An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

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.

The Dependence of Shallow Cumulus Macrophysical Properties on Large‐Scale Meteorology as Observed in ASTER Imagery

“…On the other hand, Genkova et al (2007) show that the ASTER infrared-window retrieval exhibits a low height bias of about 250 m on average compared to the more accurate stereo height retrieval, although the difference might have been caused by inadequate cloud motion correction in the stereo retrievals. In broken low-level cloud scenes, Greenwald et al (2018) found a 450-m low bias in MODIS Collection 6 CTHs compared to Cloud-Aerosol Lidar with Orthogonal Polarization layer top heights, which was attributed to the influence of the warm ocean surface on 1-km MODIS brightness temperatures in partly cloudy pixels. This potential warm surface effect, however, is likely to be much smaller in the 90-m ASTER brightness temperatures.…”

“…In broken low‐level cloud scenes, Greenwald et al. () found a 450‐m low bias in MODIS Collection 6 CTHs compared to Cloud‐Aerosol Lidar with Orthogonal Polarization layer top heights, which was attributed to the influence of the warm ocean surface on 1‐km MODIS brightness temperatures in partly cloudy pixels. This potential warm surface effect, however, is likely to be much smaller in the 90‐m ASTER brightness temperatures.…”

Understanding Global Model Systematic Shortwave Radiation Errors in Subtropical Marine Boundary Layer Cloud Regimes

“…While the Lebsock and Su () study uses an alternative CERES‐MODIS retrieval (Collection 5.1; Platnick et al, ) to the one used here and in Painemal et al (), the conclusions are likely to apply more broadly, given that both CERES‐MODIS retrievals produce comparable results in MBL clouds (Zhang et al, ). Greenwald et al () show that if a bias correction is applied to the satellite MWR retrieval accounting for precipitation‐related biases (among others), the LWP retrieved from satellite MWR is reduced and becomes more comparable to the lower water paths from MAGIC in broken cloud conditions.…”

“…A shift of the distribution mode can be achieved by reducing the model's bulk condensate amount in the cumulus regime and increasing it in the stratocumulus regime, a change that is consistent with adjusting the model toward observed all-sky LWP values from the CERES-MODIS SSF retrieval. Given the recent insights from Lebsock and Su (2014) and Greenwald et al (2018) on precipitation-related biases from the satellite MWR retrievals, the CERES-MODIS SSF LWP therefore seems to the be the more appropriate reference choice here, despite the remaining uncertainties on the magnitude of the all-sky LWP.…”

“…Seethala and Horváth () find that LWP retrievals from passive microwave instruments using the Wentz algorithm (Hilburn & Wentz, ; Wentz & Spencer, ) are subject to retrieval biases particularly in areas with broken cloud cover, such as those found in the trade cumulus regions. Studies comparing satellite retrievals based on microwave radiometer data with retrievals based on optical depth/effective radius (Clouds and the Earth's Radiant Energy System/Moderate‐resolution Imaging Spectroradiometer, CERES/MODIS) conclude that agreement is good where cloud cover is overcast and nonprecipitating (Bennartz, ; Greenwald, ; Painemal et al, ; Seethala & Horváth, ) but diverge for broken cloud cover and in the presence of precipitation (Greenwald et al, ; Lebsock & Su, ).…”