Improved rain event detection in Commercial Microwave Link time series via combination with MSG SEVIRI data. Wagner, A., Chwala, C., Graf, M., Polz, J., Lliso, L., Lahuerta, J. A., & Kunstmann, H. October, 2023. ![Improved rain event detection in Commercial Microwave Link time series via combination with MSG SEVIRI data [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex Abstract. The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods.
@misc{wagner_improved_2023,
title = {Improved rain event detection in {Commercial} {Microwave} {Link} time series via combination with {MSG} {SEVIRI} data},
copyright = {https://creativecommons.org/licenses/by/4.0/},
url = {https://amt.copernicus.org/preprints/amt-2023-175/amt-2023-175.pdf},
doi = {10.5194/amt-2023-175},
abstract = {Abstract. The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods.},
urldate = {2024-11-15},
publisher = {Others (Wind, Precipitation, Temperature, etc.)/Remote Sensing/Data Processing and Information Retrieval},
author = {Wagner, Andreas and Chwala, Christian and Graf, Maximilian and Polz, Julius and Lliso, Llorenç and Lahuerta, José Alberto and Kunstmann, Harald},
month = oct,
year = {2023},
}
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CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. 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The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time-series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. 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