Abstract
Abstract. Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.
Highlights
Data assimilation in numerical weather prediction (NWP) optimally blends observations with an atmospheric model in order to obtain the spatial distribution of atmospheric variables and to produce the best possible model initial state
It has earlier been demonstrated that adaption of variational bias correction (Dee, 2005) to be used together with Global Navigation Satellite System (GNSS) zenith total delays (ZTDs) data was successful for handling systematic observation errors (Sánchez-Arriola et al, 2016)
In addition we used the degrees of freedom for signal (DFS) to study the relative impact of observations in the assimilation system (Chapnik et al, 2006)
Summary
Data assimilation in numerical weather prediction (NWP) optimally blends observations with an atmospheric model in order to obtain the spatial distribution of atmospheric variables and to produce the best possible model initial state. The forecast model used within MetCoOp is developed in the framework of the shared Aire Limitée Adaptation dynamique Developpement InterNational (ALADIN) High Resolution Limited Area Model (HIRLAM) NWP system. ZTD observations obtained from the E-GVAP network of ground-based GNSS receivers contain horizontally dense information and are available with a temporal resolution of up to 5 min and have the potential to provide humidityrelated data for kilometre-scale short-range weather forecasting. It has earlier been demonstrated that adaption of variational bias correction (Dee, 2005) to be used together with GNSS ZTD data was successful for handling systematic observation errors (Sánchez-Arriola et al, 2016). Aspects of the GNSS ZTD observation handling and data assimilation was investigated The evaluation includes both an individual case study and statistics based on extended parallel experiments.
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