Analysis of Precipitation Totals Based on Radar and Rain Gauge Data
The relationship between radar reflectivity (Z) and rainfall intensity (R) plays a crucial role in estimating precipitation and serves as a foundation for flood risk assessment. However, empirical Z–R relationships often introduce considerable uncertainty, making the correction of rainfall estimatio...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2157 |
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| author | Karol Dzwonkowski Ireneusz Winnicki Sławomir Pietrek Jolanta Siewert |
| author_facet | Karol Dzwonkowski Ireneusz Winnicki Sławomir Pietrek Jolanta Siewert |
| author_sort | Karol Dzwonkowski |
| collection | DOAJ |
| description | The relationship between radar reflectivity (Z) and rainfall intensity (R) plays a crucial role in estimating precipitation and serves as a foundation for flood risk assessment. However, empirical Z–R relationships often introduce considerable uncertainty, making the correction of rainfall estimation errors a key challenge in remote-sensing-based applications. Developing an effective approach to reduce these deviations is, therefore, essential to improve the accuracy of radar-based precipitation measurements. This study aims to develop a methodology for analyzing radar-derived precipitation using dual-polarization radar measurements, with validation based on rain gauge observations. Three well-established Z–R relationships—Marshall–Palmer, Muchnik, and Joss—were applied to radar reflectivity values measured at two heights, 1 km and 1.5 km above ground level. The Marshall–Palmer relationship applied at a height of 1.5 km yielded the smallest deviations from rain gauge measurements. Both the mean absolute error (MAE) and average precipitation difference at this height were consistent, amounting to 1.99 mm, compared to 2.32 mm at 1 km. The range of deviations in all cases was 0.54–7.64 mm at 1.5 km and 0.65–7.18 mm at 1 km. Furthermore, all tested Z–R relationships demonstrated a strong linear correlation with rain gauge data, as indicated by a Pearson correlation coefficient of 0.98. These findings enable the identification of the most accurate Z–R relationships and optimal measurement heights for radar-based precipitation estimation. These results may have important implications for operational applications and the calibration of radar precipitation products. |
| format | Article |
| id | doaj-art-2b77214fa41d4d03bb7876f137036e3c |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-2b77214fa41d4d03bb7876f137036e3c2025-08-20T03:50:20ZengMDPI AGRemote Sensing2072-42922025-06-011713215710.3390/rs17132157Analysis of Precipitation Totals Based on Radar and Rain Gauge DataKarol Dzwonkowski0Ireneusz Winnicki1Sławomir Pietrek2Jolanta Siewert3Institute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 2 Gen. S. Kaliskiego St., 00-908 Warsaw, PolandInstitute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 2 Gen. S. Kaliskiego St., 00-908 Warsaw, PolandInstitute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 2 Gen. S. Kaliskiego St., 00-908 Warsaw, PolandInstitute of Geospatial Engineering and Geodesy, Faculty of Civil Engineering and Geodesy, Military University of Technology, 2 Gen. S. Kaliskiego St., 00-908 Warsaw, PolandThe relationship between radar reflectivity (Z) and rainfall intensity (R) plays a crucial role in estimating precipitation and serves as a foundation for flood risk assessment. However, empirical Z–R relationships often introduce considerable uncertainty, making the correction of rainfall estimation errors a key challenge in remote-sensing-based applications. Developing an effective approach to reduce these deviations is, therefore, essential to improve the accuracy of radar-based precipitation measurements. This study aims to develop a methodology for analyzing radar-derived precipitation using dual-polarization radar measurements, with validation based on rain gauge observations. Three well-established Z–R relationships—Marshall–Palmer, Muchnik, and Joss—were applied to radar reflectivity values measured at two heights, 1 km and 1.5 km above ground level. The Marshall–Palmer relationship applied at a height of 1.5 km yielded the smallest deviations from rain gauge measurements. Both the mean absolute error (MAE) and average precipitation difference at this height were consistent, amounting to 1.99 mm, compared to 2.32 mm at 1 km. The range of deviations in all cases was 0.54–7.64 mm at 1.5 km and 0.65–7.18 mm at 1 km. Furthermore, all tested Z–R relationships demonstrated a strong linear correlation with rain gauge data, as indicated by a Pearson correlation coefficient of 0.98. These findings enable the identification of the most accurate Z–R relationships and optimal measurement heights for radar-based precipitation estimation. These results may have important implications for operational applications and the calibration of radar precipitation products.https://www.mdpi.com/2072-4292/17/13/2157dual-polarization radarquantitative precipitation estimationprecipitation measurementsempirical relationships |
| spellingShingle | Karol Dzwonkowski Ireneusz Winnicki Sławomir Pietrek Jolanta Siewert Analysis of Precipitation Totals Based on Radar and Rain Gauge Data Remote Sensing dual-polarization radar quantitative precipitation estimation precipitation measurements empirical relationships |
| title | Analysis of Precipitation Totals Based on Radar and Rain Gauge Data |
| title_full | Analysis of Precipitation Totals Based on Radar and Rain Gauge Data |
| title_fullStr | Analysis of Precipitation Totals Based on Radar and Rain Gauge Data |
| title_full_unstemmed | Analysis of Precipitation Totals Based on Radar and Rain Gauge Data |
| title_short | Analysis of Precipitation Totals Based on Radar and Rain Gauge Data |
| title_sort | analysis of precipitation totals based on radar and rain gauge data |
| topic | dual-polarization radar quantitative precipitation estimation precipitation measurements empirical relationships |
| url | https://www.mdpi.com/2072-4292/17/13/2157 |
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