OSSE實驗架構下利用系集預報敏感度工具探討觀測對於颱風路徑預報及結構之影響

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周哲維(Che-Wei Chou) 查詢紙本館藏

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大氣物理研究所

論文名稱

OSSE實驗架構下利用系集預報敏感度工具探討觀測對於颱風路徑預報及結構之影響

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摘要(中)

在颱風資料同化預報議題中，觀測資料除了本身的品質之外，其位置亦是對於能
否獲得有效分析修正量的重要因素之一。本研究透過一系列觀測系統模擬實驗(OSSE)
模擬2008 年颱風辛樂克，並使用Local ensemble transform Kalman filter (LETKF)，
Weather Research and Forecasting (WRF) model 及系集預報敏感度工具來探討對於颱風
路徑及結構的影響。本研究利用真實場建立出五組不同位置之探空資料，分別包含海
洋及陸地(ALL)、陸地(LAND)、海洋(OCEAN)、陸地加飛機穿越(LAND_PF)，海陸加
飛機穿越(ALL_PF)。同化預報實驗結果顯示，在海洋上有觀測的實驗組可成功掌握改
善颱風北側及東側高度場因此其系集平均路徑都較與真實場較接近。而含有穿越颱風
之觀測資訊的實驗則是能夠使得颱風的環流結構更加準確，但若僅使用飛機穿越資料
則仍會因對環境場掌握度不佳而無法提高預報路徑準確度。
此外，本研究使用Kalnay et al.(2012)提出之系集預報敏感度計算觀測影響，並以
ALL_PF 實驗結果討論同化哪些位置的觀測有助於減少預報誤差，並進一步以系集路
徑預報誤差來印證此方法之結果是否合理。結果顯示，大部分在颱風環流範圍以外的
海洋觀測在24hr 預報中能夠發揮其最大效益，其修正量亦多位於有效修正區(颱風北側
及東側)。因此透過此敏感度工具可確認同化這個區域的觀測能夠有效改善路徑預報。
而穿越中心的飛機投落送觀測則是在短期預報中就能提供正面貢獻，尤其是靠近颱風
中心點的幾個觀測。
本研究另外使用兩組敏感度測試實驗亦驗證外圍環境場的掌握度對於路徑預報的
影響，其結果與利用系集敏感度工具評估而得的觀測影響相呼應。

摘要(英)

For typhoon assimilation and prediction, the quality of observations and its location
plays an important role in the problem of acquiring useful analysis increment. To discuss the
impact of observation location on typhoon track and structure, a series of OSSEs were carried
out with the Weather Research and Forecasting (WRF) Local ensemble transform Kalman
filter (LETKF) system and the ensemble based forecast sensitivity method is used to estimate
the observation impact. In this study, five different sets of observation locations were
constructed based on the natural run, including ALL (on ocean and land), LAND (on land),
OCEAN (on ocean), LAND_PF (on land and dropsonde of penetrated flight) and ALL_PF (on
ocean and land and dropsonde of penetrated flight). The experiment results show that the
ensemble mean track forecasts are closer to the natural run when there are observations over
ocean. Mainly, the effective corrections for improving the track prediction are over the
northern and eastern side of typhoon. In this case, we also found that assimilating the
penetrating flight dropsonde is helpful for establishing reliable typhoon circulation, but is not
useful enough for improving the overall track prediction, due to the lack of environment
information.
Based on the results of ALL_PF, we use the ensemble-based forecast sensitivity to
observation (EFSO) method (Kalnay et al., 2012) to estimate the observation impact, and
investigate which location of observations can significantly reduce the forecast error. The
result indicates that most of observations outside the typhoon circulation and over the ocean
region can bring out best benefit within the 24hr forecast. Results also confirm that
observations with positive corrections are mainly located near north and east region of
typhoon, as we expected that assimilating these observations can improve the track forecast.
In addition, assimilating dropsondes provides positive contribution to the short forecast,
iii
especially with the observations in the typhoon inner core.
In addition, two sensitivity experiments were carried out to test the effectiveness of the
environment observations on improving the track forecast. Results are able to echoed the
observation impact calculated by EFSO.

關鍵字(中)

★ 系集預報敏感度
★ 系集卡爾曼濾波器
★ 颱風系集預報
★ 觀測對於颱風預報影響

關鍵字(英)

論文目次

摘要 i
Abstract ii
Acknowledgement iv
目錄 v
圖表目錄 vii
第一章前言 1
1.1.背景及文獻回顧 1
1.2.研究動機 3
第二章研究方法 5
2.1.數值模式 5
2.2.資料同化系統 5
2.2.1. 系集卡爾曼濾波器 (Ensemble Kalman Filter, EnKF) 6
2.2.2. 局地系集轉換卡爾曼濾波器 (Local Ensemble Transform
Kalman Filter,LETKF) 9
2.3.利用系集預報敏感度評估觀測影響 11
第三章 OSSE 實驗設定 13
3.1. 觀測系統模擬實驗介紹 13
3.2.2008 年辛樂克颱風 14
3.3. 真實場與初始系集設定 15
3.4.觀測資料設定 15
3.5 實驗設定 16
第四章實驗結果與討論 18
4.1. 觀測位置配置對於路徑預報之影響 18
4.1.1. 9 月10 日1200UTC 之系集平均路徑預報 18
4.1.2 9 月11 日0000UTC 之系集平均路徑預報 19
4.1.3 9 月11 日0000UTC 之系集路徑預報 20
4.2.觀測位置配置對於颱風結構以及環境場之影響 21
4.2.1.颱風風場結構 21
4.2.2.駛流場 22
4.2.3.500hPa 高度場 23
4.3. ALL_PF 2008 年9 月11 日0000UTC 之觀測影響 23
4.3.1.水平觀測影響 24
4.3.2 垂直觀測影響 25
4.4.敏感度測試1 26
4.4.1 路徑預報 26
4.4.2 觀測影響 27
4.5.敏感度測試2 28
第五章總結與未來展望 29
5.1.總結 29
5.2 未來展望 31
參考文獻 32

參考文獻

陳占慧 (2009)，策略性颱風觀測理論之動力分析與比較。國立台灣大學大氣科
學研究所博士論文，110 頁。
林冠任 (2011)，改善區域系集卡爾曼濾波器在颱風同化及預報中的spin-up 問
題。國立中央大學大氣科學研究所碩士論文，68 頁。
張誌謙 (2013)，利用系集重新定位法改善颱風路徑預報– 2011 年南瑪都颱風個
案研究。國立中央大學大氣科學研究所碩士論文，78 頁。
Abserson, S. D., and J. L. Franklin, 1999: impact on hurricane track and intensity
forecast of GPS dropsonde observation from the fist-season Flights of the NOAA
Gulfstream-IV jet aircraft. Bull. Amer. Meteor. Soc., 80, 421-427.
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.
Bergot, T., 1999: Adaptive observations during FASTEX: A systematic survry of upstream
flights. Quart. J. Roy. Meteor. Soc.
Buckingham, C., T. Marchok, I. Ginis, L. Rothstein and D. Rowe, 2010: Short- and
Medium-Range Prediction of Tropical and Transitioning Cyclone Track within the NECP
Global Ensemble Forecasting System, Mon. Wea. Rev., 25,736-753
Burpee, R. W., J. L. Franklin, S.J. Lord, R. E. Tuleya, and S. D. Aberson, 1996: The
impact of omega dropsomdes on operational hurricane track forecast models. Bull. Amer.
Mereor. Soc., 77, 925-933.
Chan, JCL. 1985. Identification of the steering flow for tropical cyclone motion using
objectively-analyzed wind fields. Mon. Weather Rev. 113:106–116.
Chen, Y., and C. Snyder, 2007: Assimilation vortex position with an ensemble Kalman
Filter. Mon. Wea. Rev., 135, 1828-1845.
Chou, K.-H., C.-C. Wu, 2008: Development of the typhoon initialization in a
mesoscale model – Combination of the bogused vortex with the dropwindsonde data in
DOTSTAR. Mon. Wea. Rev. 136, 865-879.
＿＿＿＿, ＿＿＿＿, P.-H. Lin, S. D. Aberson, M. Weissmann, F. Harnisch, T.
Nakazawa, 2011: The impact of dropwindsonde observations on typhoon track forecasts
in DOTSTAR and T-PARC. Mon. Wea. Rev., 139, 1728-1743.
Elsberry, R. L., and P. A. Harr, 2008: Tropical cyclone structure (TCS08) field
experiment science basis, observational platforms and strategy. Asis-Pa. J. Atmos. Sci.,
44,209-231
Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics. J. Geophys Res., 99, 10
143-10 162.
＿＿＿＿, 2003: the ensemble Kalman filter: Theoretical formulation and practical
implementation. Ocean Dyn., 53, 343-367.
Fiorino, M., and R. L. Elsberry, 1989: Some aspects of vortex structure related to tropical
cyclone motion. J. Atmos. Sci., 46, 975–990.
Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for
spatiotemporal chaos: A local ensemble transform Kalman filter. Physuca D, 230,112-
126
Kalnay, E., Y. Ota, T. Miyoshi and J. Liu, 2012: A simpler formulation of forecast
sensitivity to observations: application to ensemble Kalman filters. Tellus A, 64, 18462.
Kelly, G. J.-N. Thépaut, R. Buizza, and C. Cardinali, 2007: The value of observations.
I: Data denial experiments for the Atlantic and the Pacific. Quart. J. Roy. Meteor. Soc.,
133, 1803-1815.
Kunii, M., T. Miyoshi, and E. Kalnay, 2011: Estimating the impact of real
observations in regional numerical weather prediction using an Ensemble Kalman Filter.
Mon. Wea. Rev., 140, 1975-1987.
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
＿＿＿＿, ＿＿＿＿, R. E. Tuleya, and R. J. Ross, 1995:Improvements in the
GFDL hurricane prediction system. Mon. Wea. Rev., 123,2791-2801.
＿＿＿＿, R. E. Tuleya, and M. A. Bender, 1998: The GFDL hurricane prediction
system and its performance in the 1995 hurricane season. Mon. Wea. Rev., 126,1306-
1322.
Langland, R. H. and N. L. Baker, 2004: Estimation of observation impact using the
NRL atmospheric variational data assimilation adjoint system. Tellus A, 56, 189-201.
Li, H., Liu, J. and E. Kalnay,. 2010. Correction of ‘Estimating observation impact
without adjoint model in an ensemble Kalman filter’. Quart. J. Roy. Meteor. Soc. 136,
1652-1654.
Liu, J., and E. Kalnay, 2008: Estimating observation impact without adjoint model in
an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 134, 1327-1335.
Neumann, C. J., 1979: On the use of deep-layer-mean geopotential height fields in statistical
prediction of tropical cyclone motion. Preprints, Sixth Conf. on Probability and Statistics
in Atmospheric Sciences, Banff, Alberta, Canada, Amer. Meteor. Soc., 32–38.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and
J.G. Powers, 2005: A description of the Advanced Research WRF version2. NCAR Tech.
Note TN-468_STR, 88 pp.
Park, K., and X. Zou, 2004: Toward developing an objective 4DVAR BDA scheme
for hurricane initialization based on TPC observed parameters. Mon. Wea. Rev., 132,
2054-2069.
Pu, Z.-X., and S. A. Braun, 2001: Evaluation of bogus vortex techniques with fourdimensional
variational data assimilation. Mon. Wea. Rev., 129, 2023-2039.
Torn, R. D., and G. J. Hakim, 2009: Ensemble data assimilation applied to RAINEX
observations of Hurricane Katrina (2005). Mon. Wea. Rev., 137, 2817-2829.
Tsai, C. -C., S.-C. Yang, and Y.-C. Liou, 2014: Improving quantitative precipitation
nowcasting with a local ensemble transform Kalman filter radar data assimilation
system: observing system simulation experiments. Tellus A, 66, 21804.
Wu, C.-C., K.-H. Chou, Y. Wang, and Y.-H. Kuo, 2006: Tropical cyclone initialization
and prediction based on four-dimensional variational data assimilation. J. Atmos. Sci.,
63, 2383-2395.
＿＿＿＿, ＿＿＿＿, P.-H. Lin, S. D. Aberson, M. S. Peng, T. Nakazawa, 2007:
The impact of dropwindsonde data on typhoon track forecasts in DOTSTAR. Wea.
Forecasting, 22, 1157-1176.
＿＿＿＿, G.-Y. Lien, J.-H. Chen, and F. Zhang, 2010:Assimilation of tropical
cyclone track and structure based on ensemble Kalman Filter (EnKF). J. Atmos. Sci., 67,
3806-3822.
＿＿＿＿, K.A. Emanuel, 1995: Potential vorticity diagnostics of hurricane movement. Part
1: a case study of Hurricane Bob 1991. Mon. Weather Rev. 123,69–92
＿＿＿＿, ＿＿＿＿, 1995: Potential vorticity diagnostics of hurricane movement. Part
2: Tropical storm Ana 1991 and Hurricane Andrew 1992. Mon. Weather Rev. 123,93–109
Weissmann,M., and Coauthors, 2011: The influence of assimilating dropsonde data on
typhoon track and midlatitude forecasts.Mon. Wea. Rev., 139, 908–920.
Xiao, Q., X. Zou, Y-H. Kuo, 2000: Incorporating the SSM/I-Derived Precipitable
Water and Rainfall Rate into a Numerical Model: A Case Study for the ERICA IOP-4
Cyclone. Mon. Wea. Rev., 128, 87-108
Yang, S.-C., E. Kalnay,.and T. Miyoshi, 2011: Improving EnKF spin-up for typhoon
assimilation amd prediction, Wea. Forecasting, 27, 878–897..
＿＿＿＿, ＿＿＿＿, and B. Hunt, 2012: Handling Nonlinearity in an Ensemble
Kalman Filter: Experiments with the Three-Variable Lorenz Model. Mon. Wea.
Rev., 140, 2628–2646.

指導教授

楊舒芝(Shu-Chih Yang)

審核日期

2015-1-8

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