Articles | Volume 16, issue 7
https://doi.org/10.5194/amt-16-1803-2023
© Author(s) 2023. 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-16-1803-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Apr 2023
Research article |
| 05 Apr 2023
| 05 Apr 2023
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, USA
Aviad Levis
Computer and Mathematical Sciences Department, California Institute of Technology, Pasadena, CA 91125, USA
Larry Di Girolamo
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, USA
Vadim Holodovsky
Viterbi Faculty of Electrical and Computer Engineering, Technion –
Israel Institute of Technology, Haifa 3200003, Israel
Linda Forster
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Anthony B. Davis
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Yoav Y. Schechner
Viterbi Faculty of Electrical and Computer Engineering, Technion –
Israel Institute of Technology, Haifa 3200003, Israel
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Cited articles
Ahn, E., Huang, Y., Siems, S. T., and Manton, M. J.: A Comparison of Cloud
Microphysical Properties Derived From MODIS and CALIPSO With In Situ
Measurements Over the Wintertime Southern Ocean, J. Geophys. Res.-Atmos., 123, 11120–11140, https://doi.org/10.1029/2018JD028535, 2018.
Alexandrov, M. D., Emde, C., Van Diedenhoven, B., and Cairns, B.:
Application of Radon Transform to Multi-Angle Measurements Made by the
Research Scanning Polarimeter: A New Approach to Cloud Tomography. Part I:
Theory and Tests on Simulated Data, Frontiers in Remote Sensing, 2, 791130,
https://doi.org/10.3389/frsen.2021.791130, 2021.
Arridge, S. R. and Schotland, J. C.: Optical tomography: forward and inverse
problems, Inverse Problems, 25, 123010,
https://doi.org/10.1088/0266-5611/25/12/123010, 2009.
Bal, G.: Inverse transport theory and applications, Inverse Problems, 25,
053001, https://doi.org/10.1088/0266-5611/25/5/053001, 2009.
Bal, G. and Jollivet, A.: Stability estimates in stationary inverse
transport, Inverse Probl. Imag., 2, 427–454, https://doi.org/10.3934/ipi.2008.2.427, 2008.
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P.,
Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau,
A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman,
A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T.,
Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein,
M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T.,
Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative
Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660,
https://doi.org/10.1029/2019RG000660, 2020.
Bodas-Salcedo, A., Andrews, T., Karmalkar, A. V., and Ringer, M. A.: Cloud
liquid water path and radiative feedbacks over the Southern Ocean,
Geophys. Res. Lett., 43, 10938–10946, https://doi.org/10.1002/2016GL070770, 2016.
Box, M. A.: Radiative perturbation theory: a review, Environ. Modell. Softw., 17, 95–106, https://doi.org/10.1016/S1364-8152(01)00056-1, 2002.
Buras, R. and Mayer, B.: Efficient unbiased variance reduction techniques
for Monte Carlo simulations of radiative transfer in cloudy atmospheres: The
solution, J. Quant. Spectrosc. Ra., 112, 434–447, https://doi.org/10.1016/j.jqsrt.2010.10.005, 2011.
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A Limited Memory Algorithm
for Bound Constrained Optimization, SIAM J. Sci. Comput., 16, 1190–1208,
https://doi.org/10.1137/0916069, 1995.
Cahalan, R. F., Oreopoulos, L., Marshak, A., Evans, K. F., Davis, A. B.,
Pincus, R., Yetzer, K. H., Mayer, B., Davies, R., Ackerman, T. P., Barker,
H. W., Clothiaux, E. E., Ellingson, R. G., Garay, M. J., Kassianov, E.,
Kinne, S., Macke, A., O'hirok, W., Partain, P. T., Prigarin, S. M., Rublev,
A. N., Stephens, G. L., Szczap, F., Takara, E. E., Várnai, T., Wen, G., and Zhuravleva, T. B.: THE I3RC: Bringing Together the Most Advanced
Radiative Transfer Tools for Cloudy Atmospheres, B. Am. Meteorol. Soc., 86, 1275–1294, https://doi.org/10.1175/BAMS-86-9-1275, 2005.
Chance, B., Cooper, C. E., Delpy, D. T., Reynolds, E. O. R., Arridge, S. R.,
and Schweiger, M.: Image reconstruction in optical tomography, Philos.
T. Roy. Soc. B, 352, 717–726, https://doi.org/10.1098/rstb.1997.0054, 1997.
Chazette, P., Totems, J., Baron, A., Flamant, C., and Bony, S.: Trade-wind clouds and aerosols characterized by airborne horizontal lidar measurements during the EUREC4A field campaign, Earth Syst. Sci. Data, 12, 2919–2936, https://doi.org/10.5194/essd-12-2919-2020, 2020.
Chen, K., Li, Q., and Wang, L.: Stability of stationary inverse transport
equation in diffusion scaling, Inverse Problems, 34, 025004,
https://doi.org/10.1088/1361-6420/aa990c, 2018.
Cornet, C. and Davies, R.: Use of MISR measurements to study the radiative
transfer of an isolated convective cloud: Implications for cloud optical
thickness retrieval, J. Geophys. Res.-Atmos., 113, D04202,
https://doi.org/10.1029/2007JD008921, 2008.
Culver, J. P., Ntziachristos, V., Holboke, M. J., and Yodh, A. G.:
Optimization of optode arrangements for diffuse optical tomography: A
singular-value analysis, Opt. Lett., 26, 701–703, https://doi.org/10.1364/OL.26.000701, 2001.
Czerninski, I. and Schechner, Y. Y.: Accelerating Inverse Rendering By Using
a GPU and Reuse of Light Paths, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.00085, 30 September 2021.
Davies, R.: The Effect of Finite Geometry on the Three-Dimensional Transfer
of Solar Irradiance in Clouds, J. Atmos. Sci., 35, 1712–1725, https://doi.org/10.1175/1520-0469(1978)035<1712:TEOFGO>2.0.CO;2, 1978.
Davis, A., Marshak, A., Cahalan, R., and Wiscombe, W.: The Landsat Scale
Break in Stratocumulus as a Three-Dimensional Radiative Transfer Effect:
Implications for Cloud Remote Sensing, J. Atmos. Sci., 54, 241–260, https://doi.org/10.1175/1520-0469(1997)054<0241:TLSBIS>2.0.CO;2, 1997.
Davis, A. B. and Marshak, A.: Multiple Scattering in Clouds: Insights from
Three-Dimensional Diffusion/P1 Theory, Nucl. Sci. Eng., 137,
251–280, https://doi.org/10.13182/NSE01-A2190, 2001.
Davis, A. B. and Marshak, A.: Photon propagation in heterogeneous optical
media with spatial correlations: enhanced mean-free-paths and
wider-than-exponential free-path distributions, J. Quant. Spectrosc. Ra., 84, 3–34, https://doi.org/10.1016/S0022-4073(03)00114-6, 2004.
Davis, A. B., Forster, L., Diner, D. J., and Mayer, B.: Toward Cloud
Tomography from Space using MISR and MODIS: The Physics of Image Formation
for Opaque Convective Clouds, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14537, 27 July 2021.
Deutschmann, T., Beirle, S., Frieß, U., Grzegorski, M., Kern, C.,
Kritten, L., Platt, U., Prados-Román, C., Puḳīte, J., Wagner, T., Werner, B., and Pfeilsticker, K.: The Monte Carlo atmospheric radiative transfer model McArtim: Introduction and validation of Jacobians and 3D features, J. Quant. Spectrosc. Ra., 112, 1119–1137, https://doi.org/10.1016/j.jqsrt.2010.12.009, 2011.
Di Girolamo, L., Liang, L., and Platnick, S.: A global view of one-dimensional solar radiative transfer through oceanic water clouds, Geophys. Res. Lett., 37, L18809, https://doi.org/10.1029/2010GL044094, 2010.
Diner, D. J., Xu, F., Garay, M. J., Martonchik, J. V., Rheingans, B. E., Geier, S., Davis, A., Hancock, B. R., Jovanovic, V. M., Bull, M. A., Capraro, K., Chipman, R. A., and McClain, S. C.: The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing, Atmos. Meas. Tech., 6, 2007–2025, https://doi.org/10.5194/amt-6-2007-2013, 2013.
Doicu, A. and Efremenko, D. S.: Linearizations of the Spherical Harmonic
Discrete Ordinate Method (SHDOM), Atmosphere, 10, 292, https://doi.org/10.3390/atmos10060292, 2019.
Doicu, A., Mishchenko, M. I., and Trautmann, T.: Electromagnetic scattering
by discrete random media illuminated by a Gaussian beam II: Solution of the
radiative transfer equation, J. Quant. Spectrosc. Ra., 256, 107297,
https://doi.org/10.1016/j.jqsrt.2020.107297, 2020.
Doicu, A., Doicu, A., Efremenko, D., and Trautmann, T.: Cloud tomographic
retrieval algorithms. I: Surrogate minimization method, J. Quant. Spectrosc. Ra., 277, 107954, https://doi.org/10.1016/j.jqsrt.2021.107954, 2022a.
Doicu, A., Doicu, A., Efremenko, D., and Trautmann, T.: Cloud tomographic
retrieval algorithms. II: Adjoint method, J. Quant. Spectrosc. Ra., 108177,
https://doi.org/10.1016/j.jqsrt.2022.108177, 2022b.
Dubovik, O., Holben, B. N., Lapyonok, T., Sinyuk, A., Mishchenko, M. I.,
Yang, P., and Slutsker, I.: Non-spherical aerosol retrieval method employing
light scattering by spheroids, Geophys. Res. Lett., 29, 54-1–54-4,
https://doi.org/10.1029/2001GL014506, 2002.
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011.
Dwight, R. P. and Brezillon, J.: Effect of Approximations of the Discrete
Adjoint on Gradient-Based Optimization, AIAA J., 44, 3022–3031,
https://doi.org/10.2514/1.21744, 2006.
Dzambo, A. M., L'Ecuyer, T., Sinclair, K., van Diedenhoven, B., Gupta, S., McFarquhar, G., O'Brien, J. R., Cairns, B., Wasilewski, A. P., and Alexandrov, M.: Joint cloud water path and rainwater path retrievals from airborne ORACLES observations, Atmos. Chem. Phys., 21, 5513–5532, https://doi.org/10.5194/acp-21-5513-2021, 2021.
Efremenko, D. S., Loyola, D. G., Doicu, A., and Spurr, R. J. D.:
Multi-core-CPU and GPU-accelerated radiative transfer models based on the
discrete ordinate method, Comput. Phys. Commun., 185, 3079–3089,
https://doi.org/10.1016/j.cpc.2014.07.018, 2014.
Emde, C., Barlakas, V., Cornet, C., Evans, F., Korkin, S., Ota, Y.,
Labonnote, L. C., Lyapustin, A., Macke, A., Mayer, B., and Wendisch, M.:
IPRT polarized radiative transfer model intercomparison project – Phase A, J. Quant. Spectrosc. Ra., 164, 8–36, https://doi.org/10.1016/j.jqsrt.2015.05.007, 2015.
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016.
Emde, C., Barlakas, V., Cornet, C., Evans, F., Wang, Z., Labonotte, L. C.,
Macke, A., Mayer, B., and Wendisch, M.: IPRT polarized radiative transfer
model intercomparison project – Three-dimensional test cases (phase B),
J. Quant. Spectrosc. Ra., 209, 19–44, https://doi.org/10.1016/j.jqsrt.2018.01.024, 2018.
Endo, S., Fridlind, A. M., Lin, W., Vogelmann, A. M., Toto, T., Ackerman, A.
S., McFarquhar, G. M., Jackson, R. C., Jonsson, H. H., and Liu, Y.: RACORO
continental boundary layer cloud investigations: 2. Large-eddy simulations
of cumulus clouds and evaluation with in situ and ground-based observations,
J. Geophys. Res.-Atmos., 120, 5993–6014, https://doi.org/10.1002/2014JD022525, 2015.
Eppstein, M. J., Fedele, F., Laible, J., Zhang, C., Godavarty, A., and
Sevick-Muraca, E. M.: A comparison of exact and approximate adjoint
sensitivities in fluorescence tomography, IEEE T. Med. Imaging, 22, 1215–1223, https://doi.org/10.1109/TMI.2003.818165, 2003.
Evans, K. F.: The Spherical Harmonics Discrete Ordinate Method for
Three-Dimensional Atmospheric Radiative Transfer, J. Atmos. Sci., 55, 429–446, https://doi.org/10.1175/1520-0469(1998)055<0429:TSHDOM>2.0.CO;2, 1998.
Evans, K. F.: Spherical Harmonics Discrete Ordinate Method, University of Colorado, Boulder [code], https://nit.coloradolinux.com/~evans/shdom/shdom.tar.gz, last access: 23 March 2023.
Ewald, F., Groß, S., Wirth, M., Delanoë, J., Fox, S., and Mayer, B.: Why we need radar, lidar, and solar radiance observations to constrain ice cloud microphysics, Atmos. Meas. Tech., 14, 5029–5047, https://doi.org/10.5194/amt-14-5029-2021, 2021.
Eytan, E., Khain, A., Pinsky, M., Altaratz, O., Shpund, J., and Koren, I.:
Shallow Cumulus Properties as Captured by Adiabatic Fraction in
High-Resolution LES Simulations, J. Atmos. Sci., 79, 409–428, https://doi.org/10.1175/JAS-D-21-0201.1, 2022.
Fielding, M. D., Chiu, J. C., Hogan, R. J., and Feingold, G.: A novel
ensemble method for retrieving properties of warm cloud in 3-D using
ground-based scanning radar and zenith radiances, J. Geophys. Res.-Atmos., 119, 10912–10930, https://doi.org/10.1002/2014JD021742, 2014.
Forster, L., Davis, A. B., Diner, D. J., and Mayer, B.: Toward Cloud
Tomography from Space Using MISR and MODIS: Locating the “Veiled Core” in
Opaque Convective Clouds, J. Atmos. Sci., 78, 155–166,
https://doi.org/10.1175/JAS-D-19-0262.1, 2020.
Fu, D., Di Girolamo, L., Liang, L., and Zhao, G.: Regional Biases in MODIS
Marine Liquid Water Cloud Drop Effective Radius Deduced Through Fusion With
MISR, J. Geophys. Res.-Atmos., 124, 13182–13196, https://doi.org/10.1029/2019JD031063, 2019.
Gao, M., Franz, B. A., Knobelspiesse, K., Zhai, P.-W., Martins, V., Burton, S., Cairns, B., Ferrare, R., Gales, J., Hasekamp, O., Hu, Y., Ibrahim, A., McBride, B., Puthukkudy, A., Werdell, P. J., and Xu, X.: Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model, Atmos. Meas. Tech., 14, 4083–4110, https://doi.org/10.5194/amt-14-4083-2021, 2021.
Garay, M. J., Davis, A. B., and Diner, D. J.: Tomographic reconstruction of
an aerosol plume using passive multiangle observations from the MISR
satellite instrument, Geophys. Res. Lett., 43, 12590–12596,
https://doi.org/10.1002/2016GL071479, 2016.
Gerber, H., Jensen, J. B., Davis, A. B., Marshak, A., and Wiscombe, W. J.:
Spectral Density of Cloud Liquid Water Content at High Frequencies, J. Atmos. Sci., 58, 497–503, https://doi.org/10.1175/1520-0469(2001)058<0497:SDOCLW>2.0.CO;2, 2001.
Girolamo, L. D.: Reciprocity principle applicable to reflected radiance measurements and the searchlight problem, Appl. Optics, 38, 3196–3198, https://doi.org/10.1364/AO.38.003196, 1999.
Girolamo, L. D.: Reciprocity principle for radiative transfer models that use periodic boundary conditions, J. Quant. Spectrosc. Ra., 73, 23–27, https://doi.org/10.1016/S0022-4073(01)00166-2, 2002.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M.
D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M.,
Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A.,
Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D.,
Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K.,
Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang,
Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm
Clouds: A Review of the Current State of Knowledge and Perspectives, Rev.
Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018.
Guillaume, A., Kahn, B. H., Yue, Q., Fetzer, E. J., Wong, S., Manipon, G.
J., Hua, H., and Wilson, B. D.: Horizontal and Vertical Scaling of Cloud
Geometry Inferred from CloudSat Data, J. Atmos. Sci., 75, 2187–2197, https://doi.org/10.1175/JAS-D-17-0111.1, 2018.
Hack, J. J., Caron, J. M., Danabasoglu, G., Oleson, K. W., Bitz, C., and
Truesdale, J. E.: CCSM–CAM3 Climate Simulation Sensitivity to Changes in
Horizontal Resolution, J. Climate, 19, 2267–2289, https://doi.org/10.1175/JCLI3764.1, 2006.
Hasekamp, O. P. and Landgraf, J.: Linearization of vector radiative transfer
with respect to aerosol properties and its use in satellite remote sensing,
J. Geophys. Res.-Atmos., 110, D04203, https://doi.org/10.1029/2004JD005260, 2005.
Hill, P. G., Hogan, R. J., Manners, J., and Petch, J. C.: Parametrizing the
horizontal inhomogeneity of ice water content using CloudSat data products,
Q. J. Roy. Meteor. Soc., 138, 1784–1793, https://doi.org/10.1002/qj.1893, 2012.
Holodovsky, V., Schechner, Y. Y., Levin, A., Levis, A., and Aides, A.:
In-situ multi-view multi-scattering stochastic tomography, in: 2016 IEEE
International Conference on Computational Photography (ICCP), Evanston, IL, 13–15 May 2016, IEEE, 1–12, https://doi.org/10.1109/ICCPHOT.2016.7492869, 2016.
Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python,
Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017.
Huang, D., Liu, Y., and Wiscombe, W.: Determination of cloud liquid water
distribution using 3D cloud tomography, J. Geophys. Res.-Atmos., 113, D13201, https://doi.org/10.1029/2007JD009133, 2008.
Huang, D., Gasiewski, A., and Wiscombe, W.: Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 2: Observation system simulation experiments, Atmos. Chem. Phys., 10, 6699–6709, https://doi.org/10.5194/acp-10-6699-2010, 2010.
Jiang, W., Zhan, Y., Xi, S., Huang, D. D., and Lu, J.: Compressive
Sensing-Based 3-D Rain Field Tomographic Reconstruction Using Simulated
Satellite Signals, IEEE T. Geosci. Remote, 60, 1–13, https://doi.org/10.1109/TGRS.2021.3063617, 2022.
Jimenez, P. A., Hacker, J. P., Dudhia, J., Haupt, S. E., Ruiz-Arias, J. A.,
Gueymard, C. A., Thompson, G., Eidhammer, T., and Deng, A.: WRF-Solar:
Description and Clear-Sky Assessment of an Augmented NWP Model for Solar
Power Prediction, B. Am. Meteorol. Soc., 97, 1249–1264, https://doi.org/10.1175/BAMS-D-14-00279.1, 2016.
Jones, A. L. and Di Girolamo, L.: Design and Verification of a New
Monochromatic Thermal Emission Component for the I3RC Community Monte Carlo
Model, J. Atmos. Sci., 75, 885–906, https://doi.org/10.1175/JAS-D-17-0251.1, 2018.
Kahn, R. A., Li, W.-H., Moroney, C., Diner, D. J., Martonchik, J. V., and
Fishbein, E.: Aerosol source plume physical characteristics from space-based
multiangle imaging, J. Geophys. Res.-Atmos., 112, D11205, https://doi.org/10.1029/2006JD007647, 2007.
Kanewala, U. and Bieman, J. M.: Testing scientific software: A systematic
literature review, Inform. Software Tech., 56, 1219–1232,
https://doi.org/10.1016/j.infsof.2014.05.006, 2014.
Kato, S. and Marshak, A.: Solar zenith and viewing geometry-dependent errors
in satellite retrieved cloud optical thickness: Marine stratocumulus case,
J. Geophys. Res.-Atmos., 114, D01202, https://doi.org/10.1029/2008JD010579, 2009.
King, N. J. and Vaughan, G.: Using passive remote sensing to retrieve the
vertical variation of cloud droplet size in marine stratocumulus: An
assessment of information content and the potential for improved retrievals
from hyperspectral measurements, J. Geophys. Res.-Atmos., 117, D15206, https://doi.org/10.1029/2012JD017896, 2012.
Klose, A. D. and Hielscher, A. H.: Optical tomography using the
time-independent equation of radiative transfer – Part 2: inverse model,
J. Quant. Spectrosc. Ra., 72, 715–732, https://doi.org/10.1016/S0022-4073(01)00151-0, 2002.
Langmore, I., Davis, A. B., and Bal, G.: Multipixel Retrieval of Structural
and Optical Parameters in a 2-D Scene With a Path-Recycling Monte Carlo
Forward Model and a New Bayesian Inference Engine, IEEE T. Geosci. Remote, 51, 2903–2919, https://doi.org/10.1109/TGRS.2012.2217380, 2013.
Lebsock, M. and Su, H.: Application of active spaceborne remote sensing for
understanding biases between passive cloud water path retrievals, J.
Geophys. Res.-Atmos., 119, 8962–8979, https://doi.org/10.1002/2014JD021568, 2014.
Lee, B., Di Girolamo, L., Zhao, G., and Zhan, Y.: Three-Dimensional Cloud Volume Reconstruction from the Multi-angle Imaging SpectroRadiometer, Remote Sens.-Basel, 10, 1858, https://doi.org/10.3390/rs10111858, 2018.
Lee, E. K. H., Wardenier, J. P., Prinoth, B., Parmentier, V., Grimm, S. L.,
Baeyens, R., Carone, L., Christie, D., Deitrick, R., Kitzmann, D., Mayne,
N., Roman, M., and Thorsbro, B.: 3D Radiative Transfer for Exoplanet
Atmospheres. gCMCRT: A GPU-accelerated MCRT Code, Astrophys. J., 929, 180,
https://doi.org/10.3847/1538-4357/ac61d6, 2022.
Levis, A., Schechner, Y. Y., Aides, A., and Davis, A. B.: Airborne
Three-Dimensional Cloud Tomography, in: 2015 IEEE International Conference
on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015, IEEE, 3379–3387, https://doi.org/10.1109/ICCV.2015.386, 2015.
Levis, A., Schechner, Y. Y., and Davis, A. B.: Multiple-Scattering
Microphysics Tomography, in: 2017 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017, IEEE, 5797–5806, https://doi.org/10.1109/CVPR.2017.614, 2017.
Levis, A., Schechner, Y. Y., Davis, A. B., and Loveridge, J.: Multi-View
Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size,
Remote Sens., 12, 2831, https://doi.org/10.3390/rs12172831, 2020.
Liemert, A. and Kienle, A.: Exact and efficient solution of the radiative
transport equation for the semi-infinite medium, Sci. Rep.-UK, 3, 2018,
https://doi.org/10.1038/srep02018, 2013.
Loeub, T., Levis, A., Holodovsky, V., and Schechner, Y. Y.: Monotonicity
Prior for Cloud Tomography, in: Computer Vision – ECCV 2020, edited by: Vedaldi, A., Bischof, H., Brox, T., and Frahm, J. M., Lecture Notes in Computer Science, Springer, Cham, 12363, 283–299, https://doi.org/10.1007/978-3-030-58523-5_17, 2020.
Loveridge, J., Levis, A., Aides, A., Forster, L., and Holodovsky, V.: Atmospheric Tomography with 3D Radiative Transfer, v4.1.2, Zenodo [code], https://doi.org/10.5281/zenodo.7062466, 2022.
Loveridge, J., Levis, A., Di Girolamo, L., Holodovsky, V., Forster, L., Davis, A. B., and Schechner, Y. Y.: Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization, Atmos. Meas. Tech., submitted, 2023a.
Loveridge, J., Levis, A., Holodovsky, V., and Forster, L.: Atmospheric Tomography with 3D Radiative Transfer, GitHub [code], https://github.com/CloudTomography/AT3D, last access: 28 March 2023b.
Marchand, R. and Ackerman, T.: Evaluation of radiometric measurements from
the NASA Multiangle Imaging Spectroradiometer (MISR): Two- and
three-dimensional radiative transfer modeling of an inhomogeneous
stratocumulus cloud deck, J. Geophys. Res.-Atmos., 109, D18208,
https://doi.org/10.1029/2004JD004710, 2004.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Radiative smoothing
in fractal clouds, J. Geophys. Res.-Atmos., 100, 26247–26261, https://doi.org/10.1029/95JD02895, 1995.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Scale Invariance in
Liquid Water Distributions in Marine Stratocumulus. Part II: Multifractal
Properties and Intermittency Issues, J. Atmos. Sci., 54, 1423–1444, https://doi.org/10.1175/1520-0469(1997)054<1423:SIILWD>2.0.CO;2, 1997.
Marshak, A., Davis, A., Cahalan, R. F., and Wiscombe, W.: Nonlocal independent pixel approximation: direct and inverse problems, IEEE
T. Geosci. Remote, 36, 192–205, https://doi.org/10.1109/36.655329, 1998a.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Radiative effects of
sub-mean free path liquid water variability observed in stratiform clouds,
J. Geophys. Res.-Atmos., 103, 19557–19567, https://doi.org/10.1029/98JD01728, 1998b.
Marshak, A., Platnick, S., Várnai, T., Wen, G., and Cahalan, R. F.: Impact of three-dimensional radiative effects on satellite retrievals of cloud droplet sizes, J. Geophys. Res.-Atmos., 111, D09207, https://doi.org/10.1029/2005JD006686, 2006.
Martelli, F., Binzoni, T., Pifferi, A., Spinelli, L., Farina, A., and
Torricelli, A.: There's plenty of light at the bottom: statistics of photon
penetration depth in random media, Sci. Rep.-UK, 6, 27057,
https://doi.org/10.1038/srep27057, 2016.
Martin, W., Cairns, B., and Bal, G.: Adjoint methods for adjusting
three-dimensional atmosphere and surface properties to fit
multi-angle/multi-pixel polarimetric measurements, J. Quant. Spectrosc. Ra., 144, 68–85, https://doi.org/10.1016/j.jqsrt.2014.03.030, 2014.
Martin, W. G. K. and Hasekamp, O. P.: A demonstration of adjoint methods for
multi-dimensional remote sensing of the atmosphere and surface, J. Quant. Spectrosc. Ra., 204, 215–231, https://doi.org/10.1016/j.jqsrt.2017.09.031, 2018.
McBride, B. A., Martins, J. V., Barbosa, H. M. J., Birmingham, W., and Remer, L. A.: Spatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurements, Atmos. Meas. Tech., 13, 1777–1796, https://doi.org/10.5194/amt-13-1777-2020, 2020.
McFarlane, S. A., Mather, J. H., and Mlawer, E. J.: ARM's Progress on
Improving Atmospheric Broadband Radiative Fluxes and Heating Rates,
Meteor. Mon., 57, 20.1–20.24, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0046.1, 2016.
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van
Diedenhoven, B., and Xue, L.: Confronting the Challenge of Modeling Cloud
and Precipitation Microphysics, J. Adv. Model. Earth Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020.
Muller, J.-P., Mandanayake, A., Moroney, C., Davies, R., Diner, D. J., and
Paradise, S.: MISR stereoscopic image matchers: techniques and results, IEEE
T. Geosci. Remote, 40, 1547–1559, https://doi.org/10.1109/TGRS.2002.801160, 2002.
Muller, J.-P., Denis, M.-A., Dundas, R. D., Mitchell, K. L., Naud, C., and
Mannstein, H.: Stereo cloud-top heights and cloud fraction retrieval from
ATSR-2, Int. J. Remote Sens., 28, 1921–1938, https://doi.org/10.1080/01431160601030975, 2007.
Nakajima, T. and Tanaka, M.: Algorithms for radiative intensity calculations
in moderately thick atmospheres using a truncation approximation, J. Quant. Spectrosc. Ra., 40, 51–69, https://doi.org/10.1016/0022-4073(88)90031-3, 1988.
Niu, H., Lin, Z.-J., Tian, F., Dhamne, S., and Liu, H.: Comprehensive
investigation of three-dimensional diffuse optical tomography with depth
compensation algorithm, J. Biomed. Opt., 15, 046005,
https://doi.org/10.1117/1.3462986, 2010.
Nocedal, J. and Wright, S.: Fundamentals of Unconstrained Optimization, in:
Numerical Optimization, Springer New York, New York, NY, 10–29,
https://doi.org/10.1007/978-0-387-40065-5_2, 2006.
Painemal, D., Spangenberg, D., Smith Jr., W. L., Minnis, P., Cairns, B., Moore, R. H., Crosbie, E., Robinson, C., Thornhill, K. L., Winstead, E. L., and Ziemba, L.: Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign, Atmos. Meas. Tech., 14, 6633–6646, https://doi.org/10.5194/amt-14-6633-2021, 2021.
Peterson, P.: F2PY: a tool for connecting Fortran and Python programs,
International Journal of Computational Science and Engineering, 4, 296–305,
https://doi.org/10.1504/IJCSE.2009.029165, 2009.
Pincus, R. and Evans, K. F.: Computational Cost and Accuracy in Calculating
Three-Dimensional Radiative Transfer: Results for New Implementations of
Monte Carlo and SHDOM, J. Atmos. Sci., 66, 3131–3146, https://doi.org/10.1175/2009JAS3137.1, 2009.
Ramon, D., Steinmetz, F., Jolivet, D., Compiègne, M., and Frouin, R.:
Modeling polarized radiative transfer in the ocean-atmosphere system with
the GPU-accelerated SMART-G Monte Carlo code, J. Quant. Spectrosc. Ra., 222–223, 89–107, https://doi.org/10.1016/j.jqsrt.2018.10.017, 2019.
Raschke, E., Ohmura, A., Rossow, W. B., Carlson, B. E., Zhang, Y.-C.,
Stubenrauch, C., Kottek, M., and Wild, M.: Cloud effects on the radiation
budget based on ISCCP data (1991 to 1995), Int. J. Climatol., 25, 1103–1125, https://doi.org/10.1002/joc.1157, 2005.
Ronen, R., Schechner, Y. Y., and Eytan, E.: 4D Cloud Scattering Tomography,
in: Proceedings of the IEEE/CVF International Conference on Computer Vision
(ICCV), Montreal, QC, Canada, 10–17 October 2021, IEEE, 5520–5529, https://doi.org/10.1109/ICCV48922.2021.00547, 2021.
Ronen, R., Holodovsky, V., and Schechner, Y. Y.: Variable Imaging Projection
Cloud Scattering Tomography, IEEE T. Pattern Anal., 1–12, https://doi.org/10.1109/TPAMI.2022.3195920, 2022.
Saito, M., Yang, P., Hu, Y., Liu, X., Loeb, N., Smith Jr., W. L., and Minnis,
P.: An Efficient Method for Microphysical Property Retrievals in Vertically
Inhomogeneous Marine Water Clouds Using MODIS-CloudSat Measurements, J. Geophys. Res.-Atmos., 124, 2174–2193, https://doi.org/10.1029/2018JD029659, 2019.
Saito, M., Yang, P., Ding, J., and Liu, X.: A Comprehensive Database of the
Optical Properties of Irregular Aerosol Particles for Radiative Transfer
Simulations, J. Atmos. Sci., 78, 2089–2111, https://doi.org/10.1175/JAS-D-20-0338.1, 2021.
Schilling, K., Schechner, Y. Y., and Koren, I.: CloudCT - Computed
Tomography Of Clouds By A Small Satellite Formation, in: 12th IAA symposium on Small Satellites for Earth Observation, Berlin Germany, 6–10 May 2019, https://www.cloudct.space/_files/ugd/aef793_e2334d0332014022aeb4a5891ff12e92.pdf (last access: 20 March 2023), 2019.
Seethala, C.: Evaluating the state-of-the-art of and errors in 1D satellite
cloud liquid water path retrievals with large eddy simulations and realistic
radiative transfer models, PhD thesis, University of Hamburg, Hamburg, https://doi.org/10.17617/2.1404650, 2012.
Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42,
https://doi.org/10.1038/nature12829, 2014.
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M.,
Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J.,
Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L.,
Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J.
R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and
Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple
Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020.
Shi, H.-J. M., Xie, Y., Byrd, R., and Nocedal, J.: A Noise-Tolerant
Quasi-Newton Algorithm for Unconstrained Optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.04352, 9 September 2021.
Sourdeval, O., Labonnote, L. C., Brogniez, G., Jourdan, O., Pelon, J., and Garnier, A.: A variational approach for retrieving ice cloud properties from infrared measurements: application in the context of two IIR validation campaigns, Atmos. Chem. Phys., 13, 8229–8244, https://doi.org/10.5194/acp-13-8229-2013, 2013.
Stephens, G. L. and Kummerow, C. D.: The Remote Sensing of Clouds and
Precipitation from Space: A Review, J. Atmos. Sci., 64, 3742–3765, https://doi.org/10.1175/2006JAS2375.1, 2007.
Tian, F., Niu, H., Khadka, S., Lin, Z.-J., and Liu, H.: Algorithmic depth
compensation improves quantification and noise suppression in functional
diffuse optical tomography, Biomed. Opt. Express, 1, 441–452,
https://doi.org/10.1364/BOE.1.000441, 2010.
van Harten, G., Diner, D. J., Daugherty, B. J. S., Rheingans, B. E., Bull,
M. A., Seidel, F. C., Chipman, R. A., Cairns, B., Wasilewski, A. P., and
Knobelspiesse, K. D.: Calibration and validation of Airborne Multiangle
SpectroPolarimetric Imager (AirMSPI) polarization measurements, Appl. Optics,
57, 4499–4513, https://doi.org/10.1364/AO.57.004499, 2018.
Van Weverberg, K., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H.-Y.,
Zhang, C., Xie, S., Tang, Q., Gustafson Jr., W. I., Qian, Y., Berg, L. K.,
Liu, Y., Huang, M., Ahlgrimm, M., Forbes, R., Bazile, E., Roehrig, R., Cole,
J., Merryfield, W., Lee, W.-S., Cheruy, F., Mellul, L., Wang, Y.-C.,
Johnson, K., and Thieman, M. M.: CAUSES: Attribution of Surface Radiation
Biases in NWP and Climate Models near the U.S. Southern Great Plains,
J. Geophys. Res.-Atmos., 123, 3612–3644, https://doi.org/10.1002/2017JD027188, 2018.
Vial, J., Bony, S., Stevens, B., and Vogel, R.: Mechanisms and Model
Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review, in:
Shallow Clouds, Water Vapor, Circulation, and Climate Sensitivity, edited
by: Pincus, R., Winker, D., Bony, S., and Stevens, B., Springer
International Publishing, Cham, 159–181,
https://doi.org/10.1007/978-3-319-77273-8_8, 2018.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ.,
Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M.,
Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020.
Wang, C., Platnick, S., Zhang, Z., Meyer, K., and Yang, P.: Retrieval of ice
cloud properties using an optimal estimation algorithm and MODIS infrared
observations: 1. Forward model, error analysis, and information content,
J. Geophys. Res.-Atmos., 121, 5809–5826, https://doi.org/10.1002/2015JD024526, 2016.
Wang, Y., Guo, M., Zhao, Y., and Jiang, J.: GPUs-RRTMG_LW: high-efficient and scalable computing for a longwave radiative transfer model on multiple GPUs, J. Supercomput., 77, 4698–4717, https://doi.org/10.1007/s11227-020-03451-3, 2021.
Wang, Z., Cui, S., Yang, J., Gao, H., Liu, C., and Zhang, Z.: A novel hybrid
scattering order-dependent variance reduction method for Monte Carlo
simulations of radiative transfer in cloudy atmosphere, J. Quant. Spectrosc. Ra., 189, 283–302, https://doi.org/10.1016/j.jqsrt.2016.12.002, 2017.
Wiscombe, W. J.: The Delta–M Method: Rapid Yet Accurate Radiative Flux
Calculations for Strongly Asymmetric Phase Functions, J. Atmos. Sci., 34, 1408–1422, https://doi.org/10.1175/1520-0469(1977)034<1408:TDMRYA>2.0.CO;2, 1977.
Xie, X. and Zhang, M.: Scale-aware parameterization of liquid cloud
inhomogeneity and its impact on simulated climate in CESM, J. Geophys. Res.-Atmos., 120, 8359–8371, https://doi.org/10.1002/2015JD023565, 2015.
Xu, F., Diner, D. J., Dubovik, O., and Schechner, Y.: A Correlated
Multi-Pixel Inversion Approach for Aerosol Remote Sensing, Remote Sens., 11, 746, https://doi.org/10.3390/rs11070746, 2019.
Yao, R., Intes, X., and Fang, Q.: Direct approach to compute Jacobians for
diffuse optical tomography using perturbation Monte Carlo-based photon
“replay”,” Biomed. Opt. Express, 9, 4588–4603,
https://doi.org/10.1364/BOE.9.004588, 2018.
Ye, J. C., Webb, K. J., Millane, R. P., and Downar, T. J.: Modified distorted Born iterative method with an approximate Fréchet derivative for optical diffusion tomography, J. Opt. Soc. Am. A, 16, 1814–1826, https://doi.org/10.1364/JOSAA.16.001814, 1999.
Zawada, D. J., Bourassa, A. E., and Degenstein, D. A.: Two-dimensional
analytic weighting functions for limb scattering, J. Quant. Spectrosc. Ra., 200, 125–136, https://doi.org/10.1016/j.jqsrt.2017.06.008, 2017.
Zawada, D. J., Rieger, L. A., Bourassa, A. E., and Degenstein, D. A.: Tomographic retrievals of ozone with the OMPS Limb Profiler: algorithm description and preliminary results, Atmos. Meas. Tech., 11, 2375–2393, https://doi.org/10.5194/amt-11-2375-2018, 2018.
Zhang, L. and Zhang, G.: Brief review on learning-based methods for optical
tomography, J. Innov. Opt. Heal. Sci., 12, 1930011, https://doi.org/10.1142/S1793545819300118, 2019.
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, D19208,
https://doi.org/10.1029/2012JD017655, 2012.
Zhao, G. and Di Girolamo, L.: Statistics on the macrophysical properties of
trade wind cumuli over the tropical western Atlantic, J. Geophys. Res.-Atmos., 112, D10204, https://doi.org/10.1029/2006JD007371, 2007.
Zhao, H. and Zhong, Y.: Instability of an Inverse Problem for the Stationary
Radiative Transport Near the Diffusion Limit, SIAM J. Math. Anal., 51,
3750–3768, https://doi.org/10.1137/18M1222582, 2019.
Zhou, C., Zelinka, M. D., and Klein, S. A.: Impact of decadal cloud
variations on the Earth's energy budget, Nat. Geosci., 9, 871–874,
https://doi.org/10.1038/ngeo2828, 2016.
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B:
Fortran subroutines for large-scale bound-constrained optimization, ACM
T. Math. Software, 23, 550–560, https://doi.org/10.1145/279232.279236, 1997.
Zinner, T., Mayer, B., and Schröder, M.: Determination of
three-dimensional cloud structures from high-resolution radiance data,
J. Geophys. Res.-Atmos., 111, D08204, https://doi.org/10.1029/2005JD006062, 2006.
Short summary
We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
We describe a new method for measuring the 3D spatial variations in water within clouds using...
