| 研究生: |
陳致穎 Chih-Ying Chen |
|---|---|
| 論文名稱: |
颱風渦漩初始化與資料同化對颱風預報的影響 |
| 指導教授: |
林沛練
Pay-Liam Lin 陳景森 Chin-Sen Chen 陳宇能 Yi-Leng Chen |
| 口試委員: | |
| 學位類別: |
博士 Doctor |
| 系所名稱: |
地球科學學院 - 大氣物理研究所 Graduate Institute of Atmospheric Physics |
| 論文出版年: | 2014 |
| 畢業學年度: | 102 |
| 語文別: | 中文 |
| 論文頁數: | 108 |
| 中文關鍵詞: | 颱風渦漩初始化 、資料同化 |
| 外文關鍵詞: | Tropical Cyclone Initialization, Data Assimilation |
| 相關次數: | 點閱:18 下載:0 |
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本研究探討使用三維變分(3DVAR)和系集調整卡爾曼濾波器(EAKF)同化全球電信觀測系統(GTS)以及GPS無線電掩星(GPSRO)資料對莫拉克颱風預報的影響。從模式預報降雨與地面降雨資料的統計校驗中發現,使用WRF-EAKF同化GTS或GPSRO明顯提高了WRF 模式在24 - 48小時間的降雨預報。主要因為颱風初始渦旋經由2天的EAKF同化方法循環同化後強度增加。然而,當模式預報時間超過48小時後,同化後的降雨預報並沒有顯著改善。綜合而言同化GPSRO資料,對於莫拉克颱風0-24小時降雨預報明顯高估,而48-72小時則是明顯低估。我們發現,使用EAKF同化方式作為低解析度WRF模式的初始化方法可以產生較好的降雨預報。相較之下,3DVAR資料同化的整體表現則較差。這主要是由於不同的循環資料同化是否能產生接近實際觀測的颱風初始渦旋的緣故。
其次,我們利用18公里解析度模式預報資料對莫拉克颱風進行2公里高解析度降尺度預報。我們發現使用NCEP GFS作為對流尺度高解析度模式的側邊界條件,將會使模式明顯改善24小時以後的降雨預報,而且提供較佳的預報結果。然而使用EAKF或者冷起動模式的預報資料作為側邊界條件,其預報結果並不如預期的好 。由此結果顯示,側邊界條件對小區域的雲解析度模式預報之影響相當重要。因為,模式預報24-48小時颱風的路徑以及降雨是受環境流場影響為主,而初始渦旋強度以及結構主要是影響模式在最初0-24小時內(CTRL,EAKF和GFS)的降雨量預測,這結果相當明顯呈現在我們的敏感度實驗中。
由於,前兩個部分針對莫拉克颱風的同化與敏感度實驗已證實初始渦旋以及側邊界條件對模式預報的重要性。基於資料同化對於颱風初始渦旋掌握的不確定性,以及無法在模式初始場提供正確的颱風渦旋雨帶結構。因此,我們利用了Nguyen and Chen (2011)的颱風初始化方法針對2004 – 2013年18個西北太平洋的颱風進行颱風初始化對颱風結構的影響以及分析,主要目的是要瞭解這個初始化方法能否初始化出接近實際衛星觀測的雨帶結構。初步結果顯示,透過這個方法可以正確的呈現環境場與颱風渦旋間的交互作用的過程,而且可以有效提供模式初始條件較合理的颱風渦旋結構。因此,最後我們將NC2011以及3DVAR資料同化方法進行結合對2012年颱風杰拉華進行實驗性預報,並討論部分預報結果。初步結果顯示,結合NC2011以及3DVAR資料同化方法是可行的,不過由於杰拉華颱風的路徑預報受到大尺度環流的影響過於顯著,透過使用Nataional Center for Environmental Prediction Climate Forecast System Reanalysis (NCEP CFSR)作為側邊界條件可以證明模式中LBCs的重要性。雖然預報路徑與實際最佳路徑相比仍有相當差距,不過颱風結構仍舊呈現出與衛星觀測接近的特徵。
This study focused on investigating the impacts of assimilating Global Telecommunication System (GTS) and/or GPS Radio Occultation (GPSRO) data using two assimilation systems 3-Dimension variational (3DVAR) and Ensemble Adjustment Kalman Filter (EAKF) on the realtime forecast for Typhoon Morakot (2009) using a 18-km grid. From statistical verifications of simulated rainfall with dense ground base rain guage data, we found that assimilation GTS and GPSRO has improved the 24-48 h rainfall forecast because the initial vortex is better resolved with data assimilation. However, beyond 48 h model runs, there is no significant improvement in rainfall forecast skill. We also found that model initialization using the EAKF assimilation system produces better rainfall forecast as compared with the 3DVAR data assimilation system simply because the EAKF data assimilation system provides better initial hurricane vortex as compared with the 3DVAR data assimilation scheme.
We conduct a series of sensitivity tests by nest down the initial vortex after data assimilation to a nested domain with a 2-km grid. We found that the lateral boundary conditions for the 2-km convection-allowing model provided by National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) produces better rainfall forecast after 24 h of model integration than the lateral boundary conditions from both the EAKF and 3DVAR runs with a 18-km grid. It is apparent that with different initial vortex (GFS, EAKF and GFS) rainfall forecast within the first 24 h is mainly affected by the strength and structure of the initial vortex. After 24-48 h model integration, the influences of lateral boundary conditions on model forecasts become important. It is apparent that the track and rainfall forecasts after 24-48 h model integration are dominated by environment flow.
We also examine 18 storms (2004-2013) over the western Pacific using TC method developed by Nguyen and Chen (2011). The preliminary results of this study show that the environment has a significant effect on the initial storm structure. During the early season, storms embedded within the southwesterly monsoon flow have a tendency to exhibit a “9” type asymmetric structure with an upper level outflow channel extending southwestward from the southeastern quadrate of the storm. At low levels, the convergence area between the storm circulation and the southwesterly flow is a favorable location for the development of spiral rainbands. Late season storms have a tendency to produce a “6” type storm structure with an outflow channel extending northeastward from the northwestern part of the eyewall, especially when an upper-level cold low or trough is present to the northwest of the storm. At low levels, the convergence of the northeasterly monsoon flow and the cyclonic circulation of the storm are favorable for the occurrences of spiral rainbands. For intense storms that underwent an eye-wall replacement cycle, the NC2011 scheme also shows considerable skill in reproducing the double eye-wall structure in the model initial conditions. So, finally we will combine NC2011 and 3DVAR data assimilation methods to study TC Jelawat (2012) and make some discussion about the forecasting results. Our preliminary results show that combining NC2011 and 3DVAR data assimilation method is feasible, however, because the track forecast affected by large-scale circulation is too significant, so the results did not fully achieve the desired effect, but still showing some characteristics close to satellite observations.
巫佳玲,林沛練,利用WRF 3DVAR與EAKF探討GPSRO資料同化對莫拉克颱風模擬之影響,飛航天氣第十七期,2012年,4月,12頁。
Bender, M. A., R. J. Ross, R. E. Tuleya, and Y. Kurihara, 1993: Improvements in Tropical Cyclone Track and Intensity Forecasts Using the GFDL Initialization System. Mon. Wea. Rev., 121, 2046-2061.
——, 1997: The Effect of Relative Flow on the Asymmetric Structure in the Interior of Hurricanes. J. Atmos. Sci., 54, 703-724.
Chen, S.-H., and W.-Y. Sun, 2002: A one-dimensional time dependent cloud model. J. Meteor. Soc. Japan , 80, 99-118.
Cha, D.-H., and Y. Wang, 2012: A Dynamical Initialization Scheme for Real-Time Forecasts of Tropical Cyclones Using the WRF Model*. Mon. Wea. Rev., 141, 964-986.
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.
Chen, S.-Y., C.-Y. Huang, Y.-H. Kuo, Y.-R. Guo, and S. Sokolovskiy, 2009: Assimilation of GPS Refractivity from FORMOSAT-3/COS MIC Using a Nonlocal Operator with WRF 3DVAR and Its Im pact on the Prediction of a Typhoon Event. Terr. Atmos. Ocean. Sci, 20, 133-154.
Chien, F.-C., and H.-C. Kuo, 2011: On the extreme rainfall of Typhoon Morakot (2009). J. Geophys. Res., 116, D05104.
Chou, M.-D., and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo, 104606, 85.
Davidson, N. E., and H. C. Weber, 2000: The BMRC High-Resolution Tropical Cyclone Prediction System: TC-LAPS. Mon. Wea. Rev., 128, 1245.
Barker, D. M., W. Huang, Y.-R. Guo, and Q. N. Xiao, 2004: A Three-Dimensional (3DVAR) Data Assimilation System For Use With MM5: Implementation and Initial Results. Mon. Wea. Rev., 132, 897–914.
DeMaria, M., 1996: The effect of vertical shear on tropical cyclone intensity change. J. Atmos. Sci., 53, 2076–2087.
Fang, X., Y.-H. Kuo, and A. Wang, 2011: The Impacts of Taiwan Topography on the Predictability of Typhoon Morakot's Record-Breaking Rainfall: A High-Resolution Ensemble Simulation. Wea. Forecasting, 26, 613-633.
Frank, W. M., and E. A. Ritchie, 1999: Effects of Environmental Flow upon Tropical Cyclone Structure. Mon. Wea. Rev., 127, 2044-2061.
——, and ——, 2001: Effects of Vertical Wind Shear on the Intensity and Structure of Numerically Simulated Hurricanes. Mon. Wea. Rev., 129, 2249-2269.
Fujita, T., 1952: Pressure distribution within typhoon. Geophys. Mag., 23, 437–451.
Hendricks, E. A., M. S. Peng, X. Ge, and T. Li, 2011: Performance of a Dynamic Initialization Scheme in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC). Wea. Forecasting, 26, 650-663.
Hong, S.-Y., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322-2339.
——, and J.-O. J. Lim, 2006: The WRF Single-moment 6-class Microphysics Scheme (WSM6). J. Korean Meteor. Soc., 42, 129-151.
Hsiao, L.-F., M. S. Peng, C. Der-Song, H. Kang-Ning, and Y. Tien-Chiang, 2009: Sensitivity of Typhoon Track Predictions in a Regional Prediction System to Initial and Lateral Boundary Conditions. J. Appl. Meteor. Climatol., 48, 1913-1928.
Huang, C.-Y., Chan-Seng Wong, and Y. Tien-Chiang, 2011: Extreme rainfall mechanisms exhibited by Typhoon Morakot (2009). Terr. Atmos. Ocean. Sci, 22, 613-632.
——, Y.-H. Kuo, S.-H. Chen, and F. Vandenberghe, 2005: Improvements in typhoon forecasts with assimilated GPS occultation refractivity. Wea. Forecasting, 20, 931-953.
Jones, S. C., 1995: The evolution of vortices in vertical shear: I: Initially barotropic vortices. Quart. J. Roy. Meteor. Soc. 121, 821–851.
Anderson, Jeffrey L., 2001: An Ensemble Adjustment Kalman Filter for Data Assimilation. Mon. Wea. Rev., 129, 2884–2903.
Kain, J., 1993: Convective parameterization for mesoscale models: The Kain-Fritsch scheme. The representation of cumulus convection in numerical models, Meteor. Monogr, 46, 165-170.
——, 2004: The Kain-Fritsch convective parameterization: an update. J. Appl. Meteor., 43, 170-181.
Kurihara, Y., M. A. Bender, and R. J. Ross, 1993: An Initialization Scheme of Hurricane Models by Vortex Specification. Mon. Wea. Rev., 121, 2030-2045.
Kwon, I.-H., and H.-B. Cheong, 2010: Tropical Cyclone Initialization with a Spherical High-Order Filter and an Idealized Three-Dimensional Bogus Vortex. Mon. Wea. Rev., 138, 1344-1367.
Kuo, Y. H., T. K. Wee, S. Sokolovskiy, C. Rocken, W. Schreiner, D. Hunt, and R. A. Anthes, 2004: Inversion and Error Estimation of GPS Radio Occultation Data. J. Meteor. Soc. Japan, 82, 507-531.
Kursinski, E. R., G. A. Hajj, K. R. Hardy, L. J. Romans, and J. T. Schofield, 1995: Observing tropospheric water vapor by radio occultation using the Global Positioning System. Geophys. Res. Lett., 22, 2365-2368.
Leslie, L. M., and G. J. Holland, 1995: On the Bogussing of Tropical Cyclones in Numerical Models: A Comparison of Vortex Profiles. Meteor. Atmos. Phys., 56, 101-110.
Liou, C.-S., and K. Sashegyi, 2012: On the initialization of tropical cyclones with a three dimensional variational analysis. Nat Hazards, 63, 1375-1391.
Liu, H., J. Anderson, and Y.-H. Kuo, 2011: Improved Analyses and Forecasts of Hurricane Ernesto’s Genesis Using Radio Occultation Data in an Ensemble Filter Assimilation System. Mon. Wea. Rev., 140, 151-166.
Maclay, K. S., M. DeMaria, and T. H. Vonder Haar, 2008: Tropical Cyclone Inner-Core Kinetic Energy Evolution. Mon. Wea. Rev., 136, 4882-4898.
Meng, Z., and F. Zhang, 2008: Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study. Mon. Wea. Rev., 136, 522-540.
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16663-16616,16682.
Molinari, J., D. Vollaro, and K. L. Corbosiero, 2004: Tropical Cyclone Formation in a Sheared Environment: A Case Study. J. Atmos. Sci., 61, 2493-2509.
Nguyen, H. V., and Y.-L. Chen, 2011: High-Resolution Initialization and Simulations of Typhoon Morakot (2009). Mon. Wea. Rev., 139, 1463-1491.
——, and Y.-L. Chen, 2014: On the Spin-up Process of a Typhoon Vortex in a Tropical Cyclone Initialization Scheme and Its Impacts on the Intensity Simulations. Mon. Wea. Rev.(In Review).
Pasch, R. J., L. A. Avila, and J. L. Guiney, 2001: Atlantic Hurricane Season of 1998. Mon. Wea. Rev., 129, 3085-3123.
Pu, Z.-X., and S. A. Braun, 2001: Evaluation of Bogus Vortex Techniques with Four-Dimensional Variational Data Assimilation. Mon. Wea. Rev., 129, 2023-2039.
Rogers, R., S. Chen, J. Tenerelli, and H. Willoughby, 2003: A Numerical Study of the Impact of Vertical Shear on the Distribution of Rainfall in Hurricane Bonnie (1998). Mon. Wea. Rev., 131, 1577-1599.
Rosenthal, S. L., 1971: The Response of A Tropical Cyclone Model to Variations in Boundary Layer Parameters, Initial Conditions, Lateral Boundary Conditions, and Domain Size. Mon. Wea. Rev., 99, 767-777.
Tao, W.-K., and Coauthors, 2013: High-Resolution Numerical Simulation of the Extreme Rainfall Associated with Typhoon Morakot. Part I: Comparing the Impact of Microphysics and PBL Parameterizations with Observations, Terr. Atmos. Ocean. Sci, 22, 673-696.
Viéé, B., O. Nuissier,, and V. Ducrocq, 2011: Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions. Mon. Wea. Rev., 139, 403-423.
Wang, C.-C., K. Hung-Chi, C. Yu-Han, H. Hsiao-Ling, C. Chao-Hsuan, and K. Tsuboki, 2012: Effects of Asymmetric Latent Heating on Typhoon Movement Crossing Taiwan: The Case of Morakot (2009) with Extreme Rainfall. J. Atmos. Sci., 69, 3172-3196.
——, and Coauthors, 2013: High-resolution quantitative precipitation forecasts and simulations by the Cloud-Resolving Storm Simulator (CReSS) for Typhoon Morakot (2009). J. Hydrol., 506, 26-41.
Ware, R., and Coauthors, 1996: GPS Sounding of the Atmosphere from Low Earth Orbit: Preliminary Results. Bull. Amer. Meteor. Soc., 77, 19-40.
Wu, C.-C., H.-C. Kuo, H.-H. Hsu, and B. J.-D. Jou, 2000: Weather and climate research in Taiwan: Potential application of GPS/MET data. Terr. Atmos. Ocean. Sci, 11, 211.
Wu, C.-C., C. Kun-Hsuan, W. Yuqing, and K. Ying-Hwa, 2006: Tropical Cyclone Initialization and Prediction Based on Four-Dimensional Variational Data Assimilation. J. Atmos. Sci., 63, 2383-2395.
Wu, C.-C., Y.-H. Huang, and G.-Y. Lien, 2012: Concentric eyewall formation in Typhoon Sinlaku (2008) – Part I: Assimilation of T-PARC data based on the Ensemble Kalman Filter (EnKF). Mon. Wea. Rev., 140, 506-527.
Xiao, Q., Y.-H. Kuo, Y. Zhang, D. M. Barker, and D.-J. Won, 2006: A Tropical Cyclone Bogus Data Assimilation Scheme in the MM5 3D-Var System and Numerical Experiments with Typhoon Rusa (2002) Near Landfall. J. Meteor. Soc. Japan, 84, 671-689.
Yang, S.-C., M. Corazza, A. Carrassi, E. Kalnay, and T. Miyoshi, 2009: Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model. Mon. Wea. Rev., 137, 693-709.
Yen, T.-H., C.-C. Wu, and G.-Y. Lien, 2011: Rainfall simulations of Typhoon Morakot with controlled translation speed based on EnKF data assimilation. Terr. Atmos. Ocean. Sci, 22, 647-660.
Yang, Y.-T., H.-C. Kuo, E. A. Hendricks, and M. S. Peng, 2013: Structural and Intensity Changes of Concentric Eyewall Typhoons in the Western North Pacific Basin. Mon. Wea. Rev., 141, 2632-2648.
Zhang, S., T. Li, X. Ge, M. Peng, and N. Pan, 2011: A 3DVAR-Based Dynamical Initialization Scheme for Tropical Cyclone Predictions*. Wea. Forecasting, 27, 473-483.
Zhang, F., Y. Weng, Y.-H. Kuo, J. S. Whitaker, and B. Xie, 2010: Predicting Typhoon Morakot''s Catastrophic Rainfall with a Convection-Permitting Mesoscale Ensemble System. Wea. Forecasting, 25, 1816-1825.
Zou, X., and Q. Xiao, 2000: Studies on the Initialization and Simulation of a Mature Hurricane Using a Variational Bogus Data Assimilation Scheme. J. Atmos. Sci., 57, 836-860.