scholarly journals Machine Learning the Warm Rain Process

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
A. Gettelman ◽  
D. J. Gagne ◽  
C.‐C. Chen ◽  
M. W. Christensen ◽  
Z. J. Lebo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warm Rain ◽  
Warm Rain Process

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

A comparison study of aerosol impacts on idealized supercell between two bulk microphysics parameterizations

2020 ◽  
Author(s):  
Wanchen Wu ◽  
Wei Huang ◽  
Baode Chen
Keyword(s):  
Weather Prediction ◽  
Cloud Droplet ◽  
Forecast Model ◽  
Cloud Microphysics ◽  
Microphysics Scheme ◽  
Freezing Level ◽  
Cold Pools ◽  
Warm Rain ◽  
Warm Rain Process ◽  
Aerosol Effects

<p>Considering aerosol effects via microphysics parameterization is an imperative work in high-resolution numerical weather prediction. This paper uses two bulk microphysics parameterizations, Aerosol-Aware Thompson and CLR schemes, with the Weather and Research Forecast model to study the impacts of aerosols and microphysics scheme on an idealized supercell storm. Our results show that the implementation of aerosols can successfully modify the cloud droplet size and influence the subsequent warm-rain, mixed-phase, and accumulated precipitation. It implies that aerosols can make numerous differences to cloud microphysics properties and processes but the uncertainty in the magnitude of aerosol effects is huge because the two schemes are different from each other since the warm-rain process including CCN activation and rainwater formation. On the other hand, it is also found that the two schemes make tremendous differences in the rainfall pattern and storm dynamics due to the presence of graupel below the freezing level. The Thompson scheme has hail-like graupel which can fall below the freezing level to chill the air temperature effectively, intensify the downdraft, and enhance the uplifting on the front of cold pools. The mean graupel size represented by the two schemes plays a much more important role than the fall-speed formula for the dynamical feedbacks. Our results suggest that particle size is the core of a myriad of microphysics processes and highly associated with key cloud and dynamical signatures.</p>

scholarly journals An Investigation of the Influence of Droplet Number Concentration and Giant Aerosol Particles upon Supercooled Large Drop Formation in Wintertime Stratiform Clouds

2008 ◽  
Vol 47 (10) ◽  
pp. 2659-2678 ◽  
Author(s):  
Sonia Lasher-Trapp ◽  
Sarah Anderson-Bereznicki ◽  
Ashley Shackelford ◽  
Cynthia H. Twohy ◽  
James G. Hudson
Keyword(s):  
Particle Number ◽  
Aerosol Particles ◽  
Liquid Water Content ◽  
Cloud Properties ◽  
Droplet Concentration ◽  
Warm Rain ◽  
Stratiform Clouds ◽  
Initial Comparison ◽  
Warm Rain Process ◽  
Small Cloud

Abstract Supercooled large drops (SLD) can be a significant hazard for aviation. Past studies have shown that warm-rain processes are prevalent, or even dominant, in stratiform clouds containing SLD, but the primary factors that control SLD production are still not well understood. Giant aerosol particles have been shown to accelerate the formation of the first drizzle drops in some clouds and thus are a viable source of SLD, but observational support for testing their effectiveness in supercooled stratiform clouds has been lacking. In this study, new observations collected during six research flights from the Alliance Icing Research Study II (AIRS II) are analyzed to assess the factors that may be relevant to SLD formation, with a particular emphasis on the importance of giant aerosol particles. An initial comparison of observed giant aerosol particle number concentrations with the observed SLD suggests that they were present in sufficient numbers to be the source of the SLD. However, microphysical calculations within an adiabatic parcel model, initialized with the observed aerosol distributions and cloud properties, suggest that the giant aerosol particles were only a limited source of SLD. More SLD was produced in the modeled clouds with low droplet concentrations, simply by an efficient warm-rain process acting at temperatures below 0°C. For cases in which the warm-rain process is limited by a higher droplet concentration and small cloud depth/liquid water content, the giant aerosol particles were then the only source of SLD. The modeling results are consistent with the observed trends in SLD across the six AIRS II cases.

scholarly journals Collection/aggregation algorithms in Lagrangian cloud microphysical models: Rigorous evaluation in box model simulations

2016 ◽  
Author(s):  
Simon Unterstrasser ◽  
Fabian Hoffmann ◽  
Marion Lerch
Keyword(s):  
Box Model ◽  
Time Step ◽  
Cloud Droplets ◽  
Constant Weight ◽  
Rigorous Evaluation ◽  
Model Simulations ◽  
Warm Rain ◽  
Higher Dimensional ◽  
Warm Rain Process

Abstract. Recently, several Lagrangian microphysical models have been developed which use a large number of (computational) particles to represent a cloud. In particular, the collision process leading to coalescence of cloud droplets or aggregation of ice crystals is implemented differently in the various models. Three existing implementations are reviewed and extended, and their performance is evaluated by a comparison with well established analytical and bin model solutions. In this first step of rigorous evaluation, box model simulations with collection/aggregation being the only process considered have been performed for the three well-known kernels of Golovin, Long and Hall. Besides numerical parameters like the time step and the number of simulation particles (SIPs) used, the details of how the initial SIP ensemble is created from a prescribed analytically defined size distribution is crucial for the performance of the algorithms. Using a constant weight technique as done in previous studies greatly underestimates the quality of the algorithms. Using better initialisation techniques considerably reduces the number of required SIPs to obtain realistic results. From the box model results recommendations for the collection/aggregation implementation in higher dimensional model setups are derived. Suitable algorithms are equally relevant to treating the warm-rain process and aggregation in cirrus.

The Supercooled Warm Rain Process and the Specification of Freezing Precipitation

1988 ◽  
Vol 116 (11) ◽  
pp. 2172-2182 ◽  
Author(s):  
George J. Huffman ◽  
Gene Alfred Norman
Keyword(s):  
Warm Rain ◽  
Warm Rain Process

scholarly journals Aerosol Effects on Clouds and Precipitation over Central Europe in Different Weather Regimes

2018 ◽  
Vol 75 (12) ◽  
pp. 4247-4264 ◽  
Author(s):  
Christian Barthlott ◽  
Corinna Hoose
Keyword(s):  
Central Europe ◽  
Environmental Conditions ◽  
Large Scale ◽  
Cloud Condensation Nuclei ◽  
Convective Available Potential Energy ◽  
Small Scale ◽  
Aerosol Effect ◽  
Warm Rain ◽  
Synoptic Forcing ◽  
Warm Rain Process

Abstract The response of clouds to changes in the aerosol concentration is complex and may differ depending on the cloud type, the aerosol regime, and environmental conditions. In this study, a novel technique is used to systematically modify the environmental conditions in realistic convection-resolving simulations for cases with weak and strong large-scale forcing over central Europe with the Consortium for Small-Scale Modeling (COSMO) model. Besides control runs with quasi-operational settings, initial and boundary temperature profiles are modified with linear increasing temperature increments from 0 to 5 K between 3 and 12 km AGL to represent different amounts of convective available potential energy (CAPE) and relative humidity. The results show a systematic decrease of total precipitation with increasing cloud condensation nuclei (CCN) concentrations for the cases with strong synoptic forcing caused by a suppressed warm-rain process, whereas no systematic aerosol effect is simulated for weak synoptic forcing. The effect of increasing CCN tends to be stronger in the simulations with increased temperatures and lower CAPE. While the large-scale domain-averaged responses to increased CCN are weak, the precipitation forming over mountainous terrain reveals a stronger sensitivity for most of the analyzed cases. Our findings also demonstrate that the role of the warm-rain process is more important for strong than for weak synoptic forcing. The aerosol effect is largest for weakly forced conditions but more predictable for the strongly forced cases. However, more accurate environmental conditions are much more important than accurate aerosol assumptions, especially for weak large-scale forcing.

Characteristic features of atmospheric aerosols over India and their role in warm rain process

1978 ◽  
Vol 117 (4) ◽  
pp. 583-598 ◽  
Author(s):  
R. K. Kapoor ◽  
K. K. Kanuga ◽  
S. K. Paul ◽  
S. K. Sharma
Keyword(s):  
Atmospheric Aerosols ◽  
Warm Rain ◽  
Characteristic Features ◽  
Warm Rain Process

scholarly journals Collection/aggregation algorithms in Lagrangian cloud microphysical models: rigorous evaluation in box model simulations

2017 ◽  
Vol 10 (4) ◽  
pp. 1521-1548 ◽  
Author(s):  
Simon Unterstrasser ◽  
Fabian Hoffmann ◽  
Marion Lerch
Keyword(s):  
Box Model ◽  
Time Step ◽  
Cloud Droplets ◽  
Constant Weight ◽  
Rigorous Evaluation ◽  
Model Simulations ◽  
Warm Rain ◽  
Higher Dimensional ◽  
Warm Rain Process

Abstract. Recently, several Lagrangian microphysical models have been developed which use a large number of (computational) particles to represent a cloud. In particular, the collision process leading to coalescence of cloud droplets or aggregation of ice crystals is implemented differently in various models. Three existing implementations are reviewed and extended, and their performance is evaluated by a comparison with well-established analytical and bin model solutions. In this first step of rigorous evaluation, box model simulations, with collection/aggregation being the only process considered, have been performed for the three well-known kernels of Golovin, Long and Hall. Besides numerical parameters, like the time step and the number of simulation particles (SIPs) used, the details of how the initial SIP ensemble is created from a prescribed analytically defined size distribution is crucial for the performance of the algorithms. Using a constant weight technique, as done in previous studies, greatly underestimates the quality of the algorithms. Using better initialisation techniques considerably reduces the number of required SIPs to obtain realistic results. From the box model results, recommendations for the collection/aggregation implementation in higher dimensional model setups are derived. Suitable algorithms are equally relevant to treating the warm rain process and aggregation in cirrus.

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