Demistify: a large-eddy simulation (LES) and single-column model (SCM) intercomparison of radiation fog

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.

Retrieval of Water Cloud Optical and Microphysical Properties from Combined Multiwavelength Lidar and Radar Data

The remote sensing of water clouds is useful for studying their spatial and temporal variations and constraining physical processes in climate and weather prediction models. However, radar-only detection provides inadequate information for the cloud droplet size distribution. Here, we propose a novel lookup-table method, which combines lidar (1064, 532 nm) and radar (8.6 mm) to retrieve profiles of cloud optical (backscatter coefficient and extinction coefficient) and microphysical properties (effective diameter and liquid water content). Through the iteration of the extinction-to-backscatter ratio, more continuous cloud optical characteristics can be obtained. Sensitivity analysis shows that a 10% error of the lidar constant will lead to a retrieval error of up to 30%. The algorithm performed precise capture of the ideal cloud signal at a specific height and at full height and the maximum relative error of the backscatter coefficients at 1064 nm and 532 nm were 6% and 4%, respectively. With the application of the algorithm in the two observation cases on single or multiple cloud layers, the results indicate that the microphysical properties mostly agree with the empirical radar measurements but are slightly different when larger particles cause signal changes of different extents. Consequently, the synergetic algorithm is capable of computing the cloud droplet size distribution. It provides continuous profiles of cloud optical properties and captures cloud microphysical properties well for water cloud studies.

Demistify: an LES and SCM intercomparison of radiation fog

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the LANFEX field campaign. 7 of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst 3 are research-grade SCMs designed for fog simulation, and the LES are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality, and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number (CDNC) conditions. The main SCM bias appears to be toward over-development of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP-SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parametrization, as it is to the underlying aerosol or CDNC.

Importance of aerosols and shape of the cloud droplet size distribution for convective clouds and precipitation

The predictability of deep moist convection is subject to large uncertainties resulting from inaccurate initial and boundary data, the incomplete description of physical processes, or microphysical uncertainties. In this study, we investigate the response of convective clouds and precipitation over central Europe to varying cloud condensation nuclei (CCN) concentrations and different shape parameters of the cloud droplet size distribution (CDSD), both of which are not well constrained by observations. We systematically evaluate the relative impact of these uncertainties in realistic convection-resolving simulations for multiple cases with different synoptic controls using the new icosahedral nonhydrostatic ICON model. The results show a large systematic increase in total cloud water content with increasing CCN concentrations and narrower CDSDs together with a reduction in the total rain water content. This is related to a suppressed warm-rain formation due to a less efficient collision-coalescence process. It is shown that the evaporation at lower levels is responsible for diminishing these impacts on surface precipitation, which lies between +13 % to −16 % compared to a reference run with continental aerosol assumption. In general, the precipitation response was larger for weakly-forced cases. We also find that the overall timing of convection is not sensitive to the microphysical uncertainties applied, indicating that different rain intensities are responsible for changing precipitation totals at the ground. Furthermore, weaker rain intensities in the developing phase of convective clouds can allow for a higher convective instability at later times, which can lead to a turning point with larger rain intensities later on. The existence of such a turning point and its location in time can have a major impact on precipitation totals. In general, we find that an increase in the shape parameter can produce almost as large a variation in precipitation as a CCN increase from maritime to polluted conditions. Narrowing of the CDSD not only decreases the absolute values of autoconversion and accretion, but also decreases the relative role of the warm-rain formation in general, independent of the prevailing weather regime. We further find that increasing CCN concentrations reduces the effective radius of cloud droplets stronger than larger shape parameters. The cloud optical depth, however, reveals a similar large increase with larger shape parameters as changing the aerosol load from maritime to polluted. By the frequency of updrafts as a function of height, we show a negative aerosol effect on updraft strength, indicating that the larger water load above the freezing level in polluted conditions does not lead to an invigoration of deep convection. These findings demonstrate that both, CCN assumptions and the CDSD shape parameter, are important for quantitative precipitation forecasting and should be carefully chosen if double-moment schemes are used for modeling aerosol-cloud interactions.

Spatial distributions of cloud droplet size distributions from cloudbow observations measured with specMACS

<p>The evolution of clouds and their impact on weather and climate is closely related to the cloud droplet size distribution, which is often represented by two parameters: the cloud droplet effective radius (reff) and the effective variance (veff). The droplet radius (reff) determines the radiative effect of clouds on climate. The effective variance is a measure of the width of the size distribution which is, for instance, important to understand the formation of precipitation or entrainment and mixing processes. We present an airborne remote-sensing technique to determine reff and veff from high-resolution polarimetric imaging observations of the LMU cloud camera system specMACS.</p><p>Recently the spectral camera system has been upgraded by a wide-field polarization resolving RGB camera which was operated for the first time on the HALO aircraft during the EUREC<sup>4</sup>A campaign. The new polarimeter is ideally suited for observing the cloudbow - an optical phenomenon which forms by scattering of sunlight by liquid water cloud droplets at cloud top. The cloudbow is dominated by single scattering which has two implications: Its visibility is significantly enhanced in polarized measurements and its structure is sensitive to the cloud droplet size distribution at cloud top. This allows the retrieval of reff and veff by fitting the observed polarized cloudbow reflectances against a look-up table of pre-computed scattering phase functions.</p><p>The characteristics of the polarimeter are optimized for the measurement of the cloudbow. The wide field-of-view is key for observing the cloudbow (scattering angle 135° -165°) for a wide range of solar positions. Another advantage is the high spatial and temporal resolution which allows the study of small-scale variability of cloud microphysics at cloud top with a horizontal resolution of up to 20 m. Combining the polarimetric cloudbow technique with an existing stereographic retrieval of cloud geometry allows to derive vertical profiles of the droplet size distribution at cloud top. Observations of different EUREC<sup>4</sup>A cloud fields are used to demonstrate the retrieval technique and to present first spatial distributions and vertical profiles of cloud droplet size distributions.</p>

The enhancement of droplet collision by electric charges and atmospheric electric fields

The effects of electric charges and fields on droplet collision–coalescence and the evolution of cloud droplet size distribution are studied numerically. Collision efficiencies for droplet pairs with radii from 2 to 1024 µm and charges from −32 r2 to +32 r2 (in units of elementary charge; droplet radius r in units of µm) in different strengths of downward electric fields (0, 200, and 400 V cm−1) are computed by solving the equations of motion for the droplets. It is seen that the collision efficiency is increased by electric charges and fields, especially for pairs of small droplets. These can be considered as being electrostatic effects. The evolution of the cloud droplet size distribution with the electrostatic effects is simulated using the stochastic collection equation. Results show that the electrostatic effect is not notable for clouds with the initial mean droplet radius of r¯=15 µm or larger. For clouds with the initial r¯=9 µm, the electric charge without a field could evidently accelerate raindrop formation compared to the uncharged condition, and the existence of electric fields further accelerates it. For clouds with the initial r¯=6.5 µm, it is difficult for gravitational collision to occur, and the electric field could significantly enhance the collision process. The results of this study indicate that electrostatic effects can accelerate raindrop formation in natural conditions, particularly for polluted clouds. It is seen that the aerosol effect on the suppression of raindrop formation is significant in polluted clouds, when comparing the three cases with r¯=15, 9, and 6.5 µm. However, the electrostatic effects can accelerate raindrop formation in polluted clouds and mitigate the aerosol effect to some extent.

Mid-level clouds are frequent above the southeast Atlantic stratocumulus clouds

Shortwave-absorbing aerosols seasonally overlay extensive low-level stratocumulus clouds over the southeast Atlantic. While much attention has focused on the interactions between the low-level clouds and the overlying aerosols, few studies have focused on the mid-level clouds that also occur over the region. The presence of mid-level clouds over the region complicates the space-based remote-sensing retrievals of cloud properties and the evaluation of cloud radiation budgets. Here we characterize the mid-level clouds over the southeast Atlantic using lidar- and radar-based satellite cloud retrievals and observations collected in September 2016 during the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) field campaign. We find that mid-level clouds over the southeast Atlantic are relatively common, with the majority of the clouds occurring between altitudes of 5 and 7 km and at temperatures between 0 and −20 ∘C. The mid-level clouds occur at the top of a moist mid-tropospheric smoke-aerosol layer, most frequently between August and October, and closer to the southern African coast than farther offshore. They occur more frequently during the night than during the day. Between July and October, approximately 64 % of the mid-level clouds had a geometric cloud thickness less than 1 km, corresponding to a cloud optical depth of less than 4. A lidar-based depolarization–backscatter relationship for September 2016 indicates that the mid-level clouds are liquid-only clouds with no evidence of the existence of ice. In addition, a polarimeter-derived cloud droplet size distribution indicates that approximately 85 % of the September 2016 mid-level clouds had an effective radius less than 7 µm, which could further discourage the ability of the clouds to glaciate. These clouds are mostly associated with synoptically modulated mid-tropospheric moisture outflow that can be linked to the detrainment from the continental-based clouds. Overall, the supercooled mid-level clouds reduce the radiative cooling rates of the underlying low-altitude cloud tops by approximately 10 K d−1, thus influencing the regional cloud radiative budget.

Cloud Droplet Size Distributions from Observations of Glory and Cloudbow during the EUREC4A Campaign

<p>The cloud droplet size distribution determines the evolution of clouds and their impact on weather and climate. First, droplet size determines<br>the cloud radiative effect. Second, evolution of clouds and formation of precipitation are determined by droplet size and the shape of the size distribution. Therefore, measurements of the size distribution are important to further our understanding of clouds and their role in the earth system. We present a remote sensing technique for droplet size and width of the size distribution based on polarized observations of the glory and the cloudbow.<br>Glory and cloudbow are caused by backscattering of sunlight by spherical droplets in liquid clouds. This backscattering results in colorful concentric rings surrounding the observer’s shadow; the formation is described quantitatively by Mie theory. The rings of the glory appear in an angular range of 170° – 180° scattering angle and the larger cloudbow rings in a range of about 130° – 160° . The angular radius of the rings is the most accurate and direct measure of the droplet size at cloud edge. In addition, the sharpness of the rings conveys information about the width of the droplet size distribution. The visibility of glory and cloudbow is significantly enhanced by the use of polarized observations which reduce the contribution of multiple scattering.<br>The specMACS sensor of LMU Munich has been upgraded recently by a polarization-sensitive wide-angle imager which was operated for the first time on the HALO aircraft during the EUREC4A campaign. The newly installed sensor offers a high spatial and temporal resolution, allowing to study small-scale variability of cloud microphysics at cloud top with a resolution of about 20 m. specMACS measurements and first retrieval results using the glory-cloudbow technique are presented.</p>