InScAPE

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====== Cloud geometry ======


The earth´s radiation budget is heavily influenced by the variability in cloud cover (( R.F. Cahalan, W. Ridgway, W.J. Wiscombe, and T.L. Bell (1994). //The Albedo of Fractal Stratocumulus Clouds//. J. Atmos. Sci, **51** )). In addition, individual clouds (in particular cumulus) can be very irregular, associated with inefficient vertical overlap. It is therefore essential to understand the geometry and spatial structure of shallow cumulus (Scu) cloud fields to be able to simulate the radiative fluxes adequately. Reliable cloud population information in terms of size is also necessary for the scale-aware parameterization schemes for convection that are currently being developed to address the [[greyzone|grey zone]] problem. 

==== 3D cloud reconstructions using stereo cameras ====

{{:movie_joyce_tsi_dales_20140724-hr014_rs256_20s.mp4?&300|Time lapse movie of simulated cumulus clouds on 24 July 2014 at the JOYCE site. The ray tracing renderings are generated using the open-source Blender tool. A hemispheric projection is used, allowing comparison to Total Sky Imager (TSI) data.}}

The SOCLES project (Stereo Observations of Clouds for LES Validation and Sub-scale Cloud Parameterizations) combines high-frequency cloud observations by multiple hemispheric cameras with high-resolution Large-Eddy Simulations (LES) at the [[https://atmos.meteo.uni-koeln.de/ag_crewell/doku.php?id=sites:joyce|JOYCE]] meteorological site. The main science objective is to better understand and quantify the fine-scale spatial and temporal structures of shallow cumulus cloud populations. These transient cloud fields are highly heterogeneous, a behavior that has complicated their representation in numerical weather prediction and climate models for decades. 

SOCLES aims to fill a critical data gap concerning cumulus cloud geometry with new tools and methods, thus creating new opportunities for making progress. The high-resolution sampling capability of stereo reconstruction in four dimensions, combined with its considerable spatial coverage, allows capturing cumulus cloud populations in unprecedented detail. The LES realizations supplement this observational data, used as a virtual laboratory for gaining insight and for testing measurement strategies. Assessing the realism of the LES is of key importance, involving the determination of the resolution at which simulated cloud irregularity starts to match the stereo camera observations. For this purpose, the open source [[http://blender.org|Blender]] tool and its path-tracing engine are used to generate realistic renderings of three-dimensional cloud fields from LES, including optical effects such as absorption, scattering, sun glare, and haze effects. Among others, hemispheric projections are used as a camera instrument simulator for LES, exactly mimicking the way visual instruments like Total Sky Imagers (TSI) view the world. This allows a fair comparison between models and measurements. An example hemispheric Blender movie of simulated clouds over JOYCE is shown on the right.

SOCLES is an ongoing joint effort by scientists at the University of Bonn and the University of Cologne, supported by DFG: https://gepris.dfg.de/gepris/projekt/430226822




==== Size statistics of shallow cumulus ====

This project is looking into detail into the spatial buildup of cumulus cloud populations, and how best to describe them. Patterns in cloud fields are studied by means of cloud size distributions and nearest neighbour spacing. The shape of the cloud size distribution gives information about the clustering and merging in the cloud field and is important for the [[edmf|EDMF]] scheme. The organization in a cloud population can also be defined by the distances between the clouds, or the nearest neighbour spacing. 

In a recent study by [[https://doi.org/10.1175/JAS-D-18-0084.1|Van Laar et al (2019)]], a total of 146 Scu days are simulated using [[models#dales|DALES]], which provides a broad parameter space and the possibility to compute cloud size statistics. Some first results show that the shape of the cloud size distribution greatly depends on the cloud fraction and the same is the case for the maximum cloud size in the domain (Fig. 1). Previous research points to a correlation between cloud size and spacing, this is the next thing that will be investigated. 

{{:csd-2.png?300|}}

//Figure 1 – Cloud size distributions for five different days with all a different cloud fraction. The distribution is described by a power law–exponential function.// 

In a follow-up study by [[https://doi.org/10.1002/essoar.10506509.1|Van Laar and Neggers (2021)]], we investigated cloud spacing in superlarge ICON-LEM simulations of subtropical marine cumulus clouds fields in the Caribbean as observed during NARVAL. These simulations were generated as part of the HD(CP)2 project. More specifically, we studied how nearest neighbor spacing depends on cloud size, exploring both classic and new definitions of the spacing. We find that spacing between clouds of any size behaves logarithmically, while spacing between clouds of equal size follows an exponential size-dependence. The exponential dependence reflects the increasing role of mesoscale circulations associated with the largest clusters in the cloud field.

{{ ::snapshot-2013-12-20t00_00_00z_iconbox_pythonised.png?direct&600 |}} 

//Figure 2 – MODIS true color image of the cloud field during NARVAL.// 

{{ ::csd_l_narval_fullsim-2panel_icon_modis.png?direct&600 |}}

//Figure 3 – Cloud size distributions derived from MODIS (left) and ICON-LEM (right)// 

{{ ::nns_fit_short-crop.png?direct&300 |}}

//Figure 4 – Logarithmic and exponential dependence on cloud size in the Nearest Neighbor Spacing (NNS) between clouds of any size (top) and clouds of equal size (bottom)//