Articles | Volume 13, issue 9
https://doi.org/10.5194/amt-13-4947-2020
© Author(s) 2020. 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-13-4947-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Sep 2020
Research article |
| 18 Sep 2020
| 18 Sep 2020
A new Orbiting Carbon Observatory 2 cloud flagging method and rapid retrieval of marine boundary layer cloud properties
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 90095, USA
Matthew D. Lebsock
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
James McDuffie
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Graeme L. Stephens
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 90095, USA
Department of Meteorology, University of Reading, Reading, RG6 7BE, UK
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Cited articles
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman, S. A.,
Heidinger, A. K., and Yang, P.: MODIS Cloud-Top Property Refinements for Collection 6,
J. Appl. Meteorol. Clim., 51, 1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012.
Bennartz, R.: Global assessment of marine boundary layer cloud droplet number
concentration from satellite, J. Geophys. Res., 112, D02201, https://doi.org/10.1029/2006JD007547, 2007.
Bodas-Salcedo, A., Mulcahy, J. P., Andrews, T., Williams, K. D., Ringer, M. A., Field,
P. R., and Elsaesser, G. S.: Strong Dependence of Atmospheric Feedbacks on Mixed-Phase
Microphysics and Aerosol-Cloud Interactions in HadGEM3, J. Adv. Model. Earth Sy., 11,
1735–1758, https://doi.org/10.1029/2019MS001688, 2019.
Boesch, H., Brown, L., Castano, R., Christi, M., Connor, B., Crisp, D., Eldering, A.,
Fisher, B., Frankenberg, C., Gunson, M., Granat, R., McDuffie, J., Miller, C., Natraj, V.,
O'Brien, D., O'Dell, C., Osterman, G., Oyafuso, F., Payne, V., Polonsky, I., Smyth, M., Spurr, R.,
Thompson, D., and Toon, G.: Orbiting Carbon Observatory (OCO)-2 Level 2 Full Physics Algorithm
Theoretical Basis Document, Pasadena, CA, available at: https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_L2_ATBD.V8.pdf (last access: 7 September 2020), 2017.
Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the heart of tropical
cloud feedback uncertainties in climate models, Geophys. Res. Lett., 32, L20806,
https://doi.org/10.1029/2005GL023851, 2005.
CloudSat Data Processing Center: http://www.cloudsat.cira.colostate.edu/data-products/level-aux/oco2cld-lidar-aux, last access: 9 September 2020.
Crisp, D.: NASA Orbiting Carbon Observatory: measuring the column averaged carbon
dioxide mole fraction from space, J. Appl. Remote Sens., 2, 023508, https://doi.org/10.1117/1.2898457, 2008.
Crisp, D., Atlas, R. M., Breon, F.-M., Brown, L. R., Burrows, J. P., Ciais, P., Connor,
B. J., Doney, S. C., Fung, I. Y., Jacob, D. J., Miller, C. E., O'Brien, D., Pawson, S., Randerson,
J. T., Rayner, P., Salawitch, R. J., Sander, S. P., Sen, B., Stephens, G. L., Tans, P. P., Toon,
G. C., Wennberg, P. O., Wofsy, S. C., Yung, Y. L., Kuang, Z., Chudasama, B., Sprague, G., Weiss,
B., Pollock, R., Kenyon, D., and Schroll, S.: The Orbiting Carbon Observatory (OCO) mission,
Adv. Space Res., 34, 700–709, https://doi.org/10.1016/j.asr.2003.08.062, 2004.
Davis, A. B. and Knyazikhin, Y.: A Primer in 3D Radiative Transfer, in: 3D Radiative
Transfer in Cloudy Atmospheres, Springer-Verlag, Berlin/Heidelberg, 153–242, 2005.
Davis, A. B., Merlin, G., Cornet, C., Labonnote, L. C., Riédi, J., Ferlay, N.,
Dubuisson, P., Min, Q., Yang, Y., and Marshak, A.: Cloud information content in EPIC/DSCOVR's
oxygen A- and B-band channels: An optimal estimation approach,
J. Quant. Spectrosc. Ra., 216, 6–16, https://doi.org/10.1016/j.jqsrt.2018.05.007, 2018.
Eldering, A., O'Dell, C. W., Wennberg, P. O., Crisp, D., Gunson, M. R., Viatte, C.,
Avis, C., Braverman, A., Castano, R., Chang, A., Chapsky, L., Cheng, C., Connor, B., Dang, L.,
Doran, G., Fisher, B., Frankenberg, C., Fu, D., Granat, R., Hobbs, J., Lee, R. A. M., Mandrake,
L., McDuffie, J., Miller, C. E., Myers, V., Natraj, V., O'Brien, D., Osterman, G. B., Oyafuso, F.,
Payne, V. H., Pollock, H. R., Polonsky, I., Roehl, C. M., Rosenberg, R., Schwandner, F., Smyth,
M., Tang, V., Taylor, T. E., To, C., Wunch, D., and Yoshimizu, J.: The Orbiting Carbon
Observatory-2: first 18 months of science data products, Atmos. Meas. Tech., 10, 549–563, https://doi.org/10.5194/amt-10-549-2017, 2017.
Ferlay, N., Thieuleux, F., Cornet, C., Davis, A. B., Dubuisson, P., Ducos, F., Parol,
F., Riédi, J., and Vanbauce, C.: Toward New Inferences about Cloud Structures from
Multidirectional Measurements in the Oxygen A Band: Middle-of-Cloud Pressure and Cloud Geometrical
Thickness from POLDER-3/ PARASOL, J. Appl. Meteorol. Climatol., 49, 2492–2507,
https://doi.org/10.1175/2010JAMC2550.1, 2010.
Fischer, J. and Grassl, H.: Detection of Cloud-Top Height from Backscattered Radiances
within the Oxygen A Band. Part 1: Theoretical Study, J. Appl. Meteorol., 30, 1245–1259,
https://doi.org/10.1175/1520-0450(1991)030<1245:DOCTHF>2.0.CO;2, 1991.
Grisel, O., Mueller, A., Lars, Gramfort, A., Louppe, G., Prettenhofer, P., Blondel, M., Niculae, V., Nothman, J., Joly, A., Vanderplas, J., Kumar, M., Fan, T. J., Qin, H., Varoquaux, N., Estève, L., Layton, R., Hug, N., Metzen, J. H., Dawe, N., Lemaitre, G., Jalali, A., Rajagopalan, V. R., Schönberger, J., Yurchak, R., Li, W., Woolam, C., Eren, K., Dupré la Tour, T., and Eustache: scikit-learn/scikit-learn: scikit-learn 0.23.2, Zenodo, https://doi.org/10.5281/zenodo.3971965, 2020.
Grosvenor, D. P., Sourdeval, O., and Wood, R.: Parameterizing cloud top effective radii
from satellite retrieved values, accounting for vertical photon transport: quantification and
correction of the resulting bias in droplet concentration and liquid water path retrievals,
Atmos. Meas. Tech., 11, 4273–4289, https://doi.org/10.5194/amt-11-4273-2018, 2018.
Heidinger, A. K. and Stephens, G. L.: Molecular Line Absorption in a Scattering
Atmosphere. Part III: Pathlength Characteristics and Effects of Spatially Heterogeneous Clouds,
J. Atmos. Sci., 59, 1641–1654,
https://doi.org/10.1175/1520-0469(2002)059<1641:MLAIAS>2.0.CO;2, 2002.
Koelemeijer, R. B. A., Stammes, P., Hovenier, J. W., and de Haan, J. F.: A fast method
for retrieval of cloud parameters using oxygen A band measurements from the Global Ozone
Monitoring Experiment, J. Geophys. Res. Atmos., 106, 3475–3490, https://doi.org/10.1029/2000JD900657,
2001.
Kokhanovsky, A. A., Rozanov, V. V., Burrows, J. P., Eichmann, K.-U., Lotz, W., and
Vountas, M.: The SCIAMACHY cloud products: Algorithms and examples from ENVISAT, Adv. Space Res.,
36, 789–799, https://doi.org/10.1016/j.asr.2005.03.026, 2005.
Kokhanovsky, A. A., Mayer, B., Rozanov, V. V., Wapler, K., Burrows, J. P. and
Schumann, U.: The influence of broken cloudiness on cloud top height retrievals using nadir
observations of backscattered solar radiation in the oxygen A-band,
J. Quant. Spectrosc. Ra., 103, 460–477, https://doi.org/10.1016/j.jqsrt.2006.06.003, 2007.
L'Ecuyer, T. S. and Jiang, J. H.: Touring the atmosphere aboard the A-Train,
Phys. Today, 63, 36–41, https://doi.org/10.1063/1.3463626, 2010.
Lindstrot, R., Preusker, R., Ruhtz, T., Heese, B., Wiegner, M., Lindemann, C., and
Fischer, J.: Validation of MERIS Cloud-Top Pressure Using Airborne Lidar Measurements,
J. Appl. Meteorol. Clim., 45, 1612–1621, https://doi.org/10.1175/JAM2436.1, 2006.
Loyola, D. G., Gimeno García, S., Lutz, R., Argyrouli, A., Romahn, F., Spurr,
R. J. D., Pedergnana, M., Doicu, A., Molina García, V., and Schüssler, O.: The
operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor,
Atmos. Meas. Tech., 11, 409–427, https://doi.org/10.5194/amt-11-409-2018, 2018.
McDuffie, J., Bowman, K., Hobbs, J., Natraj, V., Sarkissian, E., Smyth, M., Thill, M., and Val, S.: Reusable Framework for Retrieval of Atmospheric Composition (ReFRACtor) (Version 1.09), Zenodo, https://doi.org/10.5281/zenodo.4019567, 2020.
Merlin, G., Riedi, J., Labonnote, L. C., Cornet, C., Davis, A. B., Dubuisson, P.,
Desmons, M., Ferlay, N., and Parol, F.: Cloud information content analysis of multi-angular
measurements in the oxygen A-band: application to 3MI and MSPI, Atmos. Meas. Tech., 9, 4977–4995,
https://doi.org/10.5194/amt-9-4977-2016, 2016.
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and Effective
Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part I: Theory,
J. Atmos. Sci., 47, 1878–1893,
https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
Natraj, V. and Spurr, R. J. D.: A fast linearized pseudo-spherical two orders of
scattering model to account for polarization in vertically inhomogeneous scattering–absorbing
media, J. Quant. Spectrosc. Ra., 107, 263–293, https://doi.org/10.1016/j.jqsrt.2007.02.011,
2007.
O'Brien, D. M. and Mitchell, R. M.: Error Estimates for Retrieval of Cloud-Top Pressure
Using Absorption in the A Band of Oxygen, J. Appl. Meteorol., 31, 1179–1192,
https://doi.org/10.1175/1520-0450(1992)031<1179:EEFROC>2.0.CO;2, 1992.
O'Dell, C. W.: Acceleration of multiple-scattering, hyperspectral radiative transfer
calculations via low-streams interpolation, J. Geophys. Res., 115, D10206,
https://doi.org/10.1029/2009JD012803, 2010.
O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C., Castano, R.,
Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J., Miller, C. E., Natraj, V.,
Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The
ACOS CO2 retrieval algorithm – Part 1: Description and validation against synthetic
observations, Atmos. Meas. Tech., 5, 99–121, https://doi.org/10.5194/amt-5-99-2012, 2012.
Painemal, D. and Zuidema, P.: Assessment of MODIS cloud effective radius and optical
thickness retrievals over the Southeast Pacific with VOCALS-REx in situ measurements,
J. Geophys. Res., 116, D24206, https://doi.org/10.1029/2011JD016155, 2011.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Preusker, R., Fischer, J., Albert, P., Bennartz, R., and Schüller, L.: Cloud-top
pressure retrieval using the oxygen A-band in the IRS-3 MOS instrument, Int. J. Remote Sens., 28,
1957–1967, https://doi.org/10.1080/01431160600641632, 2007.
Richardson, M. and Stephens, G. L.: Information content of OCO-2 oxygen A-band channels
for retrieving marine liquid cloud properties, Atmos. Meas. Tech., 11, 1515–1528,
https://doi.org/10.5194/amt-11-1515-2018, 2018.
Richardson, M., McDuffie, J., Stephens, G. L., Cronk, H. Q., and Taylor, T. E.: The
OCO-2 oxygen A-band response to liquid marine cloud properties from CALIPSO and MODIS,
J. Geophys. Res.-Atmos., 122, 8255–8275, https://doi.org/10.1002/2017JD026561, 2017.
Richardson, M., Leinonen, J., Cronk, H. Q., McDuffie, J., Lebsock, M. D., and Stephens,
G. L.: Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer,
Atmos. Meas. Tech., 12, 1717–1737, https://doi.org/10.5194/amt-12-1717-2019, 2019.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding Theory and Practice, World
Scientific, Singapore, 2000.
Rosenfeld, D., Zhu, Y., Wang, M., Zheng, Y., Goren, T., and Yu, S.: Aerosol-driven
droplet concentrations dominate coverage and water of oceanic low-level clouds, Science, 363,
eaav0566, https://doi.org/10.1126/science.aav0566, 2019.
Rozanov, V. V. and Kokhanovsky, A. A.: Semianalytical cloud retrieval algorithm as
applied to the cloud top altitude and the cloud geometrical thickness determination from
top-of-atmosphere reflectance measurements in the oxygen A band, J. Geophys. Res., 109, D05202,
https://doi.org/10.1029/2003JD004104, 2004.
Schepers, D., Butz, A., Hu, H., Hasekamp, O. P., Arnold, S. G., Schneider, M., Feist,
D. G., Morino, I., Pollard, D., Aben, I., and Landgraf, J.: Methane and carbon dioxide total
column retrievals from cloudy GOSAT soundings over the oceans, J. Geophys. Res.-Atmos., 121,
5031–5050, https://doi.org/10.1002/2015JD023389, 2016.
Schuessler, O., Loyola Rodriguez, D. G., Doicu, A., and Spurr, R.: Information Content
in the Oxygen A-Band for the Retrieval of Macrophysical Cloud Parameters, IEEE
T. Geosci. Remote Sens., 52, 3246–3255, https://doi.org/10.1109/TGRS.2013.2271986, 2014.
Spurr, R. J. D.: VLIDORT: A linearized pseudo-spherical vector discrete ordinate
radiative transfer code for forward model and retrieval studies in multilayer multiple scattering
media, J. Quant. Spectrosc. Ra., 102, 316–342, https://doi.org/10.1016/j.jqsrt.2006.05.005,
2006.
Szczodrak, M., Austin, P. H., and Krummel, P. B.: Variability of Optical Depth and
Effective Radius in Marine Stratocumulus Clouds, J. Atmos. Sci., 58, 2912–2926,
https://doi.org/10.1175/1520-0469(2001)058<2912:VOODAE>2.0.CO;2, 2001.
Taylor, T. E., O'Dell, C. W., Frankenberg, C., Partain, P. T., Cronk, H. Q.,
Savtchenko, A., Nelson, R. R., Rosenthal, E. J., Chang, A. Y., Fisher, B., Osterman, G. B.,
Pollock, R. H., Crisp, D., Eldering, A., and Gunson, M. R.: Orbiting Carbon Observatory-2 (OCO-2)
cloud screening algorithms: validation against collocated MODIS and CALIOP data,
Atmos. Meas. Tech., 9, 973–989, https://doi.org/10.5194/amt-9-973-2016, 2016.
Toll, V., Christensen, M., Quaas, J., and Bellouin, N.: Weak average liquid-cloud-water
response to anthropogenic aerosols, Nature, 572, 51–55, https://doi.org/10.1038/s41586-019-1423-9, 2019.
Vanbauce, C., Buriez, J. C., Parol, F., Bonnel, B., Sèze, G., and Couvert, P.:
Apparent pressure derived from ADEOS-POLDER observations in the oxygen A-band over ocean,
Geophys. Res. Lett., 25, 3159–3162, https://doi.org/10.1029/98GL02324, 1998.
Várnai, T. and Marshak, A.: Observations of Three-Dimensional Radiative Effects
that Influence MODIS Cloud Optical Thickness Retrievals, J. Atmos. Sci., 59, 1607–1618,
https://doi.org/10.1175/1520-0469(2002)059<1607:OOTDRE>2.0.CO;2, 2002.
Vidot, J., Bennartz, R., O'Dell, C. W., Preusker, R., Lindstrot, R., and Heidinger,
A. K.: CO2 Retrieval over Clouds from the OCO Mission: Model Simulations and Error
Analysis, J. Atmos. Ocean. Tech., 26, 1090–1104, https://doi.org/10.1175/2009JTECHA1200.1, 2009.
Werner, F., Zhang, Z., Wind, G., Miller, D. J., and Platnick, S.: Quantifying the
Impacts of Subpixel Reflectance Variability on Cloud Optical Thickness and Effective Radius
Retrievals Based On High-Resolution ASTER Observations, J. Geophys. Res.-Atmos., 123, 4239–4258,
https://doi.org/10.1002/2017JD027916, 2018.
Yamamoto, G. and Wark, D. Q.: Discussion of the letter by R. A. Hanel, “Determination
of cloud altitude from a satellite,” J. Geophys. Res., 66, 3596–3596,
https://doi.org/10.1029/JZ066i010p03596, 1961.
Yang, Y., Marshak, A., Mao, J., Lyapustin, A., and Herman, J.: A method of retrieving
cloud top height and cloud geometrical thickness with oxygen A and B bands for the Deep Space
Climate Observatory (DSCOVR) mission: Radiative transfer simulations,
J. Quant. Spectrosc. Ra., 122, 141–149, https://doi.org/10.1016/j.jqsrt.2012.09.017, 2013.
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P.,
Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models,
Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020.
Zhang, Z., Ackerman, A. S., Feingold, G., Platnick, S., Pincus, R., and Xue, H.:
Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective
radius: Case studies based on large-eddy simulations, J. Geophys. Res. Atmos., 117, 19208,
https://doi.org/10.1029/2012JD017655, 2012.
Zhou, Y., Yang, Y., Gao, M., and Zhai, P.-W.: Cloud detection over snow and ice with
oxygen A- and B-band observations from the Earth Polychromatic Imaging Camera (EPIC),
Atmos. Meas. Tech., 13, 1575–1591, https://doi.org/10.5194/amt-13-1575-2020, 2020.
Short summary
We previously combined CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data and reflected-sunlight measurements from OCO-2 (Orbiting Carbon Observatory 2) for information about low clouds over oceans. The satellites are no longer formation-flying, so this work is a step towards getting new information about these clouds using only OCO-2. We can rapidly and accurately identify liquid oceanic clouds and obtain their height better than a widely used passive sensor.
We previously combined CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite...
