPR is further limited for all of the low-altitude clouds (tops below 3km) as they are predominantly in the temperature range from freezing to -20 C, where understanding the CPR reflectivity becomes difficult due to the unknown thermodynamic phase. These shortcomings are seen to flow through to the merged CloudSat-CALIPSO product. A cloud-top phase climatology comparison has been made between CALIPSO, the DARDAR-MASK and MODIS. All three products highlight the extensive presence of supercooled liquid water over the Southern Ocean, particularly during summer. The DARDAR-MASK recorded substantially more ice at cloud-tops as well as mixed phase in the low-elevation cloud-tops in comparison to CALIPSO and MODIS. Moving beneath the cloud-top, the DARDAR-MASK finds ice to be dominant at heights greater than 1 km, once the lidar signal is attenuated. The limitations demonstrated in this study highlight the enormous challenge that remains in better defining the energy and water budget over the Southern Ocean.

In this presentation we will show a cloud properties climatology using radar-lidar synergy from CloudSat/CALIPSO above the Hymex area. This work aims to aide preparation for the future Hymex airborne measurements campaign in Autumn 2012 and also to understand the role of stratiform clouds as "water reservoirs" for heavy precipitation. This climatology is derived using DARDAR products (http://www.icare.univ-lille1.fr/projects/dardar/) from the CloudSat/CALIPSO measurements, including cloud phase discrimination and ice cloud properties from 2006 to 2010 for autumn. Firstly, we will look at the vertical distribution of liquid and ice clouds. We will then identify the location of high values of IWC and effective radius where most of the condensate water accumulates before melting, causing strong rainfall over land. We will also target ten critical regions from Spain to Italy where rainfall causes severe floods.

International audience; This paper presents a numerical simulation of a Mesoscale Convective System (MCS) observed during the AMMA (African Monsoon Multidisciplinary Analysis) experiment with the BRAMS model (Brazilian Regional Atmospheric Modelling System). The aim is to document the life cycle of the MCS and to identify key cloud microphysical processes and their signatures by making use of synthetic observations calculated from the simulated fields. These observations: ARM (Atmospheric Radiation Measurement) 95 GHz equivalent radar reflectivity factor and Doppler velocity and infrared brightness temperatures in three SEVIRI (Spinning Enhanced Visible and InfraRed Imager) channels centred at 8.7, 10.6 and 12 µm are simulated using respectively Mie scattering theory and FASDOM (Fast Discrete Ordinate Method), a fast radiative transfer code. Synthetic observations and model variables are compared to various measurements from several platforms (W-band and Massachusetts Institute of Technology (MIT) ground-based Doppler radars, soundings, aircraft measurements, and Meteosat Second Generation) to evaluate the model at different scales and to identify the signatures of microphysical properties with a focus on the anvil part of the MCS. A method using both the ARM and the MIT radar data is used to identify the different regimes within the MCS. A relatively good agreement with direct comparisons is found, as well as discrepancies in the microphysical scheme parametrization that clearly need improvements (using in situ measurements). Microphysical signatures are also studied using joint radar reflectivity/Doppler-height histograms. Their analysis shows that the model tends to overplay the role of the riming processes, even in the anvil part of the MCS. Comparisons of the Particle Size Distributions (simulated and measured in situ) show the model's ability to reproduce complex PSDs (e.g. a multimodal behaviour)

Backscatter lidar observations such as those provided by the CALIPSO mission are expected to give complementary information to long-used radiometric observations for aerosol properties characterization important to climate and environment issues. However, retrieving aerosol optical depth (AOD) and profiling the aerosol extinction cannot be done accurately applying a standard inversion procedure to the backscatter lidar measurements, without a precise knowledge of aerosol properties on the vertical. The objective of this first study is to propose a new approach to quantify the AOD over the ocean combining the surface return signals from the lidar and radar onboard the CALIPSO and CloudSat platforms, respectively. Taking advantage of the satellite formation within the AQUA-train, first comparisons of AODs retrieved with our method and MODIS ones at tropical latitudes show an overall bias smaller than 1%, and a standard deviation of about 0.07. These first results are presented and error sources are discussed.

The Rali (radar-lidar) project is a new tool which combines a radar and a lidar for observing weakly- precipitating clouds. This paper des- cribes the instruments used or improved for Rali, together with the algorithms for reproducing cloud parameters and shows some of the first scientific results. Around March 2005, the Rali instrument will be installed in the new French research aircraft and will allow the scientific community to get coupled observa- tions of dynamical, microphysical and radiative properties of aerosols and weakly-precipitating clouds., Le projet Rali (radar-lidar) développe un nouveau moyen d’observation des nuages faiblement précipitants, qui repose sur la combinaison d’un radar et d’un lidar. On décrit ici les instru- ments utilisés ou mis au point dans le cadre de Rali et les algorithmes de res- titution des paramètres des nuages, puis on présente quelques exemples des premiers résultats scientifiques obtenus. À l’horizon mars 2005, l’ins- trument Rali sera intégré dans les nouveaux avions de recherche fran- çais et permettra à la communauté scientifique d’accéder à des observa- tions couplées de la dynamique, de la microphysique et des propriétés radia- tives des nuages faiblement précipi- tants et des aérosols.

International audience; A case study analysis has been conducted on the diurnal cycle of boundary layer clouds observed at the IPSL station in Palaiseau (France) on the period from the 22 nd to the 26 th March 2004, during the CLOUDNET project. The cloud properties have been analysed using remote sensing (ground based radar and lidar), applying the Klett inversion method in a new way to retrieve microphysical properties of a stratocumulus cloud layer. Comparisons of results obtained on March 24 th at the end of the morning with satellite observations (MODIS) and with forecasts from three numerical models (METEOFRANCE ARPEGE, UKMO Unified Model and ECMWF) are presented and discussed here.

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.