Articles | Volume 23, issue 11
https://doi.org/10.5194/nhess-23-3319-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-3319-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2023
Research article |
| 01 Nov 2023
| 01 Nov 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
Rosa Claudia Torcasio
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), via del Fosso del Cavaliere 100, 00133 Rome, Italy
Alessandra Mascitelli
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), via del Fosso del Cavaliere 100, 00133 Rome, Italy
University “Gabriele d'Annunzio” of Chieti–Pescara, Center for Advanced Studies and Technology (CAST), Department of Advanced Technologies in Medicine and Dentistry (DTM&O), Via dei Vestini 31, 66100 Chieti, Italy
Eugenio Realini
Geomatics Research & Development srl (GReD), via Cavour 2, 22074 Lomazzo, Italy
Stefano Barindelli
Geomatics Research & Development srl (GReD), via Cavour 2, 22074 Lomazzo, Italy
Giulio Tagliaferro
BIPM Time Department, Sèvres, France
Silvia Puca
Civil Protection Department, via Vitorchiano 4, 00189 Rome, Italy
Stefano Dietrich
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), via del Fosso del Cavaliere 100, 00133 Rome, Italy
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), via del Fosso del Cavaliere 100, 00133 Rome, Italy
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Cited
15 citations as recorded by crossref.
- Impact of assimilating GNSS ground-based and Space-based observations on Extreme rainfall prediction over Karnataka, India S. Purwar et al.
- A Fusion Model Enabling Encryption of Ground-Based GNSS ZTD Datasets for Enhanced Precipitation Forecasting P. Wei et al.
- Assimilation of high-resolution GNSS tropospheric delays and its effects on a severe convective event nowcasting Y. Zheng et al.
- GNSS-CORS as water vapor sensors for local atmospheric monitoring: Comparing high-end geodetic-grade and low-cost stations in S Spain M. Garrido-Carretero et al.
- Analysis of atmospheric variations using GNSS signal as atmospheric sensor (PWV-GNSS) in the extreme rainfall events in Rio Grande do Sul (Brazil) in 2024 T. Gouveia et al.
- Synergistic Assimilation of Radar-Derived Precipitation and GNSS Zenith Total Delay to Improve Heavy Rainfall Forecasts: A Case Study Over Northern Germany in 2017 J. Li et al.
- Assimilation of anisotropic GNSS observations in the WRF model: preliminary results of the NEW-ARGENT project R. Torcasio et al.
- Forecasting convective precipitation over northern Italy: A comparison of lightning and GNSS-ZTD data assimilation S. Federico et al.
- A novel approach for assimilating GNSS tropospheric gradient information to improve numerical weather prediction Y. Zheng et al.
- Assimilating Ground-Based and High-Dynamic Airborne GNSS Zenith Total Delays Into Numerical Weather Predictions Z. Zhang et al.
- News from the BIPM laboratories—2023 M. Stock et al.
- Improving forecast of “21.7” Henan extreme heavy rain by assimilating high spatial resolution GNSS ZTDs M. Liu et al.
- Exploring the impacts of assimilating AMSR2 and GNSS PWV data on rainfall prediction in South China Y. Gong et al.
- A multi hazard extreme weather event in Southern Italy: Assessment and sensitivity tests of the WRF model E. Avolio et al.
- Assimilating WIVERN windpseudo-observations in WRF model: an application to the outstanding case of the Medicane Ianos S. Federico et al.
15 citations as recorded by crossref.
- Impact of assimilating GNSS ground-based and Space-based observations on Extreme rainfall prediction over Karnataka, India S. Purwar et al.
- A Fusion Model Enabling Encryption of Ground-Based GNSS ZTD Datasets for Enhanced Precipitation Forecasting P. Wei et al.
- Assimilation of high-resolution GNSS tropospheric delays and its effects on a severe convective event nowcasting Y. Zheng et al.
- GNSS-CORS as water vapor sensors for local atmospheric monitoring: Comparing high-end geodetic-grade and low-cost stations in S Spain M. Garrido-Carretero et al.
- Analysis of atmospheric variations using GNSS signal as atmospheric sensor (PWV-GNSS) in the extreme rainfall events in Rio Grande do Sul (Brazil) in 2024 T. Gouveia et al.
- Synergistic Assimilation of Radar-Derived Precipitation and GNSS Zenith Total Delay to Improve Heavy Rainfall Forecasts: A Case Study Over Northern Germany in 2017 J. Li et al.
- Assimilation of anisotropic GNSS observations in the WRF model: preliminary results of the NEW-ARGENT project R. Torcasio et al.
- Forecasting convective precipitation over northern Italy: A comparison of lightning and GNSS-ZTD data assimilation S. Federico et al.
- A novel approach for assimilating GNSS tropospheric gradient information to improve numerical weather prediction Y. Zheng et al.
- Assimilating Ground-Based and High-Dynamic Airborne GNSS Zenith Total Delays Into Numerical Weather Predictions Z. Zhang et al.
- News from the BIPM laboratories—2023 M. Stock et al.
- Improving forecast of “21.7” Henan extreme heavy rain by assimilating high spatial resolution GNSS ZTDs M. Liu et al.
- Exploring the impacts of assimilating AMSR2 and GNSS PWV data on rainfall prediction in South China Y. Gong et al.
- A multi hazard extreme weather event in Southern Italy: Assessment and sensitivity tests of the WRF model E. Avolio et al.
- Assimilating WIVERN windpseudo-observations in WRF model: an application to the outstanding case of the Medicane Ianos S. Federico et al.
Saved (final revised paper)
Latest update: 02 May 2026
Short summary
This work shows how local observations can improve precipitation forecasting for severe weather events. The improvement lasts for at least 6 h of forecast.
This work shows how local observations can improve precipitation forecasting for severe weather...
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