參考文獻 |
陳如瑜、張偉裕、陳台琦,2017:北台灣 S 與 C 波段雙偏極化雷達定量降雨估計之比較,大氣科學,45(1),57-80。
游承融,2019:利用雙偏極化雷達觀測資料進行極短期天氣預報評估─2008 年西南氣流實驗 IOP8 期間颮線系統個案,碩士論文,國立中央大學大氣物理研究所,105 頁。
邱顯榮,2020:利用多頻道衛星觀測評估 WRF 數值模式於不同微物理方案之雲特性:以梅雨鋒面降水系統個案為例。碩士論文,國立中央大學大氣物理研究所, 1-73頁。
Arakawa, A. 1975: Modelling clouds and cloud processes for use in climate models. WMO The Phys. Basis of Climate and Climate Modelling, 183-197
Bae, S.Y.; Hong, S.-Y.; Tao, W.-K. 2019: Development of a Single-Moment Cloud Microphysics Scheme With Prognostic Hail for the Weather Research and Forecasting (WRF) Model. Asia-Pac. J. Atmos. Sci., 55, 233–245.
Cao, Q., Zhang, G., Brandes, E., Schuur, T., Ryzhkov, A., Ikeda, K., 2008. Analysis of video disdrometer and polarimetric radar data to characterize rain microphysics in Oklahoma.J. Appl. Meteorol. Climatol. 47, 2238–2255.
Chakraborty, T., Pattnaik, S., Jenamani, R., & Baisya, H. 2021: Evaluating the performances of cloud microphysical parameterizations in WRF for the heavy rainfall event of Kerala.Meteorology and Atmospheric Physics, 133, 707–737.
Chen, J.-P., and S.-T. Liu, 2004: Physically-based two-moment bulk-water parameterization for warm cloud microphysics. Q. J. Royal Meteor. Soc., 130, Part A, 51–78.
Choudhury, D. and Das, S., 2017: The sensitivity to the microphysical schemes on the skill of forecasting the track and intensity of tropical cyclones using WRF-ARW model, J. Earth Syst. Sci., 126, 1–10.
Cohard, J.-M., and J.-P. Pinty., 2000a: A comprehensive two-moment warm microphysical bulk scheme. I: Description and tests. Quart. J. Roy. Meteor. Soc., 126, 1815–1842.
Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077–3107.
Ferrier, B. S., 1994: A double-moment multiple-phase four-class bulk ice scheme. Part I:Description. J. Atmos. Sci., 51, 249–280.
Fovell, R., Y. P. Bu, K. L, Corbosiero, W. Tung, Y. Cao, H.-C. Kuo, L. Hsu, and H. Su, 2016:Influence of cloud microphysics and radiation on tropical cyclone structure and motion.
Multiscale Convection-Coupled Systems in the Tropics: A Tribute to Dr. Michio Yanai,Meteor. Monogr., No. 56., Amer. Meteor. Soc, 11.1–11.27
Grell, G. A. and Dévényi, D, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques, Geophys, Res, Lett„ 29, 38-31–
38-38.
Grubišić, V., R. Vellore, and A. Huggins, 2005: Quantitative precipitation forecasting of wintertime storms in the Sierra Nevada: Sensitivity to the microphysical parameterization and horizontal resolution. Mon.Wea. Rev., 133, 2834–2859
García-Ortega, E., J. Lorenzana, A. Merino, S. Fernández-González, L. López, and J. L.Sánchez, 2017: Performance of multi-physics ensembles in convective precipitation
events over northeastern Spain. Atmos. Res., 190, 55–67.
Hong, S.-Y., J. Dudhia, S.-H. Chen, 2004: A revised approach to ice-microphysical processes for the bulk parameterization of cloud and precipitation., Mon. Wea. Rev., 132, 103-120.
——, and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc.,42, 129–151.
——, J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103–120.
——, Noh, Y., and Dudhia, J.,2006: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341.
——, and K.-S. S. Lim, 2009: The WRF double-moment cloud microphysics scheme (WDM). Proc. The Third East Asia WRF Workshop and Tutorial, Seoul, South Korea,Joint Center for High-Impact Weather and Climate Research, 14.
Jang, S., Lim, K.S., Ko, J., Kim, K., Lee, G.W., Cho, S.-J., Ahn, K.-D., Lee, Y.-H., 2021. Revision of WDM7 microphysics scheme and evaluation precipitating convection over
the Korean peninsula. Rem. Sens. 13, 3860.
Johnson, M., Y. Jung, D. Dawson, and M. Xue, 2016: Comparison of simulated polarimetric signatures in idealized supercell storms using two-moment bulk microphysics schemes in WRF. Mon. Wea. Rev., 144, 971–996
Jung, Y., M. Xue, and G. Zhang, 2010: Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme. J. Appl. Meteor.
Climatol., 49, 146–163.
Kang, I.S., Yang, Y.M. and Tao, W.K. 2015: GCMs with implicit and explicit representation of cloud microphysics for simulation of extreme precipitation frequency. Climate
Dynamics, 45(1–2), 325–335
Kessler, E. 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulations. Meteor. Monogr., No.32, Amer. Meteor. Soc., 84 pp.
Kumjian, M. R., & Prat, O. P. 2014: The impact of raindrop collisional processes on the polarimetric radar variables. Journal of the Atmospheric Sciences, 71(8), 3052–3067.
Kumjian, M.R.; Prat, O.P.; Reimel, K.J.; van Lier-Walqui, M.; Morrison, H.C. 2022:DualPolarization Radar Fingerprints of Precipitation Physics: A Review. Remote Sens., 14,
3706.
Lang, S., W.-K. Tao, R. Cifelli, W. Olson, J. Halverson, S. Rutledge, and J. Simpson, 2007: Improving simulations of convective system from TRMM LBA: Easterly and westerly
regimes, J. Atmos. Sci.,64, 1141–1164.
——, W.-K. Tao, X. Zeng, and Y. Li, 2011: Reducing the biases in simulated radar reflectivities from a bulk microphysics scheme: Tropical convective systems,J. Atmos. Sci.,68, 2306–2320.
——, W.-K. Tao, J.-D. Chern, D. Wu, and X. Li , 2014: Benefits of a 4th ice class in the simulated radar reflectivities of convective systems using a bulk microphysics scheme, J.Atmos. Sci.,71, 3583–3612.
Lim, J.-O. J., and S.-Y. Hong, 2005: Effects of bulk ice microphysics on the simulated monsoonal precipitation over east Asia, J. Geophys. Res., 110(D24), 06 166-06 181.
Lim, K.-S. S., and S.-Y., Hong, 2011: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138(5), 1587–1612.
Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 1065–1092
Marshall, J. S., and W. M. Palmer, 1948: The distribution of raindrops with size. J.Meteor., 5, 165–166.
McCumber, M., W.-K. Tao, J. Simpson, R. Penc, and S.-T. Soong, 1991: Comparison of icephase microphysical parameterization schemes using numerical simulations of
convection. J. Appl. Meteor., 30, 985-1004.
Meyers, M. P., R. L. Walko, J. Y. Harrington, and W. R. Cotton, 1997: New RAMS cloud microphysics parameterization. Part II: The two-moment scheme. Atmos. Res., 45, 3–39.
Milbrandt, J. A., and M. K. Yau, 2005a: A multi-moment bulk microphysics parameterization.Part I: Analysis of the role of the spectral shape parameter.J. Atmos. Sci., 62, 3051–3064
Milbrandt, J.A., Morrison, H. 2013: Prediction of graupel density in an bulk microphysics scheme. J. Atmos. Sci. 70,410–429
——, Morrison, H., Dawson, D. T., II, & Paukert, M. 2021: A triple‐moment representation of ice in the Predicted Particle Properties(P3) microphysics scheme. Journal of the
Atmospheric Sciences, 78(2), 439–458.
Min, K., S. Choo, D. Lee, and G. Lee. 2015: Evaluation of WRF Cloud Microphysics Schemes Using Radar Observations. Wea. Forecasting, 30, 1571–1589,Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A. 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663– 16682.
Morrison, H., G. Thompson, and V. Tatarskii. 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev.,137, 991–1007
——, and J. Milbrandt. 2011: Comparison of two-moment bulk microphysics schemes in idealized supercell thunderstorm simulations. Mon. Wea. Rev., 139, 1103–1130,.
——, and J. A. Milbrandt. 2015: Parameterization of ice microphysics based on the prediction of bulk particle properties. Part I: Scheme description and idealized tests. J. Atmos. Sci.,72, 287–311,
——, J. A. Milbrandt, G. Bryan, K. Ikeda, S. A. Tessendorf, and G. Thompson. 2015:Parameterization of cloud microphysics based on the prediction of bulk ice particle
properties. PartII: Case study comparisons with observations and other schemes.J.Atmos.Sci., 72, 312–339,
Park J, Cha D, Lee M K, Moon J, Hahm S, Noh K, Chan J C L and Bell M 2020 Impact of cloud microphysics schemes on tropical cyclone forecast over the western North Pacific
J. Geophys. Res. Atmos. 125
Putnam, B.J., M. Xue, Y. Jung, N. Snook, and G. Zhang. 2014: The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter. Mon. Wea. Rev., 142, 141–162.
Reshmi Mohan, P., Venkata Srinivas, C., Yesubabu, V., Baskaran, R., Venkatraman, B., 2019:Tropical cyclone simulations over Bay of Bengal with ARW model: sensitivity to cloud microphysics schemes. Atmos. Res. 230, 104651.
Roberts, N., Lean H. 2008: Scale‐selective verification of rainfall accumulations from high‐resolution forecasts of convective events. Mon. Weather Rev. 136: 78– 97.
Robber P.-J. 2009: Notes and Correspondence - Visualizing Multiple Measures of Forecast Quality. Wea. Forecasting, 24, 601-608.
Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in mid-latitude cyclones. Part VIII: A model for the ‘‘seeder feeder’’ process in warm-frontal rainbands. J. Atmos. Sci., 40, 1185–1206.
——, and ——. 1984: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. Part XII: A diagnostic modeling study of
precipitation development in narrow cold-frontal rainbands. J. Atmos. Sci., 41, 2949–2972,
Salzmann, M., Y. Ming, J.-C. Golaz, P. A. Ginoux, H. Morrison, A. Gettelman, M. Krämer,and L. J. Donner. 2010: Two-moment bulk stratiform cloud microphysics in the GFDL
AM3 GCM: Description, evaluation, and sensitivity tests. Atmos. Chem. Phys., 10,8037–8064.
Song X, Zhang GJ, and Li JL. 2012: Evaluation of Microphysics Parameterization for Convective Clouds in the NCAR Community Atmosphere Model CAM5. J Climate,25(24):8568–90.
Steiner, M., R. A. Houze, Jr., and S. E. Yuter. 1995: Climatological Characterization of ThreeDimensional Storm Structure from Operational Radar and Rain Gauge Data. J. Appl.Meteor., 34, 1978-2007.
Tao, J. Simpson and M. McCumber, 1989: An ice-water saturation adjustment. Mort. Wea.Rev.,117, 231-235.
——, W.-K., and J. Simpson, 1993: The Goddard Cumulus Ensemble Model. Part I:Model description. Terr. Atmos. Ocean.Sci., 4, 19–54.
——, and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97–137
——, and M. Moncrieff, 2009: Multi-scale cloud-system modeling. Rev. Geophys., 47,RG4002
——, Chen, J.-P., Li, Z.-Q., Wang, C., and Zhang, C.-D., 2012: The impact of aerosol on convective cloud and precipitation. Rev. Geophys. 50, RG2001.
——, and Coauthors, 2014: The Goddard Cumulus Ensemble model (GCE): Improvements and applications for studying precipitation processes. Atmos. Res., 143, 392–424
——, 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 eophysical Research: Atmospheres, 121(3), 1278–1305
Tiedtke, M., 1984: The effect of penetrative cumulus convection on the largescale flow in a general circulation model. Beitr. Phys. Atmos. 57, 216–239.
Twomey, S., 1959a: The nuclei of natural cloud formation, Part I: The chemical diffusion method and its application to atmospheric nuclei. Pure Appl.Geophys., 43, 227–242.
—— 1959b: The nuclei of natural cloud formation, Part II: The supersaturation in natural clouds and the variation of cloud droplet concentration. Pure Appl.Geophys., 43, 243–249
Ulbrich, C. W., 1983: Natural variations in the analytical form of the raindrop size distributions. J. Appl. Meteor. Climatol., 22, 1764–1775
Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110,504–520.
——, and ——, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112,2479–2498.
Yang, J. and Yau, M.: A new triple-moment blowing snow model, Bound.-Lay. Meteorol., 126,137–155.
Yuter, S. E., and R. A. Houze Jr., 1995: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus.Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123,1941–1963
Zeng, X., Tao, W.-K., Lang, S., Hou, A.Y., Zhang, M., Simpson, J., 2008: Onthe sensitivity of atmospheric ensembles to cloud microphysics in longterm cloud-resolving model simulations. J. Meteorol. Soc. Jpn., 86A, 45–65
Zhang, Y., Fan, J., Li, Z., and Rosenfeld, D, 2021: Impacts of cloud microphysics parameterizations on simulated aerosol–cloud interactions for deep convective clouds
over Houston, Atmos. Chem. Phys., 21, 2363–2381. |