22 Feb 2023
 | 22 Feb 2023
Status: this preprint is currently under review for the journal NHESS.

The impact of GNSS Zenith Total Delay data assimilation on the short-term precipitable water vapor and precipitation forecast over Italy using the WRF model

Rosa Claudia Torcasio, Alessandra Mascitelli, Eugenio Realini, Stefano Barindelli, Giulio Tagliaferro, Silvia Puca, Stefano Dietrich, and Stefano Federico

Abstract. The impact of assimilating GNSS-ZTD (Global Navigation Satellite Systems - Zenith Total Delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, characterized by several moderate to intense precipitation events. The WRF (Weather Research and Forecasting) model 4.1.3 is used with its 3DVar data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area.

Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation.

The water vapor forecast is verified for the forecast hours 1–6 h after the last data assimilation time, while the precipitation forecast is verified in the phases 0–3 h and 3–6 h after the last data assimilation time. More than 3000 rain gauges spread over Italy were used to verify the precipitation forecast.

The application of GNSS-ZTD data assimilation to a case study showed an improvement of the precipitation forecast in different ways, the main drawback being the prediction of false alarms.

Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on the precipitable water vapor and rainfall forecast, with an improvement of the performance up to 6 hours, and with statistically significant results for moderate to intense rainfall thresholds (25–30 mm/3 h).

Rosa Claudia Torcasio et al.

Status: open (until 15 Apr 2023)

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Rosa Claudia Torcasio et al.

Rosa Claudia Torcasio et al.


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Short summary
This work shows how local observations can improve the precipitation forecast for severe weather events.