Cloud Optical Depth Retrieval

Cloud Optical Depth Retrieval is a methodological framework in atmospheric remote sensing that belongs to the class of cloud property inference techniques, distinguished by its specific aim of quantitatively estimating the cloud optical depth from satellite or ground-based radiometric measurements through inversion of radiative transfer models.

441

Publications

25.8K

Citations

1.3K

Authors

380

Institutions

Publications per year

2017–2026

85

Authors

1.3K

  1. SP
    Steven Platnick

    Goddard Space Flight Center, United States

    30 publications · 1.8K citations

    23

    h-index

  2. AM
    Alexander Marshak

    Goddard Space Flight Center, United States

    24 publications · 1.6K citations

    21

    h-index

  3. PM
    Patrick Minnis

    Langley Research Center, United States

    21 publications · 1.5K citations

    18

    h-index

  4. PY
    Ping Yang

    Texas A&M University, United States

    21 publications · 1.4K citations

    17

    h-index

  5. KM
    Kerry Meyer

    Goddard Space Flight Center, United States

    19 publications · 819 citations

    15

    h-index

1–5 of 1.3K

Venues

  1. Journal of Geophysical Research Atmospheres

    JGR · 75 publications · 5.1K citations

    43

    h-index

  2. Atmospheric measurement techniques

    48 publications · 2K citations

    26

    h-index

  3. Journal of the Atmospheric Sciences

    32 publications · 3.5K citations

    25

    h-index

  4. Journal of Geophysical Research Atmospheres

    JGR · 34 publications · 1.6K citations

    24

    h-index

  5. Atmospheric chemistry and physics

    31 publications · 1.6K citations

    23

    h-index

1–5