Articles | Volume 14, issue 2
Atmos. Meas. Tech., 14, 961–974, 2021
https://doi.org/10.5194/amt-14-961-2021
Atmos. Meas. Tech., 14, 961–974, 2021
https://doi.org/10.5194/amt-14-961-2021
Research article
08 Feb 2021
Research article | 08 Feb 2021

Detection of anomalies in the UV–vis reflectances from the Ozone Monitoring Instrument

Nick Gorkavyi et al.

Related authors

Tracking aerosols and SO2 clouds from the Raikoke eruption: 3D view from satellite observations
Nick Gorkavyi, Nickolay Krotkov, Can Li, Leslie Lait, Peter Colarco, Simon Carn, Matthew DeLand, Paul Newman, Mark Schoeberl, Ghassan Taha, Omar Torres, Alexander Vasilkov, and Joanna Joiner
Atmos. Meas. Tech., 14, 7545–7563, https://doi.org/10.5194/amt-14-7545-2021,https://doi.org/10.5194/amt-14-7545-2021, 2021
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
High-resolution typhoon precipitation integrations using satellite infrared observations and multisource data
You Zhao, Chao Liu, Di Di, Ziqiang Ma, and Shihao Tang
Atmos. Meas. Tech., 15, 2791–2805, https://doi.org/10.5194/amt-15-2791-2022,https://doi.org/10.5194/amt-15-2791-2022, 2022
Short summary
Continuous temperature soundings at the stratosphere and lower mesosphere with a ground-based radiometer considering the Zeeman effect
Witali Krochin​​​​​​​, Francisco Navas-Guzmán, David Kuhl, Axel Murk, and Gunter Stober
Atmos. Meas. Tech., 15, 2231–2249, https://doi.org/10.5194/amt-15-2231-2022,https://doi.org/10.5194/amt-15-2231-2022, 2022
Short summary
Retrieval of solar-induced chlorophyll fluorescence (SIF) from satellite measurements: comparison of SIF between TanSat and OCO-2
Lu Yao, Yi Liu, Dongxu Yang, Zhaonan Cai, Jing Wang, Chao Lin, Naimeng Lu, Daren Lyu, Longfei Tian, Maohua Wang, Zengshan Yin, Yuquan Zheng, and Sisi Wang
Atmos. Meas. Tech., 15, 2125–2137, https://doi.org/10.5194/amt-15-2125-2022,https://doi.org/10.5194/amt-15-2125-2022, 2022
Short summary
Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images
Biao Tong, Xiangfei Sun, Jiyang Fu, Yuncheng He, and Pakwai Chan
Atmos. Meas. Tech., 15, 1829–1848, https://doi.org/10.5194/amt-15-1829-2022,https://doi.org/10.5194/amt-15-1829-2022, 2022
Short summary
Time evolution of temperature profiles retrieved from 13 years of infrared atmospheric sounding interferometer (IASI) data using an artificial neural network
Marie Bouillon, Sarah Safieddine, Simon Whitburn, Lieven Clarisse, Filipe Aires, Victor Pellet, Olivier Lezeaux, Noëlle A. Scott, Marie Doutriaux-Boucher, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 1779–1793, https://doi.org/10.5194/amt-15-1779-2022,https://doi.org/10.5194/amt-15-1779-2022, 2022
Short summary

Cited articles

Butz, A., Guerlet, S., Hasekamp, O. P., Kuze, A., and Suto, H.: Using ocean-glint scattered sunlight as a diagnostic tool for satellite remote sensing of greenhouse gases, Atmos. Meas. Tech., 6, 2509–2520, https://doi.org/10.5194/amt-6-2509-2013, 2013. 
Cao, X., Hu, Y., Zhu, X., Shi, F., Zhuo, L., and Chen, J.: A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images, Remote Sens. Environ., 224, 401–411, https://doi.org/10.1016/j.rse.2019.02.019, 2019. 
Chan Miller, C., Gonzalez Abad, G., Wang, H., Liu, X., Kurosu, T., Jacob, D. J., and Chance, K.: Glyoxal retrieval from the Ozone Monitoring Instrument, Atmos. Meas. Tech., 7, 3891–3907, https://doi.org/10.5194/amt-7-3891-2014, 2014. 
Cheng, L., Tao, J., Valks, P., Yu, Ch., Liu, S., Wang, Y., Xiong, X., Wang, Z., and Chen, L.: NO2 retrieval from the Environmental trace gases Monitoring Instrument (EMI): preliminary results and intercomparison with OMI and TROPOMI, Remote Sens.-Basel, 11, 3017, https://doi.org/10.3390/rs11243017, 2019. 
Cox, C. and Munk, W.: Measurement of the roughness of the sea surface from photographs of the Sun's glitter, J. Opt. Soc. Am., 44, 838–850, https://doi.org/10.1364/JOSA.44.000838, 1954. 
Download
Short summary
Various instrumental or geophysical artifacts, such as saturation, stray light or obstruction of light, negatively impact satellite measured ultraviolet and visible Earthshine radiance spectra. Here, we introduce a straightforward detection method that is based on the correlation, r, between the observed Earthshine radiance and solar irradiance spectra over a 10 nm spectral range; our decorrelation index (DI for brevity) is simply defined as DI of 1–r.