Influences of Sensitive Initial Error on the Numerical Forecast of Typhoon Kammuri (0809)

Huang Jiangping; Dong Peiming; Li Chao; Liu Jun

Vol.24, NO.4, 2013

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Article Navigation > Journal of Applied Meteorological Science > 2013 > 24(4): 425-434

Huang Jiangping, Dong Peiming, Li Chao, et al. Influences of sensitive initial error on the numerical forecast of typhoon Kammuri (0809). J Appl Meteor Sci, 2013, 24(4): 425-434.

Citation:

Huang Jiangping, Dong Peiming, Li Chao, et al. Influences of sensitive initial error on the numerical forecast of typhoon Kammuri (0809). J Appl Meteor Sci, 2013, 24(4): 425-434.

Huang Jiangping, Dong Peiming, Li Chao, et al. Influences of sensitive initial error on the numerical forecast of typhoon Kammuri (0809). J Appl Meteor Sci, 2013, 24(4): 425-434.

Citation:

Huang Jiangping, Dong Peiming, Li Chao, et al. Influences of sensitive initial error on the numerical forecast of typhoon Kammuri (0809). J Appl Meteor Sci, 2013, 24(4): 425-434.

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Influences of Sensitive Initial Error on the Numerical Forecast of Typhoon Kammuri (0809)

Huang Jiangping^1,2,
Dong Peiming^{1,3
,
,},
Li Chao^4,5,
Liu Jun⁶

1.
Beijing Aviation Meteorological Institute, Beijing 100085
2.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
3.
Numerical Weather Prediction Center of CMA, Beijing 100081
4.
Chinese Academy of Meteorological Sciences, Beijing 100081
5.
University of Chinese Academy of Sciences, Beijing 100049
6.
PLA Unit No.94578, Yancheng 224003

Received Date: 2012-09-07
Rev Recd Date: 2013-04-15
Publish Date: 2013-08-31

Abstract

Abstract

Initial error is one of the key factors that have great effects on the accuracy of numerical forecast. To study the characteristics of initial error and its influence on the numerical prediction, an analysis procedure of the sensitive initial error of numerical forecast is developed based on WRF adjoint model and is used in the investigation of typhoon Kammuri (0809). The validity of the linear assumption on the study of typhoon case is firstly assessed prior to discussing any adjoint analysis results. It is done by evaluating the evolution differences of the perturbation between linear and nonlinear development, showing that the nonlinear perturbation evolution is well represented by the linear assumption during 24-h forecast. The sensitive initial error is then constructed using the information derived from adjoint sensitivity analysis, finding that the reference coefficient from 0.01 to 0.08 is proper to build the sensitive initial error. The result of 0.08 is the best in this case study. The numerical forecast error could be reduced and the prediction bias of typhoon trace could be improved greatly by removing the sensitive initial error from the initial field. This effect of the sensitive initial error derived from 24-h numerical forecast error affects the numerical forecast even within 60 hours. In addition, the analysis reveals that the sensitive initial error of regional short-term numerical forecast concentrates mainly around the weather system. It goes with typhoon circle and the pattern is almost consistent for all physical variables. The sensitive initial error in the middle-upper troposphere has slightly more contribution to the forecast than that in lower troposphere. Comparing the contribution of different physical variable, it is found that wind is the main contributor with pressure and humidity following.
- WRF adjoint model;
- typhoon Kammuri;
- initial error;
- numerical forecast

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References(18)

Figures and Tables

图 1 850 hPa风场u分量小扰动24 h演变结果 (单位：m·s^-1)

(a) 线性演变, (b) 非线性演变

Fig. 1 24-h development of u perturbation at 850 hPa (unit: m·s^-1)

(a)linear development, (b)nolinear development

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Fig. 2 The dry energy of 24-h linear and nolinear perturbation development at model levels

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Fig. 3 The bias of typhoon track between experiments and NCEP reanalysis

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图 4 控制试验CNR和敏感性试验EXP4 δ=0.4735模式面扰动气压 (单位：Pa) 和500 hPa风场u分量 (单位：m·s^-1) 24 h预报误差对比(黑框区域为目标区)

(a) CNR扰动气压, (b) EXP4扰动气压, (c) CNR u分量, (d) EXP4 u分量

Fig. 4 The contrast of 24-h forecast error of pressure perturbation (unit:Pa) at δ=0.4735 and u perturbation (unit:m·s^-1) at 500 hPa between control experiment CNR and sensitivity experiment EXP4 (the black box denotes the target area)

(a) pressure perturbation in CNR, (b) pressure perturbation in EXP4, (c) u perturbation in CNR, (d) u perturbation in EXP4

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图 5 敏感初始误差在700 hPa的水平分布 (扰动气压在δ=0.7365模式面上，背景均为水平风矢量)

(a) u分量 (单位：m·s^-1)，(b) v分量 (单位：m·s^-1)，(c) 风矢量, (d) 扰动气压 (单位：Pa)，(e) 位温 (单位：K)，(f) 比湿 (单位：g·kg^-1)

Fig. 5 The horizontal distribution of sensitive initial error at 700 hPa (expect that pressure perturbation is at δ=0.7365, and all with background wind vector)

(a) u (unit: m·s^-1), (b) v (unit: m·s^-1), (c) wind vector, (d) pressure perturbation (unit: Pa), (e) potential temperature(unit: K), (f) humility (unit: g·kg^-1)

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图 6 沿20°N台风中心敏感初始误差的垂直剖面

(a) u分量 (单位：m·s^-1)，(b) v分量 (单位：m·s^-1)，(c) 扰动气压 (单位：Pa)，(d) 位温 (单位：K)，(e) 比湿 (单位：g·kg^-1)

Fig. 6 The section of sensitive initial error along 20°N

(a) u (unit:m·s^-1), (b) v (unit: m·s^-1), (c) pressure perturbation (unit: Pa), (d) potential temperature (unit: K), (e) humility(unit: g·kg^-1)

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图 7 不同时刻台风路径预报

Fig. 7 The forecast of typhoon track for different initial time

(a) 0000 UTC 5 Aug 2008, (b) 1200 UTC 5 Aug 2008, (c) 0000 UTC 6 Aug 2008

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Table 1 The experiment design and the integrated dry energy of 24-h forecast error in the target area

试验方案	参考系数	24 h预报误差干能量 /(10⁵ J)	误差减少率/%
CNR		15.4869
EXP1	0.01	14.3733	7.19
EXP2	0.03	12.6838	18.10
EXP3	0.05	11.5815	25.22
EXP4	0.08	10.5205	32.07

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Table 2 The dry energy of forecast error

试验	24 h预报误差干能量/(10⁵J)	误差减少率/%
试验1	12.7473	17.69
试验2	12.3248	20.42
EXP4	10.5205	32.07
CNR	15.4869

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Table 3 The dry energy of forecast error

物理量	24 h预报误差干能量/(10⁵J)	减少率/%
水平风场	11.3685	26.48
位温	13.9362	10.01
扰动气压	14.2984	7.67
湿度	15.2984	1.22

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Table 4 The integrated dry energy of 24-h perturbation between linear and nolinear development for different initial time

起报时间	非线性演变/(10⁵J)	线性演变/(10⁵J)	误差/%
2008-08-03T18:00	1.8083	1.40394	22.36
2008-08-05T00:00	3.9387	3.63357	14.85
2008-08-06T00:00	3.0678	1.98890	35.16

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