Articles | Volume 14, issue 8
Atmos. Meas. Tech., 14, 5577–5591, 2021
https://doi.org/10.5194/amt-14-5577-2021
Atmos. Meas. Tech., 14, 5577–5591, 2021
https://doi.org/10.5194/amt-14-5577-2021

Research article 17 Aug 2021

Research article | 17 Aug 2021

Introducing the MISR level 2 near real-time aerosol product

Marcin L. Witek et al.

Related authors

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product
Michael J. Garay, Marcin L. Witek, Ralph A. Kahn, Felix C. Seidel, James A. Limbacher, Michael A. Bull, David J. Diner, Earl G. Hansen, Olga V. Kalashnikova, Huikyo Lee, Abigail M. Nastan, and Yan Yu
Atmos. Meas. Tech., 13, 593–628, https://doi.org/10.5194/amt-13-593-2020,https://doi.org/10.5194/amt-13-593-2020, 2020
Short summary
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing
Andrew M. Sayer, Yves Govaerts, Pekka Kolmonen, Antti Lipponen, Marta Luffarelli, Tero Mielonen, Falguni Patadia, Thomas Popp, Adam C. Povey, Kerstin Stebel, and Marcin L. Witek
Atmos. Meas. Tech., 13, 373–404, https://doi.org/10.5194/amt-13-373-2020,https://doi.org/10.5194/amt-13-373-2020, 2020
Short summary
New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water
Marcin L. Witek, Michael J. Garay, David J. Diner, Michael A. Bull, and Felix C. Seidel
Atmos. Meas. Tech., 11, 429–439, https://doi.org/10.5194/amt-11-429-2018,https://doi.org/10.5194/amt-11-429-2018, 2018
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Optimization of Aeolus' aerosol optical properties by maximum-likelihood estimation
Frithjof Ehlers, Thomas Flament, Alain Dabas, Dimitri Trapon, Adrien Lacour, Holger Baars, and Anne Grete Straume-Lindner
Atmos. Meas. Tech., 15, 185–203, https://doi.org/10.5194/amt-15-185-2022,https://doi.org/10.5194/amt-15-185-2022, 2022
Short summary
A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data
Alberto Sorrentino, Alessia Sannino, Nicola Spinelli, Michele Piana, Antonella Boselli, Valentino Tontodonato, Pasquale Castellano, and Xuan Wang
Atmos. Meas. Tech., 15, 149–164, https://doi.org/10.5194/amt-15-149-2022,https://doi.org/10.5194/amt-15-149-2022, 2022
Short summary
Biomass burning aerosol heating rates from the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) 2016 and 2017 experiments
Sabrina P. Cochrane, K. Sebastian Schmidt, Hong Chen, Peter Pilewskie, Scott Kittelman, Jens Redemann, Samuel LeBlanc, Kristina Pistone, Michal Segal Rozenhaimer, Meloë Kacenelenbogen, Yohei Shinozuka, Connor Flynn, Rich Ferrare, Sharon Burton, Chris Hostetler, Marc Mallet, and Paquita Zuidema
Atmos. Meas. Tech., 15, 61–77, https://doi.org/10.5194/amt-15-61-2022,https://doi.org/10.5194/amt-15-61-2022, 2022
Short summary
Aeolus L2A aerosol optical properties product: standard correct algorithm and Mie correct algorithm
Thomas Flament, Dimitri Trapon, Adrien Lacour, Alain Dabas, Frithjof Ehlers, and Dorit Huber
Atmos. Meas. Tech., 14, 7851–7871, https://doi.org/10.5194/amt-14-7851-2021,https://doi.org/10.5194/amt-14-7851-2021, 2021
Short summary
Methodology to obtain highly resolved SO2 vertical profiles for representation of volcanic emissions in climate models
Oscar S. Sandvik, Johan Friberg, Moa K. Sporre, and Bengt G. Martinsson
Atmos. Meas. Tech., 14, 7153–7165, https://doi.org/10.5194/amt-14-7153-2021,https://doi.org/10.5194/amt-14-7153-2021, 2021
Short summary

Cited articles

Ackerman, S., Richard, F., Kathleen, S., Yinghui, L., Liam, G., Bryan, B., and Paul, M.: Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document (MOD35), Univ. Wisconsin – Madison, 6th Edn. (October), 129, available at: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.4885 (last access: 6 August 2021)​​​​​​​, 2010. 
Benedetti, A., Reid, J. S., and Colarco, P. R.: International cooperative for aerosol prediction workshop on aerosol forecast verification, B. Am. Meteorol. Soc., 92, ES48–ES53, https://doi.org/10.1175/BAMS-D-11-00105.1,​​​​​​​ 2011. 
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015. 
Buchard, V., da Silva, A. M., Colarco, P. R., Darmenov, A., Randles, C. A., Govindaraju, R., Torres, O., Campbell, J., and Spurr, R.: Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis, Atmos. Chem. Phys., 15, 5743–5760, https://doi.org/10.5194/acp-15-5743-2015, 2015. 
Download
Short summary
This article documents the development and testing of a new near real-time (NRT) aerosol product from the MISR instrument on NASA’s Terra platform. The NRT product capitalizes on the unique attributes of the MISR retrieval approach, which leads to a high-quality and reliable aerosol data product. Several modifications are described that allow for rapid product generation within a 3 h window following acquisition. Implications for the product quality and consistency are discussed.