Articles | Volume 12, issue 7
https://doi.org/10.5194/amt-12-3943-2019
https://doi.org/10.5194/amt-12-3943-2019
Research article
 | 
18 Jul 2019
Research article |  | 18 Jul 2019

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

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
Improvements to a long-term Rayleigh-scatter lidar temperature climatology by using an optimal estimation method
Ali Jalali, Robert J. Sica, and Alexander Haefele
Atmos. Meas. Tech., 11, 6043–6058, https://doi.org/10.5194/amt-11-6043-2018,https://doi.org/10.5194/amt-11-6043-2018, 2018
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations
Leon Kuhn, Steffen Beirle, Sergey Osipov, Andrea Pozzer, and Thomas Wagner
Atmos. Meas. Tech., 17, 6485–6516, https://doi.org/10.5194/amt-17-6485-2024,https://doi.org/10.5194/amt-17-6485-2024, 2024
Short summary
Improved convective cloud differential (CCD) tropospheric ozone from S5P-TROPOMI satellite data using local cloud fields
Swathi Maratt Satheesan, Kai-Uwe Eichmann, John P. Burrows, Mark Weber, Ryan Stauffer, Anne M. Thompson, and Debra Kollonige
Atmos. Meas. Tech., 17, 6459–6484, https://doi.org/10.5194/amt-17-6459-2024,https://doi.org/10.5194/amt-17-6459-2024, 2024
Short summary
Atmospheric propane (C3H8) column retrievals from ground-based FTIR observations in Xianghe, China
Minqiang Zhou, Pucai Wang, Bart Dils, Bavo Langerock, Geoff Toon, Christian Hermans, Weidong Nan, Qun Cheng, and Martine De Mazière
Atmos. Meas. Tech., 17, 6385–6396, https://doi.org/10.5194/amt-17-6385-2024,https://doi.org/10.5194/amt-17-6385-2024, 2024
Short summary
Can the remote sensing of combustion phase improve estimates of landscape fire smoke emission rate and composition?
Farrer Owsley-Brown, Martin J. Wooster, Mark J. Grosvenor, and Yanan Liu
Atmos. Meas. Tech., 17, 6247–6264, https://doi.org/10.5194/amt-17-6247-2024,https://doi.org/10.5194/amt-17-6247-2024, 2024
Short summary
Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS
Sora Seo, Pieter Valks, Ronny Lutz, Klaus-Peter Heue, Pascal Hedelt, Víctor Molina García, Diego Loyola, Hanlim Lee, and Jhoon Kim
Atmos. Meas. Tech., 17, 6163–6191, https://doi.org/10.5194/amt-17-6163-2024,https://doi.org/10.5194/amt-17-6163-2024, 2024
Short summary

Cited articles

Boersma, K. F., Eskes, H. J., and Brinksma, E. J.: Error analysis for tropospheric NO2 retrieval from space, J. Geophys. Res., 109, D04311, https://doi.org/10.1029/2003JD003962, 2004. a
Brocard, E., Philipona, R., Haefele, A., Romanens, G., Mueller, A., Ruffieux, D., Simeonov, V., and Calpini, B.: Raman Lidar for Meteorological Observations, RALMO – Part 2: Validation of water vapor measurements, Atmos. Meas. Tech., 6, 1347–1358, https://doi.org/10.5194/amt-6-1347-2013, 2013. a
Ceccherini, S., Raspollini, P., and Carli, B.: Optimal use of the information provided by indirect measurements of atmospheric vertical profiles, Opt. Express, 17, 4944–4958, https://doi.org/10.1364/OE.17.004944, 2009. a
Committee on Extension to the Standard Atmosphere: U.S. standard atmosphere, US Government Printing Office, 1–227, NASA-TM-X-74335, NOAA-S/T-76-1562, 1976. a
Cunnold, D. M., Chu, W., Barnes, R. A., McCormick, M. P., and Veiga, R. E.: Validation of SAGE II ozone measurements, J. Geophys. Res., 94, 8447–8460, https://doi.org/10.1029/JD094iD06p08447, 1989. a
Download
Short summary
This paper builds upon the work in von Clarmann and Grabowski (2007) concerning the a priori profile influence in the optimal estimation method applied to active remote sensing measurements, with examples given for lidar retrievals of temperature and water vapor mixing ratio. The optimal estimation method is a new technique for many active remote sensing researchers. This study gives insight into understanding the effect on retrievals of the a priori information.