Publication Abstracts

Morrison et al. 2020, in press

Morrison, H., M. van Lier-Walqui, A.M. Fridlind, W.W. Grabowski, J.Y. Harrington, C. Hoose, A. Korolev, M.R. Kumjian, J.A. Milbrandt, H. Pawlowska, D.J. Posselt, O.P. Prat, K.J. Reimel, S.-I. Shima, B. van Diedenhoven, and L. Xue, 2020: Confronting the challenge of modeling cloud and precipitation microphysics. J. Adv. Model. Earth Syst., in press, doi:10.1029/2019MS001689.

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose major challenges leading to uncertainty in research cloud models, numerical weather forecasts, and climate simulations. In this paper, the microphysics parameterization problem is divided into two parts: i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently-developed Lagrangian particle-based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next-generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process-level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle-based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods.

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BibTeX Citation

@unpublished{mo05200z,
  author={Morrison, H. and van Lier-Walqui, M. and Fridlind, A. M. and Grabowski, W. W. and Harrington, J. Y. and Hoose, C. and Korolev, A. and Kumjian, M. R. and Milbrandt, J. A. and Pawlowska, H. and Posselt, D. J. and Prat, O. P. and Reimel, K. J. and Shima, S.-I. and van Diedenhoven, B. and Xue, L.},
  title={Confronting the challenge of modeling cloud and precipitation microphysics},
  year={2020},
  journal={J. Adv. Model. Earth Syst.},
  doi={10.1029/2019MS001689},
  note={Manuscript accepted for publication}
}

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RIS Citation

TY  - INPR
ID  - mo05200z
AU  - Morrison, H.
AU  - van Lier-Walqui, M.
AU  - Fridlind, A. M.
AU  - Grabowski, W. W.
AU  - Harrington, J. Y.
AU  - Hoose, C.
AU  - Korolev, A.
AU  - Kumjian, M. R.
AU  - Milbrandt, J. A.
AU  - Pawlowska, H.
AU  - Posselt, D. J.
AU  - Prat, O. P.
AU  - Reimel, K. J.
AU  - Shima, S.-I.
AU  - van Diedenhoven, B.
AU  - Xue, L.
PY  - 2020
TI  - Confronting the challenge of modeling cloud and precipitation microphysics
JA  - J. Adv. Model. Earth Syst.
DO  - 10.1029/2019MS001689
ER  -

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