Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-985-2020
https://doi.org/10.5194/amt-13-985-2020
Research article
 | 
02 Mar 2020
Research article |  | 02 Mar 2020

Applying FP_ILM to the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) daily maps from UVN satellite measurements

Diego G. Loyola, Jian Xu, Klaus-Peter Heue, and Walter Zimmer

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Cited articles

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Coldewey-Egbers, M., Weber, M., Lamsal, L. N., de Beek, R., Buchwitz, M., and Burrows, J. P.: Total ozone retrieval from GOME UV spectral data using the weighting function DOAS approach, Atmos. Chem. Phys., 5, 1015–1025, https://doi.org/10.5194/acp-5-1015-2005, 2005. 
Efremenko, D. S., Loyola R., D. G., Hedelt, P., and Spurr, R. J. D.: Volcanic SO2 plume height retrieval from UV sensors using a full-physics inverse learning machine algorithm, Int. J. Remote Sens., 38, 1–27, https://doi.org/10.1080/01431161.2017.1348644, 2017. 
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Short summary
In this paper we present a novel algorithm for the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors based on the full-physics inverse learning machine (FP_ILM) retrieval. The GE_LER retrieval is optimized for the trace gas retrievals using the DOAS technique and the large amount of data of TROPOMI on board the EU/ESA Sentinel-5 Precursor mission.
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