Polarimetric X-band radar (BoXPol) data monitored in Bonn, Germany, since 2009 to 2023https://doi.org/10.60507/FK2/D0NVG5Trömel, SilkeMühlbauer, KaiLennefer, MartinSimmer, ClemensScharbach, TobiasPejcic, Veliborbonndata2026-09-252026-09-25T05:00:45ZVolume 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.Earth and Environmental SciencesPhysicsEnglishPejcic, 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/s41597-022-01656-0Borowska, 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/10.1175/2011JHM1339.1Borowska, 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-11-00036.1Kumjian, 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-11-0125.1Kumjian, 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-12-067.1Trömel, 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-13-0124.1Ryzhkov, 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-13-00038.1Trömel, 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-14-0050.1Weissmann, 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/10.1127/0941-2948/2014/0558Diederich, 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-14-0066.1Diederich, 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-14-0067.1Ryzhkov, 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-15-0020.1Xie, 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-16-7105-2016Xie, 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-15-0244.1Heinze, 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.2947Macke, 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-17-4887-2017Ryzhkov, 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-16-0098.1Sulis, 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-16-0159.1Trömel, 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-16-0187.1Evaristo, 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.2018.8447986Kollet, 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/w10111697Pejcic, 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.2018.8447923Sulis, 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/10.1029/2017JD028187Trömel, 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.2018.8448121Evaristo, 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.2019.8768150Trömel, 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-19-0056.1Pejcic, 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/2020/1039Poméon, 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/w12082157Evaristo, 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/IRS51887.2021.9466173Pejcic, 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/IRS51887.2021.9466201Trömel, 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-21-17291-2021Pejcic, 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/IRS54158.2022.9905054Shrestha, 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-15-291-2022Shrestha, 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-22-7593-2022Shrestha, 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-22-14095-2022Trömel, 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_ch7Scharbach, 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-2026-493Scharbach, 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-2026-2523Trömel, 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.pdfChen, 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-22-0111.12009-01-01Mühlbauer, KaiSimmer, ClemensTrömel, SilkeMühlbauer, KaiLennefer, MartinGAMIC | Weather Radar and Signal ProcessingScharbach, TobiasPejcic, Velibor2026-06-242009-01-012023-03-09Scharbach, 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.22800899Scharbach, 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.20796890Pejcic, 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_leveScharbach, 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.20799703Shrestha, 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.5218717Shrestha, P. (2021). High resolution hydrological simulations over Bonn Radar Domain (Version 1) [Data set]. CRC/TR32 Database. https://doi.org/10.5880/TR32DB.40X-band radar PPi data, saved in .mvol and .h5 formatGermanyBonnNRWCC BY 4.0