scholarly journals Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars

2020 ◽  
Vol 13 (5) ◽  
pp. 2363-2379 ◽  
Author(s):  
Katia Lamer ◽  
Pavlos Kollias ◽  
Alessandro Battaglia ◽  
Simon Preval
Keyword(s):  
Boundary Layer ◽  
Cloud Cover ◽  
Marine Boundary Layer ◽  
Short Pulse ◽  
Pulse Mode ◽  
Cloud Base ◽  
Boundary Layer Clouds ◽  
Highly Sensitive ◽  
Eastern North Atlantic ◽  
Long Pulse

Abstract. Ground-based radar observations show that, over the eastern North Atlantic, 50 % of warm marine boundary layer (WMBL) hydrometeors occur below 1.2 km and have reflectivities of < −17 dBZ, thus making their detection from space susceptible to the extent of surface clutter and radar sensitivity. Surface clutter limits the ability of the CloudSat cloud profiling radar (CPR) to observe the true cloud base in ∼52 % of the cloudy columns it detects and true virga base in ∼80 %, meaning the CloudSat CPR often provides an incomplete view of even the clouds it does detect. Using forward simulations, we determine that a 250 m resolution radar would most accurately capture the boundaries of WMBL clouds and precipitation; that being said, because of sensitivity limitations, such a radar would suffer from cloud cover biases similar to those of the CloudSat CPR. Observations and forward simulations indicate that the CloudSat CPR fails to detect 29 %–43 % of the cloudy columns detected by ground-based sensors. Out of all configurations tested, the 7 dB more sensitive EarthCARE CPR performs best (only missing 9.0 % of cloudy columns) indicating that improving radar sensitivity is more important than decreasing the vertical extent of surface clutter for measuring cloud cover. However, because 50 % of WMBL systems are thinner than 400 m, they tend to be artificially stretched by long sensitive radar pulses, hence the EarthCARE CPR overestimation of cloud top height and hydrometeor fraction. Thus, it is recommended that the next generation of space-borne radars targeting WMBL science should operate interlaced pulse modes including both a highly sensitive long-pulse mode and a less sensitive but clutter-limiting short-pulse mode.

scholarly journals A new approach to retrieve cloud base height of marine boundary layer clouds

2013 ◽  
Vol 40 (16) ◽  
pp. 4448-4453 ◽  
Author(s):  
J. M. Li ◽  
Y. H. Yi ◽  
K. Stamnes ◽  
X. D. Ding ◽  
T. H. Wang ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Marine Boundary Layer ◽  
Cloud Base ◽  
New Approach ◽  
Boundary Layer Clouds ◽  
Cloud Base Height

scholarly journals Observations of aerosol, cloud, turbulence, and radiation properties at the top of the marine boundary layer over the Eastern North Atlantic Ocean: The ACORES campaign

2020 ◽  
pp. 1-59
Author(s):  
Holger Siebert ◽  
Kai-Erik Szodry ◽  
Ulrike Egerer ◽  
Birgit Wehner ◽  
Silvia Henning ◽  
...  
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
North Atlantic ◽  
Marine Boundary Layer ◽  
North Atlantic Ocean ◽  
Fine Scale ◽  
Boundary Layer Clouds ◽  
Radiation Properties ◽  
Eastern North Atlantic ◽  
Eastern North Atlantic Ocean

Capsule summary.Helicopter-borne observations with unprecedented high resolution provide new insights in the fine-scale structure of marine boundary layer clouds and aerosol stratification over the Eastern North Atlantic.

Quantification of aerosol effects on marine boundary layer clouds with machine learning

2021 ◽  
Author(s):  
Lukas Zipfel ◽  
Hendrik Andersen ◽  
Jan Cermak
Keyword(s):  
Machine Learning ◽  
Boundary Layer ◽  
Marine Boundary Layer ◽  
Cloud Droplet ◽  
Number Concentration ◽  
Satellite Observations ◽  
Radiative Properties ◽  
Cloud Base ◽  
Boundary Layer Clouds ◽  
Cloud Droplet Number Concentration

&lt;p&gt;Satellite observations are used in regional machine learning models to quantify sensitivities of marine boundary-layer clouds (MBLC) to aerosol changes.&lt;/p&gt;&lt;p&gt;MBLCs make up a large part of the global cloud coverage as they are persistently present over more than 20% of the Earth&amp;#8217;s oceans in the annual mean.They play an important role in Earth&amp;#8217;s energy budget by reflecting solar radiation and interacting with thermal radiation from the surface, leading to a net cooling effect. Cloud properties and their radiative characteristics such as cloud albedo, horizontal and vertical extent, lifetime and precipitation susceptibility are dependent on environmental conditions. Aerosols in their role as condensation nuclei affect these cloud radiative properties through changes in the cloud droplet number concentration and subsequent cloud adjustments to this perturbation. However, the magnitude and sign of these effects remain among the largest uncertainties in future climate predictions.&lt;/p&gt;&lt;p&gt;In an effort to help improve these predictions a machine learning approach in combination with observational data is pursued:&lt;/p&gt;&lt;p&gt;Satellite observations from the collocated A-Train dataset (C3M) for 2006-2011 are used in combination with ECMWF atmospheric reanalysis data (ERA5) to train regional Gradient Boosting Regression Tree (GBRT) models to predict changes in key physical and radiative properties of MBLCs. The cloud droplet number concentration (N&lt;sub&gt;d&lt;/sub&gt;) and the liquid water path (LWP) are simulated for the eastern subtropical oceans, which are characterised by a high annual coverage of MBLC due to the occurrence of semi-permanent stratocumulus sheets. Relative humidity above cloud, cloud top height and temperature below the cloud base and at the surface are identified as important predictors for both N&lt;sub&gt;d&lt;/sub&gt; and LWP.&amp;#160; The impact of each predictor variable on the GBRT model's output is analysed using Shapley values as a method of explainable machine learning, providing novel sensitivity estimates that will improve process understanding and help constrain the parameterization of MBLC processes in Global Climate Models.&lt;/p&gt;

scholarly journals Mind-the-gap part I: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars

2020 ◽  
Author(s):  
Katia Lamer ◽  
Pavlos Kollias ◽  
Alessandro Battaglia ◽  
Simon Preval
Keyword(s):  
Boundary Layer ◽  
North Atlantic ◽  
Marine Boundary Layer ◽  
Radar Observations ◽  
Boundary Layer Clouds ◽  
Eastern North Atlantic

Abstract. Ground-based radar observations show that, in the eastern north Atlantic, 50 % of warm marine boundary layer (WMBL) hydrometeors occur below 1.2 km and have reflectivities

scholarly journals Large-Eddy Simulations of a Drizzling, Stratocumulus-Topped Marine Boundary Layer

2009 ◽  
Vol 137 (3) ◽  
pp. 1083-1110 ◽  
Author(s):  
Andrew S. Ackerman ◽  
Margreet C. vanZanten ◽  
Bjorn Stevens ◽  
Verica Savic-Jovcic ◽  
Christopher S. Bretherton ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Marine Boundary Layer ◽  
Cloud Water ◽  
Large Eddy Simulations ◽  
Cloud Base ◽  
Entrainment Rate ◽  
Liquid Water Path ◽  
Average Maximum ◽  
Large Eddy ◽  
The Mean

Abstract Cloud water sedimentation and drizzle in a stratocumulus-topped boundary layer are the focus of an intercomparison of large-eddy simulations. The context is an idealized case study of nocturnal stratocumulus under a dry inversion, with embedded pockets of heavily drizzling open cellular convection. Results from 11 groups are used. Two models resolve the size distributions of cloud particles, and the others parameterize cloud water sedimentation and drizzle. For the ensemble of simulations with drizzle and cloud water sedimentation, the mean liquid water path (LWP) is remarkably steady and consistent with the measurements, the mean entrainment rate is at the low end of the measured range, and the ensemble-average maximum vertical wind variance is roughly half that measured. On average, precipitation at the surface and at cloud base is smaller, and the rate of precipitation evaporation greater, than measured. Including drizzle in the simulations reduces convective intensity, increases boundary layer stratification, and decreases LWP for nearly all models. Including cloud water sedimentation substantially decreases entrainment, decreases convective intensity, and increases LWP for most models. In nearly all cases, LWP responds more strongly to cloud water sedimentation than to drizzle. The omission of cloud water sedimentation in simulations is strongly discouraged, regardless of whether or not precipitation is present below cloud base.

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