Articles | Volume 18, issue 10
https://doi.org/10.5194/amt-18-2261-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2261-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An introduction of the Three-Dimensional Precipitation Particle Imager (3D-PPI)
Jiayi Shi
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China
Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China
Liying Liu
Aerospace NewSky Technology Co., Ltd, Wuxi, China
Peng Wang
Aerospace NewSky Technology Co., Ltd, Wuxi, China
Related authors
No articles found.
Jinfeng Ding, Yuan Shang, Yulong Shan, Jinkai Ma, Jin Ye, Xichuan Liu, Lei Liu, and Xiaoqiao Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2718, https://doi.org/10.5194/egusphere-2025-2718, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This study employed the numerical model to simulate an intense blowing snow event near Zhongshan Station, East Antarctica, from July 15–17, 2022. While primarily driven by a mid-latitude cyclone, the event’s evolution was strongly modulated by complex local topography, which influenced airflow and enhanced snow transport. Terrain-induced uplift intensified snowfall and prolonged blizzard conditions, underscoring the importance of high-resolution modeling for Antarctic weather research.
Pingyi Dong, Xingwen Jiang, Xingbing Zhao, Yuanchang Dong, Jiafeng Zheng, Chun Hu, Guolu Gao, Lei Liu, Shulei Li, and Lingbing Bu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2523, https://doi.org/10.5194/egusphere-2025-2523, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
A method is developed and validated for retrieving vertical profiles of DSD parameters from a single-frequency Ka-band radar in this study. Some unique characteristics of the vertical profiles of DSD parameters in the eastern Tibetan Plateau are found. The empirical relationships for quantitative precipitation estimates and attenuation correction in the eastern Tibetan Plateau with Ka-band radar are derived.
Runzhuo Fang, Jinfeng Ding, Wenjuan Gao, Xi Liang, Zhuoqi Chen, Chuanfeng Zhao, Haijin Dai, and Lei Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-186, https://doi.org/10.5194/essd-2025-186, 2025
Preprint under review for ESSD
Short summary
Short summary
IMPMCT is a dataset containing a 24-year record (2001–2024) of polar storms in the Nordic Seas. These storms, called Polar Mesoscale Cyclones (PMCs), sometimes cause extreme winds and waves, threatening marine operations. IMPMCT combines remote sensing measurements and reanalysis data to construct a comprehensive PMCs archive. It includes 1,184 PMCs tracks, 16,630 cloud patterns, and 4,373 wind records, providing fundamental data for advancing our understanding of their development mechanisms.
Wei Huang, Lei Liu, Bin Yang, Shuai Hu, Wanying Yang, Zhenfeng Li, Wantong Li, and Xiaofan Yang
Atmos. Meas. Tech., 16, 4101–4114, https://doi.org/10.5194/amt-16-4101-2023, https://doi.org/10.5194/amt-16-4101-2023, 2023
Short summary
Short summary
To improve the retrieval speed of the AERI optimal estimation (AERIoe) method, a fast-retrieval algorithm named Fast AERIoe is proposed on the basis of the findings that the change in Jacobians during the retrieval process had little effect on the performance of AERIoe. The results of the experiment show that the retrieved profiles from Fast AERIoe are comparable to those of AERIoe and that the retrieval speed is significantly improved, with the average retrieval time reduced by 59 %.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech., 16, 331–353, https://doi.org/10.5194/amt-16-331-2023, https://doi.org/10.5194/amt-16-331-2023, 2023
Short summary
Short summary
Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the sphere and hexagonal column models.
Cited articles
Bailey, M. and Hallett, J.: Ice Crystal Linear Growth Rates from −20 ° to −70 °C: Confirmation from Wave Cloud Studies, J. Atmos. Sci., 69, 390–402, https://doi.org/10.1175/jas-d-11-035.1, 2012.
Bataineh, B., Abdullah, S. N. H. S., and Omar, K.: An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows, Pattern Recogn. Lett., 32, 1805–1813, https://doi.org/10.1016/j.patrec.2011.08.001, 2011.
Battaglia, A., Rustemeier, E., Tokay, A., Blahak, U., and Simmer, C.: PARSIVEL Snow Observations: A Critical Assessment, J. Atmos. Ocean. Tech., 27, 333–344, https://doi.org/10.1175/2009jtecha1332.1, 2010.
Bernauer, F., Hürkamp, K., Rühm, W., and Tschiersch, J.: Snow event classification with a 2D video disdrometer – A decision tree approach, Atmos. Res., 172–173, 186–195, https://doi.org/10.1016/j.atmosres.2016.01.001, 2016.
Cheng, Q. and Huang, P.: Camera Calibration Based on Phase Estimation, IEEE T. Instrum. Meas., 72, 1–9, https://doi.org/10.1109/tim.2022.3227554, 2023.
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.
Grazioli, J., Ghiggi, G., Billault-Roux, A.-C., and Berne, A.: MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall, Sci. Data, 9, 186, https://doi.org/10.1038/s41597-022-01269-7, 2022.
Hauswiesner, S., Straka, M., and Reitmayr, G.: Temporal coherence in image-based visual hull rendering, IEEE T. Vis. Comput. Gr., 19, 1758–1767, https://doi.org/10.1109/tvcg.2013.85, 2013.
Helms, C. N., Munchak, S. J., Tokay, A., and Pettersen, C.: A comparative evaluation of snowflake particle shape estimation techniques used by the Precipitation Imaging Package (PIP), Multi-Angle Snowflake Camera (MASC), and Two-Dimensional Video Disdrometer (2DVD), Atmos. Meas. Tech., 15, 6545–6561, https://doi.org/10.5194/amt-15-6545-2022, 2022.
Kim, M.-J., Kulie, M. S., O'Dell, C., and Bennartz, R.: Scattering of Ice Particles at Microwave Frequencies: A Physically Based Parameterization, J. Appl. Meteorol. Clim., 46, 615–633, https://doi.org/10.1175/jam2483.1, 2007.
Kim, M. J.: Single scattering parameters of randomly oriented snow particles at microwave frequencies, J. Geophys. Res.-Atmos., 111, D14201, https://doi.org/10.1029/2005jd006892, 2006.
Kleinkort, C., Huang, G. J., Bringi, V. N., and Notaroš, B. M.: Visual Hull Method for Realistic 3D Particle Shape Reconstruction Based on High-Resolution Photographs of Snowflakes in Free Fall from Multiple Views, J. Atmos. Ocean. Tech., 34, 679–702, https://doi.org/10.1175/JTECH-D-16-0099.1, 2017.
Kneifel, S., Löhnert, U., Battaglia, A., Crewell, S., and Siebler, D.: Snow scattering signals in ground-based passive microwave radiometer measurements, J. Geophys. Res.-Atmos., 115, D16214, https://doi.org/10.1029/2010jd013856, 2010.
Leinonen, J., Grazioli, J., and Berne, A.: Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) images, Atmos. Meas. Tech., 14, 6851–6866, https://doi.org/10.5194/amt-14-6851-2021, 2021.
Liu, X., He, B., Zhao, S., Hu, S., and Liu, L.: Comparative measurement of rainfall with a precipitation micro-physical characteristics sensor, a 2D video disdrometer, an OTT PARSIVEL disdrometer, and a rain gauge, Atmos. Res., 229, 100–114, https://doi.org/10.1016/j.atmosres.2019.06.020, 2019.
Liu, X. C., Gao, T. C., and Liu, L.: A video precipitation sensor for imaging and velocimetry of hydrometeors, Atmos. Meas. Tech., 7, 2037–2046, https://doi.org/10.5194/amt-7-2037-2014, 2014.
Locatelli, J. D. and Hobbs, P. V.: Fall speeds and masses of solid precipitation particles, J. Geophys. Res., 79, 2185–2197, https://doi.org/10.1029/JC079i015p02185, 1974.
Loffler-Mang, M. and Joss, J.: An Optical Disdrometer for Measuring Size and Velocity of Hydrometeors, J. Atmos. Ocean. Tech., 17, 130–139, https://doi.org/10.1175/1520-0426(2000)017<0130:Aodfms>2.0.Co;2, 2000.
Maahn, M., Moisseev, D., Steinke, I., Maherndl, N., and Shupe, M. D.: Introducing the Video In Situ Snowfall Sensor (VISSS), Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, 2024.
Mason, S. L., Hogan, R. J., Westbrook, C. D., Kneifel, S., Moisseev, D., and von Terzi, L.: The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow, Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, 2019.
Minda, H., Tsuda, N., and Fujiyoshi, Y.: Three-Dimensional Shape and Fall Velocity Measurements of Snowflakes Using a Multiangle Snowflake Imager, J. Atmos. Ocean. Tech., 34, 1763–1781, https://doi.org/10.1175/jtech-d-16-0221.1, 2017.
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., Reimel, K. J., Shima, S. I., van 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.
Newman, A. J., Kucera, P. A., and Bliven, L. F.: Presenting the Snowflake Video Imager (SVI), J. Atmos. Ocean. Tech., 26, 167–179, https://doi.org/10.1175/2008JTECHA1148.1, 2009.
Notaroš, B. M., Bringi, V. N., Kleinkort, C., Kennedy, P., Huang, G.-J., Thurai, M., Newman, A. J., Bang, W., and Lee, G.: Accurate Characterization of Winter Precipitation Using Multi-Angle Snowflake Camera, Visual Hull, Advanced Scattering Methods and Polarimetric Radar, Atmosphere, 7, 81, https://doi.org/10.3390/atmos7060081, 2016.
Olson, W. S., Tian, L., Grecu, M., Kuo, K.-S., Johnson, B. T., Heymsfield, A. J., Bansemer, A., Heymsfield, G. M., Wang, J. R., and Meneghini, R.: The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer, and In Situ Observations, J. Appl. Meteorol. Clim., 55, 709–722, https://doi.org/10.1175/jamc-d-15-0131.1, 2016.
Pettersen, C., Bliven, L. F., von Lerber, A., Wood, N. B., Kulie, M. S., Mateling, M. E., Moisseev, D. N., Munchak, S. J., Petersen, W. A., and Wolff, D. B.: The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow, Atmosphere, 11, 785, https://doi.org/10.3390/atmos11080785, 2020.
Taylor, P. A.: H. R. Pruppacher and J. D. Klett, Microphysics of Clouds and Precipitation, Bound.-Lay. Meteorol., 86, 187–188, https://doi.org/10.1023/a:1000652616430, 1998.
Tyynelä, J., Leinonen, J., Moisseev, D., and Nousiainen, T.: Radar Backscattering from Snowflakes: Comparison of Fractal, Aggregate, and Soft Spheroid Models, J. Atmos. Ocean. Tech., 28, 1365–1372, https://doi.org/10.1175/jtech-d-11-00004.1, 2011.
Zhang, Y., Zhang, L., Lei, H., Xie, Y., Wen, L., Yang, J., and Wu, Z.: Characteristics of Summer Season Raindrop Size Distribution in Three Typical Regions of Western Pacific, J. Geophys. Res.-Atmos., 124, 4054–4073, https://doi.org/10.1029/2018jd029194, 2019.
Short summary
The Three-Dimensional Precipitation Particle Imager (3D-PPI) was introduced as a new instrument to measure the three-dimensional shape, size, and falling velocity of precipitation particles. Field experiments of the 3D-PPI were conducted in Tulihe, China, during the winter of 2023 to 2024. More than 880 000 snowflakes in a typical snowfall case lasting 13 h were recorded. It shows potential applications in atmospheric science, polar research, and other fields.
The Three-Dimensional Precipitation Particle Imager (3D-PPI) was introduced as a new instrument...