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M. Pinsky, L. Magaritz, A. Khain, O. Krasnov, and A. Sterkin

Abstract

A novel trajectory ensemble model of a stratocumulus cloud is described. In this model, the boundary layer (BL) is fully covered by a great number of Lagrangian air parcels that during their motion can contain either wet aerosols or aerosols and droplets. The diffusion growth of aerosols and droplets, as well as drop collisions, is accurately described in each parcel. Droplet sedimentation is taken into account, which allows simulation of precipitation formation. The Lagrangian parcels are advected by the velocity field generated by the turbulent-like flow model obeying turbulent correlation laws. The output of the numerical model includes droplet and aerosol size distributions and their moments, such as droplet concentration, droplet spectrum width, cloud water content, drizzle content, radar reflectivity, etc., calculated in each parcel. Horizontally averaged values are calculated as well.

Stratocumulus clouds observed during two research flights (RF01 and RF07) in the Second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS II) field campaign are simulated. A good agreement between the dynamical and microphysical structures simulated by the model with observations is obtained even in the nonmixing limit. A crucial role of sedimentation for the creation of a realistic cloud microphysical structure is demonstrated. In sensitivity studies, the statistical stability of the model is analyzed.

Applications of the model for the investigation of droplet size distribution and drizzle formation are discussed, as is the possible utilization of the model for remote sensing applications.

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L. Magaritz, M. Pinsky, O. Krasnov, and A. Khain

Abstract

A novel trajectory ensemble model of the cloud-topped boundary layer containing 1340 Lagrangian parcels moving with a turbulent-like flow with the observed statistical properties was applied to investigate the formation of the microphysical structure of stratocumulus clouds (Sc) in a nonmixing limit (when turbulent mixing between the parcels is not taken into account). The Sc observed in two research flights during the Second Dynamics and Chemistry of the Marine Stratocumulus field study (DYCOMS II)—RF01 (no drizzle) and RF07 (weak drizzle)—are simulated. The mechanisms leading to a high variability of droplet size distributions (DSDs) with different spectrum width and dispersion are discussed. Drizzle formation was investigated using the radar reflectivity–LWC and LWC–effective drop radius diagrams simulated by the model in the nondrizzle and drizzle cases. It is shown that in the RF07 case large cloud droplets that trigger drop collisions and drizzle formation form only in a small fraction (about 1%) of the parcels (which will be referred to as lucky parcels) in which LWC exceeds ∼1.5 g m−3. This value exceeds the horizontally averaged LWC maximum value of 0.9 g m−3 by two to three standard deviations, indicating a small amount of lucky parcels. In a nondrizzling cloud simulation this threshold is exceeded extremely rarely. The dependence of the threshold value of LWC on aerosol concentration is discussed. The lucky parcels (at least in the nonmixing limit) start their updraft in the vicinity of the surface, where the water vapor mixing ratio is maximum, and ascend to the highest levels close to the cloud top. It is shown that the lucky parcel tracks are related to the large eddies in the boundary layer, which indicates the substantial role of large eddies in drizzle formation.

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M. Pinsky, O. Krasnov, H. W. J. Russchenberg, and A. Khain

Abstract

A new method for retrieving air velocity fluctuations in the cloud-capped boundary layer (BL) using radar reflectivity and the Doppler velocity fields is proposed. The method was developed on the basis of data obtained by the Transportable Atmospheric Radar (TARA) located in Cabauw, Netherlands, at 0500–0812 UTC 8 May 2004, and tested using a detailed trajectory ensemble model of the cloud-capped BL. During the observations, the BL depth was 1200 m, and the cloud base (measured by a lidar) was at 500–550 m. No preliminary assumptions concerning the shapes of drop size distributions were made. On the basis of the TARA radar data, vertical profiles of the vertical air velocity standard deviation, of turbulent dissipation rate, etc. were estimated. The correlation functions indicate the existence of large eddies in the BL with a characteristic horizontal scale of about 600 m. Analysis of the slope (the scaling parameter) of the structure functions indicates that turbulence above 400 m can be considered to be isotropic. Below this level, the turbulence becomes anisotropic. The rate of anisotropy increases with the decrease of the height above the surface. The averaged values of the dissipation rate were evaluated as 1–2 cm2 s−3. The importance of using the cloud-capped BL model as a link between different types of observed data (radar, lidar, aircraft, etc.) is discussed. More data should be analyzed to understand the changes in the turbulent structure of the BL during its growth, as well as during cloud and drizzle formation.

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A. Khain, M. Pinsky, L. Magaritz, O. Krasnov, and H. W. J. Russchenberg

Abstract

In situ measurements indicate the complexity and nonunique character of radar reflectivity–liquid water content (Z–LWC) relationships in stratocumulus and cumulus clouds. Parameters of empirical (statistical) Z–LWC dependences vary within a wide range. Respectively, the accuracy of retrieval algorithms remains low. This situation is partially related to the fact that empirical algorithms and parameters are often derived without a corresponding understanding of physical mechanisms responsible for the formation of the Z–LWC diagrams. In this study, the authors investigate the processes of formation of the Z–LWC relationships using a new trajectory ensemble model of the cloud-topped boundary layer (BL). In the model, the entire volume of the BL is covered by Lagrangian parcels advected by a turbulent-like velocity field. The time-dependent velocity field is generated by a turbulent model and obeys the correlation turbulent laws. Each Lagrangian parcel represents the “cloud parcel model” with an accurate description of processes of diffusion growth–evaporation of aerosols and droplets and droplet collisions. The fact that parcels are adjacent to each other allows one to calculate sedimentation of droplets and precipitation (drizzle) formation. The characteristic parcel size is 50 m; the number of parcels is 1840. The model calculates droplet size distributions (DSDs), as well as their moments (e.g., aerosol and drop concentration, mass content, radar reflectivity) in each parcel. In the course of the model integration, Z–LWC relationships are calculated for each parcel, as well as the scattering diagram including all parcels. The model reproduces in situ observed types of the Z–LWC relationships. It is shown that different regimes represent different stages of cloud evolution: diffusion growth, beginning of drizzle formation, and stage of heavy drizzle, respectively. The large scattering of the Z–LWC relationships is found to be an inherent property of any drizzling cloud. Different zones on the Z–LWC diagram are related to cloud volumes located at different levels within a cloud and having different DSD. This finding allows for improvement of retrieval algorithms.

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A. C. P. Oude Nijhuis, C. M. H. Unal, O. A. Krasnov, H. W. J. Russchenberg, and A. G. Yarovoy

Abstract

In this article, five velocity-based energy dissipation rate (EDR) retrieval techniques are assessed. The EDR retrieval techniques are applied to Doppler measurements from Transportable Atmospheric Radar (TARA)—a precipitation profiling radar—operating in the vertically fixed-pointing mode. A generalized formula for the Kolmogorov constant is derived, which gives potential for the application of the EDR retrieval techniques to any radar line of sight (LOS). Two case studies are discussed that contain rain events of about 2 and 18 h, respectively. The EDR values retrieved from the radar are compared to in situ EDR values from collocated sonic anemometers. For the two case studies, a correlation coefficient of 0.79 was found for the wind speed variance (WSV) EDR retrieval technique, which uses 3D wind vectors as input and has a total sampling time of 10 min. From this comparison it is concluded that the radar is able to measure EDR with a reasonable accuracy. Almost no correlation was found for the vertical wind velocity variance (VWVV) EDR retrieval technique, as it was not possible to sufficiently separate the turbulence dynamics contribution to the radar Doppler mean velocities from the velocity contribution of falling raindrops. An important cause of the discrepancies between radar and in situ EDR values is thus due to insufficient accurate estimation of vertical air velocities.

Open access
A. C. P. Oude Nijhuis, L. P. Thobois, F. Barbaresco, S. De Haan, A. Dolfi-Bouteyre, D. Kovalev, O. A. Krasnov, D. Vanhoenacker-Janvier, R. Wilson, and A. G. Yarovoy

Abstract

This article presents the prospects of measurement systems for wind hazards and turbulence at airports, which have been explored in the Ultrafast Wind Sensors (UFO) project. At France’s Toulouse–Blagnac Airport, in situ, profiling, and scanning sensors have been used to collect measurements, from which wind vectors and turbulence intensities are estimated. A scanning 1.5-µm coherent Doppler lidar and a solid state X-band Doppler radar have been developed with improved update rates, spatial resolution, and coverage. In addition, Mode-S data downlinks have been collected for data analysis. Wind vector and turbulence intensity retrieval techniques are applied to demonstrate the capabilities of these measurement systems. An optimal combination of remote measurement systems is defined for all weather monitoring at airports. In this combination, lidar and radar systems are complementary for clear-air and rainy conditions, which are formulated in terms of visibility and rain rate. The added value of the measurement systems for high-resolution numerical weather prediction models is estimated by an observing system experiment, and a positive impact on the local wind forecast is demonstrated.

Open access

Cloudnet

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

A. J. Illingworth, R. J. Hogan, E.J. O'Connor, D. Bouniol, M. E. Brooks, J. Delanoé, D. P. Donovan, J. D. Eastment, N. Gaussiat, J. W. F. Goddard, M. Haeffelin, H. Klein Baltink, O. A. Krasnov, J. Pelon, J.-M. Piriou, A. Protat, H. W. J. Russchenberg, A. Seifert, A. M. Tompkins, G.-J. van Zadelhoff, F. Vinit, U. Willén, D. R. Wilson, and C. L. Wrench

The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasi-real time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Météo-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.

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S. Crewell, H. Bloemink, A. Feijt, S. G. García, D. Jolivet, O. A. Krasnov, A. van Lammeren, U. Löhnert, E. van Meijgaard, J. Meywerk, M. Quante, K. Pfeilsticker, S. Schmidt, T. Scholl, C. Simmer, M. Schröder, T. Trautmann, V. Venema, M. Wendisch, and U. Willén

Clouds cause uncertainties in the determination of climate sensitivity to either natural or anthropogenic changes. Furthermore, clouds dominate our perception of the weather, and the relatively poor forecast of cloud and precipitation parameters in numerical weather prediction (NWP) models is striking. In order to improve modeling and forecasting of clouds in climate and NWP models the BALTEX BRIDGE Campaign (BBC) was conducted in the Netherlands in August/September 2001 as a contribution to the main field experiment of the Baltic Sea Experiment (BALTEX) from April 1999 to March 2001 (BRIDGE). The complex cloud processes, which involve spatial scales from less than 1 mm (condensation nuclei) to 1000 km (frontal systems) require an integrated measurement approach. Advanced remote sensing instruments were operated at the central facility in Cabauw, Netherlands, to derive the vertical cloud structure. A regional network of stations was operated within a 100 km × 100 km domain to observe solar radiation, cloud liquid water path, cloud-base temperature, and height. Aircraft and tethered balloon measurements were used to measure cloud microphysical parameters and solar radiation below, in, and above the cloud. Satellite measurements complemented the cloud observations by providing the spatial structure from above. In order to better understand the effect of cloud inhomogeneities on the radiation field, three-dimensional radiative transfer modeling was closely linked to the measurement activities. To evaluate the performance of dynamic atmospheric models for the cloudy atmosphere four operational climate and NWP models were compared to the observations. As a first outcome of BBC we demonstrate that increased vertical resolution can improve the representation of clouds in these models.

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