顾及有效角动量信息的地球自转参数中长期预报方法

王乐洋; 苗威

doi:10.13203/j.whugis20220246

顾及有效角动量信息的地球自转参数中长期预报方法

王乐洋^{1, 2,},
苗威^{1, 2}

1.
东华理工大学测绘与空间信息工程学院,江西南昌,330013
2.
自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室,江西南昌,330013

基金项目:

国家自然科学基金 41874001

国家自然科学基金 42174011

江西省研究生创新基金 YC2021-S614

详细信息

作者简介:
王乐洋,博士,教授,主要研究方向为大地测量反演及大地测量数据处理。wleyang@163.com

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出版历程
- 收稿日期: 2022-05-04
- 网络出版日期: 2022-10-07
- 刊出日期: 2024-10-04

Medium-Long Term Forecasting Method for Earth Rotation Parameters Considering Effective Angular Momentum Information

WANG Leyang^{1, 2,},
MIAO Wei^{1, 2}

1.
School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang330013, China
2.
Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake,Ministry of Natural Resources,Nanchang330013, China

摘要

摘要:
地球自转参数(Earth rotation parameters, ERP)的预报精度一直是卫星自主导航和深空探测等用户关注的焦点。为了进一步提升用户的满意度,首先将激发域的有效角动量(effective angular momentum, EAM)的拟合残差序列卷积到大地测量观测域,并添加经验调节因子,构造出新的EAM拟合残差序列；然后将ERP和新的EAM拟合残差序列做减法,获得差异序列；最后联合差异序列和新的EAM残差序列的预报值,推出目标ERP序列的预报值。使用由ERP数据驱动的最小二乘外推和自回归(the least square extrapolation and autoregressive,LS+AR)模型,从2012年初到2021年初的预报实验结果显示,在1~ 365 d的预报窗口上,X方向和Y方向的调节因子均为0.7时,分别较传统方法平均提高31.86%和21.00%；日长变化的调节因子为1时,较传统方法平均提升15%。这用数值预报的方式验证了极移短时间尺度的约70%高频变化由EAM激发这一已有结论,并为ERP预报研究提供了参考。
- 地球自转参数 /
- LS+AR /
- 有效角动量 /
- 调节因子 /
- 中长期预报
Abstract:
Objectives
The prediction accuracy of Earth rotation parameters (ERP), as one of the hot spots in geodetic research, has been attracting attention from users such as autonomous satellite navigation and deep space exploration. In order to further satisfy the users' requirements, we combine the new effective angular momentum (EAM) data for the ERP prediction study.
Methods
The EAM fitted residual series in the excitation domain is first transferred to the geodetic observation domain, and an empirical adjustment factor is added to construct a new EAM fitted residual series. Then the fitted residual series of ERP and the fitted residual series of the new EAM are subtracted to obtain the difference series. Using the least square extrapolation and autoregressive (LS+AR) models driven by ERP data, prediction experiments are performed for the difference series and the new EAM residual series, respectively, and finally predictions for the target ERP series are jointly introduced.
Results
Based on experiments from the beginning of 2012 to the beginning of 2021, it is shown that an overall better result can be obtained for the adjustment factor of 0.7 in both the X-direction and Y-direction at a prediction horizons of 1-365 days, with an average improvement of 31.86% and 21.00% over the traditional method, respectively. The average improvement over the traditional method is 15% when the adjustment factor of length of day is 1.
Conclusions
This verifies the existing conclusion that about 70% of the high-frequency changes in the short time scale of polar motion are stimulated by EAM by means of numerical prediction, and provides a reference for ERP prediction research.
- Earth rotation parameters /
- LS+AR /
- effective angular momentum /
- adjustment factor /
- medium-long term
http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20220246

HTML全文

感谢IERS提供ERP数据,感谢GFZ地球系统建模小组提供EAM数据。

http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20220246

图 1 本文方法的流程图

Figure 1. Flowchart of the Proposed Method

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图 2 本文方法、传统方法和IERS公报A的MAE对比

Figure 2. MAE Comparison of the Proposed Method, Traditional Method and IERS Bulletin A

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图 3 本文方法的最佳阶数P图

Figure 3. Optimum Order P Figure of the Proposed Method

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图 4 本文方法和传统方法的AE图

Figure 4. AE Figures of the Proposed Method and Traditional Method

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表 1 本文的预报方案

Table 1 Prediction Schemes of This Paper

预报方案	调节因子	输入数据的长度
方案1	1	极移是3~10 aLOD是18 a
方案2	0.2
方案3	0.4
方案4	0.6
方案5	0.7
方案6	0.8
方案7	0.9
传统方法	0
IERS公报A

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表 2 本文方法、传统方法和IERS 公报A的MAE

Table 2 MAE Values of the Proposed Method, Traditional Method and Bulletin A

ERP	方案	不同时间窗口的MAE
ERP	方案	1 d	30 d	60 d	90 d	120 d	150 d	180 d	240 d	300 d	365 d
X/m（″）	方案1 (5 a)	0.183	7.118	12.317	15.692	17.583	18.237	18.016	19.516	22.094	22.531
	方案5 (5 a)	0.181	7.033	12.060	15.295	16.680	17.019	16.848	18.876	20.834	20.947
	方案6 (5 a)	0.181	7.005	12.055	15.359	16.906	17.339	17.159	18.954	21.217	21.306
	方案7 (5 a)	0.182	7.042	12.117	15.452	17.139	17.737	17.699	19.391	21.733	21.891
	传统方法	0.178	8.224	16.606	23.498	28.133	30.050	29.719	27.919	28.965	31.222
	IERS公报A	0.270	6.811	11.472	14.405	16.388	17.833	18.726	21.060	22.449	23.050
$i_{M A E}^{传统方法_方案 5}$ /%		－1.69	14.48	27.38	34.91	40.71	43.36	43.31	32.39	28.07	32.91
Y/m（″）	方案1 (4 a)	0.165	7.320	16.447	24.218	28.963	30.733	30.630	29.936	30.128	31.023
	方案2 (4 a)	0.150	5.811	11.393	15.326	17.470	18.345	18.835	19.043	19.299	21.387
	方案1 (5 a)	0.165	7.230	16.404	24.999	30.772	33.449	33.807	33.275	34.495	35.031
	方案5 (5 a)	0.144	5.301	10.296	14.216	16.713	18.778	20.067	21.263	21.362	24.049
	方案6 (5 a)	0.144	5.360	10.500	14.546	16.883	18.979	20.298	21.462	21.813	24.769
	方案7 (5 a)	0.146	5.407	10.713	14.906	17.051	18.853	20.229	21.580	22.464	25.337
	传统方法	0.148	5.926	12.560	19.313	24.405	27.214	27.725	24.789	25.874	28.685
	IERS公报A	0.198	4.306	7.719	11.871	16.645	20.769	23.676	25.825	25.613	25.041
$i_{M A E}^{传统方法_方案 5}$ /%		2.70	10.55	18.03	26.39	31.52	31.00	27.62	14.22	17.44	16.16
LOD/ms	方案1 (18 a)	0.019	0.154	0.168	0.178	0.183	0.196	0.212	0.231	0.260	0.277
LOD/ms	传统方法	0.029	0.166	0.192	0.213	0.224	0.242	0.256	0.268	0.285	0.293
$i_{M A E}^{传统方法_方案 1}$ /%		32.14	7.23	12.50	16.43	18.30	19.01	17.19	13.43	8.42	5.46

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参考文献(34)

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文章访问数: 1021
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顾及有效角动量信息的地球自转参数中长期预报方法

作者简介: 王乐洋,博士,教授,主要研究方向为大地测量反演及大地测量数据处理。wleyang@163.com

计量

出版历程

Medium-Long Term Forecasting Method for Earth Rotation Parameters Considering Effective Angular Momentum Information

计量

出版历程

目录

作者简介:
王乐洋,博士,教授,主要研究方向为大地测量反演及大地测量数据处理。wleyang@163.com