Polarimetric X-band radar (BoXPol) data monitored in Bonn, Germany, since 2009 to 2023

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Trömel, Silke; Mühlbauer, Kai; Lennefer, Martin; Simmer, Clemens; Scharbach, Tobias; Pejcic, Velibor, 2026, "Polarimetric X-band radar (BoXPol) data monitored in Bonn, Germany, since 2009 to 2023", https://doi.org/10.60507/FK2/D0NVG5, bonndata, V1

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Description	Volume plan-position indicator (PPI) scans collected from 2009 to 2023 with the polarimetric Doppler X-band weather radar (wavelength of 3.2 cm; frequency of 9.3 GHz) in Bonn (BoXPol) are provided. BoXPol consisted of Radome-less EEC DWSR-2001-X-SDP radar system (for detailed information, see https://www.essweather.com/products/x-band-weather-radar-from-eec), located on a 30-meter-high building in close proximity to the Institute of Geosciences, Section Meteorology, at the University of Bonn. Equipped with a random-phase magnetron and operating in Simultaneous Dual Polarization (SIDpol), the radar transmitted and received horizontally and vertically polarized electromagnetic waves (STAR/SHV mode). BoXPol was funded in 2008 by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) in the framework of the Collaborative Research Center TR32 (Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modelling and Data Assimilation). The data set comprises volume scans including 10 elevation angles between 0.5° and 28°, as well as a birdbath (90°) scan (available since 2013) and a range-height indicator (RHI) scan pointing towards its twin X-band radar, JuXPol, located in Jülich. The azimuthal resolution is 1°, while the radial resolution ranges from 25 m to 200 m. Volume scans have a temporal resolution of 5 min and are provided in daily files for each elevation angle. The following polarimetric variables are provided: Uncorrected/corrected horizontal/vertical reflectivity factor (ZH/ZV), differential reflectivity (ZDR), copolar cross-correlation coefficient (RHOHV), differential phase shift (PHIDP), specific differential phase (KDP), horizontal/vertical radial velocities (VRADH/VRADV) and horizontal/vertical spectral width of the radial velocity (WRADH/WRADV). Depending on the year, additional variables, such as a clutter map (CMAP) and signal-to-noise ratio (SNR), are also available. More detailed information about BoXPol can be found in, e.g. Scharbach et al. (2026), Pejcic et al. (2022) and Diederich et al. (2015). The dataset has demonstrated considerable scientific value over the years and has been used in quantitative precipitation estimation (QPE), (ice-)microphysical retrievals and process studies, investigations of microphysical precipitation formation in the regional climate of Western Germany, hydrometeor classification (HC), and the evaluation and improvement of numerical weather prediction (NWP) models.
Subject	Earth and Environmental Sciences; Physics
Related Publication	References: Pejcic, V., Soderholm, J., Mühlbauer, K., Louf, V., & Trömel, S. (2022). Five years calibrated observations from the University of Bonn X-band weather radar (BoXPol). Scientific Data, 9(1), 551.doi: 10.1038/s41597-022-01656-0
Notes	In addition to the peer-reviewed publications accumulated over the years of BoXPol operation, BoXPol data has also been used and analyzed in numerous conference contributions, BSc/MSc theses and dissertations. Please note, that the radar data provided is not calibrated (ZH, ZDR). The radial velocities (e.g., VRADH) have also not been de-aliased and PHIDP has not been adjusted or unfolded. The provided KDP iis generated by the signal processor and mostly not recommended to be used. Furthermore, no georeferencing has been performed yet. For the calibration of ZDR and ZH, please see the publications Pejcic et al. (2022), Scharbach et al. (2026) and Scharbach and Trömel (2026), as well as related datasets available in Scharbach (2026a/b/c) or Pejcic et al. (2021). For reading/writing, georeferencing, further processing (e.g. PHIDP, KDP derivation) and radar data visualization, we highly recommend the Python module wradlib (Heistermann et al., 2013; https://wradlib.org/), the Python module xradar (Grover et al., 2026; https://docs.openradarscience.org/projects/xradar/en/stable/), the Python module Py-ART (Helmus and Collis, 2016; https://arm-doe.github.io/pyart/#what-is-py-art) and Towerpy (Sanchez-Rivas and Rico-Ramirez, 2023; https://towerpy.readthedocs.io/en/latest/index.html), as well as the hybrid utilization of all. For more information (e.g. about the data structure) see Readme.txt file.
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Citation Metadata

Persistent Identifier	doi:10.60507/FK2/D0NVG5
Publication Date	2026-09-25
Title	Polarimetric X-band radar (BoXPol) data monitored in Bonn, Germany, since 2009 to 2023
Author	Trömel, Silke (University of Bonn) - ORCID: https://orcid.org/0009-0006-0137-7573 Mühlbauer, Kai (University of Bonn) - ORCID: https://orcid.org/0000-0001-6599-1034 Lennefer, Martin (University of Bonn) Simmer, Clemens (University of Bonn) - ORCID: https://orcid.org/0000-0003-3001-8642 Scharbach, Tobias (University of Bonn) - ORCID: https://orcid.org/0009-0003-1440-3177 Pejcic, Velibor (University of Bonn) - ORCID: https://orcid.org/0000-0003-0274-5084
Contact	Use email button above to contact. Mühlbauer, Kai (University of Bonn) Trömel, Silke (University of Bonn) Scharbach, Tobias (University of Bonn) Lennefer, Martin (University of Bonn)
Description	Volume plan-position indicator (PPI) scans collected from 2009 to 2023 with the polarimetric Doppler X-band weather radar (wavelength of 3.2 cm; frequency of 9.3 GHz) in Bonn (BoXPol) are provided. BoXPol consisted of Radome-less EEC DWSR-2001-X-SDP radar system (for detailed information, see https://www.essweather.com/products/x-band-weather-radar-from-eec), located on a 30-meter-high building in close proximity to the Institute of Geosciences, Section Meteorology, at the University of Bonn. Equipped with a random-phase magnetron and operating in Simultaneous Dual Polarization (SIDpol), the radar transmitted and received horizontally and vertically polarized electromagnetic waves (STAR/SHV mode). BoXPol was funded in 2008 by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) in the framework of the Collaborative Research Center TR32 (Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modelling and Data Assimilation). The data set comprises volume scans including 10 elevation angles between 0.5° and 28°, as well as a birdbath (90°) scan (available since 2013) and a range-height indicator (RHI) scan pointing towards its twin X-band radar, JuXPol, located in Jülich. The azimuthal resolution is 1°, while the radial resolution ranges from 25 m to 200 m. Volume scans have a temporal resolution of 5 min and are provided in daily files for each elevation angle. The following polarimetric variables are provided: Uncorrected/corrected horizontal/vertical reflectivity factor (ZH/ZV), differential reflectivity (ZDR), copolar cross-correlation coefficient (RHOHV), differential phase shift (PHIDP), specific differential phase (KDP), horizontal/vertical radial velocities (VRADH/VRADV) and horizontal/vertical spectral width of the radial velocity (WRADH/WRADV). Depending on the year, additional variables, such as a clutter map (CMAP) and signal-to-noise ratio (SNR), are also available. More detailed information about BoXPol can be found in, e.g. Scharbach et al. (2026), Pejcic et al. (2022) and Diederich et al. (2015). The dataset has demonstrated considerable scientific value over the years and has been used in quantitative precipitation estimation (QPE), (ice-)microphysical retrievals and process studies, investigations of microphysical precipitation formation in the regional climate of Western Germany, hydrometeor classification (HC), and the evaluation and improvement of numerical weather prediction (NWP) models.
Subject	Earth and Environmental Sciences; Physics
FreeKeyword	Radar data Volume scan PPi RHi Birdbath Polarimetric variables Radial (Doppler) velocity
Related Publication	References: Pejcic, V., Soderholm, J., Mühlbauer, K., Louf, V., & Trömel, S. (2022). Five years calibrated observations from the University of Bonn X-band weather radar (BoXPol). Scientific Data, 9(1), 551. doi 10.1038/s41597-022-01656-0 https://doi.org/10.1038/s41597-022-01656-0 References: Borowska, L., Zrnić, D., Ryzhkov, A., Zhang, P., & Simmer, C. (2011). Polarimetric estimates of a 1-month accumulation of light rain with a 3-cm wavelength radar. Journal of Hydrometeorology, 12(5), 1024–1039. doi 10.1175/2011JHM1339.1 https://doi.org/10.1175/2011JHM1339.1 References: Borowska, L., & Zrnić, D. (2012). Use of ground clutter to monitor polarimetric radar calibration. Journal of Atmospheric and Oceanic Technology, 29(2), 159–176. doi 10.1175/JTECH-D-11-00036.1 https://doi.org/10.1175/JTECH-D-11-00036.1 References: Kumjian, M. R., & Ryzhkov, A. V. (2012). The impact of size sorting on the polarimetric radar variables. Journal of the Atmospheric Sciences, 69(6), 2042–2060. doi 10.1175/JAS-D-11-0125.1 https://doi.org/10.1175/JAS-D-11-0125.1 References: Kumjian, M. R., Ganson, S. M., & Ryzhkov, A. V. (2012). Freezing of raindrops in deep convective updrafts: A microphysical and polarimetric model. Journal of the Atmospheric Sciences, 69(12), 3471–3490. doi 10.1175/JAS-D-12-067.1 https://doi.org/10.1175/JAS-D-12-067.1 References: Trömel, S., Kumjian, M. R., Ryzhkov, A. V., Simmer, C., & Diederich, M. (2013). Backscatter differential phase—Estimation and variability. Journal of Applied Meteorology and Climatology, 52(11), 2529–2548. doi 10.1175/JAMC-D-13-0124.1 https://doi.org/10.1175/JAMC-D-13-0124.1 References: Ryzhkov, A., Diederich, M., Zhang, P., & Simmer, C. (2014). Potential utilization of specific attenuation for rainfall estimation, mitigation of partial beam blockage, and radar networking. Journal of Atmospheric and Oceanic Technology, 31(3), 599–619. doi 10.1175/JTECH-D-13-00038.1 https://doi.org/10.1175/JTECH-D-13-00038.1 References: Trömel, S., Ryzhkov, A. V., Zhang, P., & Simmer, C. (2014). Investigations of backscatter differential phase in the melting layer. Journal of Applied Meteorology and Climatology, 53(10), 2344–2359. doi 10.1175/JAMC-D-14-0050.1 https://doi.org/10.1175/JAMC-D-14-0050.1 References: Weissmann, M., Göber, M., Hohenegger, C., Janjić, T., Keller, J., Ohlwein, C., Seifert, A., Trömel, S., Ulbrich, T., Wapler, K., Bollmeyer, C., & Deneke, H. (2014). Initial phase of the Hans-Ertel Centre for Weather Research—A virtual centre at the interface of basic and applied weather and climate research. Meteorologische Zeitschrift, 23(3), 193–208. doi 10.1127/0941-2948/2014/0558 https://doi.org/10.1127/0941-2948/2014/0558 References: Diederich, M., Ryzhkov, A., Simmer, C., Zhang, P., & Trömel, S. (2015). Use of specific attenuation for rainfall measurement at X-band radar wavelengths. Part I: Radar calibration and partial beam blockage estimation. Journal of Hydrometeorology, 16(2), 487–502. doi 10.1175/JHM-D-14-0066.1 https://doi.org/10.1175/JHM-D-14-0066.1 References: Diederich, M., Ryzhkov, A., Simmer, C., Zhang, P., & Trömel, S. (2015). Use of specific attenuation for rainfall measurement at X-band radar wavelengths. Part II: Rainfall estimates and comparison with rain gauges. Journal of Hydrometeorology, 16(2), 503–516. doi 10.1175/JHM-D-14-0067.1 https://doi.org/10.1175/JHM-D-14-0067.1 References: Ryzhkov, A., Zhang, P., Reeves, H., Kumjian, M., Tschallener, T., Trömel, S., & Simmer, C. (2016). Quasi-vertical profiles—A new way to look at polarimetric radar data. Journal of Atmospheric and Oceanic Technology, 33(3), 551–562. doi 10.1175/JTECH-D-15-0020.1 https://doi.org/10.1175/JTECH-D-15-0020.1 References: Xie, X., Evaristo, R., Simmer, C., Handwerker, J., & Trömel, S. (2016). Precipitation and microphysical processes observed by three polarimetric X-band radars and ground-based instrumentation during HOPE. Atmospheric Chemistry and Physics, 16(11), 7105–7116. doi 10.5194/acp-16-7105-2016 https://doi.org/10.5194/acp-16-7105-2016 References: Xie, X., Evaristo, R., Trömel, S., Saavedra, P., Simmer, C., & Ryzhkov, A. (2016). Radar observation of evaporation and implications for quantitative precipitation and cooling rate estimation. Journal of Atmospheric and Oceanic Technology, 33(8), 1779–1792. doi 10.1175/JTECH-D-15-0244.1 https://doi.org/10.1175/JTECH-D-15-0244.1 References: Heinze, R., Dipankar, A., Carbajal Henken, C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., … Quaas, J. (2017). Large-eddy simulations over Germany using ICON: A comprehensive evaluation. Quarterly Journal of the Royal Meteorological Society, 143(702), 69–100. doi 10.1002/qj.2947 https://doi.org/10.1002/qj.2947 References: Macke, A., Seifert, P., Baars, H., Barthlott, C., Beekmans, C., Behrendt, A., Bohn, B., Brueck, M., Bühl, J., Crewell, S., Damian, T., Deneke, H., Düsing, S., Foth, A., Di Girolamo, P., Hammann, E., Heinze, R., Hirsikko, A., Kalisch, J., … Xie, X. (2017). The HD(CP)² Observational Prototype Experiment (HOPE)—An overview. Atmospheric Chemistry and Physics, 17(7), 4887–4914. doi 10.5194/acp-17-4887-2017 https://doi.org/10.5194/acp-17-4887-2017 References: Ryzhkov, A., Matrosov, S. Y., Melnikov, V., Zrnić, D., Zhang, P., Cao, Q., Knight, M., Simmer, C., & Trömel, S. (2017). Estimation of depolarization ratio using weather radars with simultaneous transmission/reception. Journal of Applied Meteorology and Climatology, 56(7), 1797–1816. doi 10.1175/JAMC-D-16-0098.1 https://doi.org/10.1175/JAMC-D-16-0098.1 References: Sulis, M., Williams, J. L., Shrestha, P., Diederich, M., Simmer, C., Kollet, S. J., & Maxwell, R. M. (2017). Coupling groundwater, vegetation, and atmospheric processes: A comparison of two integrated models. Journal of Hydrometeorology, 18(5), 1489–1511. doi 10.1175/JHM-D-16-0159.1 https://doi.org/10.1175/JHM-D-16-0159.1 References: Trömel, S., Ryzhkov, A. V., Diederich, M., Mühlbauer, K., Kneifel, S., Snyder, J., & Simmer, C. (2017). Multisensor characterization of mammatus. Monthly Weather Review, 145(1), 235–251. doi 10.1175/MWR-D-16-0187.1 https://doi.org/10.1175/MWR-D-16-0187.1 References: Evaristo, R., Trömel, S., & Simmer, C. (2018). Dual-Doppler and polarimetric radar analysis of hail events in Germany. In 2018 19th International Radar Symposium (IRS) (pp. 1–9). IEEE. doi 10.23919/IRS.2018.8447986 https://doi.org/10.23919/IRS.2018.8447986 References: Kollet, S., Gasper, F., Brdar, S., Goergen, K., Hendricks Franssen, H.-J., Keune, J., Kurtz, W., Küll, V., Pappenberger, F., Poll, S., Trömel, S., Shrestha, P., Simmer, C., & Sulis, M. (2018). Introduction of an experimental terrestrial forecasting/monitoring system at regional to continental scales based on the Terrestrial Systems Modeling Platform (v1.1.0). Water, 10(11), 1697. doi 10.3390/w10111697 https://doi.org/10.3390/w10111697 References: Pejcic, V., Trömel, S., Mühlbauer, K., Saavedra, P., Beer, J., & Simmer, C. (2018). Synergy of GPM and ground-based radar observations for precipitation estimation and detection of microphysical processes. In 2018 19th International Radar Symposium (IRS) (pp. 1–8). IEEE. doi 10.23919/IRS.2018.8447923 https://doi.org/10.23919/IRS.2018.8447923 References: Sulis, M., Keune, J., Shrestha, P., Simmer, C., & Kollet, S. J. (2018). Quantifying the impact of subsurface–land surface physical processes on the predictive skill of subseasonal mesoscale atmospheric simulations. Journal of Geophysical Research: Atmospheres, 123(17), 9131–9151. doi 10.1029/2017JD028187 https://doi.org/10.1029/2017JD028187 References: Trömel, S., Quaas, J., Crewell, S., Bott, A., & Simmer, C. (2018). Polarimetric radar observations meet atmospheric modelling. In 2018 19th International Radar Symposium (IRS) (pp. 1–10). IEEE. doi 10.23919/IRS.2018.8448121 https://doi.org/10.23919/IRS.2018.8448121 References: Evaristo, R., Trömel, S., & Simmer, C. (2019). Comparing different methods of radar data display for microphysical studies in precipitation systems and weather nowcasting. In 2019 20th International Radar Symposium (IRS) (pp. 1–8). IEEE. doi 10.23919/IRS.2019.8768150 https://doi.org/10.23919/IRS.2019.8768150 References: Trömel, S., Ryzhkov, A. V., Hickman, B., Mühlbauer, K., & Simmer, C. (2019). Polarimetric radar variables in the layers of melting and dendritic growth at X band—Implications for a nowcasting strategy in stratiform rain. Journal of Applied Meteorology and Climatology, 58(11), 2497–2522. doi 10.1175/JAMC-D-19-0056.1 https://doi.org/10.1175/JAMC-D-19-0056.1 References: Pejcic, V., Saavedra Garfias, P., Mühlbauer, K., Trömel, S., & Simmer, C. (2020). Comparison between precipitation estimates of ground-based weather radar composites and GPM’s DPR rainfall product over Germany. Meteorologische Zeitschrift, 29(6), 451–466. doi 10.1127/metz/2020/1039 https://doi.org/10.1127/metz/2020/1039 References: Poméon, T., Wagner, N., Furusho, C., Kollet, S., & Reinoso-Rondinel, R. (2020). Performance of a PDE-based hydrologic model in a flash flood modeling framework in sparsely-gauged catchments. Water, 12(8), 2157. doi 10.3390/w12082157 https://doi.org/10.3390/w12082157 References: Evaristo, R., Reinoso-Rondinel, R., Trömel, S., & Simmer, C. (2021). Validation of wind fields retrieved by Dual-Doppler techniques using a vertically pointing radar. In 2021 21st International Radar Symposium (IRS) (pp. 1–7). IEEE. doi 10.23919/IRS51887.2021.9466173 https://doi.org/10.23919/IRS51887.2021.9466173 References: Pejcic, V., Simmer, C., & Trömel, S. (2021). Polarimetric radar-based methods for evaluation of hydrometeor mixtures in numerical weather prediction models. In 2021 21st International Radar Symposium (IRS) (pp. 1–10). IEEE. doi 10.23919/IRS51887.2021.9466201 https://doi.org/10.23919/IRS51887.2021.9466201 References: Trömel, S., Simmer, C., Blahak, U., Blanke, A., Doktorowski, S., Ewald, F., Frech, M., Gergely, M., Hagen, M., Janjić, T., Kalesse-Los, H., Kneifel, S., Knote, C., Mendrok, J., Moser, M., Köcher, G., Mühlbauer, K., Myagkov, A., Pejcic, V., Seifert, P., Shrestha, P., Teisseire, A., von Terzi, L., Tetoni, E., Vogl, T., Voigt, C., Zeng, Y., Zinner, T., & Quaas, J. (2021). Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes. Atmospheric Chemistry and Physics, 21(23), 17291–17314. doi 10.5194/acp-21-17291-2021 https://doi.org/10.5194/acp-21-17291-2021 References: Pejcic, V., Mühlbauer, K., & Trömel, S. (2022). A new dual-frequency-based hydrometeor classification approach for the Global Precipitation Measurements core satellite. In 2022 23rd International Radar Symposium (IRS) (pp. 426–430). IEEE. doi 10.23919/IRS54158.2022.9905054 https://doi.org/10.23919/IRS54158.2022.9905054 References: Shrestha, P., Mendrok, J., Pejcic, V., Trömel, S., Blahak, U., & Carlin, J. T. (2022). Evaluation of the COSMO model (v5.1) in polarimetric radar space—Impact of uncertainties in model microphysics, retrievals and forward operators. Geoscientific Model Development, 15(1), 291–313. doi 10.5194/gmd-15-291-2022 https://doi.org/10.5194/gmd-15-291-2022 References: Shrestha, P., Trömel, S., Evaristo, R., & Simmer, C. (2022). Evaluation of modelled summertime convective storms using polarimetric radar observations. Atmospheric Chemistry and Physics, 22(11), 7593–7618. doi 10.5194/acp-22-7593-2022 https://doi.org/10.5194/acp-22-7593-2022 References: Shrestha, P., Mendrok, J., & Brunner, D. (2022). Aerosol characteristics and polarimetric signatures for a deep convective storm over the northwestern part of Europe—Modeling and observations. Atmospheric Chemistry and Physics, 22(21), 14095–14117. doi 10.5194/acp-22-14095-2022 https://doi.org/10.5194/acp-22-14095-2022 References: Trömel, S., Blahak, U., Evaristo, R., Mendrok, J., Neef, L., Pejcic, V., Scharbach, T., Shrestha, P., & Simmer, C. (2023). Fusion of radar polarimetry and atmospheric modeling. In V. N. Bringi, K. V. Mishra, & M. Thurai (Eds.), Advances in weather radar: Volume 2. Precipitation science, scattering and processing algorithms (pp. 293–344). Institution of Engineering and Technology. doi 10.1049/SBRA557G_ch7 https://doi.org/10.1049/SBRA557G_ch7 References: Scharbach, T., & Trömel, S. (2026). Long-term climatology of vertical profiles of polarimetric variables and ice-microphysical retrievals at X-band. Part I: Radar calibration [Preprint]. EGUsphere. doi 10.5194/egusphere-2026-493 https://doi.org/10.5194/egusphere-2026-493 References: Scharbach, T., & Trömel, S. (2026). Long-term climatology of vertical profiles of polarimetric variables and ice-microphysical retrievals at X-band. Part II: Climatology [Preprint]. EGUsphere. doi 10.5194/egusphere-2026-2523 https://doi.org/10.5194/egusphere-2026-2523 References: Trömel, S., Ryzhkov, A. V., Hickman, B., & Simmer, C. (2017, August 28–September 1). Climatology of the vertical profiles of polarimetric radar variables at X band in stratiform clouds [Extended conference paper]. 38th Conference on Radar Meteorology, Chicago, Illinois, United States. https://ams.confex.com/ams/38RADAR/webprogram/Manuscript/Paper320485/ExtAbstract_Troemel_AMS_Radar2017.pdf url https://ams.confex.com/ams/38RADAR/webprogram/Manuscript/Paper320485/ExtAbstract_Troemel_AMS_Radar2017.pdf References: Chen, J.-Y., Reinoso-Rondinel, R., Trömel, S., Simmer, C., & Ryzhkov, A. V. (2023). A radar-based quantitative precipitation estimation algorithm to overcome the impact of vertical gradients of warm-rain precipitation: The flood in western Germany on 14 July 2021. Journal of Hydrometeorology, 24(3), 521–536. 10.1175/JHM-D-22-0111.1 https://doi.org/10.1175/JHM-D-22-0111.1
Notes	In addition to the peer-reviewed publications accumulated over the years of BoXPol operation, BoXPol data has also been used and analyzed in numerous conference contributions, BSc/MSc theses and dissertations. Please note, that the radar data provided is not calibrated (ZH, ZDR). The radial velocities (e.g., VRADH) have also not been de-aliased and PHIDP has not been adjusted or unfolded. The provided KDP iis generated by the signal processor and mostly not recommended to be used. Furthermore, no georeferencing has been performed yet. For the calibration of ZDR and ZH, please see the publications Pejcic et al. (2022), Scharbach et al. (2026) and Scharbach and Trömel (2026), as well as related datasets available in Scharbach (2026a/b/c) or Pejcic et al. (2021). For reading/writing, georeferencing, further processing (e.g. PHIDP, KDP derivation) and radar data visualization, we highly recommend the Python module wradlib (Heistermann et al., 2013; https://wradlib.org/), the Python module xradar (Grover et al., 2026; https://docs.openradarscience.org/projects/xradar/en/stable/), the Python module Py-ART (Helmus and Collis, 2016; https://arm-doe.github.io/pyart/#what-is-py-art) and Towerpy (Sanchez-Rivas and Rico-Ramirez, 2023; https://towerpy.readthedocs.io/en/latest/index.html), as well as the hybrid utilization of all. For more information (e.g. about the data structure) see Readme.txt file.
Language	English
Producer	Simmer, Clemens (University of Bonn) Trömel, Silke (University of Bonn) Lennefer, Martin (University of Bonn) Mühlbauer, Kai (University of Bonn)
Production Date	2009-01-01
Production Place	Auf dem Hügel 16 and 20, 53121, Bonn, Germany
Contributor	Funder : Simmer, Clemens Supervisor : Trömel, Silke Data Curator : Mühlbauer, Kai Data Curator : Lennefer, Martin Data Collector : GAMIC \| Weather Radar and Signal Processing Researcher : Scharbach, Tobias Researcher : Pejcic, Velibor
Grant Information	Deutsche Forschungsgemeinschaft (DFG): 15232683
Depositor	Mühlbauer, Kai
Deposit Date	2026-06-24
Date of Collection	Start Date: 2009-01-01 ; End Date: 2023-03-09
Kind of Data	X-band radar PPi data, saved in .mvol and .h5 format
Software	GAMIC, Version: Hardware changes from ENGIMA signal processor 3 (to 2017-04-0) to 4 (from 2017-04-03 to 2023-03-09))
Related Datasets	Scharbach, T. (2026c). Data set of quasi-vertical profiles (QVPs) generated from 10 years (2013-2023) of the BoXPol radar in stratiform conditions, related to the study "Long-term climatology of vertical profiles of polarimetric variables and ice-microphysical retrievals at X-band. Part 2: Climatology" [Data set]. Zenodo. https://doi.org/10.5281/zenodo.22800899; Scharbach, T. (2026b). Python jupyter notebooks for calibration of ZH/ZDR and provided offset values, related to the study: "Long-term climatology of vertical profiles of polarimetric variables and ice-microphysical retrievals at X-band. Part 1: Radar calibration" (Version 2) [Data set + Code]. Zenodo. https://doi.org/10.5281/zenodo.20796890; Pejcic, V., Soderholm, J., Mühlbauer, K., Louf, V., & Trömel, S. (2021). Polarimetric X-band radar data of the University of Bonn BoXPol 5min level2 (Version 20201127) 2014–2019 (Version 1) [Data set]. World Data Center for Climate at DKRZ. https://doi.org/10.26050/WDCC/BoxPol_UniBonn_Radar_5min_leve; Scharbach, T. (2026a). ERA5 reanalysis data interpolated to the 18° QVPs/PPIs generated with the X-band radar in Bonn (BoXPol) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.20799703; Shrestha, P., Mendrok, J., Pejcic, V., Trömel, S., Blahak, U., & Carlin, J. T. (2021). Software documentation for COSMO model (v5.1) evaluation with X-band polarimetric radar data using B-PRO (v2.0) [Data set and software]. Zenodo. https://doi.org/10.5281/zenodo.5218717; Shrestha, P. (2021). High resolution hydrological simulations over Bonn Radar Domain (Version 1) [Data set]. CRC/TR32 Database. https://doi.org/10.5880/TR32DB.40
Other References	Ryzhkov, A. V., & Zrnić, D. S. (2019). Radar polarimetry for weather observations. Springer International Publishing. https://doi.org/10.1007/978-3-030-05093-1; Doviak, R. J., & Zrnić, D. S. (2006). Doppler Radar and Weather Observations (2nd ed.) ISBN-10:0486450600. Dover Publications.; Rinehart, R. E. (2004). Radar for Meteorologists (4th ed.). Rinehart Publishing. ISBN-10: 0965800210

Geospatial Metadata

Geographic Coverage	Germany, NRW, Bonn
Geographic Bounding Box	6.00 9.0 51.00 49.00

Computational Workflow Metadata

Workflow Type	Jupyter Notebook; Other Python-based workflow; Other
External Code Repository URL	https://github.com/wradlib/wradlib; https://github.com/openradar/xradar; https://github.com/ARM-DOE/pyart; https://github.com/uobwatergroup/towerpy
Documentation	https://wradlib.org/ https://docs.openradarscience.org/projects/xradar/en/stable/ https://arm-doe.github.io/pyart-docs-travis/ https://towerpy.readthedocs.io/en/latest/

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