Abstract
The derivation of global navigation satellite systems (GNSSs) tropospheric products is nowadays a state-of-the-art technique that serves both research and operational needs in a broad range of applications in meteorology. In particular, GNSS zenith tropospheric delay (ZTD) data assimilation is widely applied in Europe to enhance numerical weather predictions (NWPs). The current study presents the first attempt at introducing assimilation of ZTDs, derived from more than 48 stations of the Hellenic GNSS network, into the operational NWP system of the National Observatory of Athens (NOA) in Greece, which is based on the mesoscale Weather Research and Forecasting (WRF) model. WRF was applied during seven high-impact precipitation events covering the dry and wet season of 2018. The simulation employing the ZTD data assimilation reproduces more accurately, compared to the control experiment, the observed heavy rainfall (especially for high precipitation events, exceeding 20 mm in 24h) during both dry and wet periods. Assimilating ZTDs also improves the simulation of intense (>20 mm) convective precipitation during the time window of its occurrence in the dry season, and provides a beneficial influence during synoptic-scale events in the wet period. The above results, which are statistically significant, highlight an important positive impact of ZTD assimilation on the model’s precipitation forecast skill over Greece. Overall, the modelling system’s configuration, including the assimilation of ZTD observations, satisfactorily captures the spatial and temporal distribution of the observed rainfall and can therefore be used as the basis for examining further improvements in the future.
Highlights
The use of global navigation satellite systems (GNSSs) is essential in a variety of fields that require precise location and time information, including aviation (e.g., Sabatini et al [1]), transportation (e.g, Kubo et al [2]), search and rescue services (e.g, Molina et al [3]), agriculture (e.g., Kahveci et al [4]), and maritime operations (e.g., Ostolaza et al [5])
The frequency bias (FBIAS) values demonstrate that the model underestimates the observed frequency of higher than 20 mm daily precipitation, whereas it slightly overestimates the frequency of the observed daily rainfall for the lower than 20 mm thresholds, except those that are greater than 2 mm (5 mm and 10 mm) during the CTL experiment in the wet season (Figure 3)
This impact is more profound for heavy precipitation (>20 mm), for which statistically significant improvements are provided by the zenith tropospheric delay (ZTD) assimilation concerning the probability of detection, the false alarm ratio, the quality of forecasts (ETS), and the magnitude of errors (MAE)
Summary
The use of global navigation satellite systems (GNSSs) is essential in a variety of fields that require precise location and time information, including aviation (e.g., Sabatini et al [1]), transportation (e.g, Kubo et al [2]), search and rescue services (e.g, Molina et al [3]), agriculture (e.g., Kahveci et al [4]), and maritime operations (e.g., Ostolaza et al [5]). The methodology is based on the fact that the radio signals transmitted from the satellites to the receivers on the ground are delayed when propagating through the troposphere due to the presence of dry gases and water vapor [7]. Special meteorological interest derives from the near-real time (NRT) ZTDs, which are estimated based on raw GNSS observations. The ZTD is a standard GNSS product expressing the total signal delay in the zenith direction above a receiver [6,15]. This vertical lag contains information on the total columnar amount of water vapor [16]
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