future improvements in eop prediction
Download
Skip this Video
Download Presentation
Future improvements in EOP prediction

Loading in 2 Seconds...

play fullscreen
1 / 29

Future improvements in EOP prediction - PowerPoint PPT Presentation


  • 76 Views
  • Uploaded on

Future improvements in EOP prediction. W iesław Kosek Space Research Centre, Polish Academy of Sciences, Warsaw, Poland. Geodesy for Planet Earth, Buenos Aires , Aug. 31 – Sep. 4, 2009. Summary: - introduction - input data - EOP prediction algorithms

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Future improvements in EOP prediction' - kasper-chase


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
future improvements in eop prediction

Future improvements in EOP prediction

Wiesław Kosek

Space Research Centre, Polish Academy of Sciences,

Warsaw, Poland

Geodesy for Planet Earth, Buenos Aires , Aug. 31 – Sep. 4, 2009

slide2

Summary:

- introduction

- input data

- EOP prediction algorithms

- EOPPCC results

- possible causes of EOP prediction errors

- prediction of PM by Kalman filter

- MAR prediction of UT1-UTC

- application of the wavelet transform filter

- conclusions

slide3

Determination errors ofx, y and UT1-UTC (EOPC04_IAU2000.62-now)data

in 1968-2008

~3÷4 mm

EOP mean prediction errors and their ratio to determination errors in 2008

slide4

Future EOP data are neededto compute real-time transformation between the celestial and terrestrial reference frames. This transformation is important for the NASA Deep Space Network, which is an international network of antennas that supports:

- interplanetary spacecraft missions,

- radio and radar astronomy observations,

- selected Earth-orbiting missions.

slide5
DATA
  • x, y, UT1-UTC and Δdata from the IERS: EOPC04_IAU2000.62-now (1962 - 2009.6), Δt = 1 day, http://hpiers.obspm.fr/iers/eop/eopc04_05/,
  • Equatorial and axial components of atmospheric angular momentum from NCEP/NCAR, aam.ncep.reanalysis.* (1948 - 2009.3) Δt = 0.25 day, ftp://ftp.aer.com/pub/anon_collaborations/sba/,
  • Equatorial components of ocean angular momentum: c20010701.oam (Jan. 1980 - Mar. 2002) Δt = 1 day, ECCO_kf066b.oam (Jan. 1993 - Dec. 2008), Δt = 1 day, http://euler.jpl.nasa.gov/sbo/sbo_data.html,
slide6

Prediction of x, y by combination of the LS+AR method

x, y

LS model

x, y

LSresiduals

x, y

LS

AR

Prediction of

x, y

LS extrapolation

of x, y

AR prediction of

x, y residuals

slide7

Prediction of UT1-UTC by combination of the LS+AR method

diff

Δ

UT1-TAI

-- leap seconds

UT1-UTC

-- Tides

Δ- δΔ

LS model

Δ- δΔ

LSresiduals

Δ- δΔ

LS

AR

Prediction of

Δ- δΔ

LS extrapolation

ofΔ- δΔ

AR prediction of

Δ- δΔ residuals

+ Tides

Prediction of

Δ

Prediction of

UT1-TAI

Prediction of

UT1-UTC

int

+ leap seconds

slide8

Prediction of UT1-UTC by combination of the DWT+AC method

diff

Δ

UT1-TAI

-- leap seconds

UT1-UTC

-- Tides

DWT BPF

Δ- δΔ

Δ-δΔ(ω1), Δ-δΔ(ω2),…, Δ-δΔ(ωp)

AC

AC

AC

Prediction of

Δ- δΔ

Δ-δΔ(ω1) + Δ-δΔ(ω2) + … + Δ-δΔ(ωp)

+ Tides

Prediction of

Δ

Prediction of

UT1-TAI

Prediction of

UT1-UTC

int

+ leap seconds

slide10
Mean prediction errors of x (thin line), y (dashed line) pole coordinates data computed by the LS and LS+AR methods in 1984-2009
the chosen mae of pole coordinates data from the eoppcc kalarus et al prepared to j geodesy
The chosen MAE of pole coordinates data from the EOPPCC (Kalarus et al., prepared to J. Geodesy)
the chosen mae of ut1 utc and data from the eoppcc kalarus et al prepared to j geodesy
The chosen MAE of UT1-UTC and Δ data from the EOPPCC (Kalarus et al., prepared to J. Geodesy)
slide15

Amplitudes and phases of the most energetic oscillations in x, y pole coordinates data

Chandler

Amplitudes

Annual

Semi-annual

bold line – prograde

thin line - retrograde

Chandler

Phases

Annual

Semi-annual

slide16

Amplitudes and phases of the most energetic oscillations in Δ-δΔ data

Amplitudes

Annual

Semi-annual

Semi-annual

Phases

Annual

x y pole coordinates model data computed from fluid excitation functions
x, y pole coordinates model data computed from fluid excitation functions

Differential equation of polar motion:

- pole coordinates,

  • equatorial fluid excitation functions (AAM, OAM),
  • complex-valued Chandler frequency,
  • where and is the quality factor

Approximate solution of this equation in discrete time moments can be obtained using the trapezoidal rule of numerical integration:

slide18

LS+AR prediction errors of IERS x, y pole coordinates data and of x, y pole coordinates model data computed from AAM, OAM and AAM+OAM excitation functions

slide19

The mean LS+AR prediction errors of IERS x, y pole coordinates data (black), and of x, y pole coordinates model data computed from AAM, OAM and AAM+OAM excitation functions

x y pole coordinates data prediction by the kalman filter

The linear state equation (Gelb 1974):

x, y pole coordinates data prediction by the Kalman filter

- state vector

- observation vector

equatorial

excitation

functions

residual

excitation

functions

pole

coordinates

- constant coefficient matrix,

- constant coefficients

- zero mean excitation process satisfying:

prediction of the state vector:

variances of white noise processes

slide22

Prediction of Δ-ΔR data by LS+AR and LS+MAR algorithms (Niedzielski and Kosek, J. Geodes 2008)

εAAMχ3

residuals

AAMχ3

LS model

Δ-ΔRLSmodel

ε(Δ-ΔR)

residuals

&

Δ-ΔR

AAMχ3

AR

LS

AR

prediction

ε(Δ-ΔR)

MAR

Δ-ΔR

LS

extrapolation

Prediction

of Δ-ΔR

MAR

prediction

ε(Δ-ΔR)

slide24
The frequency components of x (black), y (blue) pole coordinates data computed by the Shannon wavelet decomposition

longer

period

Ch+An

Sa

shorter

period

slide25

The mean LS+AR prediction errors of IERS x, y pole coordinates data, and x, y pole coordinates model data computed by summing the chosen DWTBPF components

the frequency components of data with indices i 1 13 computed by the meyer wavelet decomposition
The frequency components of Δ-δΔ data with indices i=1,...,13,computed by the Meyer wavelet decomposition

longer

period

An

Sa

shorter

period

slide27

The mean LS+AR prediction errors of IERS UT1-UTC data, and UT1-UTC model data computed by summing the chosen DWTBPF frequency components

conclusions
CONCLUSIONS
  • The influence of variable amplitudes and phases of the most energetic oscillations in EOP data on their short term prediction errors is negligible.
  • Short term prediction errors of pole coordinates data are caused by wideband short period oscillations in these data. Some big prediction errors of pole coordinates data in 1981-82 are caused by wideband oscillations in ocean excitation functions and in 2006-07 are caused by wideband oscillations in joint atmospheric-ocean excitation functions.
  • Short term prediction errors of UT1-UTC are caused by short period wideband oscillations in these data.
  • Recommended prediction method for pole coordinates data is the combination of the least squares and autoregressive prediction.
  • Recommended prediction method for UT1-UTC data is the Kalman filter.
  • Longer term variations of UT1-UTC data can be predicted successfully by combination of the LS and multivariate autoregressive method.
  • To reduced short term EOP prediction errors Wavelet transform low pass filter can be used.
slide29
Thank You

Acknowledgements

The research was financed by Polish Ministry of Science and Education through the grant no. N N526 160136 under leadership of Dr Tomasz Niedzielski.

ad