Articles | Volume 18, issue 18
https://doi.org/10.5194/amt-18-4839-2025
© 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-18-4839-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Sep 2025
Research article |
| 26 Sep 2025
| 26 Sep 2025
A hybrid algorithm for ship clutter identification in pulse compression polarimetric radar observations
Shuai Zhang
Meteorological Observation Center, China Meteorological Administration, Beijing, China
State Key Laboratory of Environment Characteristics and Effects for Near-space, Beijing, China
Engineering Technology Research Center for Meteorological Observation, China Meteorological Administration, Beijing, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Dmitri Moisseev
CORRESPONDING AUTHOR
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Finnish Meteorological Institute, Helsinki, Finland
Matti Leskinen
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
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Jiayi Sun, Junmei Lv, Haoran Li, Wenqian Zhang, Biao Tian, and Minghu Ding
EGUsphere, https://doi.org/10.5194/egusphere-2026-862, https://doi.org/10.5194/egusphere-2026-862, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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This study reports the first-year Micro Rain Radar observations from Great Wall Station (March 2024–November 2025). Surface precipitation (snowfall) occurs 0.32 (0.23), and the radar signal changes little with height, indicating frequent shallow snowfall. Wind effects are weak below 10 m s⁻¹ but strong at higher speeds, consistent with blowing snow affecting signals up to 0.7 km. A locally fitted radar reflectivity–snowfall rate relationship shows ERA5 underestimates cumulative snowfall.
Bernd Mom and Dmitri Moisseev
EGUsphere, https://doi.org/10.5194/egusphere-2026-507, https://doi.org/10.5194/egusphere-2026-507, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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A method for estimating wet radome and rain attenuation in cloud radar observations using a disdrometer is developed. Because of the uncertainty in disdrometer measurements, we created a statistical model that provides an estimate for the drop size distribution parameters and radar variables. Analysis of selected case studies indicates that wet radome attenuation can be successfully estimated. The mitigation of rain attenuation, however, is affected by cloud layers embedded in rain.
Qinghui Li, Haoran Li, Xuejin Sun, Yun Zhang, Weitao Lyu, Zheng Ruan, Liping Liu, Aiming Liu, and Chunsheng Zhang
Atmos. Chem. Phys., 26, 1249–1264, https://doi.org/10.5194/acp-26-1249-2026, https://doi.org/10.5194/acp-26-1249-2026, 2026
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Despite the increasing complexity of snow microphysics schemes employed in numerical models, whether the dominant snow microphysical process is reasonably identified remains an open question. This study using unprecedented triple-frequency radar observations for the first time unravels the key snow growth processes over diverse geographies. The unique cross-continental datasets from triple-frequency campaigns shed new insights for model evaluation and future satellite missions.
Jenna Ritvanen, Martin Aregger, Dmitri Moisseev, Urs Germann, Alessandro Hering, and Seppo Pulkkinen
EGUsphere, https://doi.org/10.5194/egusphere-2025-5697, https://doi.org/10.5194/egusphere-2025-5697, 2025
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Convective storms pose several hazards, like heavy rainfall, but operational short-term forecasting (nowcasting) suffers from limited models of storm development. Cell tracking, commonly used for nowcasting of convective storms and analyzing storm development, is complicated by splits and merges. We show how splits and merges can be integrated into cell track analysis, using case studies and analysis of split and merge events with operational data from the Swiss weather radar network.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025, https://doi.org/10.5194/amt-18-793-2025, 2025
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Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
Chuanhong Zhao, Yijun Zhang, Dong Zheng, Haoran Li, Sai Du, Xueyan Peng, Xiantong Liu, Pengguo Zhao, Jiafeng Zheng, and Juan Shi
Atmos. Chem. Phys., 24, 11637–11651, https://doi.org/10.5194/acp-24-11637-2024, https://doi.org/10.5194/acp-24-11637-2024, 2024
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Understanding lightning activity is important for meteorology and atmospheric chemistry. However, the occurrence of lightning activity in clouds is uncertain. In this study, we quantified the difference between isolated thunderstorms and non-thunderstorms. We showed that lightning activity was more likely to occur with more graupel volume and/or riming. A deeper ZDR column was associated with lightning occurrence. This information can aid in a deeper understanding of lighting physics.
Zoé Brasseur, Julia Schneider, Janne Lampilahti, Ville Vakkari, Victoria A. Sinclair, Christina J. Williamson, Carlton Xavier, Dmitri Moisseev, Markus Hartmann, Pyry Poutanen, Markus Lampimäki, Markku Kulmala, Tuukka Petäjä, Katrianne Lehtipalo, Erik S. Thomson, Kristina Höhler, Ottmar Möhler, and Jonathan Duplissy
Atmos. Chem. Phys., 24, 11305–11332, https://doi.org/10.5194/acp-24-11305-2024, https://doi.org/10.5194/acp-24-11305-2024, 2024
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Ice-nucleating particles (INPs) strongly influence the formation of clouds by initiating the formation of ice crystals. However, very little is known about the vertical distribution of INPs in the atmosphere. Here, we present aircraft measurements of INP concentrations above the Finnish boreal forest. Results show that near-surface INPs are efficiently transported and mixed within the boundary layer and occasionally reach the free troposphere.
Deli Meng, Jianping Guo, Xiaoran Guo, Yinjun Wang, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Haoran Li, Fan Zhang, Bing Tong, Hui Xu, and Tianmeng Chen
Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, https://doi.org/10.5194/acp-24-8703-2024, 2024
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The turbulence in the planetary boundary layer (PBL) over the Tibetan Plateau (TP) remains unclear. Here we elucidate the vertical profile of and temporal variation in the turbulence dissipation rate in the PBL over the TP based on a radar wind profiler (RWP) network. To the best of our knowledge, this is the first time that the turbulence profile over the whole TP has been revealed. Furthermore, the possible mechanisms of clouds acting on the PBL turbulence structure are investigated.
Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe
Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, https://doi.org/10.5194/amt-17-899-2024, 2024
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The open-source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for characterizing particle shape, size, and sedimentation velocity in snowfall. It combines a large observation volume with relatively high resolution and a design that limits wind perturbations. The open-source nature of the VISSS hardware and software invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
Roberto Cremonini, Tanel Voormansik, Piia Post, and Dmitri Moisseev
Atmos. Meas. Tech., 16, 2943–2956, https://doi.org/10.5194/amt-16-2943-2023, https://doi.org/10.5194/amt-16-2943-2023, 2023
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Extreme rainfall for a specific location is commonly evaluated when designing stormwater management systems. This study investigates the use of quantitative precipitation estimations (QPEs) based on polarimetric weather radar data, without rain gauge corrections, to estimate 1 h rainfall total maxima in Italy and Estonia. We show that dual-polarization weather radar provides reliable QPEs and effective estimations of return periods for extreme rainfall in climatologically homogeneous regions.
Haoran Li, Dmitri Moisseev, Yali Luo, Liping Liu, Zheng Ruan, Liman Cui, and Xinghua Bao
Hydrol. Earth Syst. Sci., 27, 1033–1046, https://doi.org/10.5194/hess-27-1033-2023, https://doi.org/10.5194/hess-27-1033-2023, 2023
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A rainfall event that occurred at Zhengzhou on 20 July 2021 caused tremendous loss of life and property. This study compares different KDP estimation methods as well as the resulting QPE outcomes. The results show that the selection of the KDP estimation method has minimal impact on QPE, whereas the inadequate assumption of rain microphysics and unquantified vertical air motion may explain the underestimated 201.9 mm h−1 record.
Jenna Ritvanen, Ewan O'Connor, Dmitri Moisseev, Raisa Lehtinen, Jani Tyynelä, and Ludovic Thobois
Atmos. Meas. Tech., 15, 6507–6519, https://doi.org/10.5194/amt-15-6507-2022, https://doi.org/10.5194/amt-15-6507-2022, 2022
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Doppler lidars and weather radars provide accurate wind measurements, with Doppler lidar usually performing better in dry weather conditions and weather radar performing better when there is precipitation. Operating both instruments together should therefore improve the overall performance. We investigate how well a co-located Doppler lidar and X-band radar perform with respect to various weather conditions, including changes in horizontal visibility, cloud altitude, and precipitation.
Han Ding, Haoran Li, and Liping Liu
Atmos. Meas. Tech., 15, 6181–6200, https://doi.org/10.5194/amt-15-6181-2022, https://doi.org/10.5194/amt-15-6181-2022, 2022
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In this study, a framework for processing the Doppler spectra observations of a multi-mode pulse compression Ka–Ku cloud radar system is presented. We first proposed an approach to identify and remove the clutter signals in the Doppler spectrum. Then, we developed a new algorithm to remove the range sidelobe at the modes implementing the pulse compression technique. The radar observations from different modes were then merged using the shift-then-average method.
Silvia M. Calderón, Juha Tonttila, Angela Buchholz, Jorma Joutsensaari, Mika Komppula, Ari Leskinen, Liqing Hao, Dmitri Moisseev, Iida Pullinen, Petri Tiitta, Jian Xu, Annele Virtanen, Harri Kokkola, and Sami Romakkaniemi
Atmos. Chem. Phys., 22, 12417–12441, https://doi.org/10.5194/acp-22-12417-2022, https://doi.org/10.5194/acp-22-12417-2022, 2022
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The spatial and temporal restrictions of observations and oversimplified aerosol representation in large eddy simulations (LES) limit our understanding of aerosol–stratocumulus interactions. In this closure study of in situ and remote sensing observations and outputs from UCLALES–SALSA, we have assessed the role of convective overturning and aerosol effects in two cloud events observed at the Puijo SMEAR IV station, Finland, a diurnal-high aerosol case and a nocturnal-low aerosol case.
Victoria Anne Sinclair, Jenna Ritvanen, Gabin Urbancic, Irene Erner, Yurii Batrak, Dmitri Moisseev, and Mona Kurppa
Atmos. Meas. Tech., 15, 3075–3103, https://doi.org/10.5194/amt-15-3075-2022, https://doi.org/10.5194/amt-15-3075-2022, 2022
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We investigate the boundary-layer (BL) height and surface stability in southern Finland using radiosondes, a microwave radiometer and ERA5 reanalysis. Accurately quantifying the BL height is challenging, and the diagnosed BL height can depend strongly on the method used. Microwave radiometers provide reliable estimates of the BL height but only in unstable conditions. ERA5 captures the BL height well except under very stable conditions, which occur most commonly at night during the warm season.
Zoé Brasseur, Dimitri Castarède, Erik S. Thomson, Michael P. Adams, Saskia Drossaart van Dusseldorp, Paavo Heikkilä, Kimmo Korhonen, Janne Lampilahti, Mikhail Paramonov, Julia Schneider, Franziska Vogel, Yusheng Wu, Jonathan P. D. Abbatt, Nina S. Atanasova, Dennis H. Bamford, Barbara Bertozzi, Matthew Boyer, David Brus, Martin I. Daily, Romy Fösig, Ellen Gute, Alexander D. Harrison, Paula Hietala, Kristina Höhler, Zamin A. Kanji, Jorma Keskinen, Larissa Lacher, Markus Lampimäki, Janne Levula, Antti Manninen, Jens Nadolny, Maija Peltola, Grace C. E. Porter, Pyry Poutanen, Ulrike Proske, Tobias Schorr, Nsikanabasi Silas Umo, János Stenszky, Annele Virtanen, Dmitri Moisseev, Markku Kulmala, Benjamin J. Murray, Tuukka Petäjä, Ottmar Möhler, and Jonathan Duplissy
Atmos. Chem. Phys., 22, 5117–5145, https://doi.org/10.5194/acp-22-5117-2022, https://doi.org/10.5194/acp-22-5117-2022, 2022
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The present measurement report introduces the ice nucleation campaign organized in Hyytiälä, Finland, in 2018 (HyICE-2018). We provide an overview of the campaign settings, and we describe the measurement infrastructure and operating procedures used. In addition, we use results from ice nucleation instrument inter-comparison to show that the suite of these instruments deployed during the campaign reports consistent results.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, https://doi.org/10.5194/amt-15-365-2022, 2022
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We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
Anna Franck, Dmitri Moisseev, Ville Vakkari, Matti Leskinen, Janne Lampilahti, Veli-Matti Kerminen, and Ewan O'Connor
Atmos. Meas. Tech., 14, 7341–7353, https://doi.org/10.5194/amt-14-7341-2021, https://doi.org/10.5194/amt-14-7341-2021, 2021
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We proposed a method to derive a convective boundary layer height, using insects in radar observations, and we investigated the consistency of these retrievals among different radar frequencies (5, 35 and 94 GHz). This method can be applied to radars at other measurement stations and serve as additional way to estimate the boundary layer height during summer. The entrainment zone was also observed by the 5 GHz radar above the boundary layer in the form of a Bragg scatter layer.
Haoran Li, Ottmar Möhler, Tuukka Petäjä, and Dmitri Moisseev
Atmos. Chem. Phys., 21, 14671–14686, https://doi.org/10.5194/acp-21-14671-2021, https://doi.org/10.5194/acp-21-14671-2021, 2021
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In natural clouds, ice-nucleating particles are expected to be rare above –10 °C. In the current paper, we found that the formation of ice columns is frequent in stratiform clouds and is associated with increased precipitation intensity and liquid water path. In single-layer shallow clouds, the production of ice columns was attributed to secondary ice production, despite the rime-splintering process not being expected to take place in such clouds.
Haoran Li, Alexei Korolev, and Dmitri Moisseev
Atmos. Chem. Phys., 21, 13593–13608, https://doi.org/10.5194/acp-21-13593-2021, https://doi.org/10.5194/acp-21-13593-2021, 2021
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Kelvin–Helmholtz (K–H) clouds embedded in a stratiform precipitation event were uncovered via radar Doppler spectral analysis. Given the unprecedented detail of the observations, we show that multiple populations of secondary ice columns were generated in the pockets where larger cloud droplets are formed and not at some constant level within the cloud. Our results highlight that the K–H instability is favorable for liquid droplet growth and secondary ice formation.
Julia Schneider, Kristina Höhler, Paavo Heikkilä, Jorma Keskinen, Barbara Bertozzi, Pia Bogert, Tobias Schorr, Nsikanabasi Silas Umo, Franziska Vogel, Zoé Brasseur, Yusheng Wu, Simo Hakala, Jonathan Duplissy, Dmitri Moisseev, Markku Kulmala, Michael P. Adams, Benjamin J. Murray, Kimmo Korhonen, Liqing Hao, Erik S. Thomson, Dimitri Castarède, Thomas Leisner, Tuukka Petäjä, and Ottmar Möhler
Atmos. Chem. Phys., 21, 3899–3918, https://doi.org/10.5194/acp-21-3899-2021, https://doi.org/10.5194/acp-21-3899-2021, 2021
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By triggering the formation of ice crystals, ice-nucleating particles (INP) strongly influence cloud formation. Continuous, long-term measurements are needed to characterize the atmospheric INP variability. Here, a first long-term time series of INP spectra measured in the boreal forest for more than 1 year is presented, showing a clear seasonal cycle. It is shown that the seasonal dependency of INP concentrations and prevalent INP types is driven by the abundance of biogenic aerosol.
Tanel Voormansik, Roberto Cremonini, Piia Post, and Dmitri Moisseev
Hydrol. Earth Syst. Sci., 25, 1245–1258, https://doi.org/10.5194/hess-25-1245-2021, https://doi.org/10.5194/hess-25-1245-2021, 2021
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A long set of operational polarimetric weather radar rainfall accumulations from Estonia and Italy are generated and investigated. Results show that the combined product of specific differential phase and horizontal reflectivity yields the best results when compared to rain gauge measurements. The specific differential-phase-based product overestimates weak precipitation, and the horizontal-reflectivity-based product underestimates heavy rainfall in all analysed accumulation periods.
Cited articles
Argenti, F. and Facheris, L.: Radar pulse compression methods based on nonlinear and quadratic optimization, IEEE T. Geosci. Remote, 59, 3904–3916, https://doi.org/10.1109/TGRS.2020.3010414, 2020.
Bharadwaj, N. and Chandrasekar, V.: Wideband waveform design principles for solid-state weather radars, J. Atmos. Ocean. Tech., 29, 14–31, https://doi.org/10.1175/JTECH-D-11-00030.1, 2012.
Billingsley, J. B.: Low-angle radar land clutter: Measurements and empirical models, IET, ISBN 9780852962305, 2002.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Chen, Y., Zheng, H., Sun, T., Meng, D., Qin, L., and Yin, J.: Improving forecasts of the “21 ⋅ 7” Henan extreme rainfall event using a radar assimilation scheme that considers hydrometeor background error covariance, Mon. Weather Rev., 152, 1379–1397, https://doi.org/10.1175/MWR-D-23-0190.1, 2024.
Conrick, R., Zagrodnik, J. P., and Mass, C. F.: Dual-polarization radar retrievals of coastal pacific northwest raindrop size distribution parameters using random forest regression, J. Atmos. Ocean. Tech., 37, 229–242, https://doi.org/10.1175/JTECH-D-19-0107.1, 2020.
Cook, C. E. and Bernfeld, M.: Radar signals: An introduction to theory and application, Academic Press, New York, ISBN 9780121867508, 1967.
Crum, T. D. and Alberty, R. L.: The WSR-88D and the WSR-88D operational support facility, B. Am. Meteorol. Soc., 74, 1669–1687, https://doi.org/10.1175/1520-0477(1993)074<1669:TWATWO>2.0.CO;2, 1993.
Feng, Y. C. and Fabry, F.: The imperfect phase pattern of real parabolic radar antenna and data quality, J. Atmos. Ocean. Tech., 33, 2655–2661, https://doi.org/10.1175/JTECH-D-16-0143.1, 2016.
Geng, F. and Liu, L.: Study on attenuation correction for the reflectivity of X-band dual-polarization phased-array weather radar based on a network with S-band weather radar, Remote Sens., 15, 1333, https://doi.org/10.3390/rs15051333, 2023.
Golbon-Haghighi, M. H., Zhang, G., and Doviak, R. J.: Ground clutter detection for weather radar using phase fluctuation index, IEEE T. Geosci. Remote, 57, 2889–2895, https://doi.org/10.1109/TGRS.2018.2878378, 2018.
Hanft, W., Zhang, J., and Simpson, M.: Dual-pol VPR corrections for improved operational radar QPE in MRMS, J. Hydrometeorol., 24, 353–371, https://doi.org/10.1175/JHM-D-22-0010.1, 2023.
Ho, J., Zhang, G., Bukovcic, P., Parsons, D. B., Xu, F., Gao, J., Carlin, J. T., and Snyder, J. C.: Improving polarimetric radar-based drop size distribution retrieval and rain estimation using a deep neural network, J. Hydrometeorol., 24, 2057–2073, https://doi.org/10.1175/JHM-D-22-0166.1, 2023.
Ho, T. K.: The random subspace method for constructing decision forests, IEEE T. Pattern Anal., 20, 832–844, https://doi.org/10.1109/34.709601, 1998.
Hubbert, J. C., Dixon, M., and Ellis, S. M.: Weather radar ground clutter. Part II: Real-time identification and filtering, J. Atmos. Ocean. Technol., 26, 1181–1197, https://doi.org/10.1175/2009JTECHA1160.1, 2009a.
Hubbert, J. C., Dixon, M., Ellis, S. M., and Meymaris, G.: Weather radar ground clutter. Part I: Identification, modeling, and simulation, J. Atmos. Ocean. Tech., 26, 1165–1180, https://doi.org/10.1175/2009JTECHA1159.1, 2009b.
Hubbert, J. C., Meymaris, G., Romatschke, U., and Dixon, M.: Using a regression ground clutter filter to improve weather radar signal statistics: Theory and simulations, J. Atmos. Ocean. Tech., 38, 1353–1375, 2021.
Keeler, R. J. and Passarelli, R. E.: Signal processing for atmospheric radars, in: Radar in Meteorology: Battan Memorial and 40th Anniversary Radar Meteorology Conference, American Meteorological Society, Boston, MA, 199–229, ISBN 9780933876866, 1990.
Kilambi, A., Fabry, F., and Meunier, V.: A simple and effective method for separating meteorological from nonmeteorological targets using dual-polarization data, J. Atmos. Ocean. Tech., 35, 1415–1424, https://doi.org/10.1175/JTECH-D-17-0175.1, 2018.
Kollias, P., Palmer, R., Bodine, D., Adachi, T., Bluestein, H., Cho, J. Y. N., Griffin, C., Houser, J., Kirstetter, P. E., Kumjian, M. R., Kurdzo, J. M., Lee, W. C., Luke, E. P., Nesbitt, S., Oue, M., Shapiro, A., Rowe, A., Salazar, J., Tanamachi, R., Tuftedal, K. S., Wang, X., Zrnić, D., and Treserras, B. P.: Science applications of phased array radars, B. Am. Meteorol. Soc., 103, E2370–E2390, https://doi.org/10.1175/BAMS-D-21-0173.1, 2022.
Kumjian, M. R.: Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables, Journal of Operational Meteorology, 1, 226–242, https://doi.org/10.15191/nwajom.2013.0119, 2013.
Kurdzo, J. M., Cheong, B. L., Palmer, R. D., Zhang, G., and Meier, J. B.: A pulse compression waveform for improved-sensitivity weather radar observations, J. Atmos. Ocean. Tech., 31, 2713–2731, https://doi.org/10.1175/JTECH-D-13-00021.1, 2014.
Kurdzo, J. M., Bennett, B. J., Smalley, D. J., and Donovan, M. F.: The WSR-88D inanimate hydrometeor class, J. Appl. Meteorol. Clim., 59, 841–858, https://doi.org/10.1175/JAMC-D-19-0271.1, 2020.
Li, H., Moisseev, D., Luo, Y., Liu, L., Ruan, Z., Cui, L., and Bao, X.: Assessing specific differential phase (KDP)-based quantitative precipitation estimation for the record- breaking rainfall over Zhengzhou city on 20 July 2021, Hydrol. Earth Syst. Sci., 27, 1033–1046, https://doi.org/10.5194/hess-27-1033-2023, 2023.
Li, H., Yin, J., and Kumjian, M.: ZDR backwards arc: Evidence of multi-directional size sorting in the storm producing 201.9 mm hourly rainfall, Geophys. Res. Lett., 51, e2024GL109192, https://doi.org/10.1029/2024GL109192, 2024.
Mao, Y. and Sorteberg, A.: Improving radar-based precipitation nowcasts with machine learning using an approach based on random forest, Weather Forecast., 35, 2461–2478, https://doi.org/10.1175/WAF-D-20-0080.1, 2020.
Marshall, J. S. and Palmer, W. M. K.: The distribution of raindrops with size, J. Meteorol., 5, 165–166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2, 1948.
Overeem, A., Uijlenhoet, R., and Leijnse, H.: Full-year evaluation of nonmeteorological echo removal with dual-polarization fuzzy logic for two C-band radars in a temperate climate, J. Atmos. Ocean. Tech., 37, 1643–1660, https://doi.org/10.1175/JTECH-D-19-0149.1, 2020.
Palmer, R., Bodine, D., Kollias, P., Schvartzman, D., Zrnić, D., Kirstetter, P., Zhang, G., Yu, T. Y., Kumjian, M., Cheong, B., Collis, S., Frasier, S., Fulton, C., Hondl, K., Kurdzo, J., Ushio, T., Rowe, A., Salazar-CerrenŻo, J., Torres, S., Weber, M., and Yeary, M.: A primer on phased array radar technology for the atmospheric sciences, B. Am. Meteorol. Soc., 103, E2391–E2416, https://doi.org/10.1175/BAMS-D-21-0172.1, 2022.
Palmer, R. D., Yeary, M. B., Schvartzman, D., Salazar-Cerreno, J. L., Fulton, C., McCord, M., Cheong, B., Bodine, D., Kirstetter, P., Sigmarsson, H. H., Yu, T. Y., Zrnić, D., Kelley, R., Meier, J., and Herndon, M.: Horus – A fully digital polarimetric phased array radar for next-generation weather observations, IEEE Transactions on Radar Systems, 1, 96–117, https://doi.org/10.1109/TRS.2023.3280033, 2023.
Pang, C., Hoogeboom, P., Le Chevalier, F., Russchenberg, H. W. J., Dong, J., Wang, T., and Wang, X.: A pulse compression waveform for weather radars with solid-state transmitters, IEEE Geosci. Remote S., 12, 2026–2030, https://doi.org/10.1109/LGRS.2015.2443551, 2015.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., and Prettenhofer, P.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Pérez Hortal, A. A. and Michelson, D.: Using a continental-scale data quality monitoring framework to evaluate a new nonweather filter for radar observations, J. Appl. Meteorol. Clim., 62, 411–425, https://doi.org/10.1175/JAMC-D-22-0014.1, 2023.
Rihaczek, A. W.: Principles of high-resolution radar, McGraw-Hill, New York, ISBN 978-0070528901, 1969.
Sandmæl, T. N., Smith, B. R., Reinhart, A. E., Schick, I. M., Ake, M. C., Madden, J. G., Steeves, R. B., Williams, S. S., Elmore, K. L., and Meyer, T. C.: The tornado probability algorithm: A probabilistic machine learning tornadic circulation detection algorithm, Weather Forecast., 38, 445–466, https://doi.org/10.1175/WAF-D-22-0123.1, 2023.
Schvartzman, D., Torres, S. M., and Yu, T. Y.: Distributed beams: Concept of operations for polarimetric rotating phased array radar, IEEE T. Geosci. Remote, 59, 9173–9191, https://doi.org/10.1109/TGRS.2020.3047090, 2021.
Seliga, T. A. and Bringi, V. N.: Potential use of radar differential reflectivity measurements at orthogonal polarizations for measuring precipitation, J. Appl. Meteorol., 15, 69–76, https://doi.org/10.1175/1520-0450(1976)015<0069:PUORDR>2.0.CO;2, 1976.
Siggia, A. D. and Passarelli Jr., R. E.: Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation, in: Proc. 3rd ERAD, 67–73, 2004.
Stepanian, P. M., Horton, K. G., Melnikov, V. M., Zrnić, D. S., and Gauthreaux, S. A.: Dual-polarization radar products for biological applications, Ecosphere, 7, e01539, https://doi.org/10.1002/ecs2.1539, 2016.
Tang, L., Zhang, J., Langston, C., Krause, J., Howard, K., Lakshmanan, V.: A physically based precipitation–nonprecipitation radar echo classifier using polarimetric and environmental data in a real-time national system, Weather Forecast., 29, 1106–1119, https://doi.org/10.1175/WAF-D-13-00072.1, 2014.
Torres, S. M. and Warde, D. A.: Ground clutter mitigation for weather radars using the autocorrelation spectral density, J. Atmos. Ocean. Tech., 31, 2049–2066, https://doi.org/10.1175/JTECH-D-13-00117.1, 2014.
Torres, S. M., Curtis, C. D., and Schvartzman, D.: Requirement-driven design of pulse compression waveforms for weather radars, J. Atmos. Ocean. Tech., 34, 1351–1369, https://doi.org/10.1175/JTECH-D-16-0231.1, 2017.
Vaisala: User's manual: RVP900 digital receiver and signal processor; Vaisala oyj: Vantaa, Finland, 513 pp., 2016.
Weber, M., Hondl, K., Yussouf, N., Jung, Y., Stratman, D., Putnam, B., Wang, X., Schuur, T., Kuster, C., Wen, Y., Sun, J., Keeler, J., Ying, Z., Cho, J., Kurdzo, J., Torres, S., Curtis, C., Schvartzman, D., Boettcher, J., Nai, F., Thomas, H., Zrnić, D., Ivić, I., Mirković, D., Fulton, C., Salazar, J., Zhang, G., Palmer, R., Yeary, M., Cooley, K., Istok, M., and Vincent, M.: Towards the next generation operational meteorological radar, B. Am. Meteorol. Soc., 102, E1357–E1383, https://doi.org/10.1175/BAMS-D-20-0067.1, 2021.
Zhang, J., Wang, S., and Clarke, B.: WSR-88D reflectivity quality control using horizontal and vertical reflectivity structure, in: Proceedings of the llth Conference on Aviation, Range and Aerospace Meteorology, IS, 1, 5–6, 2004.
Short summary
The data quality of weather radar near coastlines can be affected by echoes from ships, and this interference is exacerbated when pulse compression technology is used. This study developed a hybrid ship clutter identification algorithm based on artificial intelligence and heuristic criteria, effectively mitigating the issue. The successful reproduction of ship tracks in the Gulf of Finland supports this conclusion.
The data quality of weather radar near coastlines can be affected by echoes from ships, and this...
