NewsGNSS atmospheric water vapor time series for NWP model assimilation
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GNSS atmospheric water vapor time series for NWP model assimilation

To evaluate the effectiveness of data assimilation routines of the NWP model across the three MAGDA demonstration sites in Romania, France, and Italy, six past case studies were chosen, two for each site (further details are available here). One of the NWP model inputs that are going to be used in MAGDA project is the GNSS atmospheric water vapor content. For these 6 case studies, the values are derived by processing the freely available data collected by the GNSS permanent stations within the two NWP domains, using GReD’s Breva software, which was extensively tested for water vapor retrieval in other previous projects.[1]
Figure 1: Location of the GNSS permanent stations belonging to the NWP domain of Italy and France demo sites.

For the Italy and France domain, a total of 397 third-party GNSS permanent stations were selected from various global, national, and regional permanent GNSS station networks.

Figure 2: Location of the GNSS permanent stations belonging to the NWP domain of Romania demo site.

For the Romania domain, a total of 74 third-party GNSS permanent stations were selected from various global, national, and regional permanent GNSS station networks.

Figure 3: GNSS atmospheric water vapor time series for one of the selected past case studies in Romania, with the spatial distribution of water vapor for one epoch during the heavy rainfall.

Figure 3 displays the temporal variation of GNSS atmospheric water vapor occurred during the rainfall events of 25th of June 2019 in Romania: each colour represents one of the 74 considered GNSS permanent stations. The time series clearly shows two peaks of atmospheric water vapor content during June 24th and 25th, followed by a general decreasing trend in content after the conclusion of the heaviest rainfall events. By spatially interpolating the GNSS atmospheric water vapor values with the GNSS meteorology post-processing routines of Breva software, it is possible to obtain water vapour maps. The maps allow for the identification of areas with high water vapor levels (e.g., the white area in the top-central part of the map), that are more suitable for the development of heavy rainfall events.

Author: Stefano Barindelli, Eugenio Realini

 

Links

Data assimilation into meteorological models– How MAGDA project is moving to help agriculture.

Keywords

GNSS, atmospheric water vapor, Breva software, NWP model, innovation, technology, drones, IoT, sensors, farming

Sources

Lagasio, M., Pulvirenti, L., Parodi, A., et al. (2019). Effect of the ingestion in the WRF model of different Sentinel-derived and GNSS-derived products: Analysis of the forecasts of a high impact weather event. European Journal of Remote Sensing, 52(sup4), 16-33.

Torcasio, R. C., Mascitelli, A., Realini, E., Barindelli, S., et al. (2023). The impact of global navigation satellite system (GNSS) zenith total delay data assimilation on the short-term precipitable water vapor and precipitation forecast over Italy using the Weather Research and Forecasting (WRF) model. Natural Hazards and Earth System Sciences, 23(11), 3319-3336.