An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path. Issue 7 (12th April 2018)

• Record Type: Journal Article
• Authors: Greenwald, Thomas J.; Bennartz, Ralf; Lebsock, Matthew; Teixeira, João
• Is Part Of: Journal of geophysical research. Volume 123:Issue 7(2018)
• Page Start: 3668
• Page End: 3687
• Publication Date: 2018-04-12
• Subjects: clouds -- microwave -- precipitation -- remote sensing -- liquid water; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027638
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9‐year record of the Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E): clear‐sky bias, cloud‐rain partition (CRP) bias, cloud‐fraction‐dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR‐E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud‐fraction‐dependent bias was found to be a combination of the CRP bias, an in‐cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m 2, dominated by the CRP and in‐cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias‐corrected AMSR‐E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible‐infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations. Key Points: Uncertainties in passive microwave‐derived cloud liquid water observations are dominated by in‐cloud and cloud‐rain partition biases Current methods for detecting rain in warm clouds from passive microwave measurements are inadequate Combining passive microwave and visible‐infrared data is essential in quantifying errors and improving the accuracy of these observations