scholarly journals Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes

2020 ◽  
Vol 12 (12) ◽  
Author(s):  
Axel Seifert ◽  
Stephan Rasp
Keyword(s):  
Machine Learning ◽  
Warm Rain ◽  
Cloud Microphysical Processes ◽  
Microphysical Processes ◽  
Rain Cloud

scholarly journals Machine Learning the Warm Rain Process

2020 ◽  
Author(s):  
Andrew Gettelman ◽  
David John Gagne ◽  
Chih-Chieh Chen ◽  
Matthew Christensen ◽  
Zachary Lebo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warm Rain ◽  
Warm Rain Process

scholarly journals Characterization and first results from LACIS-T: a moist-air wind tunnel to study aerosol–cloud–turbulence interactions

2020 ◽  
Vol 13 (4) ◽  
pp. 2015-2033 ◽  
Author(s):  
Dennis Niedermeier ◽  
Jens Voigtländer ◽  
Silvio Schmalfuß ◽  
Daniel Busch ◽  
Jörg Schumacher ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Spatial Scales ◽  
Experimental Studies ◽  
Field Measurements ◽  
Hygroscopic Growth ◽  
Moist Air ◽  
Aerosol Cloud ◽  
Cloud Microphysical Processes ◽  
First Results ◽  
Microphysical Processes

Abstract. The interactions between turbulence and cloud microphysical processes have been investigated primarily through numerical simulation and field measurements over the last 10 years. However, only in the laboratory we can be confident in our knowledge of initial and boundary conditions and are able to measure under statistically stationary and repeatable conditions. In the scope of this paper, we present a unique turbulent moist-air wind tunnel, called the Turbulent Leipzig Aerosol Cloud Interaction Simulator (LACIS-T) which has been developed at TROPOS in order to study cloud physical processes in general and interactions between turbulence and cloud microphysical processes in particular. The investigations take place under well-defined and reproducible turbulent and thermodynamic conditions covering the temperature range of warm, mixed-phase and cold clouds (25∘C>T>-40∘C). The continuous-flow design of the facility allows for the investigation of processes occurring on small temporal (up to a few seconds) and spatial scales (micrometer to meter scale) and with a Lagrangian perspective. The here-presented experimental studies using LACIS-T are accompanied and complemented by computational fluid dynamics (CFD) simulations which help us to design experiments as well as to interpret experimental results. In this paper, we will present the fundamental operating principle of LACIS-T, the numerical model, and results concerning the thermodynamic and flow conditions prevailing inside the wind tunnel, combining both characterization measurements and numerical simulations. Finally, the first results are depicted from deliquescence and hygroscopic growth as well as droplet activation and growth experiments. We observe clear indications of the effect of turbulence on the investigated microphysical processes.

scholarly journals Vertical redistribution of moisture and aerosol in orographic mixed-phase clouds

2020 ◽  
Author(s):  
Annette K. Miltenberger ◽  
Paul R. Field ◽  
Adrian H. Hill
Keyword(s):  
Air Temperature ◽  
Unified Model ◽  
Mixed Phase ◽  
Atmospheric Models ◽  
Microphysical Properties ◽  
Cloud Microphysical Processes ◽  
Microphysical Processes ◽  
Mixed Phase Clouds ◽  
Natural Laboratory

Abstract. Orographic wave clouds offer a natural laboratory to investigate cloud microphysical processes and their representation in atmospheric models. Wave clouds impact the larger-scale flow by the vertical redistribution of moisture and aerosol. Here we use detailed cloud microphysical observations from the ICE-L campaign to evaluate the recently developed Cloud Aerosol Interacting Microphysics (CASIM) module in the Met Office Unified Model (UM) with a particular focus on different parameterisations for heterogeneous freezing. Modelled and observed thermodynamic and microphysical properties agree very well (deviation of air temperature

Further Results on Moist Nearly Neutral Flow over a Ridge

2006 ◽  
Vol 63 (11) ◽  
pp. 2881-2897 ◽  
Author(s):  
M. M. Miglietta ◽  
R. Rotunno
Keyword(s):  
Coriolis Force ◽  
Numerical Solutions ◽  
Wrf Model ◽  
Rainfall Rate ◽  
Experimental Conditions ◽  
Available Water Content ◽  
Warm Rain ◽  
Temperature Regimes ◽  
Neutral Flow ◽  
Microphysical Processes

Abstract In a recent study, the authors performed numerical simulations of moist nearly neutral flows over a ridge using the Weather Research and Forecasting (WRF) Model in a regime where the Coriolis force can be neglected and with the simple Kessler (warm rain) microphysical scheme. In the present work, further numerical solutions using more general and realistic experimental conditions are discussed. The upstream-propagating disturbance, which was found in the author’s previous study to desaturate the initially saturated sounding for intermediate mountain heights, is present for all the simulations with taller mountains considered in the present work. The inclusion of the Coriolis force however suppresses the upwind propagation of the dry region and weakens the downstream development of convective cells. The sensitivity to different microphysical schemes has also been investigated. The simple Kessler scheme was compared with a more complete scheme, by Lin et al., which includes ice species. Some differences between the warm-rain-only and ice-microphysics simulations emerge mainly as a consequence of the different distributions of initial cloud water needed to produce a steady-state environmental flow. The effects of the different microphysical schemes on the rainfall rate have also been analyzed, with significant differences between them emerging in the case of narrower mountains. Finally, the sensitivity of the rainfall to the surface temperature has been studied, showing that for higher surface temperatures, the rainfall rate can be smaller although the available water content is larger, as a consequence of the differing microphysical processes activated in the different temperature regimes.

scholarly journals A Combined Multisensor Optimal Estimation Retrieval Algorithm for Oceanic Warm Rain Clouds

2009 ◽  
Vol 48 (11) ◽  
pp. 2242-2256 ◽  
Author(s):  
Anita D. Rapp ◽  
G. Elsaesser ◽  
C. Kummerow
Keyword(s):  
Deep Convection ◽  
Optimal Estimation ◽  
Hydrologic Cycle ◽  
Retrieval Algorithm ◽  
Climate Change Scenarios ◽  
Temperature Changes ◽  
Tropical Rainfall ◽  
Cloud Properties ◽  
Warm Rain ◽  
Rain Cloud

Abstract The complicated interactions between cloud processes in the tropical hydrologic cycle and their responses to changes in environmental variables have been the focus of many recent investigations. Most studies that examine the response of the hydrologic cycle to temperature changes focus on deep convection and cirrus production, but recent results suggest that warm rain clouds may be more sensitive to temperature changes. These clouds are prevalent in the tropics and make considerable contributions to the radiation budget and to total tropical rainfall, as well as serving to moisten and precondition the atmosphere for deep convection. A change in the properties of these clouds in climate-change scenarios could have significant implications for the hydrologic cycle. Existing microwave and visible retrievals of warm rain cloud liquid water path (LWP) disagree over the range of sea surface temperatures (SST) observed in the tropical western Pacific Ocean. Although both retrieval methods show similar behavior for nonraining clouds, the two methods show very different warm-rain-cloud LWP responses to SST, both in magnitude and trend. This makes changes to the relationship between precipitation and cloud properties in changing temperature regimes difficult to interpret. A combined optimal estimation retrieval algorithm that takes advantage of the strengths of the different satellite measurements available on the Tropical Rainfall Measuring Mission (TRMM) satellite has been developed. Deconvolved TRMM Microwave Imager brightness temperatures are combined with cloud fraction from the Visible and Infrared Scanner and rainwater estimates from the TRMM precipitation radar to retrieve the cloud LWP in warm rain systems. This algorithm is novel in that it takes into account the water in the rain and estimates the LWP due to only the cloud water in a raining cloud, thus allowing investigation of the effects of precipitation on cloud properties.

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