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A Quasi-Stochastic Collection Model and Cloud and Precipitation Modeling : 준확률 포착 모형과 구름 및 강수 모델링

DC Field Value Language
dc.contributor.advisor백종진-
dc.contributor.author르함잡-
dc.date.accessioned2018-05-28T17:12:37Z-
dc.date.available2018-05-28T17:12:37Z-
dc.date.issued2018-02-
dc.identifier.other000000149740-
dc.identifier.urihttps://hdl.handle.net/10371/141152-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 지구환경과학부, 2018. 2. 백종진.-
dc.description.abstractThe evolution of cloud drop size distribution due to the collision-coalescence process is generally described by a quasi-stochastic model that solves the stochastic collection equation (SCE) in a deterministic way. In this study, an improved quasi-stochastic (IQS) model, which is derived by rigorously considering a finite model time step, is examined in the context of comparison with the normal quasi-stochastic (NQS) model. The IQS model allows a large collector drop to collide with a small collected drop more than one time in a model time step even if the collision probability is small. The number distribution of collector drops then follows the Poisson distribution with respect to the number of collisions. Using a box model that takes turbulence-induced collision enhancement (TICE) into account, it is found that large drops in the IQS model tend to have larger sizes than those in the NQS model and that the IQS model accelerates large-drop formation by a few minutes compared to the NQS model. The effects of the IQS model depend on the model time step and the shape of initial drop size distribution. The IQS model is incorporated into a detailed bin cloud microphysics scheme that is coupled with the Weather Research and Forecasting (WRF) model, and a single warm cloud is simulated under idealized environmental conditions. It is found that the onset of surface precipitation is accelerated in the IQS model.
The IQS model against the NQS model in precipitation prediction is evaluated. For this, a precipitation event observed over north central Mongolia on 21 August 2014 is simulated using the WRF model with a detailed bin cloud microphysics scheme. The surface precipitation amount is larger in the IQS model than in the NQS model, particularly over the strong precipitation region. The IQS model increases the mass contents of small drops and large drops due to multiple collisions. The increased large drops contribute to the increase in surface precipitation amount. The increased small drops are transported upward, which eventually leads to an increase in snow mass content. Deposition and riming in the IQS model occurs more actively, further increasing snow mass content. The increased snow mass content also contributes to the increase in surface precipitation amount through melting.
The impacts of aerosol loading on surface precipitation from mid-latitude deep convective systems are examined. For this, a precipitation case over north central Mongolia on 21 August 2014 is simulated with aerosol number concentrations of N0 = 150, 300, 600, 1200, 2400, and 4800 cm–3. The surface precipitation amount slightly decreases with increasing aerosol number concentration in the range of N0 = 150–600 cm–3, while it notably increases in the range of N0 = 600–4800 cm–3 (22% increase with eightfold aerosol loading). An attempt is made to explain why the surface precipitation amount increases with increasing aerosol number concentration in the N0 = 600–4800 cm–3 range. Higher aerosol number concentration results in more drops of small sizes. More drops of small sizes grow through condensation while being transported upward and some of them freeze, thus increasing the mass content of ice crystals. The increased ice crystal mass content leads to an increase in the mass content of small-sized snow particles largely through deposition, and the increased mass content of small-sized snow particles leads to an increase in the mass content of large-sized snow particles largely through riming. Also, more drops of small sizes increase the mass content of supercooled drops, which leads to an increase in the mass content of large-sized snow particles through riming. The increased mass content of large-sized snow particles resulted from these pathways contributes to a larger surface precipitation amount through melting and collision-coalescence.
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dc.description.tableofcontents1. Introduction 1
2. An improved quasi-stochastic collection model for the collisional growth of drops 5
2.1. Theoretical background 5
2.2. Results and discussion 9
2.2.1. Box model 9
2.2.2. Cloud-resolving model 18
2.3. Summary and conclusions 25
3. Evaluation of an improved quasi-stochastic collection model through precipitation prediction over north central Mongolia 27
3.1. Introduction 27
3.2. Observational analysis 29
3.3. Model description and experimental setup 38
3.4. Results and discussion 40
3.4.1. Model validation 40
3.4.2. Impacts on precipitation and cloud microphysics 48
3.5. Summary and conclusions 71
4. Impacts of aerosol loading on surface precipitation from deep convective systems over north central Mongolia 73
4.1. Introduction 73
4.2. Case description and experimental setup 75
4.3. Results and discussion 79
4.4. Summary and conclusions 97
5. A hail climatology in Mongolia 100
5.1. Introduction 100
5.2. Data 104
5.3. Results and discussion 108
3.3.1. Temporal and spatial characteristics 108
5.3.2. Relations to thermodynamic factors 116
5.4. Summary 122
6. Summary and conclusions 127
Appendix 129
References 131
초 록 146
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dc.formatapplication/pdf-
dc.format.extent14081512 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectbin cloud microphysics-
dc.subjectWRF model-
dc.subjectclouds and precipitation-
dc.subjectaerosols-
dc.subjecthail climatology-
dc.subjectMongolia-
dc.subjectimproved quasi-stochastic collection model-
dc.subject.ddc550-
dc.titleA Quasi-Stochastic Collection Model and Cloud and Precipitation Modeling-
dc.title.alternative준확률 포착 모형과 구름 및 강수 모델링-
dc.typeThesis-
dc.contributor.AlternativeAuthorJambajamts Lkhamjav-
dc.description.degreeDoctor-
dc.contributor.affiliation자연과학대학 지구환경과학부-
dc.date.awarded2018-02-
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