Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-291-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-291-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
17 Jan 2022
Model evaluation paper |
| 17 Jan 2022
| 17 Jan 2022
Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators
Institute for Geosciences, Department of Meteorology, University of Bonn, Bonn, Germany
Jana Mendrok
Deutscher Wetterdienst, Offenbach, Germany
Velibor Pejcic
Institute for Geosciences, Department of Meteorology, University of Bonn, Bonn, Germany
Silke Trömel
Institute for Geosciences, Department of Meteorology, University of Bonn, Bonn, Germany
Laboratory for Clouds and Precipitation
Exploration, Geoverbund ABC/J, Bonn, Germany
Ulrich Blahak
Deutscher Wetterdienst, Offenbach, Germany
Jacob T. Carlin
Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma, USA
NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma, USA
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Atmos. Chem. Phys., 22, 14095–14117, https://doi.org/10.5194/acp-22-14095-2022, https://doi.org/10.5194/acp-22-14095-2022, 2022
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The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol dynamics and a radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. This is demonstrated using a case study of a deep convective storm, which showed that the strong updraft in the convective core of the storm produced aerosol-tower-like features, which affected the size of the hydrometeors and the simulated polarimetric features (e.g., ZDR and KDP columns).
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Atmos. Meas. Tech., 17, 4087–4107, https://doi.org/10.5194/amt-17-4087-2024, https://doi.org/10.5194/amt-17-4087-2024, 2024
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Boundary-layer height observations remain sparse in time and space. In this study we create a new fuzzy logic method for synergistically combining boundary-layer height estimates from a suite of instruments. These estimates generally compare well to those from radiosondes; plus, the approach offers near-continuous estimates through the entire diurnal cycle. Suspected reasons for discrepancies are discussed. The code for the newly presented fuzzy logic method is provided for the community to use.
Lucas Reimann, Clemens Simmer, and Silke Trömel
Atmos. Chem. Phys., 23, 14219–14237, https://doi.org/10.5194/acp-23-14219-2023, https://doi.org/10.5194/acp-23-14219-2023, 2023
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Polarimetric radar observations were assimilated for the first time in a convective-scale numerical weather prediction system in Germany and their impact on short-term precipitation forecasts was evaluated. The assimilation was performed using microphysical retrievals of liquid and ice water content and yielded slightly improved deterministic 9 h precipitation forecasts for three intense summer precipitation cases with respect to the assimilation of radar reflectivity alone.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Mohamed Saadi, Carina Furusho-Percot, Alexandre Belleflamme, Ju-Yu Chen, Silke Trömel, and Stefan Kollet
Nat. Hazards Earth Syst. Sci., 23, 159–177, https://doi.org/10.5194/nhess-23-159-2023, https://doi.org/10.5194/nhess-23-159-2023, 2023
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On 14 July 2021, heavy rainfall fell over central Europe, causing considerable damage and human fatalities. We analyzed how accurate our estimates of rainfall and peak flow were for these flooding events in western Germany. We found that the rainfall estimates from radar measurements were improved by including polarimetric variables and their vertical gradients. Peak flow estimates were highly uncertain due to uncertainties in hydrological model parameters and rainfall measurements.
Prabhakar Shrestha, Jana Mendrok, and Dominik Brunner
Atmos. Chem. Phys., 22, 14095–14117, https://doi.org/10.5194/acp-22-14095-2022, https://doi.org/10.5194/acp-22-14095-2022, 2022
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The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol dynamics and a radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. This is demonstrated using a case study of a deep convective storm, which showed that the strong updraft in the convective core of the storm produced aerosol-tower-like features, which affected the size of the hydrometeors and the simulated polarimetric features (e.g., ZDR and KDP columns).
Sachin Patade, Deepak Waman, Akash Deshmukh, Ashok Kumar Gupta, Arti Jadav, Vaughan T. J. Phillips, Aaron Bansemer, Jacob Carlin, and Alexander Ryzhkov
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This modeling study focuses on the role of multiple groups of primary biological aerosol particles as ice nuclei on cloud properties and precipitation. This was done by implementing a more realistic scheme for biological ice nucleating particles in the aerosol–cloud model. Results show that biological ice nucleating particles have a limited role in altering the ice phase and precipitation in deep convective clouds.
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The study makes use of ensemble numerical simulations with forward operator to evaluate the simulated cloud and precipitation processes with radar observations. While comparing model data with radar has its own challenges due to errors in the forward operator and processed radar measurements, the model was generally found to underestimate the high reflectivity, width/magnitude (value) of ZDR columns and high precipitation.
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, https://doi.org/10.5194/gmd-14-7497-2021, 2021
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Satellite observations of radiation from the earth can have strong sensitivity to cloud and precipitation in the atmosphere, with applications in weather forecasting and the development of models. Computing the radiation received at the satellite sensor using radiative transfer theory requires a simulation of the optical properties of a volume containing a large number of cloud and precipitation particles. This article describes the physics used to generate these
bulkoptical properties.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
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The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Yuefei Zeng, Tijana Janjic, Yuxuan Feng, Ulrich Blahak, Alberto de Lozar, Elisabeth Bauernschubert, Klaus Stephan, and Jinzhong Min
Atmos. Meas. Tech., 14, 5735–5756, https://doi.org/10.5194/amt-14-5735-2021, https://doi.org/10.5194/amt-14-5735-2021, 2021
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Short summary
Observation errors (OEs) of radar measurements are correlated. The Desroziers method has been often used to estimate statistics of OE in data assimilation. However, the resulting statistics consist of contributions from different sources and are difficult to interpret. Here, we use an approach based on samples for truncation error to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with OE statistics estimated by the Desroziers method.
Cited articles
Barrett, A. I., Wellmann, C., Seifert, A., Hoose, C., Vogel, B., and Kunz, M.:
One step at a time: How model time step significantly affects
convection-permitting simulations, J. Adv. Model. Earth
Sy., 11, 641–658, 2019. a
Battaglia, A., Kummerow, C. D., Shin, D.-B., and Williams, C.: Constraining
microwave brightness temperatures by radar bright band observations., J. Ocean. Atmos. Tech., 20, 856–871, 2003. a
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. a, b
Besic, N., Gehring, J., Praz, C., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Unraveling hydrometeor mixtures in polarimetric radar measurements, Atmos. Meas. Tech., 11, 4847–4866, https://doi.org/10.5194/amt-11-4847-2018, 2018. a, b
Blahak, U.: Towards a better representation of high density ice particles in a
state-of-the-art two-moment bulk microphysical scheme, in: Proc. 15th Int.
Conf. Clouds and Precip., Cancun, Mexico, vol. 20208, 2008. a
Blahak, U.: RADAR_MIE_LM and RADAR_MIELIB – Calculation of Radar
Reflectivity from Model Output, COSMO Technical Report 28, Consortium for
Small Scale Modeling (COSMO),
available at: http://www.cosmo-model.org/content/model/documentation/techReports/cosmo/docs/techReport28.pdf (last access: 10 January 2022),
2016. a, b, c, d, e
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small
Particles, John Wiley and Sons, Inc., https://doi.org/10.1002/9783527618156, 1983. a, b
Brandes, E. A., Zhang, G., and Vivekanandan, J.: Experiments in Rainfall
Estimation with a Polarimetric Radar in a Subtropical Environment, J.
Appl. Meteorol., 41, 674–685,
https://doi.org/10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2, 2002. a
Carlin, J. T.: The use of polarimetric radar data for informing numerical
weather prediction models, PhD thesis, University of Oklahoma,
available at: https://shareok.org/handle/11244/299801 (last access: 10 January 2022), 2018. a
Dolan, B. and Rutledge, S. A.: A theory-based hydrometeor identification
algorithm for X-band polarimetric radars, J. Atmos. Ocean.
Tech., 26, 2071–2088, https://doi.org/10.1175/2009JTECHA1208.1, 2009. a, b
Dolan, B., Rutledge, S. A., Lim, S., Chandrasekar, V., and Thurai, M.: A robust
C-band hydrometeor identification algorithm and application to a long-term
polarimetric radar dataset, J. Appl. Meteorol. Clim.,
52, 2162–2186, https://doi.org/10.1175/JAMC-D-12-0275.1, 2013. a, b
Dunnavan, E. L., Jiang, Z., Harrington, J. Y., Verlinde, J., Fitch, K., and
Garrett, T. J.: The Shape and Density Evolution of Snow Aggregates, J. Atmos. Sci., 76, 3919–3940, https://doi.org/10.1175/JAS-D-19-0066.1,
2019. a
Fabry, F. and Szyrmer, W.: Modeling of the melting layer. Part I:
Electromagnetics, J. Atmos. Sci., 56, 3593–3600, 1999. a
Fridlind, A. M., Ackerman, A., McFarquhar, G., Zhang, G., Poellot, M., DeMott,
P., Prenni, A., and Heymsfield, A.: Ice properties of single-layer
stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 2. Model
results, J. Geophys. Res.-Atmos., 112, D24202, https://doi.org/10.1029/2007JD008646, 2007. a
Fridlind, A. M., Li, X., Wu, D., van Lier-Walqui, M., Ackerman, A. S., Tao, W.-K., McFarquhar, G. M., Wu, W., Dong, X., Wang, J., Ryzhkov, A., Zhang, P., Poellot, M. R., Neumann, A., and Tomlinson, J. M.: Derivation of aerosol profiles for MC3E convection studies and use in simulations of the 20 May squall line case, Atmos. Chem. Phys., 17, 5947–5972, https://doi.org/10.5194/acp-17-5947-2017, 2017. a, b, c
Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012. a
Grazioli, J., Tuia, D., and Berne, A.: Hydrometeor classification from polarimetric radar measurements: a clustering approach, Atmos. Meas. Tech., 8, 149–170, https://doi.org/10.5194/amt-8-149-2015, 2015. a
Griffin, E. M., Schuur, T. J., and Ryzhkov, A. V.: A polarimetric analysis of
ice microphysical processes in snow, using quasi-vertical profiles, J. Appl. Meteorol. Clim., 57, 31–50,
https://doi.org/10.1175/JAMC-D-17-0033.1, 2018. a
Han, B., Fan, J., Varble, A., Morrison, H.,Williams, C. R., Chen, B., Dong, X., Giangrande, S. E., Khain, A., Mansell, E., Milbrandt J. A., Sphund J., and Thompson G.: Cloud-resolving model
intercomparison of an MC3E squall line case: Part II. Stratiform
precipitation properties, J. Geophys. Res.-Atmos., 124,
1090–1117, 2019. a, b
Janjić, T., Bormann, N., Bocquet, M., Carton, J. A., Cohn, S. E., Dance,
S. L., Losa, S. N., Nichols, N. K., Potthast, R., Waller, J. A., and Weston,
P.: On the representation error in data assimilation, Q. J. Roy. Meteorol. Soc., 144, 1257–1278, https://doi.org/10.1002/qj.3130,
2018. a
Jung, Y., Zhang, G., and Xue, M.: Assimilation of Simulated Polarimetric Radar
Data for a Convective Storm Using the Ensemble Kalman Filter. Part I:
Observation Operators for Reflectivity and Polarimetric Variables, Mon.
Weather Rev., 136, 2228–2245, 2008. a
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud formation:
Homogeneous freezing of supercooled aerosols, J. Geophys. Res.-Atmos., 107, D24010,
https://doi.org/10.1029/2002JD003220, 2002. a
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based
parameterization of cirrus cloud formation for use in global atmospheric
models, J. Geophys. Res.-Atmos., 111, D01205,
https://doi.org/10.1029/2005JD006219, 2006. a
Korolev, A. and Leisner, T.: Review of experimental studies of secondary ice production, Atmos. Chem. Phys., 20, 11767–11797, https://doi.org/10.5194/acp-20-11767-2020, 2020. a
Korolev, A., Heckman, I., Wolde, M., Ackerman, A. S., Fridlind, A. M., Ladino, L. A., Lawson, R. P., Milbrandt, J., and Williams, E.: A new look at the environmental conditions favorable to secondary ice production, Atmos. Chem. Phys., 20, 1391–1429, https://doi.org/10.5194/acp-20-1391-2020, 2020. a
Kumjian, M. R.: Weather Radars, in: Remote Sensing of Clouds and Precipitation,
edited by: Andronache, C., Springer Remote Sensing/Photogrammetry, 15–63,
2018. a
Matrosov, S., Reinking, R., and Rjalalova, I.: Inferring fall attitudes of
pristine dendritic crystals from polarimetric radar data, J.
Atmos. Sci., 62, 241–250, 2005. a
Matsui, T., Dolan, B., Rutledge, S. A., Tao, W., Iguchi, T., Barnum, J., and
Lang, S. E.: POLARRIS: A POLArimetric Radar Retrieval and Instrument
Simulator, J. Geophys. Res.-Atmos., 124, 4634–4657,
https://doi.org/10.1029/2018JD028317, 2019. a, b
Maxwell Garnett, J. C.: Colours in metal glasses, in metallic films and in
metallic solutions – II, Proc. Roy. Soc. A, 76, 370–373,
https://doi.org/10.1098/rspa.1905.0039, 1905. a
Mishchenko, M. I.: Calculation of the amplitude matrix for a nonspherical
particle in a fixed orientation, Appl. Optics, 39, 1026–1031,
https://doi.org/10.1364/AO.39.001026, 2000. a
Morrison, H. and Milbrandt, J. A.: Parameterization of Cloud Microphysics Based
on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description
and Idealized Tests, J. Atmos. Sci., 72, 287–311,
https://doi.org/10.1175/JAS-D-14-0065.1, 2015. a
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W., Harrington, J.
Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A., Pawlowska, H., Posselt, D. J., Prat,
O. P., Remel, K. J., Shima, S.-I., Diedenhoven, B., and Xue, L.: Confronting the challenge of modeling cloud and
precipitation microphysics, J. Adv. Model. Earth Sy.,
12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a
Noppel, H., Blahak, U., Seifert, A., and Beheng, K. D.: Simulations of a
hailstorm and the impact of CCN using an advanced two-moment cloud
microphysical scheme, Atmos. Res., 96, 286–301, 2010. a
Ori, D., Schemann, V., Karrer, M., Dias Neto, J., von Terzi, L., Seifert, A.,
and Kneifel, S.: Evaluation of ice particle growth in ICON using statistics
of multi-frequency Doppler cloud radar observations, Q. J.
Roy. Meteor. Soc., 146, 3830–3849, 2020. a
Pejcic, V., Simmer, C., and Trömel, S.: Polarimetric radar-based methods
for evaluation of hydrometeor mixtures in numerical weather prediction
models, in: 2021 21st International Radar Symposium (IRS), 1–10,
https://doi.org/10.23919/IRS51887.2021.9466201, 2021. a
Pejcic, V., Soderholm, J., Mühlbauer, K., Louf, V., and Trömel, S.: Five
Years Calibrated Observations from the University of Bonn X-band Weather
Radar (BoXPol), Sci. Data, in review, 2022. a
Peters-Lidard, C. D., Kemp, E. M., Matsui, T., Santanello Jr., J. A., Kumar, S. V., Jacob, J.
P., Clune, T., Tao, W.-K., Chin, M., Hou, A., Case, J. L., Kim, D., Kim K.-M., Lau, W., Liu, Y.,
Shi, J., Starr, D., Tan, Q., Tao, Z., Zaitchik, B. F., Zavodsky, B., Zhang, S. Q., and Zupanski, M.:
Integrated modeling of aerosol, cloud, precipitation and land processes at
satellite-resolved scales, Environ. Modell. Softw., 67, 149–159,
2015. a
Petty, G. W. and Huang, W.: The Modified Gamma Size Distribution Applied to
Inhomogeneous and Nonspherical Particles: Key Relationships and Conversions,
J. Atmos. Sci., 68, 1460–1473,
https://doi.org/10.1175/2011JAS3645.1, 2011. a
Ribaud, J.-F., Machado, L. A. T., and Biscaro, T.: X-band dual-polarization radar-based hydrometeor classification for Brazilian tropical precipitation systems, Atmos. Meas. Tech., 12, 811–837, https://doi.org/10.5194/amt-12-811-2019, 2019. a
Ryzhkov, A., Zhang, P., Reeves, H., Kumjian, M., Tschallener, T., Trömel,
S., and Simmer, C.: Quasi-vertical profiles – A new way to look at
polarimetric radar data, J. Atmos. Ocean. Tech., 33,
551–562, 2016. a
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Khain, A. P.: Polarimetric
Radar Characteristics of Melting Hail. Part I: Theoretical Simulations Using
Spectral Microphysical Modeling, J. Appl. Meteorol.
Clim., 52, 2849–2870, https://doi.org/10.1175/JAMC-D-13-073.1,
2013a. a
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Zhang, P.: Polarimetric
Radar Characteristics of Melting Hail. Part II: Practical Implications,
J. Appl. Meteorol. Clim., 52, 2871–2886,
https://doi.org/10.1175/JAMC-D-13-074.1, 2013b. a
Ryzhkov, A. V., Snyder, J., Carlin, J. T., Khain, A., and Pinsky, M.: What
polarimetric weather radars offer to cloud modelers: forward radar operators
and microphysical/thermodynamic retrievals, Atmosphere, 11, 362,
https://doi.org/10.3390/atmos11040362, 2020. a
Schrom, R. S. and Kumjian, M. R.: Bulk-Density Representations of Branched
Planar Ice Crystals: Errors in the Polarimetric Radar Variables, J.
Appl. Meteorol. Clim., 57, 333–346,
https://doi.org/10.1175/JAMC-D-17-0114.1, 2018. a
Segal, Y. and Khain, A.: Dependence of droplet concentration on aerosol
conditions in different cloud types: Application to droplet concentration
parameterization of aerosol conditions, J. Geophys. Res.-Atmos., 111, D15204, https://doi.org/10.1029/2005JD006561, 2006. a
Seifert, A.: On the parameterization of evaporation of raindrops as simulated
by a one-dimensional rainshaft model, J. Atmos. Sci.,
65, 3608–3619, 2008. a
Shrestha, P.: Clouds and Vegetation Modulate Shallow Groundwater Table Depth,
J. Hydrometeorol., 22, 753–763, https://doi.org/10.1175/JHM-D-20-0171.1,
2021. a
Shrestha, P., Mendrok, J., Pejcic, V., Trömel, S., Blahak, U., and Carlin,
J. T.: Software documentation for COSMO model (v5.1) evaluation with X-band
polarimetric radar data using B-PRO (v2.0), Zenodo [data set], https://doi.org/10.5281/zenodo.5218717,
2021. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang,
W., and Powers, J. G.: A description of the Advanced Research WRF version 3.
NCAR Technical note-475+ STR, Tech. rep., University Corporation for
Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008. a
Snyder, J. C., Bluestein, H. B., Dawson II, D. T., and Jung, Y.: Simulations of
polarimetric, X-band radar signatures in supercells. Part I: Description of
experiment and simulated ρ hv rings, J. Appl. Meteorol.
Climatol., 56, 1977–1999, 2017. a
Steppeler, J., Doms, G., Schättler, U., Bitzer, H., Gassmann, A., Damrath,
U., and Gregoric, G.: Meso-gamma scale forecasts using the nonhydrostatic
model LM, Meteorol. Atmos. Phys., 82, 75–96, 2003. a
Stewart, R. E., Marwitz, J. D., Pace, J. C., and Carbone, R. E.:
Characteristics through the melting layer of stratiform clouds, J. Atmos. Sci., 41, 3227–3237, 1984. a
Straka, J. M., Zrnić, D. S., and Ryzhkov, A. V.: Bulk hydrometeor
classification and quantification using polarimetric radar data: Synthesis of
relations, J. Appl. Meteorol., 39, 1341–1372,
https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2, 2000. a, b, c
Takahashi, T., Nagao, Y., and Kushiyama, Y.: Possible High Ice Particle
Production during Graupel–Graupel Collisions, J. Atmos.
Sci., 52, 4523–4527,
https://doi.org/10.1175/1520-0469(1995)052<4523:PHIPPD>2.0.CO;2, 1995. a
Tao, W.-K. and Simpson, J.: The Goddard cumulus ensemble model. Part I: Model
description, Terr.-Atmos. Ocean. Sci, 4, 35–72, 1993. a
Thompson, G., Berner, J., Frediani, M., Otkin, J. A., and Griffin, S. M.: A
Stochastic Parameter Perturbation Method to Represent Uncertainty in a
Microphysics Scheme, Mon. Weather Rev., 149, 1481–1497, https://doi.org/10.1175/MWR-D-20-0077.1,
2021. a
Trömel, S., Ryzhkov, A. V., Zhang, P., and Simmer, C.: Investigations of
backscatter differential phase in the melting layer, J. Appl.
Meteorol. Clim., 53, 2344–2359, 2014. a
Trömel, S., Ryzhkov, A. V., Hickman, B., Mühlbauer, K., and Simmer, C.:
Polarimetric Radar Variables in the Layers of Melting and Dendritic Growth at
X Band–Implications for a Nowcasting Strategy in Stratiform Rain, J.
Appl. Meteorol. Clim., 58, 2497–2522, 2019. a
Trömel, S., Simmer, C., Blahak, U., Blanke, A., Doktorowski, S., Ewald, F., Frech, M., Gergely, M., Hagen, M., Janjic, 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., and Quaas, J.: Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes, Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, 2021.
a, b
van Lier-Walqui, M., Vukicevic, T., and Posselt, D. J.: Quantification of Cloud
Microphysical Parameterization Uncertainty Using Radar Reflectivity, Mon.
Weather Rev., 140, 3442–3466, https://doi.org/10.1175/MWR-D-11-00216.1, 2012. a
van Weverberg, K., Goudenhoofdt, E., Blahak, U., Marbaix, P., and van Ypersele,
J.-P.: Comparison of One-Moment and Two-Moment Bulk Microphysics for
High-Resolution Climate Simulations of Intense Precipitation, Atmos.
Res., 147–148, 145–161, https://doi.org/10.1016/j.atmosres.2014.05.012, 2014. a, b
Ward, J. H. J.: Hierarchical Grouping to Optimize an Objective Function,
J. Am. Stat. Assoc., 58, 236–244,
https://doi.org/10.1080/01621459.1963.10500845, 1963. a
Wolfensberger, D. and Berne, A.: From model to radar variables: a new forward polarimetric radar operator for COSMO, Atmos. Meas. Tech., 11, 3883–3916, https://doi.org/10.5194/amt-11-3883-2018, 2018. a, b, c
Wolfensberger, D., Scipion, D., and Berne, A.: Detection and characterization
of the melting layer based on polarimetric radar scans, Q. J. Roy. Meteor.
Soc., 142, 108–124, 2016. a
Xie, X., Shrestha, P., Mendrok, J., Carlin, J., Trömel, S., and Blahak, U.:
Bonn Polarimetric Radar forward Operator (B-PRO), Collaborative Research Centre / Transregio 32 Database [code], https://doi.org/10.5880/TR32DB.41,
2021. a, b
Zeng, Y., Blahak, U., and Jerger, D.: An efficient modular volume-scanning
radar forward operator for NWP models: description and coupling to the COSMO
model, Q. J. Roy. Meteor. Soc., 142,
3234–3256, https://doi.org/10.1002/qj.2904, 2016. a, b
Zrnic, D. S., Ryzhkov, A., Straka, J., Liu, Y., and Vivekanandan, J.: Testing
a Procedure for Automatic Classification of Hydrometeor Types, J.
Atmos. Ocean. Tech., 18, 892–913,
https://doi.org/10.1175/1520-0426(2001)018<0892:TAPFAC>2.0.CO;2, 2001. a, b, c
Short summary
The article focuses on the exploitation of radar polarimetry for model evaluation of stratiform precipitation. The model exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models in the forward operator to draw valid conclusions.
The article focuses on the exploitation of radar polarimetry for model evaluation of stratiform...
