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An assessment of ground‐based GNSS Zenith Total Delay observation errors and their correlations using the Met Office UKV model
Author(s) -
Bennitt G. V.,
Johnson H. R.,
Weston P. P.,
Jones J.,
Pottiaux E.
Publication year - 2017
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3097
Subject(s) - zenith , gnss applications , data assimilation , meteorology , environmental science , numerical weather prediction , geodesy , remote sensing , global positioning system , geography , computer science , telecommunications
Ground‐based GNSS Zenith Total Delay (ZTD) observations have been assimilated into the Met Office numerical weather prediction (NWP) models since 2007, and into the Met Office UKV model since its introduction in 2009. The UKV model is a 1.5 km resolution convective‐scale model and uses a 3D‐Var assimilation system. There is a plan to upgrade the UKV assimilation system from 3D‐Var to 4D‐Var in the near future, giving the opportunity to increase the temporal resolution of ZTDs assimilated. The ZTD observation‐error covariances used operationally are assumed to be uncorrelated in both space and time despite the expectation that ZTDs have temporally and spatially correlated observation errors due to the production method (e.g. batch processing using a sliding window and (time) relative constraints). To assess whether these error correlations should be accounted for in order to use ZTDs at higher temporal resolution, a posteriori diagnostics to estimate the extent of temporal and spatial error correlations in ZTD observations over the UK, BENELUX and Northern France are used. Over two separate month‐long periods, we find that ZTD observations within the same processing batch are correlated, and that correlations persist between different batches to at least 1 h. Spatially, ZTD observations are found to be correlated to a minimum of 62.5 km. We find that the extent of the diagnosed correlation between observations separated in space and time is affected by the value of the relative constraints parameter chosen by the processing centre in the GNSS processing software. The impact of the relative constraints parameter on the diagnosed error variances is greater than that revealed by innovation statistics alone.