Publications

Fu, DW; Di Girolamo, L; Liang, LS; Zhao, GY (2019). Regional Biases in MODIS Marine Liquid Water Cloud Drop Effective Radius Deduced Through Fusion With MISR. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 124(23), 13182-13196.

Abstract
Satellite measurements from Terra's Moderate Resolution Imaging Spectroradiometer (MODIS) represent our longest, single-platform, global record of the effective radius (Re) of the cloud drop size distribution. Quantifying its error characteristics has been challenging because systematic errors in retrieved Re covary with the structural characteristics of the cloud and the Sun-view geometry. Recently, it has been shown that the bias in MODIS Re can be estimated by fusing MODIS data with data from Terra's Multi-angle Imaging SpectroRadiometer (MISR). Here, we relate the bias to the observed underlying conditions to derive regional-scale, bias-corrected, monthly-mean Re-1.6, Re-2.1, and Re-3.7 values retrieved from the 1.6, 2.1, and 3.7 mu m MODIS spectral channels. Our results reveal that monthly-mean bias in Re-2.1 exhibits large regional dependency, ranging from at least similar to 1 to 10 mu m (15 to 60%) varying with scene heterogeneity, optical depth, and solar zenith angle. Regional bias-corrected monthly-mean Re-2.1 ranges from 4 to 17 mu m, compared to 10 to 25 mu m for uncorrected Re-2.1, with estimated uncertainties of 0.1 to 1.8 mu m. The bias-corrected monthly-mean Re-3.7 and Re-2.1 show difference of approximately +0.6 mu m in the coastal marine stratocumulus regions and down to approximately -2 mu m in the cumuliform cloud regions, compared to uncorrected values of about -1 to -6 mu m, respectively. Bias-corrected Re values compare favorably to other independent data sources, including field observations, global model simulations, and satellite retrievals that do not use retrieval techniques similar to MODIS. This work changes the interpretation of global Re distributions from MODIS Re products and may further impact studies, which use the original MODIS Re products to study, for example, aerosol-cloud interactions and cloud microphysical parameterization.

DOI:
10.1029/2019JD031063

ISSN:
2169-897X