Separating physical impacts from natural variability using piggybacking technique
Wojciech W. Grabowski
CORRESPONDING AUTHOR
National Center for Atmospheric Science, Boulder, Colorado, USA
Editorial note: Please note that the ADGEO paper and its title originally included an alternative name for the piggybacking approach that is now recognized to be culturally insensitive and inappropriate. The editors of the journal ADGEO, the journal owner European Geosciences Union, and the publisher Copernicus Publications foster equality, diversity, and inclusiveness in scientific exchange, and do not condone in any way racism, discrimination, or cultural appropriation. The author did not intend to insult any ethnic groups by using this technique name when compiling the manuscript in 2019, apologizes for any offense taken, and is grateful for the opportunity to correct this mistake.
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A thermodynamic parcel analysis of several high-resolution soundings from Pune, India, investigating pre-monsoon and monsoon conditions, is carried out in this study. A simple theoretical approach for cloud base height estimation is illustrated. Results illustrate the role of surface forcing in contrasting conditions of the pre-monsoon and monsoon. Large eddy simulations, observational data, and theoretical explanation are presented.
Wojciech W. Grabowski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 11, 103–120, https://doi.org/10.5194/gmd-11-103-2018, https://doi.org/10.5194/gmd-11-103-2018, 2018
Short summary
Short summary
This paper introduces a novel approach to simulating ice-free clouds. The key process is formation and transport of cloud droplets that are represented through Lagrangian particles referred to as super-droplets. Each super-droplet represents a multitude of natural cloud droplets. The essential component of the scheme that makes it different and more efficient from previous approaches is the presence of super-droplets only within a cloud.
Muhammad E. E. Hassim, W. W. Grabowski, and T. P. Lane
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-402, https://doi.org/10.5194/acp-2016-402, 2016
Revised manuscript not accepted
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Short summary
Model simulations show that there is more surface rainfall, less shallow clouds below 3 km and more deep clouds above 9 km in pristine air conditions than in a polluted environment, contrary to previous studies. This is due to more efficient rain processes below the freezing level, enhanced ice growth above and the off-loading of precipitation that increases cloud buoyancy aloft. Our results demonstrate that microphysical effects dominate the aerosol impact on rainfall more than cloud dynamics.
M. E. E. Hassim, T. P. Lane, and W. W. Grabowski
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This paper introduces a free and open-source C++ library of algorithms for representing cloud microphysics in numerical models. In the current release, the library covers three warm-rain schemes: the single- and double-moment bulk schemes, and the particle-based scheme with Monte Carlo coalescence. The three schemes are intended for modelling frameworks of different dimensionalities and complexities ranging from parcel models to multi-dimensional cloud-resolving (e.g. large-eddy) simulations.
W. W. Grabowski, L.-P. Wang, and T. V. Prabha
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D. Jarecka, H. Pawlowska, W. W. Grabowski, and A. A. Wyszogrodzki
Atmos. Chem. Phys., 13, 8489–8503, https://doi.org/10.5194/acp-13-8489-2013, https://doi.org/10.5194/acp-13-8489-2013, 2013
A. A. Wyszogrodzki, W. W. Grabowski, L.-P. Wang, and O. Ayala
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Short summary
In a chaotic system, like moist convection, it is difficult to separate the impact of a physical process from effects of natural variability. This is because modifying even a small element of the system physics typically leads to a different system evolution. This paper discusses a relatively simple and computationally efficient modelling methodology that allows separation of the physical impact from differences originating from contrasting flow realizations.
In a chaotic system, like moist convection, it is difficult to separate the impact of a physical...