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William I. Gustafson Jr.
,
Andrew M. Vogelmann
,
Zhijin Li
,
Xiaoping Cheng
,
Kyle K. Dumas
,
Satoshi Endo
,
Karen L. Johnson
,
Bhargavi Krishna
,
Tami Fairless
, and
Heng Xiao
Full access
William I. Gustafson Jr
,
Andrew M. Vogelmann
,
Zhijin Li
,
Xiaoping Cheng
,
Kyle K. Dumas
,
Satoshi Endo
,
Karen L. Johnson
,
Bhargavi Krishna
,
Tami Fairless
, and
Heng Xiao

Abstract

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.

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