[G01-1] International terrestrial reference frame

[G01-1-03] JTRF2014: A Time Series Representation of the ITRF

^○Richard Gross, Claudio Abbondanza, T. Mike Chin, Mike Heflin, Jay Parker, Xiaoping Wu (Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA)

invited

The Jet Propulsion Laboratory (JPL) is pursuing an approach to determining ITRF-like terrestrial reference frames based upon the use of a Kalman filter/smoother. Kalman filters are commonly used to estimate the parameters of some system when a stochastic model of the system is available and when the data contain noise. For the purpose of determining a terrestrial reference frame, the system consists of the positions and velocities of geodetic observing stations and associated EOPs along with their full covariance matrices. The data consist of time series of observed VLBI, SLR, GNSS, and DORIS station positions and EOPs along with the data measurement covariance matrices. In addition, measurements from ground surveys of the positions of reference marks of co-located stations are used as constraints to tie the technique-specific measurements to each other. JPL's Kalman filter and smoother for reference frame determination (KALREF) combines these measurements to determine ITRF-like reference frames subject to constraints imposed on the allowed evolution of the station positions. KALREF includes options to model the station motion as linear, linear and annual, or linear, annual, and semiannual. Through the use of stochastic models for the process noise, the station positions can be constrained to follow these models of the station motion (by setting the process noise to zero), to recover the observed station positions (by setting the process noise to a large value), or to follow a smoothed path (by setting the process noise to some intermediate value). The time series approach to determining terrestrial reference frames that is being pursued at JPL will be described along with its use to determine JTRF2014, JPL's realization of a terrestrial reference frame using the ITRF2014 input data sets.