skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: An Intercomparison of Microphysical Retrieval Algorithms for Upper-Tropospheric Ice Clouds

Journal Article · · Bulletin of the American Meteorological Society, 88(2):191-204

The large horizontal extent, location in the cold upper troposphere, and ice composition make cirrus clouds important modulators of the earth’s radiation budget and climate. Cirrus cloud microphysical properties are difficult to measure and model because they are inhomogeneous in nature and their ice crystal size distribution and habit are not well characterized. Accurate retrievals of cloud properties are crucial for improving the representation of cloud scale processes in large-scale models and for accurately predicting the earth’s future climate. A number of passive and active remote sensing retrievals exist for estimating the microphysical properties of upper tropospheric clouds. We believe significant progress has been made in the evolution of these retrieval algorithms in the last decade; however, there is room for improvement. Members of the Atmospheric Radiation Measurement program (ARM) Cloud Properties Working Group are involved in an intercomparison of optical depth (tau), ice water path, and characteristic particle size in ice clouds retrieved using ground-based instruments. The goals of this intercomparison are to evaluate the accuracy of state-of-the-art algorithms, quantify the uncertainties, and make recommendations for improvement. Currently, there is significant scatter in the algorithms for difficult clouds with very small optical depths (tau<0.3) and thick ice clouds (tau>1). The good news is that for thin cirrus (0.3<1) the algorithms tend to converge. In this first stage of the intercomparison, we present results from a representative case study, compare the retrieved cloud properties with aircraft and satellite measurements, and perform a radiative closure experiment to begin gauging the accuracy of these retrieval algorithms.

Research Organization:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-76RL01830
OSTI ID:
985043
Report Number(s):
PNNL-SA-48156; ISSN 1520-0477; KP1205010; TRN: US201016%%1657
Journal Information:
Bulletin of the American Meteorological Society, 88(2):191-204, Vol. 88, Issue 2; ISSN 0003-0007
Country of Publication:
United States
Language:
English