{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:53:18Z","timestamp":1773247998027,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA GSFC internal research and development funds"},{"name":"NASA Earth Science Technology Office"},{"name":"NASA Atmosphere Observing System mission"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006\u20132023), Cloud-Aerosol Transport System (CATS, 2015\u20132017), and Advanced Topographic Laser Altimeter System (ATLAS 2018\u2013present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model\u2019s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~7.3\u00d710\u22124 km\u22121sr\u22121, which is lower than the CALIOP 1064 nm night MDB value of 8.6\u00d710\u22124 km\u22121sr\u22121. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7\u201333 times finer resolution than the CATS L2 products.<\/jats:p>","DOI":"10.3390\/rs16152735","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T13:04:59Z","timestamp":1721999099000},"page":"2735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3519-0599","authenticated-orcid":false,"given":"Patrick","family":"Selmer","sequence":"first","affiliation":[{"name":"Science Systems and Applications, Inc., Lanham, MD 20706, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4276-0708","authenticated-orcid":false,"given":"John E.","family":"Yorks","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8015-594X","authenticated-orcid":false,"given":"Edward P.","family":"Nowottnick","sequence":"additional","affiliation":[{"name":"NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7897-5922","authenticated-orcid":false,"given":"Amanda","family":"Cresanti","sequence":"additional","affiliation":[{"name":"Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4592-1343","authenticated-orcid":false,"given":"Kenneth E.","family":"Christian","sequence":"additional","affiliation":[{"name":"Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E815","DOI":"10.1175\/BAMS-D-21-0179.1","article-title":"A SmallSat Concept to Resolve Diurnal and Vertical Variations of Aerosols, Clouds, and Boundary Layer Height","volume":"104","author":"Yorks","year":"2023","journal-title":"Bull. 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