Speaker
Dr
Anna Klos
(Military University of Technology, Warsaw, Poland)
Description
A task of homogenisation of tropospheric data estimated in GNSS processing is based on a proper detection of possible offsets due to changes in hardware or equipment, earthquakes or any other artificial events. This task is aimed at determination of reliable trend and its uncertainty that is used in climate studies since any undetected offset influences the value of trend and increases its error. In this research, we used Monte Carlo simulations of 25 years long data of two different noise types: white and autoregressive. These were chosen, since white noise is being widely assumed in analysis of tropospheric data and autoregressive noise is what tropospheric data follows in reality. The seasonal signals with amplitudes between 5 and 40 mm’s along with few strictly defined offsets were added to simulated series, which were then subjected to homogenisation task. We made blinded tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signals on indicated epochs was noticed here. The autoregressive series were much more demanding when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within.
Primary author
Dr
Anna Klos
(Military University of Technology, Warsaw, Poland)
Co-authors
Dr
Addisu Hunegnaw
(University of Luxembourg, Luxembourg)
Prof.
Felix Norman Teferle
(University of Luxembourg, Luxembourg)
Dr
Furqan Ahmed
(1) University of Luxembourg, Luxembourg, 2) IGN, France)
Dr
Janusz Bogusz
(Military University of Technology, Warsaw, Poland)
Mr
Maciej Gruszczynski
(Military University of Technology, Warsaw, Poland)
Mrs
Marta Gruszczynska
(Military University of Technology, Warsaw, Poland)
