![]() ![]() Varun Narayan Mishra, in Atmospheric Remote Sensing, 2023 5.12 Radiometric correction This chapter is beyond the scope to highlight the challenges there and requires a separate discussion.Īrjun Pratap Shahi. In addition to CF, various cloud microphysical parameters are also routinely retrieved and reported by satellites. Without improvement in global cloud climatology, it is difficult to reduce the uncertainty in climate forcing. It is recommended that measurements from sun-synchronous and geostationary satellites need to be synergized, and cloud detection methods need to be standardized. Efforts are ongoing to fuse passive and active remote sensing cloud products and understand the ability of the sensors in identifying individual cloud types based on their morphological, physical, and optical characteristics. View-angle correction has been applied to MODIS and ground-based observations. The finite resolution scale effect has been corrected in the MISR standard CF data product. The global cloud community has started addressing these critical issues. Once the CF climatology from various datasets converges after correcting these uncertainties, it will be more prudent to fuse multiple datasets according to their strengths and derive a unified global height-stratified cloud climatology. In the absence of a “true” global cloud dataset, the only way forward is to address the primary causes of uncertainties (resolution-effect, view-angle effect, ability of the sensors to detect low, mid, and high-level clouds, and the differences in sampling frequency) in the respective datasets. Currently, a large discrepancy in CF statistics exists among various popular data products, which hinders in deriving a “true” global CF climatology and reducing the uncertainties in cloud radiative feedback estimation. ![]() ![]() Satellites are the primary and indispensable observational data sources for cloud information on a global scale. Larry Di Girolamo, in Atmospheric Remote Sensing, 2023 9.3 Summary ![]()
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