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| projects:hydrocolumn [2026/09/07 16:29] – ayush | projects:hydrocolumn [2026/09/07 16:33] (current) – ayush |
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| Birdbath scans for the 17 C-band dual-polarized Doppler radars of the operational DWD radar network have been routinely used for calibrating differential reflectivity (ZDR; Frech and Hubbert 2020). Within HydroColumn, we have updated the operational birdbath scan strategy to also allow for a meteorological interpretation of these vertically pointing Doppler measurements. To the scientists’ knowledge, DWD is the only meteorological service worldwide that currently derives Doppler spectra operationally from vertically pointing observations across the radar network. Figure 1a shows a summary of the radar output in one of the two polarization channels recorded for one birdbath scan with the DWD research radar at the Hohenpeissenberg observatory (MOHp) that is identical to the operational DWD radars. For a detailed interpretation of the radar data, the weather signal has to be isolated from the non-meteorological clutter and background signals, and the individual Doppler spectra should be checked for the existence of multiple precipitation modes representing different precipitation regimes. Within HydroColumn, we have developed a flexible algorithm to automatically identify and isolate the weather signal by leveraging unsupervised machine-learning techniques. Hierarchical density-based clustering (Campello et al. 2013) was used to cluster the radar output data based on the polarimetric characteristics of the Doppler measurements in the two orthogonal polarization channels (resulting in Figure 1b). By then applying a fast clustering algorithm based on the dip test of unimodality (Maurus and Plant 2016), statistically significant precipitation modes can be identified in the Doppler spectra at all height levels (Figure 1c). For each mode, relevant modal properties, such as radar reflectivity (Zh), mean Doppler velocity or mode width, are calculated and their uncertainties are estimated. If multiple modes are found, multimodal parameters describing the relative strength, separation, or mixing of the individual modes can be computed. The results then give a detailed profile of quantitative precipitation characteristics and uncertainty estimates. Figure. 1, for example, indicates one fast-falling precipitation mode (typical velocities faster than 1.5 m s<sup>-1</sup> downward) and a slower-falling mode below a height of about 1 km, suggesting secondary ice processes associated with riming, which agrees well with in situ data collected next to the radar site. | Birdbath scans for the 17 C-band dual-polarized Doppler radars of the operational DWD radar network have been routinely used for calibrating differential reflectivity (ZDR; Frech and Hubbert 2020). Within HydroColumn, we have updated the operational birdbath scan strategy to also allow for a meteorological interpretation of these vertically pointing Doppler measurements. To the scientists’ knowledge, DWD is the only meteorological service worldwide that currently derives Doppler spectra operationally from vertically pointing observations across the radar network. Figure 1a shows a summary of the radar output in one of the two polarization channels recorded for one birdbath scan with the DWD research radar at the Hohenpeissenberg observatory (MOHp) that is identical to the operational DWD radars. For a detailed interpretation of the radar data, the weather signal has to be isolated from the non-meteorological clutter and background signals, and the individual Doppler spectra should be checked for the existence of multiple precipitation modes representing different precipitation regimes. Within HydroColumn, we have developed a flexible algorithm to automatically identify and isolate the weather signal by leveraging unsupervised machine-learning techniques. Hierarchical density-based clustering (Campello et al. 2013) was used to cluster the radar output data based on the polarimetric characteristics of the Doppler measurements in the two orthogonal polarization channels (resulting in Figure 1b). By then applying a fast clustering algorithm based on the dip test of unimodality (Maurus and Plant 2016), statistically significant precipitation modes can be identified in the Doppler spectra at all height levels (Figure 1c). For each mode, relevant modal properties, such as radar reflectivity (Zh), mean Doppler velocity or mode width, are calculated and their uncertainties are estimated. If multiple modes are found, multimodal parameters describing the relative strength, separation, or mixing of the individual modes can be computed. The results then give a detailed profile of quantitative precipitation characteristics and uncertainty estimates. Figure. 1, for example, indicates one fast-falling precipitation mode (typical velocities faster than 1.5 m s<sup>-1</sup> downward) and a slower-falling mode below a height of about 1 km, suggesting secondary ice processes associated with riming, which agrees well with in situ data collected next to the radar site. |
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| {{ hydro_column_doppler_polarization.png?direct&850 }} | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:hydro_column_doppler_polarization.png" |
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| | title="Figure 1" |
| | caption="(a) Summary of Doppler spectra in one polarization channel from birdbath scan recorded with the MOHp C-band radar during a winter precipitation event; (b) isolated weather signal, based on polarimetric characteristics and the developed machine-learning algorithm; horizontal dashed line indicates (c) one example of a bimodal Doppler spectrum, with peak intervals found by UniDip clustering and with final mode limits for calculating quantitative (multi)modal properties and uncertainty estimates." |
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| <WRAP tablewidth 60% center>**Figure 1:** (a) Summary of Doppler spectra in one polarization channel from birdbath scan recorded with the MOHp C-band radar during a winter precipitation event; (b) isolated weather signal, based on polarimetric characteristics and the developed machine-learning algorithm; horizontal dashed line indicates (c) one example of a bimodal Doppler spectrum, with peak intervals found by UniDip clustering and with final mode limits for calculating quantitative (multi)modal properties and uncertainty estimates.</WRAP> | |
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| Owing to the high flexibility in operating the MOHp C-band radar, e.g., for testing scan strategies or modifying range sampling intervals, we were able to carry out a combined measurement campaign of radar measurements and airborne in situ observations provided by the German Aerospace Center (DLR) Falcon research aircraft during the BLUESKY campaign (Voigt et al. 2021) within the PROM phase-1 POLICE project (Trömel et al. 2021). Figure 2 summarizes results from this campaign: high-resolution quasi-vertical profiles of polarimetric variables, vertically pointing Doppler measurements, short-term predicitions from the operational COSMO-D2 model (replaced by ICON-D2 in February 2021), and particle images collected in flight within a horizontal distance of about 20 km from the radar site and within 15 min of the radar measurements. Clearly, the polarimetric measurements already provide a great amount of information; the region of maximum ZDR and increasing Zh toward the ground approximately between altitude levels L3 and L4, for example, faithfully indicates the increased amount of preferentially horizontally oriented two-dimensional dendritic or plate-like frozen precipitation particles visible in the particle images around these levels. In the low-ZDR region sourrounding level L6, however, the polarimetric fingerprint alone does not provide a clear indication of whether the dominant precipitation process is aggregation or riming. Here, the Doppler measurements add valuable information, because the enhanced values up to about 1.8 m s<sup>-1</sup> indicate a transition from aggregates to moderately rimed particles. The particle images tend to confirm increasing riming of polycrystals and aggregates, especially when comparing them to the level L5. Finally, we can see in Figure 2c that transitions between regions of characteristic features in the radar data also correspond to changes in (thermo)dynamic variables obtained from COSMO-D2, e.g., the transition to low ZDR is also marked by a spike in Doppler spectra standard deviation and a change in the atmospheric wind speed and direction around -7 °C. | Owing to the high flexibility in operating the MOHp C-band radar, e.g., for testing scan strategies or modifying range sampling intervals, we were able to carry out a combined measurement campaign of radar measurements and airborne in situ observations provided by the German Aerospace Center (DLR) Falcon research aircraft during the BLUESKY campaign (Voigt et al. 2021) within the PROM phase-1 POLICE project (Trömel et al. 2021). Figure 2 summarizes results from this campaign: high-resolution quasi-vertical profiles of polarimetric variables, vertically pointing Doppler measurements, short-term predicitions from the operational COSMO-D2 model (replaced by ICON-D2 in February 2021), and particle images collected in flight within a horizontal distance of about 20 km from the radar site and within 15 min of the radar measurements. Clearly, the polarimetric measurements already provide a great amount of information; the region of maximum ZDR and increasing Zh toward the ground approximately between altitude levels L3 and L4, for example, faithfully indicates the increased amount of preferentially horizontally oriented two-dimensional dendritic or plate-like frozen precipitation particles visible in the particle images around these levels. In the low-ZDR region sourrounding level L6, however, the polarimetric fingerprint alone does not provide a clear indication of whether the dominant precipitation process is aggregation or riming. Here, the Doppler measurements add valuable information, because the enhanced values up to about 1.8 m s<sup>-1</sup> indicate a transition from aggregates to moderately rimed particles. The particle images tend to confirm increasing riming of polycrystals and aggregates, especially when comparing them to the level L5. Finally, we can see in Figure 2c that transitions between regions of characteristic features in the radar data also correspond to changes in (thermo)dynamic variables obtained from COSMO-D2, e.g., the transition to low ZDR is also marked by a spike in Doppler spectra standard deviation and a change in the atmospheric wind speed and direction around -7 °C. |
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| {{ summary_of_data.png?direct&850 }} | <met figure |
| \\ | src="https://www2.meteo.uni-bonn.de/spp2115/lib/exe/fetch.php?media=projects:summary_of_data.png" |
| <WRAP tablewidth 60% center>**Figure 2:** Summary of data collected during a spring precipitation event over MOHp C-band radar. (a) high-resolution quasi-vertical profiles (25° elevation scans) of polarimetric variables; (b) summary of birdbath scan with profiles of derived radar reflectivity (Zh), mean velocity, and standard deviation indicated by blue, yellow, and black lines, respectively; (c) several variables indicating the atmospheric (thermo)dynamics according to COSMO-D2 model short-term predictions; (L1-L9) particle images collected at altitudes indicated in panel a (modified from Trömel et al. 2021).</WRAP> | width="850" |
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| | title="Figure 2" |
| | caption="Summary of data collected during a spring precipitation event over MOHp C-band radar. (a) high-resolution quasi-vertical profiles (25° elevation scans) of polarimetric variables; (b) summary of birdbath scan with profiles of derived radar reflectivity (Zh), mean velocity, and standard deviation indicated by blue, yellow, and black lines, respectively; (c) several variables indicating the atmospheric (thermo)dynamics according to COSMO-D2 model short-term predictions; (L1-L9) particle images collected at altitudes indicated in panel a (modified from Trömel et al. 2021)." |
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| We have planned several upcoming combined measurement campaigns with DLR aircraft in situ observations where we will further investigate the co-variability of radar variables and weather forecast model outputs specifying the atmospheric (thermo)dynamics to characterize a more diverse set of precipitation conditions. Here, we also intend to build on first results for retrieving the rime mass fraction from the mean Doppler velocity (Kneifel and Moisseev 2020). In collaboration with TROPOS Leipzig, we continue to analyze the potential of using spectrally resolved C-band polarimetric measurements collected at various elevation angles for quantitative microphysical retrievals. | We have planned several upcoming combined measurement campaigns with DLR aircraft in situ observations where we will further investigate the co-variability of radar variables and weather forecast model outputs specifying the atmospheric (thermo)dynamics to characterize a more diverse set of precipitation conditions. Here, we also intend to build on first results for retrieving the rime mass fraction from the mean Doppler velocity (Kneifel and Moisseev 2020). In collaboration with TROPOS Leipzig, we continue to analyze the potential of using spectrally resolved C-band polarimetric measurements collected at various elevation angles for quantitative microphysical retrievals. |
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