Confronting the Challenge of Modeling Cloud and Precipitation Microphysics
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 a major ch...
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| Vydáno v: | Journal of advances in modeling earth systems Ročník 12; číslo 8; s. e2019MS001689 - n/a |
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| Hlavní autoři: | , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Washington
John Wiley & Sons, Inc
01.08.2020
American Geophysical Union (AGU) John Wiley and Sons Inc |
| Témata: | |
| ISSN: | 1942-2466, 1942-2466 |
| On-line přístup: | Získat plný text |
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| Abstract | 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 a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models 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 parameterization 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.
Plain Language Summary
In the atmosphere, microphysics—the small‐scale processes affecting cloud and precipitation particles such as their growth by condensation, evaporation, and melting—is a critical part of Earth's weather and climate. Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent the evolution of particle populations statistically. There are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricacy of their shapes. The difficulty of representing cloud and precipitation particle populations and knowledge gaps in cloud processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations, including climate change assessments. We discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented, we advocate a “particle‐based” approach that addresses several limitations of traditional approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing gaps in knowledge of microphysical processes. We also advocate using statistical modeling tools to improve how these observations are used to constrain model microphysics. Finally, we discuss a hierarchical approach that combines the various pieces discussed in this article, providing a possible blueprint for accelerating progress in how microphysics is represented in cloud, weather, and climate models.
Key Points
Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertain
Two critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physics
A possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes |
|---|---|
| AbstractList | 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 a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models 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 parameterization 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.
Plain Language Summary
In the atmosphere, microphysics—the small‐scale processes affecting cloud and precipitation particles such as their growth by condensation, evaporation, and melting—is a critical part of Earth's weather and climate. Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent the evolution of particle populations statistically. There are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricacy of their shapes. The difficulty of representing cloud and precipitation particle populations and knowledge gaps in cloud processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations, including climate change assessments. We discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented, we advocate a “particle‐based” approach that addresses several limitations of traditional approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing gaps in knowledge of microphysical processes. We also advocate using statistical modeling tools to improve how these observations are used to constrain model microphysics. Finally, we discuss a hierarchical approach that combines the various pieces discussed in this article, providing a possible blueprint for accelerating progress in how microphysics is represented in cloud, weather, and climate models.
Key Points
Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertain
Two critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physics
A possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes 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 a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models 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 parameterization 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. 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 a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models 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 parameterization 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.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 a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models 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 parameterization 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. 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 a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models 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 parameterization 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. Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertainTwo critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physicsA possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes Abstract 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 a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models 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 parameterization 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. 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 a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models 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 parameterization 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. In the atmosphere, microphysics—the small‐scale processes affecting cloud and precipitation particles such as their growth by condensation, evaporation, and melting—is a critical part of Earth's weather and climate. Because it is impossible to simulate every cloud particle individually owing to their sheer number within even a small cloud, atmospheric models have to represent the evolution of particle populations statistically. There are critical gaps in knowledge of the microphysical processes that act on particles, especially for atmospheric ice particles because of their wide variety and intricacy of their shapes. The difficulty of representing cloud and precipitation particle populations and knowledge gaps in cloud processes both introduce important uncertainties into models that translate into uncertainty in weather forecasts and climate simulations, including climate change assessments. We discuss several specific challenges related to these problems. To improve how cloud and precipitation particle populations are represented, we advocate a “particle‐based” approach that addresses several limitations of traditional approaches and has recently gained traction as a tool for cloud modeling. Advances in observations, including laboratory studies, are argued to be essential for addressing gaps in knowledge of microphysical processes. We also advocate using statistical modeling tools to improve how these observations are used to constrain model microphysics. Finally, we discuss a hierarchical approach that combines the various pieces discussed in this article, providing a possible blueprint for accelerating progress in how microphysics is represented in cloud, weather, and climate models. Microphysics is an important component of weather and climate models, but its representation in current models is highly uncertain Two critical challenges are identified: representing cloud and precipitation particle populations and knowledge gaps in cloud physics A possible blueprint for addressing these challenges is proposed to accelerate progress in improving microphysics schemes |
| Author | Fridlind, Ann M. Xue, Lulin Harrington, Jerry Y. Korolev, Alexei Hoose, Corinna Posselt, Derek J. Morrison, Hugh Milbrandt, Jason A. Pawlowska, Hanna Shima, Shin‐Ichiro Lier‐Walqui, Marcus Kumjian, Matthew R. Diedenhoven, Bastiaan Reimel, Karly J. Grabowski, Wojciech W. Prat, Olivier P. |
| AuthorAffiliation | 7 Atmospheric Numerical Prediction Research Environment and Climate Change Canada Dorval Quebec Canada 4 Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA 10 North Carolina Institute for Climate Studies North Carolina State University Asheville NC USA 8 Institute of Geophysics, Faculty of Physics University of Warsaw Warsaw Poland 6 Observation Based Research Section Environment and Climate Change Canada Toronto Ontario Canada 11 University of Hyogo and RIKEN Center for Computational Science Kobe Japan 3 NASA Goddard Institute for Space Studies New York NY USA 2 NASA Goddard Institute for Space Studies and Center for Climate Systems Research Columbia University New York NY USA 1 National Center for Atmospheric Research Boulder CO USA 5 Institute of Meteorology and Climate Research Karlsruhe Institute of Technology Karlsruhe Germany 9 Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA |
| AuthorAffiliation_xml | – name: 5 Institute of Meteorology and Climate Research Karlsruhe Institute of Technology Karlsruhe Germany – name: 8 Institute of Geophysics, Faculty of Physics University of Warsaw Warsaw Poland – name: 11 University of Hyogo and RIKEN Center for Computational Science Kobe Japan – name: 10 North Carolina Institute for Climate Studies North Carolina State University Asheville NC USA – name: 2 NASA Goddard Institute for Space Studies and Center for Climate Systems Research Columbia University New York NY USA – name: 6 Observation Based Research Section Environment and Climate Change Canada Toronto Ontario Canada – name: 3 NASA Goddard Institute for Space Studies New York NY USA – name: 7 Atmospheric Numerical Prediction Research Environment and Climate Change Canada Dorval Quebec Canada – name: 9 Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA – name: 1 National Center for Atmospheric Research Boulder CO USA – name: 4 Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park PA USA |
| Author_xml | – sequence: 1 givenname: Hugh orcidid: 0000-0001-8480-9787 surname: Morrison fullname: Morrison, Hugh email: morrison@ucar.edu organization: National Center for Atmospheric Research – sequence: 2 givenname: Marcus orcidid: 0000-0002-0628-0045 surname: Lier‐Walqui fullname: Lier‐Walqui, Marcus organization: Columbia University – sequence: 3 givenname: Ann M. orcidid: 0000-0002-9020-0852 surname: Fridlind fullname: Fridlind, Ann M. organization: NASA Goddard Institute for Space Studies – sequence: 4 givenname: Wojciech W. orcidid: 0000-0002-9496-4462 surname: Grabowski fullname: Grabowski, Wojciech W. organization: National Center for Atmospheric Research – sequence: 5 givenname: Jerry Y. surname: Harrington fullname: Harrington, Jerry Y. organization: The Pennsylvania State University – sequence: 6 givenname: Corinna orcidid: 0000-0003-2827-5789 surname: Hoose fullname: Hoose, Corinna organization: Karlsruhe Institute of Technology – sequence: 7 givenname: Alexei orcidid: 0000-0003-3877-8419 surname: Korolev fullname: Korolev, Alexei organization: Environment and Climate Change Canada – sequence: 8 givenname: Matthew R. orcidid: 0000-0003-1131-5609 surname: Kumjian fullname: Kumjian, Matthew R. organization: The Pennsylvania State University – sequence: 9 givenname: Jason A. surname: Milbrandt fullname: Milbrandt, Jason A. organization: Environment and Climate Change Canada – sequence: 10 givenname: Hanna orcidid: 0000-0002-5345-778X surname: Pawlowska fullname: Pawlowska, Hanna organization: University of Warsaw – sequence: 11 givenname: Derek J. surname: Posselt fullname: Posselt, Derek J. organization: California Institute of Technology – sequence: 12 givenname: Olivier P. surname: Prat fullname: Prat, Olivier P. organization: North Carolina State University – sequence: 13 givenname: Karly J. surname: Reimel fullname: Reimel, Karly J. organization: The Pennsylvania State University – sequence: 14 givenname: Shin‐Ichiro orcidid: 0000-0001-5540-713X surname: Shima fullname: Shima, Shin‐Ichiro organization: University of Hyogo and RIKEN Center for Computational Science – sequence: 15 givenname: Bastiaan orcidid: 0000-0001-5622-8619 surname: Diedenhoven fullname: Diedenhoven, Bastiaan organization: Columbia University – sequence: 16 givenname: Lulin orcidid: 0000-0002-5501-9134 surname: Xue fullname: Xue, Lulin organization: National Center for Atmospheric Research |
| BackLink | https://www.osti.gov/servlets/purl/1632264$$D View this record in Osti.gov |
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| Publisher | John Wiley & Sons, Inc American Geophysical Union (AGU) John Wiley and Sons Inc |
| Publisher_xml | – name: John Wiley & Sons, Inc – name: American Geophysical Union (AGU) – name: John Wiley and Sons Inc |
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| Snippet | In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various... In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various... Abstract In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the... |
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| SubjectTerms | Atmosphere Atmospheric Processes Atmospheric water Bayesian analysis Climate change Cloud physics Clouds Clouds and Aerosols Commissioned Manuscript Computational Geophysics Hydrology Ice Informatics Instruments Inverse problems Laboratories Laboratory experiments Microphysics Model Calibration Model Verification and Validation Modeling Modelling Natural Hazards Numerical Modeling Oceanography: General Parameterization Physical Modeling Physics Precipitation Probability theory Statistical methods Turbulence models Uncertainty Weather forecasting |
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| Title | Confronting the Challenge of Modeling Cloud and Precipitation Microphysics |
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