Articles | Volume 15, issue 6
https://doi.org/10.5194/amt-15-1689-2022
https://doi.org/10.5194/amt-15-1689-2022
Research article
 | 
22 Mar 2022
Research article |  | 22 Mar 2022

Detecting wave features in Doppler radial velocity radar observations

Matthew A. Miller, Sandra E. Yuter, Nicole P. Hoban, Laura M. Tomkins, and Brian A. Colle

Related authors

In-cloud characteristics observed in US Northeast and Midwest non-orographic winter storms with implications for ice particle mass growth and residence time
Luke R. Allen, Sandra E. Yuter, Declan M. Crowe, Matthew A. Miller, and K. Lee Thornhill
EGUsphere, https://doi.org/10.5194/egusphere-2024-3808,https://doi.org/10.5194/egusphere-2024-3808, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Objectively identified mesoscale surface air pressure waves in the context of winter storm environments and radar reflectivity features: a 3+ year analysis
Luke R. Allen, Sandra E. Yuter, Matthew A. Miller, and Laura M. Tomkins
EGUsphere, https://doi.org/10.5194/egusphere-2024-2160,https://doi.org/10.5194/egusphere-2024-2160, 2024
Short summary
Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024,https://doi.org/10.5194/amt-17-3377-2024, 2024
Short summary
Objective identification of pressure wave events from networks of 1 Hz, high-precision sensors
Luke R. Allen, Sandra E. Yuter, Matthew A. Miller, and Laura M. Tomkins
Atmos. Meas. Tech., 17, 113–134, https://doi.org/10.5194/amt-17-113-2024,https://doi.org/10.5194/amt-17-113-2024, 2024
Short summary
Image muting of mixed precipitation to improve identification of regions of heavy snow in radar data
Laura M. Tomkins, Sandra E. Yuter, Matthew A. Miller, and Luke R. Allen
Atmos. Meas. Tech., 15, 5515–5525, https://doi.org/10.5194/amt-15-5515-2022,https://doi.org/10.5194/amt-15-5515-2022, 2022
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Determination of low-level temperature profiles from microwave radiometer observations during rain
Andreas Foth, Moritz Lochmann, Pablo Saavedra Garfias, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 7169–7181, https://doi.org/10.5194/amt-17-7169-2024,https://doi.org/10.5194/amt-17-7169-2024, 2024
Short summary
Aeolus lidar surface return (LSR) at 355 nm as a new Aeolus Level-2A product
Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Damien Josset, and Piet Stammes
Atmos. Meas. Tech., 17, 7183–7208, https://doi.org/10.5194/amt-17-7183-2024,https://doi.org/10.5194/amt-17-7183-2024, 2024
Short summary
Sampling the diurnal and annual cycles of the Earth's energy imbalance with constellations of satellite-borne radiometers
Thomas Hocking, Thorsten Mauritsen, and Linda Megner
Atmos. Meas. Tech., 17, 7077–7095, https://doi.org/10.5194/amt-17-7077-2024,https://doi.org/10.5194/amt-17-7077-2024, 2024
Short summary
Retrieval of top-of-atmosphere fluxes from combined EarthCARE lidar, imager, and broadband radiometer observations: the BMA-FLX product
Almudena Velázquez Blázquez, Carlos Domenech, Edward Baudrez, Nicolas Clerbaux, Carla Salas Molar, and Nils Madenach
Atmos. Meas. Tech., 17, 7007–7026, https://doi.org/10.5194/amt-17-7007-2024,https://doi.org/10.5194/amt-17-7007-2024, 2024
Short summary
Analysis of the measurement uncertainty for a 3D wind lidar
Wolf Knöller, Gholamhossein Bagheri, Philipp von Olshausen, and Michael Wilczek
Atmos. Meas. Tech., 17, 6913–6931, https://doi.org/10.5194/amt-17-6913-2024,https://doi.org/10.5194/amt-17-6913-2024, 2024
Short summary

Cited articles

Allen, G., Vaughan, G., Toniazzo, T., Coe, H., Connolly, P., Yuter, S. E., Burleyson, C. D., Minnis, P., and Ayers, J. K.: Gravity-wave-induced perturbations in marine stratocumulus, Q. J. Roy. Meteor. Soc., 139, 32–45, https://doi.org/10.1002/qj.1952, 2013. a, b
Battan, L. J.: Radar meteorology. By L. J. Battan (University of Chicago Press), 1959. Pp. xi, 161; 77 Figs.; 16 Tables. 45s, Q. J. Roy. Meteor. Soc., 86, 292–292, https://doi.org/10.1002/qj.49708636830, 1960. a
Doviak, R. J. and Zrnić, D. S.: Doppler Radar and Weather Observations, 2nd edn., Academic Press, New York, ISBN-13: 978-0486450605, ISBN-10: 0486450600, 1993. a
Fovell, R. G., Mullendore, G. L., and Kim, S.-H.: Discrete Propagation in Numerically Simulated Nocturnal Squall Lines, Mon. Weather Rev., 134, 3735–3752, https://doi.org/10.1175/MWR3268.1, 2006. a, b
Gaffin, D. M., Parker, S. S., and Kirkwood, P. D.: An Unexpectedly Heavy and Complex Snowfall Event across the Southern Appalachian Region, Weather Forecast., 18, 224–235, https://doi.org/10.1175/1520-0434(2003)018<0224:AUHACS>2.0.CO;2, 2003. a, b
Download
Short summary
Apparent waves in the atmosphere and similar features in storm winds can be detected by taking the difference between successive Doppler weather radar scans measuring radar-relative storm air motions. Applying image filtering to the difference data better isolates the detected signal. This technique is a useful tool in weather research and forecasting since such waves can trigger or enhance precipitation.