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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
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Confronting the Challenge of Modeling Cloud and Precipitation Microphysics
Hugh Morrison1, Marcus van Lier-Walqui2, Ann M Fridlind3
1National Center for Atmospheric Research Boulder CO USA.
Journal of Advances in Modeling Earth Systems
|October 1, 2020
Summary
Improving atmospheric microphysics in models is crucial for accurate weather forecasts and climate simulations. This study proposes advanced methods for representing cloud particles and addressing knowledge gaps in cloud physics.
Area of Science:
- Atmospheric science, specifically cloud and precipitation microphysics.
- Numerical modeling of weather and climate systems.
Background:
- Atmospheric microphysics, the study of cloud and precipitation particles, is vital for understanding Earth's water and energy cycles.
- Current models face challenges in representing microphysical processes, leading to uncertainties in weather forecasts and climate simulations.
Purpose of the Study:
- To address the dual challenges of representing cloud particle populations and uncertainties in microphysical process rates within atmospheric models.
- To propose novel methods for improving the accuracy and reducing uncertainty in microphysics parameterizations.
Main Methods:
- Advocating for the Lagrangian particle-based method to represent particle populations, overcoming limitations of traditional bulk and bin schemes.
- Highlighting the need for enhanced observational data from laboratory experiments, new probes, and space-based instruments to fill knowledge gaps in cloud physics.
- Proposing a probabilistic framework combining statistical and physical modeling, using Bayesian statistics for inverse problems to constrain microphysics schemes and quantify uncertainty.
Main Results:
- The Lagrangian particle-based method offers a conceptual and practical solution for representing particle populations in models.
- A probabilistic framework allows for rigorous constraint of microphysics schemes and systematic quantification of uncertainty.
- A hierarchical approach integrating process modeling, laboratory work, observations, and statistical methods is proposed for accelerated improvements.
Conclusions:
- Addressing microphysics representation and knowledge gaps requires a multi-faceted approach combining advanced modeling techniques and robust observational strategies.
- Increased emphasis on laboratory experiments and systematic use of observational data are essential for improving process-level understanding.
- The proposed probabilistic and hierarchical frameworks provide a pathway to significantly reduce uncertainties in atmospheric models.
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