ABSTRACT
Aims: To develop a new satellite-based mixed-phase cloud retrieval algorithm for improving mixed-phase cloud liquid water path (LWP) retrievals by combining Moderate Resolution Imaging Spectroradiometer (MODIS), CloudSat, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements. Study Design: Algorithm development and evaluation by using collocated NASA A-Train and the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) measurements at the North Slope Alaska (NSA) site. Place and Duration of Study: Collocated MODIS and ground-based measurements at NSA site from March 2000 to October 2004, MODIS measurements and retrievals during July 2006 over Eastern Pacific, and MODIS, CloudSat and CALIPSO measurements on April 04, 2007 over the Arctic Region. Methodology: The stratiform mixed-phase cloudswere treated as two adjunct water and ice layers for radiative calculations with the Discrete Ordinate Radiative Transfer (DISORT) model. The ice-phase properties were provided with the 2C-ICE product, which is produced from CloudSat radar and CALIPSO lidar measurements, and they were used as inputs in DISORT for the calculations. Then, the calculated mixed-phase cloud reflectances at selected wavelengths were compared with MODIS reflectances to retrieve liquid-phase cloud properties. Results: A new algorithm was developed to retrieve LWP in stratiform mixed-phase clouds by using MODIS radiances and ice cloud properties from active sensor measurements. The algorithm was validated separately by using Operational MODIS retrievals of warm marine stratiform clouds and collocated surface measurements of Arctic stratiform mixed-phase clouds. The results show that the new algorithm reduced the positive LWP biases in the Operational MODIS LWP retrievals for stratiform mixedphase clouds from 35 and 68% to 10 and 22% in the temperature ranges of -5 to -10ºC and -10 to -20ºC, respectively. Conclusion: The combined A-Train active and MODIS measurements can be used to improve global mixed-phase cloud property retrievals.