Dr. Zhaoyan Liu Profile

Dr. Zhaoyan Liu

at Nasa Langley Research Ctr

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Author

Publications (6)

Proceedings Article | 21 December 2024 Paper

Differential absorption Lidar for searching water sources on Mars

Zhaoyan Liu, Joel Campbell, Bing Lin, Jirong Yu, Jihong Geng

Proceedings Volume 13265, 132650D (2024) https://doi.org/10.1117/12.3045077

KEYWORDS: Mars, Absorption, Water, LIDAR, Spectroscopy, Spatial resolution, Mars Reconnaissance Orbiter, Liquids, Ions, Hydrogen

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Proceedings Article | 9 August 2023 Poster

Polarization calibration using solar radiation background signal scattered from dense cirrus clouds in the visible and ultraviolet wavelength regime

Zhaoyan Liu, Yongxiang Hu, Pengwang Zhai, Shan Zeng, Mark Vaughan, Sharon Rodier, Xiaomei Lu

Proceedings Volume 12327, 123271O (2023) https://doi.org/10.1117/12.2665858

KEYWORDS: Polarization, LIDAR, Visible radiation, Ultraviolet radiation, Solar radiation, Scattering, Clouds, Calibration, Particles, Solar radiation models

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Proceedings Article | 5 December 2016 Presentation

Optical depth distribution of optically thin clouds and surface elevation variability derived from CALIPSO lidar measurements (Conference Presentation)

Zhaoyan Liu, Bing Lin, Michael Obland, Joel Campbell

Proceedings Volume 10006, 1000606 (2016) https://doi.org/10.1117/12.2240080

KEYWORDS: Carbon dioxide, LIDAR, Clouds, Aerosols, Earth's atmosphere, Atmospheric sensing, Passive sensors, Satellites, Atmospheric particles, Atmospheric optics

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Atmospheric carbon dioxide (CO2) is one of the major greenhouse gases in the Earth’s climate system. The CO2 concentration in the atmosphere has been significantly increased over the last 150 years, due mainly to anthropogenic activities. Comprehensive measurements of global atmospheric CO2 distributions are urgently needed to develop a more complete understanding of CO2 sources and sinks. Because of the importance of the atmospheric CO2 measurements, satellite missions with passive sensors such as GOSAT and OCO-2 have been launched, and those with active sensors like Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) using an integrated path differential absorption (IPDA) lidar are being studied. The required accuracy and precision for the column-integrated CO2 mixing ratios (XCO2) is high, within 1.0 ppm or approximately 0.26%, which calls for unbiased CO2 measurements and accurate determinations of the path length. The presence of clouds and aerosols can make the measurement complicated, especially for passive instruments. The heterogeneity generated by the surface elevation changes within the field of view of the sensors and the grid boxes of averaged values of atmospheric CO2 would also cause significant uncertainties in XCO2 estimates if the path length is not accurately known. Thus, it is required to study the cloud and aerosol distributions as well as the surface elevation variability in assessing the performance of the CO2 measurements from both active and passive instruments. The CALIPSO lidar has acquired nearly 10 years of global measurement data. It provides a great opportunity to study the global distribution of clouds and aerosols as well as the statistics of the surface elevation variations. In this study we have analyzed multiple years of the CALIPSO Level 2 data to derive the global occurrence of aerosols and optically thin clouds. The results show that clear sky does not occur as frequently as expected. The global average occurrence is only about 8% for very clean air with columnar OD at 532 nm < 0.01. It increases to ~29% when OD < 0.1, and ~42% when OD < 0.3, which is close the clear atmospheric threshold from regular passive remote sensing instruments. This calls for a capability to make precise retrievals in the presence of relatively dense aerosols or thin clouds. Multiple years of surface elevation data derived from the CALIPSO lidar has also been used in the assessment of surface elevation variability for passive sensor observations. It is shown that the variability of the surface elevation generally increases with increases in footprint size and surface elevation. For a footprint of 1-2 km typical for passive sensors, the mean standard deviation is 5-10 meters when elevation < 1 km and can reach 100 meters as the elevation increases. The occurrence frequency for a standard deviation < 10 m is greater than 20%, which can cause significant biases in the CO2 retrieval if the presence of the cloud and/or aerosol cannot be identified and corrected. With ranging capability, the ASCENDS lidar system supported by NASA will reliably measure CO2 even in the presence of multiple backscatter targets (surface and transparent clouds) as shown during the experiments of recent airborne system demonstrations. However, it is very challenging for passive satellites to make reliable retrievals in the multiple-layer target case, because of the lack of path length information.

Proceedings Article | 27 September 2013 Paper

CALIOP receiver transient response study

Xiaomei Lu, Yongxiang Hu, Zhaoyan Liu, Shan Zeng, Charles Trepte

Proceedings Volume 8873, 887316 (2013) https://doi.org/10.1117/12.2033589

KEYWORDS: LIDAR, Receivers, Linear filtering, Backscatter, Sensors, Clouds, Aerosols, Signal detection, Polarization, Satellites

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Proceedings Article | 26 October 2010 Paper

The CALIPSO Mission: results and progress

Dave Winker, Yong Hu, Michael Pitts, Melody Avery, Brian Getzewich, Jason Tackett, Chieko Kittaka, Zhaoyan Liu, Mark Vaughan

Proceedings Volume 7832, 78320B (2010) https://doi.org/10.1117/12.864901

KEYWORDS: Clouds, Aerosols, Climatology, LIDAR, Satellites, Atmospheric modeling, Environmental sensing, Backscatter, Ocean optics, Pulsed laser operation

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Proceedings Article | 4 November 2004 Paper

Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products

Mark Vaughan, Stuart Young, David Winker, Kathleen Powell, Ali Omar, Zhaoyan Liu, Yongxiang Hu, Chris Hostetler

Proceedings Volume 5575, (2004) https://doi.org/10.1117/12.572024

KEYWORDS: Aerosols, Clouds, LIDAR, Backscatter, Feature extraction, Scattering, Signal attenuation, Atmospheric particles, Optical properties, Atmospheric modeling

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Showing 5 of 6 publications

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