An assessment of ground‐based GNSS Zenith Total Delay observation errors and their correlations using the Met Office UKV model

Abstract

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.

Characteristics and Effects of Ground-Based GNSS Zenith Total Delay Observation Errors in the Convective-Scale Model

“…The observation error and precipitable water showed a similar seasonal variation, and the observation error in summer was approximately 2.1 times larger than that in winter. In contrast with a study using the same observation error for each season and station (Bennitt et al 2017), utilizing different observation errors for different seasons or months was considered to be more appropriate for the Korean climate. Three one-month experiments were performed on the LDAPS using data from July 2016 for stable quality.…”

“…These errors include measurement, observation operator (forward model), representativeness, and quality control error Waller et al 2015). In addition to devicebased errors, observation errors also occur during data processing because of the mapping functions that convert the slant path signals of multiple satellites into the zenith direction (Bennitt et al 2017;Lindskog et al 2017).…”

“…Generally, errors in non-synoptic data use the standard deviation of the innovation, which is determined as the difference between the observation and model data (Boniface et al 2009;Mahfouf et al 2015). In the United Kingdom (UK) Met Office convective-scale forecasting model, almost no difference was found between the standard deviation of the innovations and the observation errors calculated by Desroziers et al (2005), and these results were very similar for the summer and winter seasons (Bennitt et al 2017). Furthermore, Poli et al (2007) found that the overall ZTD observation errors for the entire European region in the winter and summer were 10 and 20 mm, respectively, and the ratios of the background error to the observation error were 1.0 and 0.8, respectively.…”

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

“…Then, the corrections were estimated as the mean values of the ZTD differences and they were subtracted from the ZTD observations that were lined up for data assimilation. Even though this method provides statistical corrections, this is a standard bias correction approach for ZTD data that proved to be successful in reducing the observations-model divergences and capturing the systematic errors between the ZTD observations and model forecasts [16,[18][19][20][21][22][23][24][25]28]. The last stage of the ZTD data pre-processing included a selection algorithm based on the following conditions: (a) The formal ZTD error to be lower than the standard deviation of the difference between the observed and modeled ZTD, (b) the ZTD difference between observations and model output to be lower than five times the ZTD formal error, and (c) the difference between the receiver height and the model's orography to be below 100 m. Similar criteria have been applied in previous studies [19][20][21][22][23][24][25].…”

“…Boniface et al [24] identified that the impact of ZTD assimilation in AROME model depends on the rainfall synoptic conditions, while Arriola et al [16] highlighted the importance of ZTD observations processing before the assimilation application. More recent studies focused on investigating different observational bias correction methods and on estimating spatiotemporal correlations of observations errors for application on ZTD data used for data assimilation [25][26][27].The literature review shows that improvements in the precipitation forecast skill may be gained by assimilating ZTD observations into NWP models. However, regional studies using the WRF modeling system are relatively rare (e.g., Rohm et al [28]), while no research focusing on Greece has ever been conducted.…”