Articles | Volume 11, issue 11
https://doi.org/10.5194/amt-11-6043-2018
https://doi.org/10.5194/amt-11-6043-2018
Research article
 | 
08 Nov 2018
Research article |  | 08 Nov 2018

Improvements to a long-term Rayleigh-scatter lidar temperature climatology by using an optimal estimation method

Ali Jalali, Robert J. Sica, and Alexander Haefele

Related authors

A comparison of carbon monoxide retrievals between the MOPITT satellite and Canadian high-Arctic ground-based NDACC and TCCON FTIR measurements
Ali Jalali, Kaley A. Walker, Kimberly Strong, Rebecca R. Buchholz, Merritt N. Deeter, Debra Wunch, Sébastien Roche, Tyler Wizenberg, Erik Lutsch, Erin McGee, Helen M. Worden, Pierre Fogal, and James R. Drummond
Atmos. Meas. Tech., 15, 6837–6863, https://doi.org/10.5194/amt-15-6837-2022,https://doi.org/10.5194/amt-15-6837-2022, 2022
Short summary
A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals
Ali Jalali, Shannon Hicks-Jalali, Robert J. Sica, Alexander Haefele, and Thomas von Clarmann
Atmos. Meas. Tech., 12, 3943–3961, https://doi.org/10.5194/amt-12-3943-2019,https://doi.org/10.5194/amt-12-3943-2019, 2019
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Observations of tall-building wakes using a scanning Doppler lidar
Natalie E. Theeuwes, Janet F. Barlow, Antti Mannisenaho, Denise Hertwig, Ewan O'Connor, and Alan Robins
Atmos. Meas. Tech., 18, 1355–1371, https://doi.org/10.5194/amt-18-1355-2025,https://doi.org/10.5194/amt-18-1355-2025, 2025
Short summary
Mid-Atlantic nocturnal low-level jet characteristics: a machine learning analysis of radar wind profiles
Maurice Roots, John T. Sullivan, and Belay Demoz
Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025,https://doi.org/10.5194/amt-18-1269-2025, 2025
Short summary
Mitigating radome-induced bias in X-band weather radar polarimetric moments using an adaptive discrete Fourier transform algorithm
Padmanabhan Thiruvengadam, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Atmos. Meas. Tech., 18, 1185–1191, https://doi.org/10.5194/amt-18-1185-2025,https://doi.org/10.5194/amt-18-1185-2025, 2025
Short summary
GNSS-RO residual ionospheric error (RIE): a new method and assessment
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025,https://doi.org/10.5194/amt-18-843-2025, 2025
Short summary
Benchmarking KDP in rainfall: a quantitative assessment of estimation algorithms using C-band weather radar observations
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
Short summary

Cited articles

Argall, P. S. and Sica, R. J.: A comparison of Rayleigh and sodium lidar temperature climatologies, Ann. Geophys., 25, 27–35, https://doi.org/10.5194/angeo-25-27-2007, 2007. a, b, c
Argall, P. S., Vassiliev, O. N., Sica, R. J., and Mwangi, M. M.: Lidar measurements taken with a large-aperture liquid mirror: 2. The Sodium resonance-fluorescence system, Appl. Optics, 39, 2393–2399, 2000. a
Arnold, K. S. and She, C. Y.: Metal fluorescence lidar (light detection and ranging) and the middle atmosphere, Contemp. Phys., 44, 35–49, 2003. a
Bills, R. E., Gardner, C. S., and She, C. Y.: Narrow band lidar technique for sodium temperature and Doppler wind observations of the upper atmosphere, Opt. Eng., 30, 13–21, 1991. a
Fleming, E. L., Chandra, S., Shoeberl, M. R., and Barnett, J. J.: Monthly Mean Global Climatology of Temperature, Wind, Geopotential Height and Pressure for 0–120 km, NASA Tech. Memo., NASA TM100697, 85 pp., 1988. a, b
Download
Short summary
We use 16 years of lidar (laser radar) temperature measurements of the middle atmosphere to form a climatology for use in studying atmospheric temperature change using an optimal estimation method (OEM). Using OEM allows us to calculate a complete systematic and random uncertainty budget and allows for an additional 10–15 km in altitude for the measurement to be used, improving our ability to detect atmospheric temperature change up to 100 km of altitude.
Share