Articles | Volume 7, issue 11
https://doi.org/10.5194/amt-7-3989-2014
https://doi.org/10.5194/amt-7-3989-2014
Research article
 | 
27 Nov 2014
Research article |  | 27 Nov 2014

MISR research-aerosol-algorithm refinements for dark water retrievals

J. A. Limbacher and R. A. Kahn

Related authors

MISR empirical stray light corrections in high-contrast scenes
J. A. Limbacher and R. A. Kahn
Atmos. Meas. Tech., 8, 2927–2943, https://doi.org/10.5194/amt-8-2927-2015,https://doi.org/10.5194/amt-8-2927-2015, 2015
Short summary
Aerosol airmass type mapping over the Urban Mexico City region from space-based multi-angle imaging
F. Patadia, R. A. Kahn, J. A. Limbacher, S. P. Burton, R. A. Ferrare, C. A. Hostetler, and J. W. Hair
Atmos. Chem. Phys., 13, 9525–9541, https://doi.org/10.5194/acp-13-9525-2013,https://doi.org/10.5194/acp-13-9525-2013, 2013

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Retrieval of stratospheric aerosol extinction coefficients from sun-normalized Ozone Mapper and Profiler Suite Limb Profiler (OMPS-LP) measurements
Alexei Rozanov, Christine Pohl, Carlo Arosio, Adam Bourassa, Klaus Bramstedt, Elizaveta Malinina, Landon Rieger, and John P. Burrows
Atmos. Meas. Tech., 17, 6677–6695, https://doi.org/10.5194/amt-17-6677-2024,https://doi.org/10.5194/amt-17-6677-2024, 2024
Short summary
Total column optical depths retrieved from CALIPSO lidar ocean surface backscatter
Robert A. Ryan, Mark A. Vaughan, Sharon D. Rodier, Jason L. Tackett, John A. Reagan, Richard A. Ferrare, Johnathan W. Hair, John A. Smith, and Brian J. Getzewich
Atmos. Meas. Tech., 17, 6517–6545, https://doi.org/10.5194/amt-17-6517-2024,https://doi.org/10.5194/amt-17-6517-2024, 2024
Short summary
ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications
Annachiara Bellini, Henri Diémoz, Luca Di Liberto, Gian Paolo Gobbi, Alessandro Bracci, Ferdinando Pasqualini, and Francesca Barnaba
Atmos. Meas. Tech., 17, 6119–6144, https://doi.org/10.5194/amt-17-6119-2024,https://doi.org/10.5194/amt-17-6119-2024, 2024
Short summary
Post-process correction improves the accuracy of satellite PM2.5 retrievals
Andrea Porcheddu, Ville Kolehmainen, Timo Lähivaara, and Antti Lipponen
Atmos. Meas. Tech., 17, 5747–5764, https://doi.org/10.5194/amt-17-5747-2024,https://doi.org/10.5194/amt-17-5747-2024, 2024
Short summary
Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites
Pawan Gupta, Robert C. Levy, Shana Mattoo, Lorraine A. Remer, Zhaohui Zhang, Virginia Sawyer, Jennifer Wei, Sally Zhao, Min Oo, V. Praju Kiliyanpilakkil, and Xiaohua Pan
Atmos. Meas. Tech., 17, 5455–5476, https://doi.org/10.5194/amt-17-5455-2024,https://doi.org/10.5194/amt-17-5455-2024, 2024
Short summary

Cited articles

Abdou, W. A., Martonchik, J. V., Kahn, R. A., West, R. A., and Diner, D. J.: A modified linear-mixing method for calculating atmospheric path radiances of aerosol mixtures, J. Geophys. Res., 102, 16883–16888, https://doi.org/10.1029/96JD03434, 1997.
Antoine, D., Morel, A., Leymarie, E., Houyou, A., Gentili, B., Victori, S., Buis, J.-P., Buis, N., Meunier, S., Canini, M., Crozel, D., Fougnie, B., and Henry, P.: Underwater Radiance Distributions Measured with Miniaturized Multispectral Radiance Cameras, J. Atmos. Ocean. Tech., 30, 74–95, 2013.
Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., and Gombos, D.: A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications, B. Am. Meteorol. Soc., 92, 157–174, https://doi.org/10.1175/2010BAMS2946.1, 2011.
Barrot, G., Mangin, A., and Pinnock, S.: GlobColour Product User Guide, http://www.globcolour.info (last access: 31 January 2014), 2010.
Baum, B., Yang, P., Heymsfield, A., Platnick, S., King, M., Hu, Y., and Bedka, S.: Bulk scattering properties for the remote sensing of ice clouds: Part II. Narrowband models, J. Appl. Meteorol., 44, 1896–1911, https://doi.org/10.1175/JAM2309.1, 2005.
Short summary
We systematically explore the cumulative effect of MISR research aerosol retrieval algorithm assumptions, quantifying and correcting the main sources of uncertainty over ocean. High median spectral aerosol optical depth biases of ~0.024 at low AOD are reduced to ~0.01 with an improved, physically based ocean surface model, particle properties and mixtures, adaptive reflectance uncertainty estimates and pixel selection, minor radiometric calibration adjustments and more stringent cloud screening.