Abstract
Geostationary satellites can retrieve the cloud droplet effective radius (re) but suffer biases from cloud inhomogeneities, internal retrieval nonlinearities, and 3-D scattering/shadowing from neighboring clouds, among others. A 1-D retrieval method was applied to Geostationary Operational Environmental Satellite 13 (GOES-13) imagery, over large areas in South America (5∘ N–30∘ S; 20∘–70∘ W), the Southeast Pacific (5∘ N–30∘ S; 70∘–120∘ W), and the Amazon (2∘ N–7∘ S; 54∘–73∘ W), for four months in each year from 2014–2017. Results were compared against in situ aircraft measurements and the Moderate Resolution Imaging Spectroradiometer cloud product for Terra and Aqua satellites. Monthly regression parameters approximately followed a seasonal pattern. With up to 108,009 of matchups, slope, intercept, and correlation for Terra (Aqua) ranged from about 0.71 to 1.17, −2.8 to 2.5 μm, and 0.61 to 0.91 (0.54 to 0.78, −1.5 to 1.8 μm, 0.63 to 0.89), respectively. We identified evidence for re overestimation (underestimation) correlated with shadowing (enhanced reflectance) in the forward (backscattering) hemisphere, and limitations to illumination and viewing configurations accessible by GOES-13, depending on the time of day and season. A proposition is hypothesized to ameliorate 3-D biases by studying relative illumination and cloud spatial inhomogeneity.
Highlights
Clouds are the main radiative modulator in the atmosphere [1] and have relevant impacts on climate
We describe below the general retrieval strategy used to derive re from Geostationary Operational Environmental Satellite 13 (GOES-13) radiance measurements
We have examined how Geostationary Operational Environmental Satellites (GOESs)-13 re retrievals, for warm-phase clouds, fare against the reference
Summary
Clouds are the main radiative modulator in the atmosphere [1] and have relevant impacts on climate. In order to adequately describe these impacts, global climate models need constant improvements in the parametrization of cloud properties, and how they respond to changes in atmospheric dynamics and composition. At local and global scales, are often modeled as changes in the effective radius of cloud droplets (re ) (e.g., [7,8]), and as the net radiative forcing originating from these changes (e.g., [9]). A commonly used strategy is to apply forward modeling to simulate sensor-specific radiance measurements, for a myriad of physical conditions, with 1-D cloud properties, illumination, and viewing angles. A look-up table (LUT) with these results is compiled for use in the inverse problem, i.e., minimizing a cost function defined as the difference between actual radiance measurements and the precomputed LUT solutions
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
