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台风强度模拟的海温目标观测研究

姚佳伟 段晚锁

姚佳伟, 段晚锁. 2022. 台风强度模拟的海温目标观测研究[J]. 大气科学, 46(1): 83−97 doi: 10.3878/j.issn.1006-9895.2103.20256
引用本文: 姚佳伟, 段晚锁. 2022. 台风强度模拟的海温目标观测研究[J]. 大气科学, 46(1): 83−97 doi: 10.3878/j.issn.1006-9895.2103.20256
YAO Jiawei, DUAN Wansuo. 2022. Target Observation of Sea Surface Temperature for Tropical Cyclone Intensity Simulation [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(1): 83−97 doi: 10.3878/j.issn.1006-9895.2103.20256
Citation: YAO Jiawei, DUAN Wansuo. 2022. Target Observation of Sea Surface Temperature for Tropical Cyclone Intensity Simulation [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(1): 83−97 doi: 10.3878/j.issn.1006-9895.2103.20256

台风强度模拟的海温目标观测研究

doi: 10.3878/j.issn.1006-9895.2103.20256
基金项目: 国家重点研发计划2018YFC1506402,国家自然科学基金项目41930971
详细信息
    作者简介:

    姚佳伟,男,1993年生,博士研究生,主要从事热带气旋可预报性研究。E-mail: yaojiawei15@mails.ucas.ac.cn

    通讯作者:

    段晚锁,E-mail: duanws@lasg.iap.ac.cn

  • 中图分类号: P411

Target Observation of Sea Surface Temperature for Tropical Cyclone Intensity Simulation

Funds: National Key Research and Development Program of China (Grant 2018YFC1506402), National Natural Science Foundation of China (Grant 41930971)
  • 摘要: 根据非线性强迫奇异向量(NFSV)型海温(SST)强迫误差识别的敏感性特征,通过观测系统模拟试验(OSSE)确定了12个热带气旋(TC)的强度模拟的海温目标观测最优布局。NFSV型SST强迫误差敏感区一般沿着台风移动路径,主要位于台风快速增强阶段。结果表明,在NFSV型SST强迫目标观测敏感区内以90 km间隔加密海温观测,这些额外观测能够更加有效地改善TC强度的模拟技巧;与空间范围更大的非敏感区相比,在NFSV型SST强迫目标观测敏感区内进行目标观测,能够更加有效地改进TC强度的模拟能力,而当非敏感区内的局地观测区域逐步接近目标观测敏感区时,该局地区域的加密观测对TC强度模拟改善程度也逐步提高。进一步研究表明,上述目标观测对台风强度模拟水平的改善尤以NFSV型SST强迫误差大值区所对应的TC快速增强阶段最为明显。所以,在由NFSV型SST强迫误差确定的目标观测敏感区和敏感时段内加密观测,对台风强度模拟能力的提高最有效,而且在敏感区内以合适的间隔实施外场观测是最经济的观测策略。
  • 图  1  12个TC个例的非线性强迫奇异向量(NFSV)型SST强迫误差结构(阴影,单位:K)。其中点为TC的路径;蓝色、绿色、黄色、红色、紫色的点分别代表模拟TC强度大小在980~1000 hPa、970~980 hPa、960~970 hPa、950~960 hPa和900~950 hPa的范围内

    Figure  1.  Patterns of Nonlinear Forcing Singular Vector (NFSV)-type SST forcing errors (shaded, units: K) of the selected 12 TC cases. The dots represent the tracks of TCs and the blue, green, yellow, red, and purple dots indicate the simulated TC intensity within 980–1000 hPa, 970–980 hPa, 960–970 hPa, 950–960 hPa and 900–950 hPa

    图  2  Soulik个例的NFSV型SST强迫误差中前20、40、···、240个大值点所构成的敏感区范围

    Figure  2.  Regions of the sensitive area identified by the leading 20, 40, ···, 240 large values of the NFSV-type SST forcing error for TC Soulik

    图  3  OSSE-S-I的试验结果。柱状图表示同化不同范围的敏感区内模拟观测后,TC强度模拟的改善程度

    Figure  3.  Result of OSSE-S-I. Each bar represents the improvement rate of the TC intensity simulation after assimilating the observations in the sensitive area to a different extent

    图  4  同化敏感区范围内的观测后,SST强迫场的变化情况(填色,单位:K)。其中红色实线框出的范围表示对应的目标观测敏感区

    Figure  4.  Change of SST (shaded, units: K) forcing field after assimilating the observations in the sensitive area to different extents. The red line represents the extent of the corresponding sensitive area of the target observations

    图  5  OSSE-S-I中,同化不同范围敏感区内的模拟观测后,区域A所减小的绝对SST误差大小(单位:K)

    Figure  5.  Absolute reduced SST errors in region A after assimilating the observations in sensitive areas to a different extent in OSSE-S-I (units: K)

    图  6  OSSE-S-II的模拟结果。同化目标观测敏感区不同观测间隔的观测后,TC强度模拟的改善程度

    Figure  6.  Results of OSSE-S-II. The improvement rate in the TC intensity simulation after assimilating observations with different intervals in the sensitive area

    图  7  OSSE-T-I的模拟结果。在单个时刻同化敏感区内的模拟观测对TC强度模拟的改善情况。虚线为改善程度达5%的参考线

    Figure  7.  Result of OSSE-T-I. The improvement rate of the simulation of the TC intensity after assimilating observations in the sensitive region only once at the corresponding time. The dashed line is the reference line of the 5% improvement

    图  8  OSSE-S-III的模拟结果。红色(蓝色)柱为同化小范围的敏感区(非敏感区)内观测,对TC强度模拟的改善情况。“Sen”表示敏感区内进行的OSSE,“I+数字”表示在非敏感区进行的OSSE

    Figure  8.  Results of OSSE-S-III. The red (blue) bars represent the improvement rate of the TC intensity simulation after assimilating the observations in local sensitive (nonsensitive) areas. “Sen” represents the OSSE conducted in the sensitive area,while “I+Number”represents the OSSE conducted in nonsensitive area

    图  9  非敏感区的局地观测距敏感区的距离与TC强度模拟改善程度的散点图和线性拟合曲线(红色实线)。图中的公式为拟合函数,R代表相关系数,P为显著性水平

    Figure  9.  Scatter and linear fitting line (the red solid line) between the distance of observations in the nonsensitive area relative to the sensitive area and the improvement of the TC intensity (The formula is the fitting function between the two variables. R and P is the correlation coefficient and the significant level respectively.)

    图  10  OSSE-S-IV的模拟结果。红色柱为同化敏感区内观测对TC强度的改善程度;蓝色柱为同化非敏感区全场的观测对TC强度模拟的改善程度。“Sen”代表在敏感区进行的OSSE,“InSen-S”表示在非敏感区全场进行的OSSE

    Figure  10.  Results of OSSE-S-IV. The red bars represent the improvement of the TC intensity simulation after assimilating observations in the sensitive area. The blue bars represent the improvement of the TC intensity simulation after assimilating the same number of observations in the whole nonsensitive area. “Sen” represents the OSSE conducted in the sensitive area, and “InSen-S” indicated the OSSE conducted in the whole nonsensitive area

    图  11  OSSE-T-II的模拟结果。红色柱为同化敏感阶段的敏感区内观测对TC强度的改善程度;蓝色柱为同化非敏感阶段的敏感区内观测对TC强度模拟的改善程度。“Sen-T”代表在敏感区的敏感时段进行的OSSE,“InSen-T”表示在敏感区的非敏感时段进行的OSSE

    Figure  11.  Results of OSSE-T-II. The red bars represent the improvement of the TC intensity simulation after assimilating observations in the sensitive area during the sensitive period. The blue bars represent the improvement of the TC intensity simulation after assimilating observations in the sensitive area during the nonsensitive period. “Sen-T” represents the OSSE conducted in the sensitive area during sensitive period, and “InSen-T” indicated the OSSE conducted in the sensitive area during nonsensitive period

    表  1  OSSE-S第三步设置说明

    Table  1.   Settings of Step 3 of OSSE-S

    OSSE-S试验设置说明
    OSSE-S-I根据Soulik个例的NFSV型海温强迫误差,分别选取前20、40、60、80、···、220、240个大值格点所在区域构成目标观测敏感区,如图2所示。这12个敏感区分别记为“S-20”、“S-40”、···、“S-240”。
    OSSE-S-II在OSSE-I所确定的目标观测敏感区内以30 km、60 km、90 km、120 km为间隔确定“观测点”。这4个观测布局分别记为“Sen30”、“Sen60”、“Sen90”、“Sen120”。
    OSSE-S-III在非敏感区随机选取20个大小为300 km×300 km的方形局地区域,并在选取的局地区域以90 km为间距均匀选取16个“观测点”。这20个随机区域依次记为“I1”、“I2”、···、“I20”。
    OSSE-S-IV在非敏感区均匀选取与敏感区最优观测布局相同的观测点数。
    下载: 导出CSV

    表  2  OSSE-T第五步设置说明

    Table  2.   Settings of Step 5 of OSSE-T

    OSSE-T试验设置说明
    OSSE-T-I分别只在第0 h,6 h,12 h,···,114 h用更新的SST强迫分析场替换步骤(1)中“真实”的SST强迫场,积分模式120小时,将得到的模拟结果记为“Sensitive Run”,并与“Control Run”比较,考察目标观测在提高台风强度模拟技巧中的有效性。
    OSSE-T-II分别在敏感性阶段和非敏感性阶段用更新的SST强迫分析场替换步骤(1)中“真实”的SST强迫场,积分模式120小时,将得到的模拟结果记为“Sensitive Run”,并与“Control Run”比较,考察目标观测在提高台风强度模拟技巧中的有效性。
    下载: 导出CSV

    表  3  OSSE-II中敏感区内不同观测布局的观测数

    Table  3.   Numbers of observations with different intervals in the sensitive area of OSSE-II

    OSSE-II观测数(个)
    Sen30120
    Sen6031
    Sen9012
    Sen1207
    下载: 导出CSV

    表  4  不同个例对应的非敏感区局地观测距敏感区的距离与TC强度改善程度的相关系数($R $

    Table  4.   Correlation coefficients ($R $) between the distance of observations in the nonsensitive area relative to the sensitive area and the improvement of TC intensity simulation

    TC个例RTC个例RTC个例R
    Soulik−0.60Chanhom−0.86Goni−0.62
    Utor−0.64Bolaven−0.69Halong−0.84
    Rammasun−0.52Sanba*−0.17*Man-Yi−0.58
    Soudelor−0.46Muifa−0.69Noul−0.48
    注:*表示相关系数的置信水平低于99%的个例。
    下载: 导出CSV
  • [1] Arnold C P Jr, Dey C H. 1986. Observing-systems simulation experiments: Past, present, and future [J]. Bull. Amer. Meteor. Soc., 67(6): 687−695. doi:10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2
    [2] Buizza R, Cardinali C, Kelly G, et al. 2007. The value of observations. II: The value of observations located in singular-vector-based target areas [J]. Quart. J. Roy. Meteor. Soc., 133(628): 1817−1832. doi: 10.1002/qj.149
    [3] Davis C, Wang W, Chen S S, et al. 2008. Prediction of landfalling hurricanes with the advanced hurricane WRF model [J]. Mon. Wea. Rev., 136(6): 1990−2005. doi: 10.1175/2007MWR2085.1
    [4] Demaria M, Kaplan J. 1994. Sea surface temperature and the maximum intensity of Atlantic tropical cyclones [J]. J. Climate, 7(9): 1324−1334. doi:10.1175/1520-0442(1994)007<1324:SSTATM>2.0.CO;2
    [5] Duan W S, Zhou F F. 2013. Non-linear forcing singular vector of a two-dimensional quasi-geostrophic model [J]. Tellus A, 65(1): 18452. doi: 10.3402/tellusa.v65i0.18452
    [6] Duan W S, Zhao P. 2015. Revealing the most disturbing tendency error of Zebiak–Cane model associated with El Niño predictions by nonlinear forcing singular vector approach [J]. Climate Dyn., 44(9): 2351−2367. doi: 10.1007/s00382-014-2369-0
    [7] Duan W S, Hu J Y. 2016. The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: Results from an earth system model [J]. Climate Dyn., 46(11): 3599−3615. doi: 10.1007/s00382-015-2789-5
    [8] Dudhia J. 1989. Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [J]. J. Atmos. Sci., 46(20): 3077−3107. doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2
    [9] Emanuel K A. 1988. The maximum intensity of hurricanes [J]. J. Atmos. Sci., 45(7): 1143−1155. doi:10.1175/1520-0469(1988)045<1143:TMIOH>2.0.CO;2
    [10] Emanuel K, Zhang F Q. 2016. On the predictability and error sources of tropical cyclone intensity forecasts [J]. J. Atmos. Sci., 73(9): 3739−3747. doi: 10.1175/JAS-D-16-0100.1
    [11] Emanuel K A, Neelin J D, Bretherton C S. 1994. On large-scale circulations in convecting atmospheres [J]. Quart. J. Roy. Meteor. Soc., 120(519): 1111−1143. doi: 10.1002/qj.49712051902
    [12] Evans J L. 1993. Sensitivity of tropical cyclone intensity to sea surface temperature [J]. J. Climate, 6(6): 1133−1140. doi:10.1175/1520-0442(1993)006<1133:SOTCIT>2.0.CO;2
    [13] Hong S Y, Noh Y, Dudhia J. 2006. A new vertical diffusion package with an explicit treatment of entrainment processes [J]. Mon. Wea. Rev., 134(9): 2318−2341. doi: 10.1175/MWR3199.1
    [14] Kain J S. 2004. The Kain-Fritsch convective parameterization: An update [J]. J. Appl. Meteor., 43(1): 170−181. doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
    [15] Lin Y L, Farley R D, Orville H D. 1983. Bulk parameterization of the snow field in a cloud model [J]. Journal of Applied Meteorology and Climatology, 22(6): 1065−1092. doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2
    [16] Mlawer E J, Taubman S J, Brown P D, et al. 1997. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave [J]. J. Geophys. Res., 102(D14): 16663−16682. doi: 10.1029/97JD00237
    [17] Montgomery M T, Smith R K. 2017. Recent developments in the fluid dynamics of tropical cyclones [J]. Annual Review of Fluid Mechanics, 49: 541−574. doi: 10.1146/annurev-fluid-010816-060022
    [18] Mu M. 2013. Methods, current status, and prospect of targeted observation [J]. Science China Earth Sciences, 56(12): 1997−2005. doi: 10.1007/s11430-013-4727-x
    [19] Nasrollahi N, Aghakouchak A, Li J L, et al. 2012. Assessing the impacts of different WRF precipitation physics in hurricane simulations [J]. Wea. Forecasting, 27(4): 1003−1016. doi: 10.1175/WAF-D-10-05000.1
    [20] Parker C L, Lynch A H, Mooney P A. 2017. Factors affecting the simulated trajectory and intensification of tropical cyclone Yasi (2011) [J]. Atmospheric Research, 194: 27−42. doi: 10.1016/j.atmosres.2017.04.002
    [21] Peduzzi P, Chatenoux B, Dao H, et al. 2012. Global trends in tropical cyclone risk [J]. Nature Climate Change, 2(4): 289−294. doi: 10.1038/nclimate1410
    [22] Petersen G N, and Thorpe A J. 2007. The impact on weather forecasts of targeted observations during A-TReC [J] Q. J. R. Meteorol. Soc. 133: 417-431.
    [23] Price J F. 1981. Upper ocean response to a hurricane [J]. J. Phys. Oceanogr., 11(2): 153−175. doi:10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2
    [24] Privé N C, Errico R M, Todling R, et al. 2021. Evaluation of adjoint-based observation impacts as a function of forecast length using an observing system simulation experiment [J]. Quart. J. Roy. Meteor. Soc., 147(734): 121−138. doi: 10.1002/qj.3909
    [25] Qin X H, Mu M. 2012. Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts [J]. Quart. J. Roy. Meteor. Soc., 138(662): 185−197. doi: 10.1002/qj.902
    [26] Qin X H, Duan W S, Xu H. 2020. Sensitivity to tendency perturbations of tropical cyclone short-range intensity forecasts generated by WRF [J]. Adv. Atmos. Sci., 37(3): 291−306. doi: 10.1007/s00376-019-9187-6
    [27] Rogers R, Aberson S, Black M, et al. 2006. The intensity forecasting experiment: A NOAA multiyear field program for improving tropical cyclone intensity forecasts [J]. Bull. Amer. Meteor. Soc., 87(11): 1523−1538. doi: 10.1175/BAMS-87-11-1523
    [28] Schade L R. 2000. Tropical cyclone intensity and sea surface temperature [J]. J. Atmos. Sci., 57(18): 3122−3130. doi:10.1175/1520-0469(2000)057<3122:TCIASS>2.0.CO;2
    [29] Schade L R, Emanuel K A. 1999. The ocean’s effect on the intensity of tropical cyclones: Results from a simple coupled atmosphere–ocean model [J]. J. Atmos. Sci., 56(4): 642−651. doi:10.1175/1520-0469(1999)056<0642:TOSEOT>2.0.CO;2
    [30] Scoccimarro E, Fogli P G, Reed K A, et al. 2017. Tropical cyclone interaction with the ocean: The role of high-frequency (subdaily) coupled processes [J]. J. Climate, 30(1): 145−162. doi: 10.1175/JCLI-D-16-0292.1
    [31] Shay L K, Black P G, Mariano A J, et al. 1992. Upper ocean response to Hurricane Gilbert [J]. J. Geophys. Res., 97(C12): 20227−20248. doi: 10.1029/92JC01586
    [32] Snyder C. 1996. Summary of an informal workshop on adaptive observations and FASTEX [J]. Bull. Amer. Meteor. Soc., 77(5): 953−961. doi: 10.1175/1520-0477-77.5.953
    [33] Srinivas C V, Mohan G M, Naidu C V, et al. 2016. Impact of air–sea coupling on the simulation of tropical cyclones in the North Indian Ocean using a simple 3-D ocean model coupled to ARW [J]. J. Geophys. Res., 121(16): 9400−9421. doi: 10.1002/2015JD024431
    [34] Sun M H, Duan Y H, Zhu J R, et al. 2014. Simulation of Typhoon Muifa using a mesoscale coupled atmosphere–ocean model [J]. Acta Oceanologica Sinica, 33(11): 123−133. doi: 10.1007/s13131-014-0561-z
    [35] Torn R D. 2016. Evaluation of atmosphere and ocean initial condition uncertainty and stochastic exchange coefficients on ensemble tropical cyclone intensity forecasts [J]. Mon. Wea. Rev., 144(9): 3487−3506. doi: 10.1175/MWR-D-16-0108.1
    [36] Wen X X, Duan W S. 2019. Errors in current velocity in the low-latitude north Pacific: Results from the regional ocean modeling system [J]. Adv. Atmos. Sci., 36(4): 397−416. doi: 10.1007/s00376-018-8140-4
    [37] Winterbottom H R, Uhlhorn E W, Chassignet E P. 2012. A design and an application of a regional coupled atmosphere–ocean model for tropical cyclone prediction [J]. Journal of Advances in Modeling Earth Systems, 4(4): M10002. doi: 10.1029/2012MS000172
    [38] Wu C C, Chen J H, Lin P H, et al. 2007. Targeted observations of tropical cyclone movement based on the adjoint-derived sensitivity steering vector [J]. J. Atmos. Sci., 64(7): 2611−2626. doi: 10.1175/JAS3974.1
    [39] Yao J W, Duan W S, Qin X H. 2021. Which features of the SST forcing error most likely disturb the simulated intensity of tropical cyclones? [J]. Adv. Atmos. Sci., 38(4): 581−602. doi: 10.1007/s00376-020-0073-z
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  • 收稿日期:  2020-12-31
  • 录用日期:  2021-06-01
  • 网络出版日期:  2021-06-16
  • 刊出日期:  2022-01-18

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