Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5577-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5577-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Introducing the MISR level 2 near real-time aerosol product
Marcin L. Witek
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, 4800
Oak Grove Drive, Pasadena, CA 91109, USA
Michael J. Garay
Jet Propulsion Laboratory, California Institute of Technology, 4800
Oak Grove Drive, Pasadena, CA 91109, USA
David J. Diner
Jet Propulsion Laboratory, California Institute of Technology, 4800
Oak Grove Drive, Pasadena, CA 91109, USA
Michael A. Bull
Jet Propulsion Laboratory, California Institute of Technology, 4800
Oak Grove Drive, Pasadena, CA 91109, USA
Felix C. Seidel
Jet Propulsion Laboratory, California Institute of Technology, 4800
Oak Grove Drive, Pasadena, CA 91109, USA
Abigail M. Nastan
Jet Propulsion Laboratory, California Institute of Technology, 4800
Oak Grove Drive, Pasadena, CA 91109, USA
Earl G. Hansen
Jet Propulsion Laboratory, California Institute of Technology, 4800
Oak Grove Drive, Pasadena, CA 91109, USA
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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.
Benedetti, A., Reid, J. S., Knippertz, P., Marsham, J. H., Di Giuseppe, F., Rémy, S., Basart, S., Boucher, O., Brooks, I. M., Menut, L., Mona, L., Laj, P., Pappalardo, G., Wiedensohler, A., Baklanov, A., Brooks, M., Colarco, P. R., Cuevas, E., da Silva, A., Escribano, J., Flemming, J., Huneeus, N., Jorba, O., Kazadzis, S., Kinne, S., Popp, T., Quinn, P. K., Sekiyama, T. T., Tanaka, T., and Terradellas, E.: Status and future of numerical atmospheric aerosol prediction with a focus on data requirements, Atmos. Chem. Phys., 18, 10615–10643, https://doi.org/10.5194/acp-18-10615-2018, 2018.
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.
Buchard, V., Randles, C. A., da Silva, A. M., Darmenov, A., Colarco, P. R.,
Govindaraju, R., Ferrare, R., Hair, J., Beyersdorf, A. J., Ziemba, L. D., and
Yu, H.: The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and
case studies, J. Climate, 30, 6851–6872, https://doi.org/10.1175/JCLI-D-16-0613.1, 2017.
Butz, A., Hasekamp, O. P., Frankenberg, C., and Aben, U.: Retrievals of
atmospheric CO2 from simulated space-borne measurements of backscattered
near-infrared sunlight: Accounting for aerosol effects, Appl. Optics, 48,
3322–3336, https://doi.org/10.1364/AO.48.003322, 2009.
Choi, M., Lim, H., Kim, J., Lee, S., Eck, T. F., Holben, B. N., Garay, M. J., Hyer, E. J., Saide, P. E., and Liu, H.: Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign, Atmos. Meas. Tech., 12, 4619–4641, https://doi.org/10.5194/amt-12-4619-2019, 2019.
Colarco, P., Da Silva, A., Chin, M., and Diehl, T.: Online simulations of
global aerosol distributions in the NASA GEOS-4 model and comparisons to
satellite and ground-based aerosol optical depth, J. Geophys. Res.-Atmos.,
115, D14207, https://doi.org/10.1029/2009JD012820, 2010.
Di Girolamo, L. and Davies, R.: A Band-Differenced Angular Signature
Technique for Cirrus Cloud Detection, IEEE T. Geosci. Remote,
32, 890–896, https://doi.org/10.1109/36.298017, 1994.
Di Girolamo, L. and Davies, R.: The Image Navigation Cloud Mask for the
Multiangle Imaging Spectroradiometer (MISR), J. Atmos. Ocean. Technol.,
12, 1215–1228, https://doi.org/10.1175/1520-0426(1995)012<1215:tincmf>2.0.co;2, 1995.
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E.,
Kahn, R. A., Martonchik, J. V., Ackerman, T. P., Davies, R., Gerstl, S. A.
W., Gordon, H. R., Muller, J. P., Myneni, R. B., Sellers, P. J., Pinty, B.,
and Verstraete, M. M.: Multiangle Image Spectroradiometer (MISR) instrument
description and experiment overview, IEEE T. Geosci. Remote,
36, 1072–1087, 1998.
Diner, D. J., Di Girolamo, L., and Clothiaux, E. E.: Level 1 Cloud Detection
Algorithm Theoretical Basis, Jet Propuls. Lab. Calif. Inst. Technol.,
D-13397 (Rev. B), available at: https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd-misr-06.pdf (last access: 9 August 2021), 1999a.
Diner, D. J., Davies, R., Di Girolamo, L., Horvath, A., Moroney, C., Muller,
J. P., Paradise, S. R., Wenkert, D., and Zong, J.: Level 2 Cloud Detection
and Classification Algorithm Theoretical Basis, Jet Propuls. Lab. Calif.
Inst. Technol., D-11399 (Rev. D), available at: https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd-misr-07.pdf (last access: 9 August 2021), 1999b.
Diner, D. J., Abdou, W. A., Ackerman, T. P., Crean, K., Gordon, H. R., Kahn,
R. A., Martonchik, J. V, McMuldroch, S., Paradise, S. R., and Pinty, B.:
Level 2 aerosol retrieval algorithm theoretical basis, Jet Propuls. Lab.
Calif. Inst. Technol., D-11400 (Rev. G), available at: https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd-misr-09.pdf (last access: 9 August 2021), 2008.
Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C.: Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107–1129, https://doi.org/10.5194/gmd-10-1107-2017, 2017.
Frankenberg, C., Hasekamp, O., O'Dell, C., Sanghavi, S., Butz, A., and Worden, J.: Aerosol information content analysis of multi-angle high spectral resolution measurements and its benefit for high accuracy greenhouse gas retrievals, Atmos. Meas. Tech., 5, 1809–1821, https://doi.org/10.5194/amt-5-1809-2012, 2012.
Frouin, R. J., Franz, B. A., Ibrahim, A., Knobelspiesse, K., Ahmad, Z.,
Cairns, B., Chowdhary, J., Dierssen, H. M., Tan, J., Dubovik, O., Huang, X.,
Davis, A. B., Kalashnikova, O., Thompson, D. R., Remer, L. A., Boss, E.,
Coddington, O., Deschamps, P. Y., Gao, B. C., Gross, L., Hasekamp, O., Omar,
A., Pelletier, B., Ramon, D., Steinmetz, F., and Zhai, P. W.: Atmospheric
Correction of Satellite Ocean-Color Imagery During the PACE Era, Front.
Earth Sci., 7, 145, https://doi.org/10.3389/feart.2019.00145, 2019.
Fu, G., Prata, F., Lin, H. X., Heemink, A., Segers, A., and Lu, S.: Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption, Atmos. Chem. Phys., 17, 1187–1205, https://doi.org/10.5194/acp-17-1187-2017, 2017.
Gao, B. C., Goetz, A. F. H., and Wiscombe, W. J.: Cirrus cloud detection from
Airborne Imaging Spectrometer data using the 1.38 µm water vapor
band, Geophys. Res. Lett., 20, 301–304, https://doi.org/10.1029/93GL00106, 1993.
Garay, M. J., Witek, M. L., Kahn, R. A., Seidel, F. C., Limbacher, J. A., Bull, M. A., Diner, D. J., Hansen, E. G., Kalashnikova, O. V., Lee, H., Nastan, A. M., and Yu, Y.: Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product, Atmos. Meas. Tech., 13, 593–628, https://doi.org/10.5194/amt-13-593-2020, 2020.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G. K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective
analysis for research and applications, version 2 (MERRA-2), J. Climate,
30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gordon, R.: Atmospheric correction of ocean color imagery in the Earth
Observing System era, J. Geophys. Res.-Atmos., 102, 17081–17106,
https://doi.org/10.1029/96JD02443, 1997.
Houweling, S., Hartmann, W., Aben, I., Schrijver, H., Skidmore, J., Roelofs, G.-J., and Breon, F.-M.: Evidence of systematic errors in SCIAMACHY-observed CO2 due to aerosols, Atmos. Chem. Phys., 5, 3003–3013, https://doi.org/10.5194/acp-5-3003-2005, 2005.
Inness, A., Baier, F., Benedetti, A., Bouarar, I., Chabrillat, S., Clark, H., Clerbaux, C., Coheur, P., Engelen, R. J., Errera, Q., Flemming, J., George, M., Granier, C., Hadji-Lazaro, J., Huijnen, V., Hurtmans, D., Jones, L., Kaiser, J. W., Kapsomenakis, J., Lefever, K., Leitão, J., Razinger, M., Richter, A., Schultz, M. G., Simmons, A. J., Suttie, M., Stein, O., Thépaut, J.-N., Thouret, V., Vrekoussis, M., Zerefos, C., and the MACC team: The MACC reanalysis: an 8 yr data set of atmospheric composition, Atmos. Chem. Phys., 13, 4073–4109, https://doi.org/10.5194/acp-13-4073-2013, 2013.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M. M. B., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York,
NY, USA, 2013.
Kahn, R. A. and Gaitley, B. J.: An analysis of global aerosol type as
retrieved by MISR, J. Geophys. Res.-Atmos., 120, 4248–4281,
https://doi.org/10.1002/2015JD023322, 2015.
Kahn, R. A., Gaitley, B. J., Garay, M. J., Diner, D. J., Eck, T. F.,
Smirnov, A., and Holben, B. N.: Multiangle Imaging SpectroRadiometer global
aerosol product assessment by comparison with the Aerosol Robotic Network,
J. Geophys. Res.-Atmos., 115, D23209, https://doi.org/10.1029/2010JD014601, 2010.
Kalashnikova, O. V., Garay, M. J., Martonchik, J. V., and Diner, D. J.: MISR Dark Water aerosol retrievals: operational algorithm sensitivity to particle non-sphericity, Atmos. Meas. Tech., 6, 2131–2154, https://doi.org/10.5194/amt-6-2131-2013, 2013.
Lamarque, J.-F., Shindell, D. T., Josse, B., Young, P. J., Cionni, I., Eyring, V., Bergmann, D., Cameron-Smith, P., Collins, W. J., Doherty, R., Dalsoren, S., Faluvegi, G., Folberth, G., Ghan, S. J., Horowitz, L. W., Lee, Y. H., MacKenzie, I. A., Nagashima, T., Naik, V., Plummer, D., Righi, M., Rumbold, S. T., Schulz, M., Skeie, R. B., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S., Voulgarakis, A., and Zeng, G.: The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): overview and description of models, simulations and climate diagnostics, Geosci. Model Dev., 6, 179–206, https://doi.org/10.5194/gmd-6-179-2013, 2013.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, 367–371, https://doi.org/10.1038/nature15371, 2015.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Liu, M., Westphal, D. L., Walker, A. L., Holt, T. R., Richardson, K. A., and
Miller, S. D.: COAMPS real-time dust storm forecasting during operation
Iraqi freedom, Weather Forecast., 22, 192–206, https://doi.org/10.1175/WAF971.1,
2007.
Lynch, P., Reid, J. S., Westphal, D. L., Zhang, J., Hogan, T. F., Hyer, E. J., Curtis, C. A., Hegg, D. A., Shi, Y., Campbell, J. R., Rubin, J. I., Sessions, W. R., Turk, F. J., and Walker, A. L.: An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences, Geosci. Model Dev., 9, 1489–1522, https://doi.org/10.5194/gmd-9-1489-2016, 2016.
Martonchik, J. V., Diner, D. J., Crean, K. A., and Bull, M. A.: Regional
aerosol retrieval results from MISR, IEEE T. Geosci. Remote,
40, 1520–1531, https://doi.org/10.1109/TGRS.2002.801142, 2002.
Martonchik, J. V., Kahn, R. A., and Diner, D. J.: Retrieval of aerosol
properties over land using MISR observations, in Satellite Aerosol Remote
Sensing over Land, edited by: Kokhanovsky, A. A. and de Leeuw, G., Springer, Berlin, Heidelberg, pp. 267–293, 2009.
NASA: Multi-angle Imaging SpectroRadiometer, NASA [data set], available at: https://asdc.larc.nasa.gov/project/MISR, last access: 6 August 2021.
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A.,
Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka,
Y., and Flynn, C. J.: The MERRA-2 aerosol reanalysis, 1980 onward. Part I:
System description and data assimilation evaluation, J. Climate, 30,
6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017.
Reid, J. S., Benedetti, A., Colarco, P. R., and Hansen, J. A.: International
operational aerosol observability workshop, B. Am.
Meteorol. Soc., 92, ES21–ES24, 2011.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G. K., Bloom,
S., Chen, J., Collins, D., Conaty, A., Da Silva, A., Gu, W., Joiner, J.,
Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P.,
Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz,
M., and Woollen, J.: MERRA: NASA's modern-era retrospective analysis for
research and applications, J. Climate, 24, 3624–3648,
https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Sayer, A. M. and Knobelspiesse, K. D.: How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth, Atmos. Chem. Phys., 19, 15023–15048, https://doi.org/10.5194/acp-19-15023-2019, 2019.
Sayer, A. M., Govaerts, Y., Kolmonen, P., Lipponen, A., Luffarelli, M., Mielonen, T., Patadia, F., Popp, T., Povey, A. C., Stebel, K., and Witek, M. L.: A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing, Atmos. Meas. Tech., 13, 373–404, https://doi.org/10.5194/amt-13-373-2020, 2020.
Sekiyama, T. T., Tanaka, T. Y., Shimizu, A., and Miyoshi, T.: Data assimilation of CALIPSO aerosol observations, Atmos. Chem. Phys., 10, 39–49, https://doi.org/10.5194/acp-10-39-2010, 2010.
Shi, Y., Zhang, J., Reid, J. S., Holben, B., Hyer, E. J., and Curtis, C.: An analysis of the collection 5 MODIS over-ocean aerosol optical depth product for its implication in aerosol assimilation, Atmos. Chem. Phys., 11, 557–565, https://doi.org/10.5194/acp-11-557-2011, 2011.
Shi, Y., Zhang, J., Reid, J. S., Hyer, E. J., and Hsu, N. C.: Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa, Atmos. Meas. Tech., 6, 949–969, https://doi.org/10.5194/amt-6-949-2013, 2013.
Shi, Y., Zhang, J., Reid, J. S., Liu, B., and Hyer, E. J.: Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products, Atmos. Meas. Tech., 7, 1791–1801, https://doi.org/10.5194/amt-7-1791-2014, 2014.
Shindell, D. T., Lamarque, J.-F., Schulz, M., Flanner, M., Jiao, C., Chin, M., Young, P. J., Lee, Y. H., Rotstayn, L., Mahowald, N., Milly, G., Faluvegi, G., Balkanski, Y., Collins, W. J., Conley, A. J., Dalsoren, S., Easter, R., Ghan, S., Horowitz, L., Liu, X., Myhre, G., Nagashima, T., Naik, V., Rumbold, S. T., Skeie, R., Sudo, K., Szopa, S., Takemura, T., Voulgarakis, A., Yoon, J.-H., and Lo, F.: Radiative forcing in the ACCMIP historical and future climate simulations, Atmos. Chem. Phys., 13, 2939–2974, https://doi.org/10.5194/acp-13-2939-2013, 2013.
Si, Y., Chen, L., Xiong, X., Shi, S., Husi, L., and Cai, K.: Evaluation of
the MISR fine resolution aerosol product using MODIS, MISR, and ground
observations over China, Atmos. Environ., 223, 117229,
https://doi.org/10.1016/j.atmosenv.2019.117229, 2020.
Sogacheva, L., Popp, T., Sayer, A. M., Dubovik, O., Garay, M. J., Heckel, A., Hsu, N. C., Jethva, H., Kahn, R. A., Kolmonen, P., Kosmale, M., de Leeuw, G., Levy, R. C., Litvinov, P., Lyapustin, A., North, P., Torres, O., and Arola, A.: Merging regional and global aerosol optical depth records from major available satellite products, Atmos. Chem. Phys., 20, 2031–2056, https://doi.org/10.5194/acp-20-2031-2020, 2020.
Tao, M., Wang, J., Li, R., Chen, L., Xu, X., Wang, L., Tao, J., Wang, Z., and
Xiang, J.: Characterization of Aerosol Type Over East Asia by 4.4 km MISR
Product: First Insight and General Performance, J. Geophys. Res.-Atmos.,
125, e2019JD031909, https://doi.org/10.1029/2019JD031909, 2020.
Turnock, S. T., Allen, R. J., Andrews, M., Bauer, S. E., Deushi, M., Emmons, L., Good, P., Horowitz, L., John, J. G., Michou, M., Nabat, P., Naik, V., Neubauer, D., O'Connor, F. M., Olivié, D., Oshima, N., Schulz, M., Sellar, A., Shim, S., Takemura, T., Tilmes, S., Tsigaridis, K., Wu, T., and Zhang, J.: Historical and future changes in air pollutants from CMIP6 models, Atmos. Chem. Phys., 20, 14547–14579, https://doi.org/10.5194/acp-20-14547-2020, 2020.
Werner, M., Kryza, M., and Guzikowski, J.: Can data assimilation of surface
PM2.5 and Satellite AOD improve WRF-Chem Forecasting? A case study for two
scenarios of particulate air pollution episodes in Poland, Remote Sens.,
11, 2364, https://doi.org/10.3390/rs11202364, 2019.
Witek, M. L., Garay, M. J., Diner, D. J., and Smirnov, A.: Aerosol optical
depths over oceans: A view from MISR retrievals and collocated MAN and
AERONET in situ observations, J. Geophys. Res.-Atmos., 118,
12620–12633, https://doi.org/10.1002/2013JD020393, 2013.
Witek, M. L., Diner, D. J., Garay, M. J., Xu, F., Bull, M. A., and Seidel, F.
C.: Improving MISR AOD Retrievals with Low-Light-Level Corrections for
Veiling Light, IEEE T. Geosci. Remote, 56, 1251–1268,
https://doi.org/10.1109/TGRS.2017.2727342, 2018a.
Witek, M. L., Garay, M. J., Diner, D. J., Bull, M. A., and Seidel, F. C.: New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water, Atmos. Meas. Tech., 11, 429–439, https://doi.org/10.5194/amt-11-429-2018, 2018b.
Witek, M. L., Garay, M. J., Diner, D. J., and Smirnov, A.: Oceanic Aerosol
Loading Derived From MISR's 4.4 km (V23) Aerosol Product, J. Geophys. Res.-Atmos., 124, 10154–10174, https://doi.org/10.1029/2019JD031065, 2019.
Xian, P., Reid, J. S., Hyer, E. J., Sampson, C. R., Rubin, J. I., Ades, M.,
Asencio, N., Basart, S., Benedetti, A., Bhattacharjee, P. S., Brooks, M. E.,
Colarco, P. R., da Silva, A. M., Eck, T. F., Guth, J., Jorba, O.,
Kouznetsov, R., Kipling, Z., Sofiev, M., Perez Garcia-Pando, C., Pradhan,
Y., Tanaka, T., Wang, J., Westphal, D. L., Yumimoto, K., and Zhang, J.:
Current state of the global operational aerosol multi-model ensemble: An
update from the International Cooperative for Aerosol Prediction (ICAP), Q.
J. Roy. Meteor. Soc., 145, 176–209, https://doi.org/10.1002/qj.3497, 2019.
Zhang, J. and Reid, J. S.: An analysis of clear sky and contextual biases
using an operational over ocean MODIS aerosol product, Geophys. Res. Lett.,
36, L15824, https://doi.org/10.1029/2009GL038723, 2009.
Zhang, J. and Reid, J. S.: A decadal regional and global trend analysis of the aerosol optical depth using a data-assimilation grade over-water MODIS and Level 2 MISR aerosol products, Atmos. Chem. Phys., 10, 10949–10963, https://doi.org/10.5194/acp-10-10949-2010, 2010.
Zhang, J., Reid, J. S., Westphal, D. L., Baker, N. L., and Hyer, E. J.: A
system for operational aerosol optical depth data assimilation over global
oceans, J. Geophys. Res.-Atmos., 113, D10208, https://doi.org/10.1029/2007JD009065,
2008.
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.
This article documents the development and testing of a new near real-time (NRT) aerosol product...