請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97299完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳健銘 | zh_TW |
| dc.contributor.advisor | Chien-Ming Wu | en |
| dc.contributor.author | 黃金德 | zh_TW |
| dc.contributor.author | Jin-De Huang | en |
| dc.date.accessioned | 2025-04-02T16:21:45Z | - |
| dc.date.available | 2025-04-03 | - |
| dc.date.copyright | 2025-04-02 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-02-24 | - |
| dc.identifier.citation | Chapter 1
Houze Jr, R. A., & Cheng, C. P. (1977). Radar characteristics of tropical convection observed during GATE: Mean properties and trends over the summer season. Monthly Weather Review, 105(8), 964-980. https://doi.org/10.1175/1520-0493(1977)105<0964:RCOTCO>2.0.CO;2 Mapes, B. E., & Houze Jr, R. A. (1993). Cloud clusters and superclusters over the oceanic warm pool. Monthly Weather Review, 121(5), 1398-1416. https://doi.org/10.1175/1520-0493(1993)121<1398:CCASOT>2.0.CO;2 Yuan, J., & Houze Jr, R. A. (2010). Global variability of mesoscale convective system anvil structure from A-Train satellite data. Journal of Climate, 23(21), 5864-5888. https://doi.org/10.1175/2010JCLI3671.1 Hamada, A., Murayama, Y., & Takayabu, Y. N. (2014). Regional characteristics of extreme rainfall extracted from TRMM PR measurements. Journal of Climate, 27(21), 8151-8169. https://doi.org/10.1175/JCLI-D-14-00107.1 Tan, J., Jakob, C., Rossow, W. B., & Tselioudis, G. (2015). Increases in tropical rainfall driven by changes in frequency of organized deep convection. Nature, 519(7544), 451-454. https://doi.org/10.1038/nature14339 Chen, P. J., Chen, W. T., Wu, C. M., & Yo, T. S. (2021). Convective cloud regimes from a classification of object‐based CloudSat observations over Asian‐Australian monsoon areas. Geophysical Research Letters, 48(10), e2021GL092733. https://doi.org/10.1029/2021GL092733 Mauritsen, T., & Stevens, B. (2015). Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nature Geoscience, 8(5), 346-351. https://doi.org/10.1038/ngeo2414 Manabe, S., and R. F. Strickler, 1964: Thermal Equilibrium of the Atmosphere with a Convective Adjustment. J. Atmos. Sci., 21, 361–385, https://doi.org/10.1175/1520-0469(1964)021<0361:teotaw>2.0.co;2 Held, I. M., R. S. Hemler, and V. Ramaswamy, 1993: Radiative-Convective Equilibrium with Explicit Two-Dimensional Moist Convection. J. Atmos. Sci., 50, 3909–3927, https://doi.org/10.1175/1520-0469(1993)050<3909:rcewet>2.0.co;2 Tompkins, A. M., and G. C. Craig, 1998: Radiative–convective equilibrium in a three-dimensional cloud-ensemble model. Q.J Royal Met. Soc., 124, 2073–2097, https://doi.org/10.1002/qj.49712455013 Bretherton, C. S., P. N. Blossey, and M. Khairoutdinov, 2005: An Energy-Balance Analysis of Deep Convective Self-Aggregation above Uniform SST. Journal of the Atmospheric Sciences, 62, 4273–4292, https://doi.org/10.1175/jas3614.1 Muller, C. J., and I. M. Held, 2012: Detailed Investigation of the Self-Aggregation of Convection in Cloud-Resolving Simulations. Journal of the Atmospheric Sciences, 69, 2551–2565, https://doi.org/10.1175/jas-d-11-0257.1 Tompkins, A. M., and A. G. Semie, 2017: Organization of tropical convection in low vertical wind shears: Role of updraft entrainment. J. Adv. Model. Earth Syst., 9, 1046–1068, https://doi.org/10.1002/2016ms000802 Arnold, N. P., and D. A. Randall, 2015: Global-scale convective aggregation: Implications for the Madden-Julian Oscillation. J. Adv. Model. Earth Syst., 7, 1499–1518, https://doi.org/10.1002/2015ms000498 Holloway, C. E., A. A. Wing, S. Bony, C. Muller, H. Masunaga, T. S. L’Ecuyer, D. D. Turner, and P. Zuidema, 2017: Observing Convective Aggregation. Surv Geophys, 38, 1199–1236, https://doi.org/10.1007/s10712-017-9419-1 Wing, A. A., 2019: Self-Aggregation of Deep Convection and its Implications for Climate. Curr Clim Change Rep, 5, 1–11, https://doi.org/10.1007/s40641-019-00120-3 Tobin, I., Bony, S., & Roca, R. (2012). Observational evidence for relationships between the degree of aggregation of deep convection, water vapor, surface fluxes, and radiation. Journal of Climate, 25(20), 6885-6904. https://doi.org/10.1175/JCLI-D-11-00258.1 Lebsock, M. D., L’Ecuyer, T. S., & Pincus, R. (2018). An observational view of relationships between moisture aggregation, cloud, and radiative heating profiles. Shallow Clouds, Water Vapor, Circulation, and Climate Sensitivity, 65-82. https://doi.org/10.1007/978-3-319-77273-8_3 Xu, K. M., Hu, Y., & Wong, T. (2019). Convective aggregation and indices examined from CERES cloud object data. Journal of Geophysical Research: Atmospheres, 124(24), 13604-13624. https://doi.org/10.1029/2019JD030816 Tsai, W. M., & Mapes, B. E. (2022). Evidence of aggregation dependence of 5°-scale tropical convective evolution using a gross moist stability framework. Journal of the atmospheric sciences, 79(5), 1385-1404. https://doi.org/10.1175/JAS-D-21-0253.1 Wing, A. A., and Coauthors, 2020: Clouds and Convective Self‐Aggregation in a Multimodel Ensemble of Radiative‐Convective Equilibrium Simulations. J Adv Model Earth Syst, 12, https://doi.org/10.1029/2020ms002138 Wing, A. A., K. A. Reed, M. Satoh, B. Stevens, S. Bony, and T. Ohno, 2018: Radiative–convective equilibrium model intercomparison project. Geosci. Model Dev., 11, 793–813, https://doi.org/10.5194/gmd-11-793-2018 Coppin, D., and S. Bony, 2015: Physical mechanisms controlling the initiation of convective self-aggregation in a General Circulation Model. J. Adv. Model. Earth Syst., 7, 2060–2078, https://doi.org/10.1002/2015ms000571 Holloway, C. E., and S. J. Woolnough, 2016: The sensitivity of convective aggregation to diabatic processes in idealized radiative‐convective equilibrium simulations. J. Adv. Model. Earth Syst., 8, 166–195, https://doi.org/10.1002/2015ms000511 Yanase, T., Nishizawa, S., Miura, H., Takemi, T., & Tomita, H. (2020). New critical length for the onset of self‐aggregation of moist convection. Geophysical Research Letters, 47(16), e2020GL088763. https://doi.org/10.1029/2020GL088763 Maher, P., Gerber, E. P., Medeiros, B., Merlis, T. M., Sherwood, S., Sheshadri, A., ... & Zurita‐Gotor, P. (2019). Model hierarchies for understanding atmospheric circulation. Reviews of Geophysics, 57(2), 250-280. https://doi.org/10.1029/2018RG000607 Jung, J.-H., & Arakawa, A. (2008). A three-dimensional anelastic model based on the vorticity equation. Monthly Weather Review., 136(1), 276–294. https://doi.org/10.1175/2007MWR2095.1 Chapter 2 Adams-Selin, R. D., van den Heever, S. C., & Johnson, R. H. (2013). Impacts of graupel parameterization schemes on idealized bow echo simulations. Monthly Weather Review, 141, 3735–3756. https://doi.org/10.1175/MWR-D-12-00343.1 Arakawa, A. (2004). The cumulus parameterization problem: Past, present, and future. Journal of Climate, 17, 2493–2525. https://doi.org/10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2 Arakawa, A., & Schubert, W. H. (1974). Interaction of a cumulus cloud ensemble with large-scale environment, Part I. Journal of the Atmospheric Sciences, 31(3), 674–701. https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2 Arakawa, A., & Wu, C.-M. (2013). A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. Journal of the Atmospheric Sciences, 70(7), 1977–1992. https://doi.org/10.1175/JAS‐D‐12‐0330.1 Chen, X., Pauluis, O. M., Leung, L. R., & Zhang, F. (2018). Multiscale Atmospheric Overturning of the Indian Summer Monsoon as Seen through Isentropic Analysis. Journal of the Atmospheric Sciences, 75, 3011–3030. https://doi.org/10.1175/JAS-D-18-0068.1 Chen, Y.-T., & Wu, C.-M. (2019). The role of interactive SST in the cloud-resolving simulations of aggregated convection. Journal of Advances in Modeling Earth Systems, 11, 3321–3340. https://doi.org/10.1029/2019MS001762 Chien, M.-H., & Wu, C.-M. (2016). Representation of topography by partial steps using the immersed boundary method in a vector vorticity equation model (VVM). Journal of Advances in Modeling Earth Systems, 8, 212–223. https://doi.org/10.1002/2015MS000514 Grabowski, W. W. (2014). Extracting microphysical impacts in large-eddy simulations of shallow convection. Journal of the Atmospheric Sciences, 71, 4493–4499. https://doi.org/10.1175/JAS-D-14-0231.1 Grabowski, W. W., Wu, X., & Moncrieff, M. W. (1999). Cloud resolving modeling of tropical cloud systems during phase III of GATE. Part III: Effects of cloud microphysics. Journal of the Atmospheric Sciences, 56, 2384–2402. https://doi.org/10.1175/1520-0469(1999)056<2384:CRMOTC>2.0.CO;2 Johnson, D. E., Tao, W.-K., & Simpson, J. (2007). A study of the response of deep tropical clouds to large-scale thermodynamic forcings. Part II: Sensitivities to microphysics, radiation and surface fluxes. Journal of the Atmospheric Sciences, 64, 869–886. https://doi.org/10.1175/JAS3846.1 Jung, J.-H., & Arakawa, A. (2008). A three-dimensional anelastic model based on the vorticity equation. Monthly Weather Review., 136(1), 276–294. https://doi.org/10.1175/2007MWR2095.1 Jung, J.‐H., & Arakawa, A. (2016). Simulation of subgrid orographic precipitation with an embedded 2‐D cloud‐resolving model. Journal of Advances in Modeling Earth Systems, 8, 31–40. https://doi.org/10.1002/2015MS000539 Jung, J.‐H., Konor, C. S., & Randall, D. (2019). Implementation of the vector vorticity dynamical core on cubed sphere for use in the Quasi‐3‐D Multiscale Modeling Framework. Journal of Advances in Modeling Earth Systems, 11, 560–577. https://doi.org/10.1029/2018MS001517 Khairoutdinov, M. F., & Randall, D. A. (2003). Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities. Journal of the Atmospheric Sciences, 60, 607–625. https://doi.org/10.1175/1520-0469(2003)060,0607:CRMOTA.2.0.CO;2 Krueger, S. K., Fu, Q., Liou, K., & Chin, H.-N. S. (1995). Improvements of an ice-phase microphysics parameterization for use in numerical simulations of tropical convection. Journal of Applied Meteorology, 34(1), 281–287. https://doi.org/10.1175/1520-0450-34.1.281 Kuo, K.-T., & Wu, C.-M. (2019). The precipitation hotspots of afternoon thunderstorms over the Taipei Basin: Idealized numerical simulations. Journal of the Meteorological Society of Japan, 97, 501–517. https://doi.org/10.2151/jmsj.2019-031 Lin, Y.-L., Farley, R. D., & Orville, H. D. (1983). Bulk parameterization of the snow field in a cloud model. Journal of Climate and Applied Meteorology, 22, 1065–1092. https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2 Milbrandt, J. A., & Morrison, H. (2016). Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part III: Introduction of multiple free categories. Journal of the Atmospheric Sciences, 73, 975–995. https://doi.org/10.1175/JAS-D-15-0204.1 Morrison, H., & Milbrandt, J. A. (2011). Comparison of two-moment bulk microphysics schemes in idealized supercell thunderstorm simulations. Monthly Weather Review, 139, 1103–1130. https://doi.org/10.1175/2010MWR3433.1 Morrison, H., & Milbrandt, J. A. (2015). Parameterization of ice microphysics based on the prediction of bulk particle properties. Part I: Scheme description and idealized tests. Journal of the Atmospheric Sciences, 72, 287–311. https://doi.org/10.1175/JAS-D-14-0065.1 Morrison, H., Milbrandt, J. A., Bryan, G. H., Ikeda, K., Tessendorf, S. A., & Thompson, G. (2015). Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part II: Case study comparisons with observations and other schemes. Journal of the Atmospheric Sciences, 72, 312–339. https://doi.org/10.1175/JAS-D-14-0066.1 Mrowiec, A. A., Pauluis, O. M., & Zhang, F. (2016). Isentropic analysis of a simulated hurricane. Journal of the Atmospheric Sciences, 73, 1857–1870. https://doi.org/10.1175/JAS-D-15-0063.1 Pauluis, O., & Mrowiec, A. A. (2013). Isentropic analysis of convective motions. Journal of the Atmospheric Sciences, 70, 3673–3688. https://doi.org/10.1175/JAS-D-12-0205.1 Pauluis, O. (2016). The mean air flow as Lagrangian dynamics approximation and its application to moist convection. Journal of the Atmospheric Sciences, 73, 4407–4425. https://doi.org/10.1175/JAS-D-15-0284.1 Tao, W.-K., Shi, J. J., Chen, S. S., Lang, S., Lin, P.-L., Hong, S.-Y., Peters-Lidard, C., & Hou, A. (2011). The impact of microphysical schemes on hurricane intensity and track. Asia-Pacific Journal of Atmospheric Sciences, 47(1), 1–16. https://doi.org/10.1007/s13143-011-1001-z Tao, W.-K., Wu, D., Lang, S., Chern, J.-D., Peters-Lidard, C., Fridlind, A., & Matsui, T. (2016). High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations, Journal of Geophysical Research: Atmospheres, 121, 1278–305. https://doi.org/10.1002/2015JD023986. Tsai, J.-Y., & Wu, C.-M. (2016). Critical transitions of stratocumulus dynamical systems due to perturbation in free atmosphere moisture. Dynamics of Atmospheres and Oceans, 76, 1–13. https://doi.org/10.1016/j.dynatmoce.2016.08.002 Tsai, W.-M., & Wu, C.-M. (2017). The environment of aggregated deep convection. Journal of Advances in Modeling Earth Systems, 9, 2061–2078, https://doi.org/10.1002/2017MS000967. Wu, C.-M., & Arakawa, A. (2011). Inclusion of surface topography into the vector vorticity equation model (VVM). Journal of Advances in Modeling Earth Systems, 3, M04002. https://doi.org/10.1029/2011MS000061 Wu, C.-M., & Arakawa, A. (2014). A unified representation of deep moist convection in numerical modeling of the atmosphere. Part II. Journal of the Atmospheric Sciences, 71(6), 2089–2103. https://doi.org/10.1175/JAS‐D‐13‐0382.1 Wu, C.-M., Lin, H.-C., Cheng, F.-Y., & Chien, M.-H. (2019). Implementation of the land surface processes into a vector vorticity equation model (VVM) to study its impact on afternoon thunderstorms over complex topography in Taiwan. Asia-Pacific Journal of Atmospheric Sciences. https://doi.org/10.1007/s13143-019-00116-x Yanai, M., Esbensen, S., & Chu, J.-H. (1973). Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. Journal of the Atmospheric Sciences, 30, 611–627. https://doi.org/10.1175/1520-0469(1973)030,0611:DOBPOT.2.0.CO;2 Chapter 3 Arakawa, A., & Wu, C.-M. (2013). A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. Journal of the Atmospheric Sciences, 70(7), 1977–1992. https://doi.org/10.1175/JAS‐D‐12‐0330.1 Bretherton, C. S., & Sobel, A. H. (2002). A simple model of a convectively coupled Walker circulation using the weak temperature gradient approximation. Journal of Climate, 15(20), 2907-2920. https://doi.org/10.1175/1520-0442(2002)015<2907:ASMOAC>2.0.CO;2 Bretherton, C. S., Blossey, P. N., & Khairoutdinov, M. (2005). An energy-balance analysis of deep convective self-aggregation above uniform SST. Journal of the Atmospheric Sciences, 62, 4273–4292. https://doi.org/10.1175/JAS3614.1 Bretherton, C.S., Blossey, P.N. & Peters, M.E. (2006). Interpretation of simple and cloud-resolving simulations of moist convection–radiation interaction with a mock-Walker circulation. Theoretical and Computational Fluid Dynamics, 20, 421–442. https://doi.org/10.1007/s00162-006-0029-7 Byrne, M. P., & Schneider, T. (2016). Energetic constraints on the width of the intertropical convergence zone. Journal of Climate, 29(13), 4709-4721. https://doi.org/10.1175/JCLI-D-15-0767.1 Chang, Y. H., Chen, W. T., Wu, C. M., Moseley, C., & Wu, C. C. (2021). Tracking the influence of cloud condensation nuclei on summer diurnal precipitating systems over complex topography in Taiwan. Atmospheric Chemistry and Physics Discussions, 1-28. https://doi.org/10.5194/acp-21-16709-2021 Chen, F., & Dudhia, J. (2001). Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Monthly Weather Review, 129(4), 569-585. https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2 Chen, P.-J., Chen, W.-T., Wu, C.-M., & Yo, T.-S. (2021). Convective cloud regimes from a classification of object-based CloudSat observations over Asian-Australian monsoon areas. Geophysical Research Letters, 48, e2021GL092733. https://doi.org/10.1029/2021GL092733 Chen, W.-T., Wu, C.-M., Tsai, W.-M., Chen, P.-J., & Chen, P.-Y. (2019). Role of coastal convection to moisture buildup during the South China Sea summer monsoon onset. Journal of the Meteorological Society of Japan, 97(6), 1155-1171. https://doi.org/10.2151/jmsj.2019-065 Chen, Y.-T., & Wu, C.-M. (2019). The role of interactive SST in the cloud-resolving simulations of aggregated convection. Journal of Advances in Modeling Earth Systems, 11, 3321–3340. https://doi.org/10.1029/2019MS001762 Chien, M.-H., & Wu, C.-M. (2016). Representation of topography by partial steps using the immersed boundary method in a vector vorticity equation model (VVM). Journal of Advances in Modeling Earth Systems, 8, 212–223. https://doi.org/10.1002/2015MS000514 Craig, G. C., & Mack, J. M. (2013). A coarsening model for self-organization of tropical convection. Journal of Geophysical Research, Atmospheres, 118, 8761–8769. https://doi.org/10.1002/jgrd.50674 Cronin, T. W., & Wing, A. A. (2017). Clouds, circulation, and climate sensitivity in a radiative-convective equilibrium channel model. Journal of Advances in Modeling Earth Systems, 9, 2883– 2905. https://doi.org/10.1002/2017MS001111 Coppin, D., & Bony, S. (2015). Physical mechanisms controlling the initiation of convective self-aggregation in a general circulation model. Journal of Advances in Modeling Earth Systems, 7, 2060–2078. https://doi.org/10.1002/2015MS000571 Emanuel, K., Wing, A. A., & Vincent, E. M. (2014). Radiative-convective instability. Journal of Advances in Modeling Earth Systems, 6, 75– 90, http://doi.org/10.1002/2013MS000270 Feng, Z., Hagos, S., Rowe, A. K., Burleyson, C. D., Martini, M. N., & de Szoeke, S. P. (2015). Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign. Journal of Advances in Modeling Earth Systems, 7(2), 357-381. https://doi.org/10.1002/2014MS000384 Grabowski, W. W., Yano, J., & Moncrieff, M. W. (2000). Cloud resolving modeling of tropical circulations driven by large-scale SST gradients. Journal of the Atmospheric Sciences, 57(13), 2022-2040. https://doi.org/10.1175/1520-0469(2000)057<2022:CRMOTC>2.0.CO;2 Grabowski, W.W., & Moncrieff, M.W. (2001). Large-scale organization of tropical convection in two-dimensional explicit numerical simulations. Quarterly Journal of the Royal Meteorological Society, 127: 445-468. https://doi.org/10.1002/qj.49712757211 Grabowski, W.W., & Moncrieff, M.W. (2004). Moisture–convection feedback in the tropics. Quarterly Journal of the Royal Meteorological Society, 130: 3081-3104. https://doi.org/10.1256/qj.03.135 Grabowski, W. W. (2006). Impact of Explicit Atmosphere–Ocean Coupling on MJO-Like Coherent Structures in Idealized Aquaplanet Simulations. Journal of the Atmospheric Sciences, 63(9), 2289-2306. https://doi.org/10.1175/JAS3740.1 Haerter, J. O. (2019). Convective self‐aggregation as a cold pool‐driven critical phenomenon. Geophysical Research Letters, 46(7), 4017-4028. https://doi.org/10.1029/2018GL081817 Haerter, J. O., Meyer, B., & Nissen, S. B. (2020). Diurnal self-aggregation. npj Climate and Atmospheric Science, 3(1), 1-11. https://doi.org/10.1038/s41612-020-00132-z Hamada, A., Murayama, Y., & Takayabu, Y. N. (2014). Regional Characteristics of Extreme Rainfall Extracted from TRMM PR Measurements. Journal of Climate, 27(21), 8151-8169. https://doi.org/10.1175/JCLI-D-14-00107.1 Held, I. M., Hemler, R. S., & Ramaswamy, V. (1993). Radiative-Convective Equilibrium with Explicit Two-Dimensional Moist Convection. Journal of Atmospheric Sciences, 50(23), 3909-3927. https://doi.org/10.1175/1520-0469(1993)050<3909:RCEWET>2.0.CO;2 Henneberg, O., Meyer, B., & Haerter, J. O. (2020). Particle‐based tracking of cold pool gust fronts. Journal of Advances in Modeling Earth Systems, 12(5), e2019MS001910. https://doi.org/10.1029/2019MS001910 Hohenegger, C., & Stevens, B. (2016). Coupled radiative convective equilibrium simulations with explicit and parameterized convection. Journal of Advances in Modeling Earth Systems, 8, 1468– 1482. https://doi.org/10.1002/2016MS000666 Holloway, C. E., & Neelin, J. D. (2010). Temporal relations of column water vapor and tropical precipitation. Journal of the Atmospheric Sciences, 67(4), 1091-1105. https://doi.org/10.1175/2009JAS3284.1 Holloway, C. E., & Woolnough, S. J. (2016). The sensitivity of convective aggregation to diabatic processes in idealized radiative-convective equilibrium simulations. Journal of Advances in Modeling Earth Systems, 8, 166– 195. https://doi.org/10.1002/2015MS000511 Holloway, C. E., Wing, A. A., Bony, S., Muller, C., Masunaga, H., L'Ecuyer, T. S., et al. (2017). Observing convective aggregation. Surveys in Geophysics, 38, 1199–1236. https://doi.org/10.1007/s10712-017-9419-1 Hoskins, B. J., Yang, G. Y., & Fonseca, R. M. (2019). The detailed dynamics of the June-August Hadley Cell. Quarterly Journal of the Royal Meteorological Society, 146(727), 557–575. https://doi.org/10.1002/qj.3702 Houze Jr, R. A. (2014). Cloud dynamics. Academic press. Huang, J.‐D., & Wu, C.‐M. (2020). Effects of microphysical processes on the precipitation Spectrum in a strongly forced environment. Earth and Space Science, 7, e2020EA001190. https://doi.org/10.1029/2020EA001190 Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. (2008), Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13103. https://doi.org/10.1029/2008JD009944 Jung, J.-H., & Arakawa, A. (2008). A three-dimensional anelastic model based on the vorticity equation. Monthly Weather Review., 136(1), 276–294. https://doi.org/10.1175/2007MWR2095.1 Khairoutdinov, M. F., & Emanuel, K. A. (2010). Aggregated convection and the regulation of tropical climate, paper presented at 29th Conference on Hurricanes and Tropical Meteorology. Am. Meteorol. Soc., Tucson, Ariz. Krueger, S. K., Fu, Q., Liou, K., & Chin, H.-N. S. (1995). Improvements of an ice-phase microphysics parameterization for use in numerical simulations of tropical convection. Journal of Applied Meteorology, 34(1), 281–287. https://doi.org/10.1175/1520-0450-34.1.281 Kuo, K.-T., & Wu, C.-M. (2019). The precipitation hotspots of afternoon thunderstorms over the Taipei Basin: Idealized numerical simulations. Journal of the Meteorological Society of Japan, 97(2), 501–517. https://doi.org/10.2151/jmsj.2019-031 Liu, C., & Moncrieff, M. W. (2008). Explicitly simulated tropical convection over idealized warm pools. Journal of Geophysical Research, Atmospheres, 113, D21121. https://doi.org/10.1029/2008JD010206 Mauritsen, T., & Stevens, B. (2015). Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nature Geoscience, 8, 346–351. https://doi.org/10.1038/ngeo2414 Meyer, B., & Haerter, J. O. (2020). Mechanical forcing of convection by cold pools: Collisions and energy scaling. Journal of Advances in Modeling Earth Systems, 12(11), e2020MS002281. https://doi.org/10.1029/2020MS002281 Moseley, C., Hohenegger, C., Berg, P., & Haerter, J. O. (2016). Intensification of convective extremes driven by cloud–cloud interaction. Nature Geoscience, 9(10), 748-752. https://doi.org/10.1038/ngeo2789 Muller, C. J., & Held, I. M. (2012). Detailed investigation of the self-aggregation of convection in cloud-resolving simulations. Journal of the Atmospheric Sciences, 69, 2551–2565. https://doi.org/10.1175/JAS-D-11-0257.1 Muller, C. J., & Bony, S. (2015). What favors convective aggregation and why? Geophysical Research Letters, 42, 5626–5634. https://doi.org/10.1002/2015GL064260 Morrison, H., & Milbrandt, J. A. (2015). Parameterization of ice microphysics based on the prediction of bulk particle properties. Part I: Scheme description and idealized tests. Journal of the Atmospheric Sciences, 72, 287–311. https://doi.org/10.1175/JAS-D-14-0065.1 Nakajima, K., & Matsuno, T. (1988). Numerical experiments concerning the origin of cloud clusters in the tropical atmosphere. Journal of the Meteorological Society of Japan, 66, 309-329. https://doi.org/10.2151/jmsj1965.66.2_309 Nishizawa, S., Yashiro, H., Sato, Y., Miyamoto, Y., & Tomita, H. (2015). Influence of grid aspect ratio on planetary boundary layer turbulence in large‐eddy simulations. Geoscientific Model Development, 8(10), 3393–3419. https://doi.org/10.5194/gmd‐8‐3393‐2015 Reed, A. K., Medeiros, B., Bacmeister, J. T., & Lauritzen, P. H. (2015). Global radiative-convective equilibrium in the Community Atmosphere Model Version 5. Journal of the Atmospheric Sciences, 72, 2183–2197. https://doi.org/10.1175/JAS-D-14-0268.1 Sato, Y., Nishizawa, S., Yashiro, H., Miyamoto, Y., Kajikawa, Y., & Tomita, H. (2015). Impacts of cloud microphysics on trade wind cumulus: Which cloud microphysics processes contribute to the diversity in a large eddy simulation? Progress in Earth and Planetary Science, 2(1), 23. https://doi.org/10.1186/s40645‐015‐0053‐6 Shutts, G. J. & Gray, M. E. B. (1994). A numerical modelling study of the geostrophic adjustment process following deep convection. Quarterly Journal of the Royal Meteorological Society, 120, 1145-1178. https://doi.org/10.1002/qj.49712051903 Su, C.-Y., Wu, C.-M., & Chen, W.-T. (2019). Object-based precipitation system bias in grey zone simulation: the 2016 South China Sea summer monsoon onset. Climate Dynamics, 53, 617–630. https://doi.org/10.1007/s00382-018-04607-x Tompkins, A. M., & Craig, G. C. (1998). Radiative-convective equilibrium in a three-dimensional cloud-ensemble model. Quarterly Journal of the Royal Meteorological Society, 124, 2073–2097. https://doi.org/10.1002/qj.49712455013 Tompkins, A. (2001). Organization of tropical convection in low wind shears: The role of water vapor. Journal of the Atmospheric Sciences, 58, 529–545. https://doi.org/10.1175/1520-0469(2001)058<0529:OOTCIL>2.0.CO;2 Tompkins, A. M., & Semie, A. G. (2017). Organization of tropical convection in low vertical wind shears: Role of updraft entrainment. Journal of Advances in Modeling Earth Systems, 9, 1046–1068. https://doi.org/10.1002/2016MS000802 Tompkins, A. M., & Semie, A. G. (2021). Impact of a mixed ocean layer and the diurnal cycle on convective aggregation. Journal of Advances in Modeling Earth Systems, 13, e2020MS002186. https://doi.org/10.1029/2020MS002186 Tsai, J.-Y., & Wu, C.-M. (2016). Critical transitions of stratocumulus dynamical systems due to perturbation in free atmosphere moisture. Dynamics of Atmospheres and Oceans, 76, 1–13. https://doi.org/10.1016/j.dynatmoce.2016.08.002 Tsai, W.-M., & Wu, C.-M. (2017). The environment of aggregated deep convection. Journal of Advances in Modeling Earth Systems, 9, 2061–2078, https://doi.org/10.1002/2017MS000967. Wing, A. A., & Emanuel, K. (2014). Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations. Journal of Advances in Modeling Earth Systems, 6, 59–74. https://doi.org/10.1002/2013MS000269 Wing, A. A., & Cronin, T. W. (2016). Self-aggregation of convection in long channel geometry. Quarterly Journal of the Royal Meteorological Society, 142, 1–15. https://doi.org/10.1002/qj.2628 Wing, A. A., Emanuel, K., Holloway, C. E., & Muller, C. (2017). Convective self-aggregation in numerical simulations: A review. Surveys in Geophysics, 38, 1173–1197. https://doi.org/10.1007/s10712-017-9408-4 Wing, A. A., Reed, K. A., Satoh, M., Stevens, B., Bony, S., & Ohno, T. (2018). Radiative‐Convective Equilibrium Model Intercomparison Project. Geoscientific Model Development, 11, 793–813. https://doi.org/10.5194/gmd-11-793-2018 Wing, A.A. (2019). Self-aggregation of deep convection and its implications for climate. Current Climate Change Reports, 5, 1–11. https://doi.org/10.1007/s40641-019-00120-3 Wing, A. A., Stauffer, C. L., Becker, T., Reed, K. A., Ahn, M.‐S., & Arnold, N. P., et al. (2020). Clouds and convective self‐aggregation in a multimodel ensemble of radiative‐convective equilibrium simulations. Journal of Advances in Modeling Earth Systems, 12(9), 1942-2466. https://doi.org/10.1029/2020MS002138 Wofsy, J., & Kuang, Z. (2012). Cloud-Resolving Model Simulations and a Simple Model of an Idealized Walker Cell. Journal of Climate, 25(23), 8090-8107. https://doi.org/10.1175/JCLI-D-11-00692.1 Wu, C.-M., & Arakawa, A. (2011). Inclusion of surface topography into the vector vorticity equation model (VVM). Journal of Advances in Modeling Earth Systems, 3, M04002. https://doi.org/10.1029/2011MS000061 Wu, C.-M., & Arakawa, A. (2014). A unified representation of deep moist convection in numerical modeling of the atmosphere. Part II. Journal of the Atmospheric Sciences, 71(6), 2089–2103. https://doi.org/10.1175/JAS‐D‐13‐0382.1 Wu, C.-M., Lo, M.-H., Chen, W.-T., and Lu, C.-T. (2015), The impacts of heterogeneous land surface fluxes on the diurnal cycle precipitation: A framework for improving the GCM representation of land-atmosphere interactions, Journal of Geophysical Research, Atmospheres, 120, 3714–3727. https://doi.org/10.1002/2014JD023030 Wu, C.-M., Lin, H.-C., Cheng, F.-Y., & Chien, M.-H. (2019). Implementation of the land surface processes into a vector vorticity equation model (VVM) to study its impact on afternoon thunderstorms over complex topography in Taiwan. Asia-Pacific Journal of Atmospheric Sciences. https://doi.org/10.1007/s13143-019-00116-x Wu, C.-M., & Chen, P.-Y. (2021). Idealized cloud-resolving simulations of land-atmosphere coupling over tropical islands. Terrestrial, Atmospheric and Oceanic sciences journal, in press. https://doi.org/ 10.3319/TAO.2020.12.16.01 Yanase, T., Nishizawa, S., Miura, H., Takemi, T., & Tomita, H. (2020). New critical length for the onset of self‐aggregation of moist convection. Geophysical Research Letters, 47, e2020GL088763. https://doi.org/10.1029/2020GL088763 Yuan, J., & Houze, R. A., Jr. (2010). Global variability of mesoscale convective system anvil structure from A-train satellite data. Journal of Climate, 23(21), 5864–5888. https://doi.org/10.1175/2010JCLI3671.1 Chapter 4 Adachi, S. A., S. Nishizawa, K. Ando, T. Yamaura, R. Yoshida, H. Yashiro, Y. Kajikawa, and H. Tomita, 2018: An evaluation method for uncertainties in regional climate projections. Atmos Sci Lett, 20, https://doi.org/10.1002/asl.877. Arakawa, A., and C.-M. Wu, 2013: A Unified Representation of Deep Moist Convection in Numerical Modeling of the Atmosphere. Part I. Journal of the Atmospheric Sciences, 70, 1977–1992, https://doi.org/10.1175/jas-d-12-0330.1. Arnold, N. P., and D. A. Randall, 2015: Global-scale convective aggregation: Implications for the Madden-Julian Oscillation. J. Adv. Model. Earth Syst., 7, 1499–1518, https://doi.org/10.1002/2015ms000498. Becker, T., and A. A. Wing, 2020: Understanding the Extreme Spread in Climate Sensitivity within the Radiative‐Convective Equilibrium Model Intercomparison Project. J Adv Model Earth Syst, 12, https://doi.org/10.1029/2020ms002165. Beljaars, A. C. M., and A. A. M. Holtslag, 1991: Flux Parameterization over Land Surfaces for Atmospheric Models. J. Appl. Meteor., 30, 327–341, https://doi.org/10.1175/1520-0450(1991)030<0327:fpolsf>2.0.co;2. Bretherton, C. S., P. N. Blossey, and M. Khairoutdinov, 2005: An Energy-Balance Analysis of Deep Convective Self-Aggregation above Uniform SST. Journal of the Atmospheric Sciences, 62, 4273–4292, https://doi.org/10.1175/jas3614.1. Brown, A. R., S. H. Derbyshire, and P. J. Mason, 1994: Large-eddy simulation of stable atmospheric boundary layers with a revised stochastic subgrid model. Q.J Royal Met. Soc., 120, 1485–1512, https://doi.org/10.1002/qj.49712052004. Chang, Y.-H., W.-T. Chen, C.-M. Wu, C. Moseley, and C.-C. Wu, 2021: Tracking the influence of cloud condensation nuclei on summer diurnal precipitating systems over complex topography in Taiwan. Atmos. Chem. Phys., 21, 16709–16725, https://doi.org/10.5194/acp-21-16709-2021. Chen, F., and J. Dudhia, 2001: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Mon. Wea. Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:caalsh>2.0.co;2. Chen, P., W. Chen, C. Wu, and T. Yo, 2021: Convective Cloud Regimes From a Classification of Object‐Based CloudSat Observations Over Asian‐Australian Monsoon Areas. Geophysical Research Letters, 48, https://doi.org/10.1029/2021gl092733. Chen, Y., and C. Wu, 2019: The Role of Interactive SST in the Cloud‐Resolving Simulations of Aggregated Convection. J. Adv. Model. Earth Syst., 11, 3321–3340, https://doi.org/10.1029/2019ms001762. Chien, M., and C. Wu, 2016: Representation of topography by partial steps using the immersed boundary method in a vector vorticity equation model (VVM). J. Adv. Model. Earth Syst., 8, 212–223, https://doi.org/10.1002/2015ms000514. Coppin, D., and S. Bony, 2015: Physical mechanisms controlling the initiation of convective self-aggregation in a General Circulation Model. J. Adv. Model. Earth Syst., 7, 2060–2078, https://doi.org/10.1002/2015ms000571. Craig, G. C., and J. M. Mack, 2013: A coarsening model for self-organization of tropical convection. J. Geophys. Res. Atmos., 118, 8761–8769, https://doi.org/10.1002/jgrd.50674. Dunion, J. P., 2011: Rewriting the Climatology of the Tropical North Atlantic and Caribbean Sea Atmosphere. Journal of Climate, 24, 893–908, https://doi.org/10.1175/2010jcli3496.1. Hamada, A., Y. Murayama, and Y. N. Takayabu, 2014: Regional Characteristics of Extreme Rainfall Extracted from TRMM PR Measurements. Journal of Climate, 27, 8151–8169, https://doi.org/10.1175/jcli-d-14-00107.1. Held, I. M., R. S. Hemler, and V. Ramaswamy, 1993: Radiative-Convective Equilibrium with Explicit Two-Dimensional Moist Convection. J. Atmos. Sci., 50, 3909–3927, https://doi.org/10.1175/1520-0469(1993)050<3909:rcewet>2.0.co;2. Hernandez-Deckers, D., and S. C. Sherwood, 2018: On the Role of Entrainment in the Fate of Cumulus Thermals. Journal of the Atmospheric Sciences, 75, 3911–3924, https://doi.org/10.1175/jas-d-18-0077.1. Hohenegger, C., and B. Stevens, 2016: Coupled radiative convective equilibrium simulations with explicit and parameterized convection. J. Adv. Model. Earth Syst., 8, 1468–1482, https://doi.org/10.1002/2016ms000666. Holloway, C. E., A. A. Wing, S. Bony, C. Muller, H. Masunaga, T. S. L’Ecuyer, D. D. Turner, and P. Zuidema, 2017: Observing Convective Aggregation. Surv Geophys, 38, 1199–1236, https://doi.org/10.1007/s10712-017-9419-1. Holloway, C. E., and S. J. Woolnough, 2016: The sensitivity of convective aggregation to diabatic processes in idealized radiative‐convective equilibrium simulations. J. Adv. Model. Earth Syst., 8, 166–195, https://doi.org/10.1002/2015ms000511. Honda, T., S. Kotsuki, G. ‐Y. Lien, Y. Maejima, K. Okamoto, and T. Miyoshi, 2018: Assimilation of Himawari‐8 All‐Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction. JGR Atmospheres, 123, 965–976, https://doi.org/10.1002/2017jd027096. Honda, T., S. Takino, and T. Miyoshi, 2019: Improving a Precipitation Forecast by Assimilating All-Sky Himawari-8 Satellite Radiances: A Case of Typhoon Malakas (2016). SOLA, 15, 7–11, https://doi.org/10.2151/sola.2019-002. Hoskins, B. J., G. ‐Y. Yang, and R. M. Fonseca, 2020: The detailed dynamics of the June–August Hadley Cell. QJR Meteorol Soc, 146, 557–575, https://doi.org/10.1002/qj.3702. Huang, J., and C. Wu, 2020: Effects of Microphysical Processes on the Precipitation Spectrum in a Strongly Forced Environment. Earth and Space Science, 7, https://doi.org/10.1029/2020ea001190. Huang, J., and C. Wu, 2022: A Framework to Evaluate Convective Aggregation: Examples With Different Microphysics Schemes. JGR Atmospheres, 127, https://doi.org/10.1029/2021jd035886. Hung, C., and H. Miura, 2021: Ensemble of Radiative‐Convective Equilibrium Simulations Near the Aggregated and Scattered Boundary. Geophysical Research Letters, 48, https://doi.org/10.1029/2021gl095279. Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, https://doi.org/10.1029/2008jd009944. Inatsu, M., S. Tanji, and Y. Sato, 2020: Toward predicting expressway closures due to blowing snow events. Cold Regions Science and Technology, 177, 103123, https://doi.org/10.1016/j.coldregions.2020.103123. Iwabuchi, H., and R. Okamura, 2017: Multispectral Monte Carlo radiative transfer simulation by the maximum cross-section method. Journal of Quantitative Spectroscopy and Radiative Transfer, 193, 40–46, https://doi.org/10.1016/j.jqsrt.2017.01.025. Jung, J.-H., and A. Arakawa, 2008: A Three-Dimensional Anelastic Model Based on the Vorticity Equation. Monthly Weather Review, 136, 276–294, https://doi.org/10.1175/2007mwr2095.1. Kuo, K.-T., and C.-M. Wu, 2019: The Precipitation Hotspots of Afternoon Thunderstorms over the Taipei Basin: Idealized Numerical Simulations. Journal of the Meteorological Society of Japan, 97, 501–517, https://doi.org/10.2151/jmsj.2019-031. Manabe, S., and R. F. Strickler, 1964: Thermal Equilibrium of the Atmosphere with a Convective Adjustment. J. Atmos. Sci., 21, 361–385, https://doi.org/10.1175/1520-0469(1964)021<0361:teotaw>2.0.co;2. Matsugishi, S., and M. Satoh, 2022: Sensitivity of the Horizontal Scale of Convective Self‐Aggregation to Sea Surface Temperature in Radiative Convective Equilibrium Experiments Using a Global Nonhydrostatic Model. J Adv Model Earth Syst, 14, https://doi.org/10.1029/2021ms002636. Mauritsen, T., and B. Stevens, 2015: Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nature Geosci, 8, 346–351, https://doi.org/10.1038/ngeo2414. Morrison, H., and J. A. Milbrandt, 2015: Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests. Journal of the Atmospheric Sciences, 72, 287–311, https://doi.org/10.1175/jas-d-14-0065.1. Muller, C. J., and I. M. Held, 2012: Detailed Investigation of the Self-Aggregation of Convection in Cloud-Resolving Simulations. Journal of the Atmospheric Sciences, 69, 2551–2565, https://doi.org/10.1175/jas-d-11-0257.1. Muller, C. J., and I. M. Held, 2012: Detailed Investigation of the Self-Aggregation of Convection in Cloud-Resolving Simulations. Journal of the Atmospheric Sciences, 69, 2551–2565, https://doi.org/10.1175/jas-d-11-0257.1. Nakajima, K., and T. Matsuno, 1988: Numerical Experiments Concerning the Origin of Cloud Clusters in the Tropical Atmosphere. Journal of the Meteorological Society of Japan, 66, 309–329, https://doi.org/10.2151/jmsj1965.66.2_309. Nishizawa, S., and Y. Kitamura, 2018: A surface flux scheme based on the Monin–Obukhov similarity for finite volume models. J. Adv. Model. Earth Syst., https://doi.org/10.1029/2018ms001534. Nishizawa, S., H. Yashiro, Y. Sato, Y. Miyamoto, and H. Tomita, 2015: Influence of grid aspect ratio on planetary boundary layer turbulence in large-eddy simulations. Geosci. Model Dev., 8, 3393–3419, https://doi.org/10.5194/gmd-8-3393-2015. Patrizio, C. R., and D. A. Randall, 2019: Sensitivity of Convective Self‐Aggregation to Domain Size. J. Adv. Model. Earth Syst., 11, 1995–2019, https://doi.org/10.1029/2019ms001672. Pauluis, O. M., 2016: The Mean Air Flow as Lagrangian Dynamics Approximation and Its Application to Moist Convection. Journal of the Atmospheric Sciences, 73, 4407–4425, https://doi.org/10.1175/jas-d-15-0284.1. Pauluis, O. M., and A. A. Mrowiec, 2013: Isentropic Analysis of Convective Motions. Journal of the Atmospheric Sciences, 70, 3673–3688, https://doi.org/10.1175/jas-d-12-0205.1. Sato, Y., S. Nishizawa, H. Yashiro, Y. Miyamoto, Y. Kajikawa, and H. Tomita, 2015: Impacts of cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation? Prog. in Earth and Planet. Sci., 2, https://doi.org/10.1186/s40645-015-0053-6. Sato, Y., S. Shima, and H. Tomita, 2018: Numerical Convergence of Shallow Convection Cloud Field Simulations: Comparison Between Double‐Moment Eulerian and Particle‐Based Lagrangian Microphysics Coupled to the Same Dynamical Core. J. Adv. Model. Earth Syst., 10, 1495–1512, https://doi.org/10.1029/2018ms001285. Sato, Y., Y. Miyamoto, S. Nishizawa, H. Yashiro, Y. Kajikawa, R. Yoshida, T. Yamaura, and H. Tomita, 2015: Horizontal Distance of Each Cumulus and Cloud Broadening Distance Determine Cloud Cover. SOLA, 11, 75–79, https://doi.org/10.2151/sola.2015-019. Scotti, A., C. Meneveau, and D. K. Lilly, 1993: Generalized Smagorinsky model for anisotropic grids. Physics of Fluids A: Fluid Dynamics, 5, 2306–2308, https://doi.org/10.1063/1.858537. Sekiguchi, M., and T. Nakajima, 2008: A k-distribution-based radiation code and its computational optimization for an atmospheric general circulation model. Journal of Quantitative Spectroscopy and Radiative Transfer, 109, 2779–2793, https://doi.org/10.1016/j.jqsrt.2008.07.013. Stauffer, C. L., and A. A. Wing, 2022: Properties, Changes, and Controls of Deep‐Convecting Clouds in Radiative‐Convective Equilibrium. J Adv Model Earth Syst, 14, https://doi.org/10.1029/2021ms002917. Su, C.-Y., C.-M. Wu, W.-T. Chen, and J.-H. Chen, 2019: Object-based precipitation system bias in grey zone simulation: the 2016 South China Sea summer monsoon onset. Clim Dyn, 53, 617–630, https://doi.org/10.1007/s00382-018-04607-x. Takahashi, H., Z. J. Luo, and G. L. Stephens, 2017: Level of neutral buoyancy, deep convective outflow, and convective core: New perspectives based on 5 years of CloudSat data. JGR Atmospheres, 122, 2958–2969, https://doi.org/10.1002/2016jd025969. Tanji, S., and M. Inatsu, 2019: Case Study of Blowing Snow Potential Diagnosis with Dynamical Downscaling. SOLA, 15, 32–36, https://doi.org/10.2151/sola.2019-007. Tobin, I., S. Bony, and R. Roca, 2012: Observational Evidence for Relationships between the Degree of Aggregation of Deep Convection, Water Vapor, Surface Fluxes, and Radiation. Journal of Climate, 25, 6885–6904, https://doi.org/10.1175/jcli-d-11-00258.1. Tomita, H., 2008: New Microphysical Schemes with Five and Six Categories by Diagnostic Generation of Cloud Ice. Journal of the Meteorological Society of Japan, 86A, 121–142, https://doi.org/10.2151/jmsj.86a.121. Tompkins, A. M., and A. G. Semie, 2017: Organization of tropical convection in low vertical wind shears: Role of updraft entrainment. J. Adv. Model. Earth Syst., 9, 1046–1068, https://doi.org/10.1002/2016ms000802. Tompkins, A. M., and A. G. Semie, 2021: Impact of a Mixed Ocean Layer and the Diurnal Cycle on Convective Aggregation. J Adv Model Earth Syst, 13, https://doi.org/10.1029/2020ms002186. Tompkins, A. M., and G. C. Craig, 1998: Radiative–convective equilibrium in a three-dimensional cloud-ensemble model. Q.J Royal Met. Soc., 124, 2073–2097, https://doi.org/10.1002/qj.49712455013. Tsai, J.-Y., and C.-M. Wu, 2016: Critical transitions of stratocumulus dynamical systems due to perturbation in free-atmosphere moisture. Dynamics of Atmospheres and Oceans, 76, 1–13, https://doi.org/10.1016/j.dynatmoce.2016.08.002. Tsai, W.-M., and B. E. Mapes, 2022: Evidence of Aggregation Dependence of 5°-Scale Tropical Convective Evolution Using a Gross Moist Stability Framework. Journal of the Atmospheric Sciences, 79, 1385–1404, https://doi.org/10.1175/jas-d-21-0253.1. Tsai, W.-M., and C.-M. Wu, 2017: The environment of aggregated deep convection. J. Adv. Model. Earth Syst., 9, 2061–2078, https://doi.org/10.1002/2017ms000967. Wilson, D. K., 2001: An Alternative Function For The Wind And Temperature Gradients In Unstable Surface Layers. Boundary-Layer Meteorology, 99, 151–158, https://doi.org/10.1023/a:1018718707419. Windmiller, J. M., and G. C. Craig, 2019: Universality in the Spatial Evolution of Self-Aggregation of Tropical Convection. Journal of the Atmospheric Sciences, 76, 1677–1696, https://doi.org/10.1175/jas-d-18-0129.1. Wing, A. A., 2019: Self-Aggregation of Deep Convection and its Implications for Climate. Curr Clim Change Rep, 5, 1–11, https://doi.org/10.1007/s40641-019-00120-3. Wing, A. A., and Coauthors, 2020: Clouds and Convective Self‐Aggregation in a Multimodel Ensemble of Radiative‐Convective Equilibrium Simulations. J Adv Model Earth Syst, 12, https://doi.org/10.1029/2020ms002138. Wing, A. A., and K. A. Emanuel, 2014: Physical mechanisms controlling self-aggregation of convection in idealized numerical modeling simulations. J. Adv. Model. Earth Syst., 6, 59–74, https://doi.org/10.1002/2013ms000269. Wing, A. A., K. A. Reed, M. Satoh, B. Stevens, S. Bony, and T. Ohno, 2018: Radiative–convective equilibrium model intercomparison project. Geosci. Model Dev., 11, 793–813, https://doi.org/10.5194/gmd-11-793-2018. Wu, C.-M., and A. Arakawa, 2011: Inclusion of Surface Topography into the Vector Vorticity Equation Model (VVM). J. Adv. Model. Earth Syst., 3, n/a-n/a, https://doi.org/10.1029/2011ms000061. Wu, C.-M., and A. Arakawa, 2014: A Unified Representation of Deep Moist Convection in Numerical Modeling of the Atmosphere. Part II. Journal of the Atmospheric Sciences, 71, 2089–2103, https://doi.org/10.1175/jas-d-13-0382.1. Wu, C.-M., and P.-Y. Chen, 2021: Idealized cloud-resolving simulations of land-atmosphere coupling over tropical islands. Terr. Atmos. Ocean. Sci., 32, 191–202, https://doi.org/10.3319/tao.2020.12.16.01. Wu, C.-M., H.-C. Lin, F.-Y. Cheng, and M.-H. Chien, 2019: Implementation of the Land Surface Processes into a Vector Vorticity Equation Model (VVM) to Study its Impact on Afternoon Thunderstorms over Complex Topography in Taiwan. Asia-Pacific J Atmos Sci, 55, 701–717, https://doi.org/10.1007/s13143-019-00116-x. Yanase, T., and T. Takemi, 2018: Diurnal Variation of Simulated Cumulus Convection in Radiative-Convective Equilibrium. SOLA, 14, 116–120, https://doi.org/10.2151/sola.2018-020. Yanase, T., S. Nishizawa, H. Miura, T. Takemi, and H. Tomita, 2020: New Critical Length for the Onset of Self‐Aggregation of Moist Convection. Geophys. Res. Lett., 47, https://doi.org/10.1029/2020gl088763. Yoshida, R., S. Nishizawa, H. Yashiro, S. A. Adachi, T. Yamaura, H. Tomita, and Y. Kajikawa, 2018: Maintenance condition of back-building squall-line in a numerical simulation of a heavy rainfall event in July 2010 in Western Japan. Atmos Sci Lett, 20, e880, https://doi.org/10.1002/asl.880. Yuan, J., and R. A. Houze Jr., 2010: Global Variability of Mesoscale Convective System Anvil Structure from A-Train Satellite Data. Journal of Climate, 23, 5864–5888, https://doi.org/10.1175/2010jcli3671.1. Chapter 5 Chen, P.-J., Chen, W.-T., Wu, C.-M., & Yo, T.-S. (2021). Convective cloud regimes from a classification of object-based CloudSat observations over Asian-Australian monsoon areas. Geophysical Research Letters, 48, e2021GL092733. https://doi.org/10.1029/2021GL092733. Huang, J. D., Hung, C. S., Wu, C. M., & Miura, H. (2023). Convective Variabilities Leading to Different Pathways of Convective Self-Aggregation in Two Cloud-Resolving Models. Journal of the Atmospheric Sciences, 80(8), 2041-2055. https://doi.org/10.1175/JAS-D-22-0250.1. Huang, J.-D., and C.-M. Wu, 2020: Effects of Microphysical Processes on the Precipitation Spectrum in a Strongly Forced Environment. Earth and Space Science, 7, https://doi.org/10.1029/2020ea001190. Huang, J.-D., and C.-M. Wu, 2022: A Framework to Evaluate Convective Aggregation: Examples With Different Microphysics Schemes. Journal of Geophysical Research: Atmospheres, 127, https://doi.org/10.1029/2021jd035886. Kuo, Y.-H., and J. D. Neelin, 2022: Conditions for convective deep inflow. Geophys. Res. Lett., 49, e2022GL100 552, doi:10.1029/2022GL100552. https://doi.org/10.1029/2022GL100552. Kuo, Y. H., & Neelin, J. D. (2025a). Anelastic Convective Entities. Part 1: Formulation and implication for nighttime convection. Journal of the Atmospheric Sciences. https://doi.org/10.1175/JAS-D-23-0214.1 Kuo, Y. H., & Neelin, J. D. (2025b). Anelastic Convective Entities. Part 2: Adjustment processes and convective cold top. Journal of the Atmospheric Science. https://doi.org/10.1175/JAS-D-24-0130.1 Ruppert, J. H., & Johnson, R. H. (2015). Diurnally modulated cumulus moistening in the preonset stage of the Madden–Julian oscillation during DYNAMO. Journal of the Atmospheric Sciences, 72(4), 1622-1647. https://doi.org/10.1175/JAS-D-14-0218.1. Stevens, B., Satoh, M., Auger, L. et al. DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains. Prog Earth Planet Sci 6, 61 (2019). https://doi.org/10.1186/s40645-019-0304-z. Su, C. Y., Chen, W. T., & Wu, C. M. (2022). Object-Based evaluation of tropical precipitation systems in DYAMOND simulations over the Maritime Continent. Journal of the Meteorological Society of Japan. Ser. II, 100(4), 647-659. https://doi.org/10.2151/jmsj.2022-033. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97299 | - |
| dc.description.abstract | 本研究構建了一個階層式的模擬框架，旨在解析物理機制，特別是雲微物理和反饋效應，如何影響對流系統的發展及大尺度大氣流場。第一層次的研究探討了微物理參數化對強迫環境中極端降水的作用。結果顯示，使用預測粒子特性（P3）微物理參數化法，由於減少了對流核心中的冰融效應，相較於傳統參數化法，產生了更強的上升氣流並促進了更極端的降水事件。第二層次進一步分析了反饋機制如何在輻射-對流平衡（RCE）情境下影響對流自發聚集（CSA）。這一階段強調了水汽對流反饋（MCF）對於促進對流組織化的重要性，P3方案模擬出更顯著的CSA及其對大尺度環流的深遠影響。最後，該框架比較了兩個雲解析模型—VVM和SCALE，發現不同的物理機制導致了不同的CSA發展過程。VVM呈現出快速的對流驅動聚集過程，而SCALE則展現了較為緩慢的輻射驅動聚集過程，揭示了模型中物理過程如何影響對流聚集過程。綜合來看，這一階層式框架提供了一個分析小尺度對流行為與大尺度反饋效應相互作用的系統化視角，對提升天氣與氣候模型的準確性具有重要的指導意義。 | zh_TW |
| dc.description.abstract | This research presents a hierarchical modeling framework to investigate how physical processes, particularly microphysics and feedback mechanisms, influence convective behavior and large-scale atmospheric circulation. The first level of analysis examines the role of microphysical parameterizations in shaping extreme precipitation under strongly forced conditions. Using the predicted particle properties (P3) microphysics scheme, the study finds that reduced melting effects in convective cores lead to stronger updrafts and more extreme precipitation than traditional schemes. The second level extends the framework to explore how feedback mechanisms drive convective self-aggregation (CSA) within radiative-convective equilibrium (RCE). This stage emphasizes the importance of moisture-convection feedback (MCF) in enhancing convective organization, with P3 simulations demonstrating stronger CSA and its broader influence on large-scale circulation. Finally, the framework compares two cloud-resolving models, VVM and SCALE, revealing that differences in physical processes lead to distinct CSA pathways. VVM follows a rapid, convection-driven aggregation, while SCALE exhibits a slower, radiation-driven pathway, highlighting how model-specific dynamics shape atmospheric behavior. This hierarchical approach provides a structured view of the interaction between small-scale convective processes and large-scale feedback mechanisms, offering valuable insights for improving weather and climate models. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-04-02T16:21:45Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-04-02T16:21:45Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 ................................................................................................................ i
摘要 ............................................................................................................... ii Abstract ........................................................................................................ iii 目次 Contents ............................................................................................... v 1. Introduction ............................................................................................... 1 1.1 Background and Motivation ............................................................................... 1 1.2 References .......................................................................................................... 9 1.3 Figures .............................................................................................................. 13 2. Effects of Microphysical Processes on the Precipitation Spectrum in a Strongly Forced Environment ..................................................................... 14 2.1 Abstract ............................................................................................................. 14 2.2 Plain Language Summary ................................................................................. 15 2.3 Introduction ...................................................................................................... 15 2.4 Materials and Methods ..................................................................................... 18 2.4.1 The Model Description .......................................................................... 18 2.4.2 The Experiment Setup ........................................................................... 19 2.4.3 Microphysics Schemes .......................................................................... 20 2.4.4 The Isentropic Analysis ......................................................................... 21 2.5 Results .............................................................................................................. 23 2.5.1 Vertical Structure ................................................................................... 23 2.5.2 Convective Statics ................................................................................. 24 2.5.3 Convective Core Cloud Analyses .......................................................... 27 2.6 Conclusions ...................................................................................................... 28 2.7 Acknowledgments ............................................................................................ 30 2.8 References ........................................................................................................ 30 2.9 Figures .............................................................................................................. 37 3. A Framework to Evaluate Convective Aggregation: Examples with Different Microphysics Schemes ................................................................ 42 3.1 Abstract ............................................................................................................. 42 3.2 Introduction ...................................................................................................... 43 3.3 Model Description, Experimental Settings, and Analyses Methods ................ 49 3.3.1 The Model and Experiment Setup ......................................................... 49 3.3.2 Moist Static Energy Variance Budget .................................................... 51 3.4 Results .............................................................................................................. 53 3.4.1 Bifurcation of Convective Aggregation ................................................. 53 3.4.2 Evaluations with FMSE Variance Budget ............................................. 56 3.5 Discussion ......................................................................................................... 59 3.5.1 Microphysical Processes ....................................................................... 59 3.5.2 Cold Pools ............................................................................................. 62 3.6 Summary ........................................................................................................... 63 3.7 Acknowledgments ............................................................................................ 65 3.8 Supporting Information .................................................................................... 66 3.9 References ........................................................................................................ 66 3.10 Tables .............................................................................................................. 80 3.11 Figures ............................................................................................................ 81 4. Convective Variabilities Leading to Different Pathways of Convective Self-aggregation in Two Cloud-resolving Models ...................................... 97 4.1 Abstract ............................................................................................................. 97 4.2 Introduction ...................................................................................................... 98 4.3 Methodology ................................................................................................... 103 4.3.1 Model Description ............................................................................... 103 4.3.2 Experiment Design .............................................................................. 105 4.3.3 Convective Variability Analyses .......................................................... 106 4.4 Results ............................................................................................................ 111 4.4.1 General Evolution of CSA ................................................................... 111 4.4.2 Development of CSA........................................................................... 113 4.4.3 Evolution of CSA in the Isentropic Space ........................................... 116 4.4.4 Convective Variability ......................................................................... 120 4.5 Mechanism-denial Experiment and Convective Variability ........................... 122 4.5.1 Mechanism-denial Experiment ............................................................ 122 4.5.2 Role of Convective Variability ............................................................ 124 4.6 Summary ......................................................................................................... 125 4.7 Acknowledgments .......................................................................................... 128 4.8 Data Availability Statement ............................................................................ 128 4.9 Supplemental Information .............................................................................. 129 4.10 References .................................................................................................... 129 4.11 Tables ............................................................................................................ 143 4.12 Figures .......................................................................................................... 144 5. Summary and Future Work ................................................................... 164 5.1 Summary ......................................................................................................... 164 5.2 Future Work .................................................................................................... 165 5.2.1 Investigating the relationship between convective systems and CSA development using cloud-resolving models ................................................. 165 5.2.2 Using an intermediate model to understand the role of convective systems in modulating large-scale circulations ............................................ 166 5.2.3 Linking convective clouds to large-scale circulations in the real world ...................................................................................................................... 168 5.2.4 Coupling ocean model with K-profile parameterization to VVM ....... 169 5.3 References ...................................................................................................... 170 5.4 Figures ............................................................................................................ 172 | - |
| dc.language.iso | en | - |
| dc.subject | 輻射對流平衡 | zh_TW |
| dc.subject | 預測粒子特性微物理參數化 | zh_TW |
| dc.subject | 階層式模擬框架 | zh_TW |
| dc.subject | 對流聚集過程 | zh_TW |
| dc.subject | 向量渦度方程雲解析模型 | zh_TW |
| dc.subject | radiative-convective equilibrium | en |
| dc.subject | vector vorticity equation cloud-resolving model (VVM) | en |
| dc.subject | convective aggregation | en |
| dc.subject | hierarchical modeling framework | en |
| dc.subject | predicted particle properties (P3) microphysics scheme | en |
| dc.title | 利用向量渦度方程雲解析模型階層式探討影響對流聚集之物理過程 | zh_TW |
| dc.title | A Hierarchical Understanding of Physical Processes Modulating Convective Aggregation Using the Vector Vorticity Equation Cloud-Resolving Model | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 陳維婷;黃彥婷;郭鴻基;陳世楠;三浦裕亮;蘇俊彥 | zh_TW |
| dc.contributor.oralexamcommittee | Wei-Ting Chen;Yen-Ting Hwang;Hung-Chi Kuo;Shih-Nan Chen;Hiroaki Miura;Chun-Yian Su | en |
| dc.subject.keyword | 向量渦度方程雲解析模型,對流聚集過程,階層式模擬框架,預測粒子特性微物理參數化,輻射對流平衡, | zh_TW |
| dc.subject.keyword | vector vorticity equation cloud-resolving model (VVM),convective aggregation,hierarchical modeling framework,predicted particle properties (P3) microphysics scheme,radiative-convective equilibrium, | en |
| dc.relation.page | 173 | - |
| dc.identifier.doi | 10.6342/NTU202500738 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-02-25 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 大氣科學系 | - |
| dc.date.embargo-lift | 2025-04-03 | - |
| 顯示於系所單位： | 大氣科學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 11.28 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。