Analysis of cloud retrieval uncertainty in MICROBASE
Poster PDF
Authors
Chuanfeng Zhao — Beijing Normal University
Shaocheng Xie — Lawrence Livermore National Laboratory
Xiao Chen — Lawrence Livermore National Laboratory
Maureen Dunn — Brookhaven National Laboratory
Michael Jensen — Brookhaven National Laboratory
Category
Cloud Properties
Description
This paper presents a simple yet general approach to estimate uncertainties in ground-based retrievals of cloud properties. This approach, called as the perturbation method, quantifies the cloud retrieval uncertainties by perturbing the cloud retrieval inputs and parameters within their error ranges. The error ranges for the cloud retrieval inputs and parameters are determined by either instrument limitations or comparisons against aircraft observations. We analyzed the relative contributions to the uncertainties of retrieved cloud properties from the inputs, assumptions and parameterizations. In this study, we apply this approach to the ARM baseline retrieval value-added product, MICROBASE. Results show that different influential factors play the dominant role in contributing to the uncertainties in different cloud properties. To reduce uncertainties in cloud retrievals, efforts should be emphasized on the major contributing factors for considered cloud properties. Our results also indicate that the cloud retrieval uncertainties are sensitive to cloud types and other factors.
