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UT1-UTC forecasting method based on time sequence intervention model

A UT1-UTC and time series technology, which is applied in weather forecasting, meteorology, measuring devices, etc., can solve problems such as not considering the impact of EPR forecasting, and achieve the effect of improving the accuracy of short-term forecasting

Pending Publication Date: 2022-07-01
SHANDONG UNIV
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Problems solved by technology

[0003] However, the above technical solutions do not take into account the impact of intervention events such as El Niño and La Niña on EPR forecasting

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  • UT1-UTC forecasting method based on time sequence intervention model
  • UT1-UTC forecasting method based on time sequence intervention model
  • UT1-UTC forecasting method based on time sequence intervention model

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Embodiment Construction

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0032] Firstly, the UT1-UTC sequence is preprocessed to obtain the UT1-UTC stationary sequence; secondly, the least squares LS fitting is performed on the UT1-UTC stationary sequence; then based on the LS fitting residuals, the autoregressive AR model is fitted; then based on the AR Model fitting residuals to establish an intervention model; finally, the LS fitting model, AR fitting model, and intervention model are added to obtain the final UT1-UTC forecast sequ...

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Abstract

The invention discloses a UT1-UTC forecasting method based on a time sequence intervention model, and the method comprises the following operation steps: firstly, carrying out the preprocessing of a UT1-UTC sequence, and removing the second jump and the simple harmonic tidal term of the earth; secondly, carrying out least square (LS) fitting on the UT1-UTC stationary sequence; carrying out auto-regression AR model fitting to obtain an AR fitting model, and calculating an AR fitting residual error; secondly, in an intervention event, an intervention model is established based on AR fitting residual errors; and finally, adding the LS fitting model, the AR fitting model and the intervention model to obtain a UT1-UTC forecast stationary sequence, and adding second jump and earth simple harmonic tidal terms to obtain a UT1-UTC forecast final sequence. The result shows that when the ENSO phenomenon occurs, the constructed intervention model can remarkably improve the short-term forecasting precision of UT1-UTC, forecasting is carried out for 30 days, and compared with a non-intervention model, the improvement degree reaches 0.02-0.2 ms.

Description

technical field [0001] The invention belongs to the technical field of surveying and mapping science and technology, and specifically relates to a UT1-UTC forecasting method based on a time series intervention model. Background technique [0002] The earth rotation parameter ERP includes polar shift, UT1-UTC / LOD, and is an important parameter to achieve precise satellite orbit determination. High-precision ERP prediction is an important factor for improving the high-precision autonomous orbit determination of navigation satellites and the autonomous positioning accuracy of deep space spacecraft. Adaptive matching of several types of key parameters required for ERP forecasting improves the accuracy of ERP forecasting. The invention adopts the LS+AR (leastsquare+auto-regressive) method for ERP forecasting, and provides an empirical setting criterion for the training length of the ERP forecasting time series; the difference method is used to improve the stationarity of the ERP...

Claims

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Application Information

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IPC IPC(8): G01W1/10G06F30/20
CPCG01W1/10G06F30/20Y02A90/10
Inventor 聂文锋赵一沣薛慧杰徐天河
Owner SHANDONG UNIV
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