Our longest single-satellite cloud record that provides stable and multi-decadal observations comes from NASA’s Terra mission. Cloud properties are derived separately from Terra’s MODIS and MISR instruments using a single-cloud-layer assumption. Extensive validation (Mitra et al. 2021) of cloud top heights (CTH) for these two datasets using a lidar on the International Space Station showed significant issues with these datasets for conditions of thin cirrus over low cloud. In these conditions, MISR accurately retrieves the low cloud CTH rather than the CTH of the thin cirrus, while MODIS mischaracterizes the thin cirrus as a thicker mid-level cloud. Hence, the major weakness of each Terra sensor (missed high clouds for MISR and multi-layered clouds for MODIS) pose a significant observational challenge in estimating CTH and cloud optical property variability, especially for multi-layered clouds. Since multi-layered clouds form ~30% of global cloud cover (with 2-layered, thin cirrus over thick water or mixed-phase clouds dominating), they are climatically important.

We have recently developed and validated a physics-based MODIS+MISR fusion algorithm that leverages the strengths of each sensor to tackle the shortcomings of the other (Mitra et al. 2023). This improvement reduces the MODIS CTH and effective emissivity bias in the retrievals of thin high clouds above thick low clouds by >75%, with a near-closure of the MODIS error budget. This leads to a reduction in error in modeled top-of-atmosphere longwave cloud radiative effects ranging between 5 to 45 W m-2, depending on the relative position and optical properties of the 2 cloud layers.

We are supported by NASA to operationalize this new algorithm to create a 22-year MODIS+MISR fusion Level 2 Earth System Data Record (ESDR) for CTH (including the heights of both layers in a two-layered system) and cloud optical properties (including thin cirrus emissivity). The support comes under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs), and involves team members from Argonne National Lab, Lawrence Livermore National Lab, and the NASA Langley Atmospheric Science Data Center. This new data will be publicly available through NASA Earthdata in 2027.


This project is supported by NASA. The publications below also include support under separate grants and contracts with NASA or NSF and are listed in the acknowledgment section of the publications.

Peer-reviewed publications related to our MEaSUREs objectives

Stubenrauch, C.J.,  S. Kinne, G. Mandori, W.B. Rossow, , D.M. Winker, S. A. Ackerman, H. Chepfer, L. Di Girolamo, A. Garnier, A. Heidinger, K.-G. Karlsson, K. Meyer, P. Minnis, S. Platnick, Stengel, S. Sun-Mack, P. Veglio, A. Walther, X. Cai, A.H. Young, and G. Zhao, 2024: Lessons learned from the updated GEWEX Cloud Assessment database. Surv. Geophys.,

Reid, J., et al., 2023: The coupling between tropical meteorology, aerosol lifecycle, convection, and radiation, during the Clouds, Aerosol Monsoon Processes Philippines Experiment (CAMP2Ex). Bull. Am. Meteorol. Soc.,

Mitra, A., L. Loveridge, and L. Di Girolamo, 2023: Fusion of MISR stereo cloud heights and Terra-MODIS thermal infrared radiances to estimate two-layered cloud properties. J. Geophys. Res. – Atmos., 128,  e2022JD038135,

Hong, Y., Trapp, R. J., Nesbitt, S. W., and Di Girolamo, L., 2023: Near Global Distributions of Overshooting Tops Derived from Terra and Aqua MODIS Observations, Atmos. Meas. Tech., 16, 1391-1406,

Foster, M.J., L. Di Girolamo, C. Phillips, M. Stengel, S. Sun-Mack, and G. Zhao, 2021: State of the Climate in 2020: Cloudiness [in “State of the Climate in 2020”], Bull. Am. Meteor. Soc., 102 (8), S61-S63, doi:10.1175/2021BAMSStateoftheClimate.1

Mitra, A., L. Di Girolamo, Y. Hong, Y. Zhan, and K.J. Mueller, 2021: Assessment and error analysis of Terra-MODIS and MISR cloud-top heights through comparison with ISS-CATS lidar. J. Geophys. Res. Atmos., 126, e2020JD034281.

Dutta, S., L. Di Girolamo, S. Dey, Y. Zhan, C.M. Moroney, and G. Zhao, 2020: The reduction in near-global cloud cover after correcting for biases caused by finite resolution measurement. Geophys. Res. Lett, 47, e2020GL090313.

Hong, Y., and L. Di Girolamo, 2020: Cloud phase characteristics over southeast Asia from A-Train satellite observations. Atmos. Phys. Chem., 20, 8267-8291,

Zhao, G., M. Yang, Y. Gao, Y. Zhan, H.-K. Lee, and L. Di Girolamo, 2020: PYTAF: a python tool for spatially resampling Earth observation data. Earth Sci. Informatics,

Mueller, K.J., D.L. Wu, A. Horvath, V.M. Jovanovic, J.-P. Mueller, L. Di Girolamo, M.J. Garay,D.J. Diner, C.M. Moroney, and S. Wanzong, 2017: Assessment of MISR Cloud Motion Vectors (CMVs) relative to GOES and MODIS Atmospheric Motion Vectors (AMVs). J. Appl. Meteor. Climatol., 56(3), 555-572, doi: 101175/JAMC-D-16-0112.1.

Zhao, G., L. Di Girolamo, D.J. Diner, C.J. Bruegge, K. Mueller, and D.L. Wu, 2016: Regional changes in Earth’s color and texture as observed from space over a 15-year period. IEEE Trans. Geosci. Remote Sens.,54(7), 4240-4249, doi:10.1109/TGRS.2016.2538723.

Cho, H.-M., Z. Zhang, K. Meyer, M. Lebsock, S. Platnick, A.S. Ackerman, L. Di Girolamo, L.C. Labonnote, C. Cornet, J. Riedi, and R.E. Holz, 2015: Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans. J. Geophys. Res. Atmos., 120, doi:10.1002/2015JD023161.

Astin, I., and L. Di Girolamo, 2014: The horizontal scale-dependence of the cloud overlap parameter α.  Atmos. Chem. Phys., 14, 9917-9922.

Stubenrauch, C.J.,  W.B. Rossow, S. Kinne, S. Ackerman, G. Cesana, H. Chepfer, L. Di Girolamo, B. Getzewich, A. Guignard, A. Heidinger, B. Maddux, P. Menzel, P. Minnis, C. Pearl, S. Platnick, C. Poulsen,  J. Riedi, S. Sun-Mack, A. Walther, D. Winker, S. Zeng, and G. Zhao, 2013: Assessment of global cloud datasets from satellites: Project and Database initiated by the GEWEX Radiation Panel. Bull. Am. Meteor. Soc., 94, 1031 – 1049.

Reid, J.S., E.J. Hyer, R. Johnson, B.N. Holben, J. Zhang, J.R. Campbell, S.A. Christopher, L. Di Girolamo, L. Giglio, R.E. Holz, C. Kearney, J. Miettinen, E.A. Reid, F.J. Turk, J. Wang, P. Xian, R.J. Yokelson, G. Zhao, R. Balasubramanian, B.N. Chew, S. Janai, N. Lagrosas, P. Lestari, N.-H.Lin, M. Mahmud, B. Norris, A.X. Nguyen, N.T.K.Oahn, M. Oo, S. Salinas, and S.C. Liew, 2013: Observing and understanding the Southeast Asian aerosol system by remote sensing: An initial review and analysis for the Seven Southeast Asian Studies (7SEAS) program. Atmos. Res., 122, 403-468

Harshvardhan, G. Zhao, L. Di Girolamo, and R.N. Green, 2009: Satellite-observed location of stratocumulus cloud-top heights in the presence of strong inversions. IEEE Trans. Geosci. Remote Sens., 47, 1421-1428.

Mueller, K., L. Di Girolamo, M. Fromm, and S. Palm, 2008: Stereo observations of polar stratospheric clouds. Geophys. Res. Lett.,35, L17813, doi:10.1029/2008GL033792.

Astin, I., and L. Di Girolamo, 2006: The relationship between a and the cross-correlation of cloud fraction. Quart. J. Roy. Metero. Soc., 132, 2475-2478.

Zhao, G., and L. Di Girolamo, 2004: A cloud fraction versus view angle technique for automatic in-scene evaluation of the MISR cloud mask. J. Appl. Meteor., 43, 860-869.