The Roles of Spatial Locations and Patterns of Initial Errors in the Uncertainties of Tropical Cyclone Forecasts

CHEN Boyu; MU Mu

doi:10.1007/s00376-011-0201-x

Impact Factor: 5.8

Volume 29 Issue 1

Jan. 2012

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2012 > 29(1): 63-78

The Roles of Spatial Locations and Patterns of Initial Errors in the Uncertainties of Tropical Cyclone Forecasts

CHEN Boyu,
MU Mu

1.
National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate University of Chinese Academy of Sciences, Beijing 100049,Key Laboratory of Ocean Circulation and Wave, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

CHEN Boyu,
MU Mu

doi: 10.1007/s00376-011-0201-x

Abstract
References
Related papers
PDF

Abstract:
In this study, a series of sensitivity experiments were performed for two tropical cyclones (TCs), TC Longwang (2005) and TC Sinlaku (2008), to explore the roles of locations and patterns of initial errors in uncertainties of TC forecasts. Specifically, three types of initial errors were generated and three types of sensitive areas were determined using conditional nonlinear optimal perturbation (CNOP), first singular vector (FSV), and composite singular vector (CSV) methods. Additionally, random initial errors in randomly selected areas were considered. Based on these four types of initial errors and areas, we designed and performed 16 experiments to investigate the impacts of locations and patterns of initial errors on the nonlinear developments of the errors, and to determine which type of initial errors and areas has the greatest impact on TC forecasts. Overall, results from the experiments indicate the following: (1) The impact of random errors introduced into the sensitive areas was greater than that of errors themselves fixed in the randomly selected areas. From the perspective of statistical analysis, and by comparison, the impact of random errors introduced into the CNOP target area was greatest. (2) The initial errors with CNOP, CSV, or FSV patterns were likely to grow faster than random errors. (3) The initial errors with CNOP patterns in the CNOP target areas had the greatest impacts on the final verification forecasts.
- sensitive area,
- conditional nonlinear optimal perturbation,
- CNOP,
- uncertainty of initial conditions,
- nonlinear development
References

[1]	Huizhen YU, Zhiyong MENG, 2022: The Impact of Moist Physics on the Sensitive Area Identification for Heavy Rainfall Associated Weather Systems, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 684-696. doi: 10.1007/s00376-021-0278-9
[2]	ZHENG Qin, SHA Jianxin, SHU Hang, and LU Xiaoqing, 2014: A Variant Constrained Genetic Algorithm for Solving Conditional Nonlinear Optimal Perturbations, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 219-229. doi: 10.1007/s00376-013-2253-6*
[3]	SUN Guodong, MU Mu, ZHANG Yale, 2010: Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP), ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1311-1321. doi: 10.1007/s00376-010-9088-1
[4]	ZHOU Feifan, MU Mu, 2012: The Time and Regime Dependencies of Sensitive Areas for Tropical Cyclone Prediction Using the CNOP Method, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 705-716. doi: 10.1007/s00376-012-1174-0
[5]	ZHOU Feifan, MU Mu, 2012: The Impact of Horizontal Resolution on the CNOP and on Its Identified Sensitive Areas for Tropical Cyclone Predictions, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 36-46. doi: 10.1007/s00376-011-1003-x
[6]	ZHOU Feifan, MU Mu, 2011: The Impact of Verification Area Design on Tropical Cyclone Targeted Observations Based on the CNOP Method, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 997-1010. doi: 10.1007/s00376-011-0120-x
[7]	JIANG Zhina, 2006: Applications of Conditional Nonlinear Optimal Perturbation to the Study of the Stability and Sensitivity of the Jovian Atmosphere, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 775-783. doi: 10.1007/s00376-006-0775-x
[8]	WANG Qiang, MU Mu, Henk A. DIJKSTRA, 2012: Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 118-134. doi: 10.1007/s00376-011-0199-0
[9]	SUN Guodong, MU Mu, 2012: Inducing Unstable Grassland Equilibrium States Due to Nonlinear Optimal Patterns of Initial and Parameter Perturbations: Theoretical Models, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 79-90. doi: 10.1007/s00376-011-0226-1
[10]	Zhenhua HUO, Wansuo DUAN, Feifan ZHOU, 2019: Ensemble Forecasts of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 231-247. doi: 10.1007/s00376-018-8001-1
[11]	Xiangjun TIAN, Xiaobing FENG, 2019: An Adjoint-Free CNOP-4DVar Hybrid Method for Identifying Sensitive Areas in Targeted Observations: Method Formulation and Preliminary Evaluation, ADVANCES IN ATMOSPHERIC SCIENCES, , 721-732. doi: 10.1007/s00376-019-9001-5
[12]	QIN Xiaohao, MU Mu, 2014: Can Adaptive Observations Improve Tropical Cyclone Intensity Forecasts?, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 252-262. doi: 10.1007/s00376-013-3008-0
[13]	Qian ZHOU, Wansuo DUAN, Xu WANG, Xiang LI, Ziqing ZU, 2021: The Initial Errors in the Tropical Indian Ocean that Can Induce a Significant “Spring Predictability Barrier” for La Niña Events and Their Implication for Targeted Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1566-1579. doi: 10.1007/s00376-021-0427-1
[14]	Bin MU, Juhui REN, Shijin YUAN, Rong-Hua ZHANG, Lei CHEN, Chuan GAO, 2019: The Optimal Precursors for ENSO Events Depicted Using the Gradient-definition-based Method in an Intermediate Coupled Model, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1381-1392. doi: 10.1007/s00376-019-9040-y
[15]	SUN Guodong, MU Mu, 2013: Using the Lund-Potsdam-Jena Model to Understand the Different Responses of Three Woody Plants to Land Use in China, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 515-524. doi: 10.1007/s00376-012-2011-1
[16]	SUN Guodong, MU Mu, 2011: Response of a Grassland Ecosystem to Climate Change in a Theoretical Model, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1266-1278. doi: 10.1007/s00376-011-0169-6
[17]	Xing ZHANG, Mu MU, Qiang WANG, Stefano PIERINI, 2017: Optimal Precursors Triggering the Kuroshio Extension State Transition Obtained by the Conditional Nonlinear Optimal Perturbation Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 685-699. doi: 10.1007/s00376-017-6263-7
[18]	WANG Bo, and HUO Zhenhua, 2013: Extended application of the conditional nonlinear optimal parameter perturbation method in the Common Land Model, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1213-1223. doi: 10.1007/s00376-012-2025-8
[19]	JIANG Zhina, WANG Xin, WANG Donghai, 2015: Exploring the Phase-Strength Asymmetry of the North Atlantic Oscillation Using Conditional Nonlinear Optimal Perturbation, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 671-679. doi: 10.1007/s00376-014-4094-3
[20]	MU Mu, DUAN Wansuo, XU Hui, WANG Bo, 2006: Applications of Conditional Nonlinear Optimal Perturbation in Predictability Study and Sensitivity Analysis of Weather and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 992-1002. doi: 10.1007/s00376-006-0992-3

PDF

Get Citation+

Export:

Article Metrics

Article Views: 1110 Times

PDF downloads: 796 Times

Cited by: Times

Proportional views

Manuscript History

Manuscript received: 10 January 2012

Manuscript revised: 10 January 2012

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

The Roles of Spatial Locations and Patterns of Initial Errors in the Uncertainties of Tropical Cyclone Forecasts

CHEN Boyu,
MU Mu

1. National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate University of Chinese Academy of Sciences, Beijing 100049,Key Laboratory of Ocean Circulation and Wave, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Manuscript received: 2012-01-10
Manuscript revised: 2012-01-10

Abstract: In this study, a series of sensitivity experiments were performed for two tropical cyclones (TCs), TC Longwang (2005) and TC Sinlaku (2008), to explore the roles of locations and patterns of initial errors in uncertainties of TC forecasts. Specifically, three types of initial errors were generated and three types of sensitive areas were determined using conditional nonlinear optimal perturbation (CNOP), first singular vector (FSV), and composite singular vector (CSV) methods. Additionally, random initial errors in randomly selected areas were considered. Based on these four types of initial errors and areas, we designed and performed 16 experiments to investigate the impacts of locations and patterns of initial errors on the nonlinear developments of the errors, and to determine which type of initial errors and areas has the greatest impact on TC forecasts. Overall, results from the experiments indicate the following: (1) The impact of random errors introduced into the sensitive areas was greater than that of errors themselves fixed in the randomly selected areas. From the perspective of statistical analysis, and by comparison, the impact of random errors introduced into the CNOP target area was greatest. (2) The initial errors with CNOP, CSV, or FSV patterns were likely to grow faster than random errors. (3) The initial errors with CNOP patterns in the CNOP target areas had the greatest impacts on the final verification forecasts.

Keywords: