A Radar-Based Fast Code for Rainfall Nowcasting over the Tuscany Region
Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally effi...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2467 |
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| author | Alessandro Mazza Andrea Antonini Samantha Melani Alberto Ortolani |
| author_facet | Alessandro Mazza Andrea Antonini Samantha Melani Alberto Ortolani |
| author_sort | Alessandro Mazza |
| collection | DOAJ |
| description | Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. |
| format | Article |
| id | doaj-art-0395e1e2d4ab4026a74c7de5651edb1b |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Remote Sensing |
| spelling | doaj-art-0395e1e2d4ab4026a74c7de5651edb1b2025-08-20T03:07:58ZengMDPI AGRemote Sensing2072-42922025-07-011714246710.3390/rs17142467A Radar-Based Fast Code for Rainfall Nowcasting over the Tuscany RegionAlessandro Mazza0Andrea Antonini1Samantha Melani2Alberto Ortolani3LaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, ItalyCNR Institute for BioEconomy, Via Madonna del Piano 10, 50019 Sesto Fiorentino, ItalyLaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, ItalyLaMMA Consortium, Via Madonna del Piano 10, 50019 Sesto Fiorentino, ItalyAccurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany.https://www.mdpi.com/2072-4292/17/14/2467nowcastingradar imagesLagrangian models |
| spellingShingle | Alessandro Mazza Andrea Antonini Samantha Melani Alberto Ortolani A Radar-Based Fast Code for Rainfall Nowcasting over the Tuscany Region Remote Sensing nowcasting radar images Lagrangian models |
| title | A Radar-Based Fast Code for Rainfall Nowcasting over the Tuscany Region |
| title_full | A Radar-Based Fast Code for Rainfall Nowcasting over the Tuscany Region |
| title_fullStr | A Radar-Based Fast Code for Rainfall Nowcasting over the Tuscany Region |
| title_full_unstemmed | A Radar-Based Fast Code for Rainfall Nowcasting over the Tuscany Region |
| title_short | A Radar-Based Fast Code for Rainfall Nowcasting over the Tuscany Region |
| title_sort | radar based fast code for rainfall nowcasting over the tuscany region |
| topic | nowcasting radar images Lagrangian models |
| url | https://www.mdpi.com/2072-4292/17/14/2467 |
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