Abstract
Abstract. This paper presents a statistical comparison of three cloud retrieval products of the Atmospheric Radiation Measurement (ARM) program at the Southern Great Plains (SGP) site from 1998 to 2006: MICROBASE, University of Utah (UU), and University of North Dakota (UND) products. The probability density functions of the various cloud liquid water content (LWC) retrievals appear to be consistent with each other. While the mean MICROBASE and UU cloud LWC retrievals agree well in the middle of cloud, the discrepancy increases to about 0.03 gm−3 at cloud top and cloud base. Alarmingly large differences are found in the droplet effective radius (re) retrievals. The mean MICROBASE re is more than 6 μm lower than the UU re, whereas the discrepancy is reduced to within 1 μm if columns containing raining and/or mixed-phase layers are excluded from the comparison. A suite of stratified comparisons and retrieval experiments reveal that the LWC difference stems primarily from rain contamination, partitioning of total liquid later path (LWP) into warm and supercooled liquid, and the input cloud mask and LWP. The large discrepancy among the re retrievals is mainly due to rain contamination and the presence of mixed-phase layers. Since rain or ice particles are likely to dominate radar backscattering over cloud droplets, the large discrepancy found in this paper can be thought of as a physical limitation of single-frequency radar approaches. It is therefore suggested that data users should use the retrievals with caution when rain and/or mixed-phase layers are present in the column.
Highlights
It has been long recognized that inadequate representation of clouds is largely responsible for the high degree of uncertainty associated with the magnitude of model-predicted climate change induced by changes of carbon dioxide, other trace gases, and aerosols (Stephens, 2005)
We focus on only low-level non-raining non-mixed-phase stratus clouds because: (1) the complication of phase-partitioning and rain contamination can be avoided; and (2) the University of North Dakota (UND) product can serve as a reference here since it uses more observation constraints
To examine if the existing ground-based cloud retrievals are able to provide a useful constraint for model evaluation and radiation budget studies, this paper statistically compares three cloud products that use Atmospheric Radiation Measurement (ARM) data as inputs over the nine-year period from 1998 to 2006
Summary
It has been long recognized that inadequate representation of clouds is largely responsible for the high degree of uncertainty associated with the magnitude of model-predicted climate change induced by changes of carbon dioxide, other trace gases, and aerosols (Stephens, 2005). For the various cloud microphysical retrieval products to be useful for various applications – including model evaluation, parameterization development, and understanding cloud processes – the uncertainty in the retrievals has to be quantified. Given the complexity of cloud processes themselves and the huge scale mismatch between in-situ and remotely sensed observations, systematic studies that employ different uncertainty quantification techniques are necessary to obtain a comprehensive image of the accuracy of cloud retrievals (Zhao et al, 2012). A fourth approach is to compare different cloud retrieval products and quantify the spread between the products in light of physical constraints on what is known from in-situ data.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
