Articles | Volume 15, issue 16
https://doi.org/10.5194/amt-15-4931-2022
https://doi.org/10.5194/amt-15-4931-2022
Research article
 | 
30 Aug 2022
Research article |  | 30 Aug 2022

Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments

Mariko Oue, Stephen M. Saleeby, Peter J. Marinescu, Pavlos Kollias, and Susan C. van den Heever

Related authors

Shallow- and deep-convection characteristics in the greater Houston, Texas, area using cell tracking methodology
Kristofer S. Tuftedal, Bernat Puigdomènech Treserras, Mariko Oue, and Pavlos Kollias
Atmos. Chem. Phys., 24, 5637–5657, https://doi.org/10.5194/acp-24-5637-2024,https://doi.org/10.5194/acp-24-5637-2024, 2024
Short summary
Detection of small drizzle droplets in a large cloud chamber using ultrahigh-resolution radar
Zeen Zhu, Fan Yang, Pavlos Kollias, Raymond A. Shaw, Alex B. Kostinski, Steve Krueger, Katia Lamer, Nithin Allwayin, and Mariko Oue
Atmos. Meas. Tech., 17, 1133–1143, https://doi.org/10.5194/amt-17-1133-2024,https://doi.org/10.5194/amt-17-1133-2024, 2024
Short summary
Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars
Mariko Oue, Pavlos Kollias, Sergey Y. Matrosov, Alessandro Battaglia, and Alexander V. Ryzhkov
Atmos. Meas. Tech., 14, 4893–4913, https://doi.org/10.5194/amt-14-4893-2021,https://doi.org/10.5194/amt-14-4893-2021, 2021
Short summary
Multifrequency radar observations of clouds and precipitation including the G-band
Katia Lamer, Mariko Oue, Alessandro Battaglia, Richard J. Roy, Ken B. Cooper, Ranvir Dhillon, and Pavlos Kollias
Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021,https://doi.org/10.5194/amt-14-3615-2021, 2021
Short summary
The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory
Mariko Oue, Aleksandra Tatarevic, Pavlos Kollias, Dié Wang, Kwangmin Yu, and Andrew M. Vogelmann
Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020,https://doi.org/10.5194/gmd-13-1975-2020, 2020
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Bayesian cloud-top phase determination for Meteosat Second Generation
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024,https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024,https://doi.org/10.5194/amt-17-3863-2024, 2024
Short summary
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024,https://doi.org/10.5194/amt-17-3679-2024, 2024
Short summary
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024,https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024,https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary

Cited articles

Adachi, T. and Mashiko, W.: High temporal-spatial resolution observation of tornadogenesis in a shallow supercell associated with Typhoon Hagibis (2019) using phased array weather radar, Geophys. Res. Lett., 47, e2020GL089635, https://doi.org/10.1029/2020GL089635, 2020. 
Barnes, S. L.: A Technique for maximizing details in numerical weather map analysis, J. Appl. Meteorol., 3, 396–409, 1964. 
Billam, E. R. and Harvey, D. H.: MESAR – An advanced experimental phased array radar, Proceedings of the IEEE International Radar Conference, 19–21 October 1987, London, UK, 37–40, 1987. 
Bousquet, O., Tabary, P., and Parent du Chtelet, J.: Operational multiple-Doppler wind retrieval inferred from long-range radial velocity measurements, J. Appl. Meteor. Climatol., 47, 2929–2945, https://doi.org/10.1175/2008JAMC1878.1, 2008. 
Bryan, G. H. and Fritsch, J. M.: A benchmark simulation for moist nonhydrostatic numerical models, Mon. Weather Rev., 130, 2917–2928, 2002. 
Download
Short summary
This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.