Atmospheric Radiation Measurement Climate Research Facility US Department of Energy


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acred > ARM Cloud Retrieval Ensemble DataVAP Type(s) > Evaluation


ARM Cloud Retrieval Ensemble Data (ACRED) is a multi-year ensemble cloud microphysical property data set, created by assembling nine existing ground-based cloud retrievals of ARM measurements from cloud radars, lidars, and the Atmospheric Emitted Radiance Interferometer (AERI). The major cloud properties in ACRED are the cloud liquid effective radius, liquid water content, liquid water path, cloud ice effective radius, ice water content, and ice water path. For each variable, three types of quantities are provided: the time means, standard deviations, and quality control flags. Currently, ACRED is available at the ARM Southern Great Plains (SGP), North Slope of Alaska (NSA), and Tropical Western Pacific atmospheric observatories (TWP). For each site, ACRED contains three to six retrieval products for multiple years. The current version of ACRED includes hourly averaged cloud properties and has 512 vertical layers with a resolution of 45 m, which are consistent with the ARM Best Estimate (ARMBE) data products.

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This data set is developed to address the uncertainty issue within current cloud retrievals (Zhao et al. 2012), as large differences exist in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. It provides a rough estimate of the uncertainties in current ARM retrieved cloud microphysical properties for climate model evaluation and development. Additionally, the ACRED serves as a useful tool to understand uncertainties or bias that are closely associated with the retrieval techniques, which is an important step to further improve the representation of cloud processes in climate models.

One concern is that the uncertainty in each ensemble member has not been determined for all meteorological conditions. This can be addressed by 1) to generate an ensemble data set for each algorithm by perturbing key parameters and/or changing key assumptions used in these selected retrieval methods (Zhao et al. 2014), 2) to create observation system simulation experiment data sets and run the algorithms on these. In general, the retrieval techniques used by the nine ARM ground-based retrieval products differ from each other in their retrieval fundamental basis, assumptions used, retrieval inputs, and retrieval constraints. We recommend that users refer to the technical report and references below for more information.


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Shaocheng Xie
Lawrence Livermore National Laboratory

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