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Cheng, Xiaoping

Publications and source records attributed to Cheng, Xiaoping.

The Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) Activity for Continental Shallow Convection

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.

54 ENVIRONMENTAL SCIENCES↗

Recommendations for the Implementation of the LASSO Workflow

The U. S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Research Fa-cility began a pilot project in May 2015 to design a routine, high-resolution modeling capability to complement ARM’s extensive suite of measurements. This modeling capability, envisioned in the ARM Decadal Vision (U.S. Department of Energy 2014), subsequently has been named the Large-Eddy Simu-lation (LES) ARM Symbiotic Simulation and Observation (LASSO) project, and it has an initial focus of shallow convection at the ARM Southern Great Plains (SGP) atmospheric observatory. This report documents the recommendations resulting from the pilot project to be considered by ARM for imple-mentation into routine operations. During the pilot phase, LASSO has evolved from the initial vision outlined in the pilot project white paper (Gustafson and Vogelmann 2015) to what is recommended in this report. Further details on the overall LASSO project are available at https://www.arm.gov/capabilities/modeling/lasso. Feedback regarding LASSO and the recommendations in this report can be directed to William Gustafson, the project principal investigator (PI), and Andrew Vogelmann, the co-principal investigator (Co-PI), via lasso@arm.gov.

54 ENVIRONMENTAL SCIENCES↗

LASSO Data Bundles

The Alpha 2 release is the second release from the LASSO Pilot Phase that builds upon the Alpha 1 release. Alpha 2 contains additional diagnostics in the data bundles and focuses on cases from spring-summer 2016. A data bundle is a unified package consisting of LASSO LES input and output, observations, evaluation diagnostics, and model skill scores. LES input include model configuration information and forcing data. LES output includes profile statistics and full domain fields of cloud and environmental variables. Model evaluation data consists of LES output and ARM observations co-registered on the same grid and sampling frequency. Model performance is quantified by skill scores and diagnostics in terms of cloud and environmental variables.

54 ENVIRONMENTAL SCIENCES↗

Description of the LASSO Alpha 1 Release

The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility began a pilot project in May 2015 to design a routine, high-resolution modeling capability to complement ARM’s extensive suite of measurements. This modeling capability has been named the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project. The availability of LES simulations with concurrent observations will serve many purposes. LES helps bridge the scale gap between DOE ARM observations and models, and the use of routine LES adds value to observations. It provides a self-consistent representation of the atmosphere and a dynamical context for the observations. Further, it elucidates unobservable processes and properties. LASSO will generate a simulation library for researchers that enables statistical approaches beyond a single-case mentality. It will also provide tools necessary for modelers to reproduce the LES and conduct their own sensitivity experiments. Many different uses are envisioned for the combined LASSO LES and observational library. For an observationalist, LASSO can help inform instrument remote-sensing retrievals, conduct Observation System Simulation Experiments (OSSEs), and test implications of radar scan strategies or flight paths. For a theoretician, LASSO will help calculate estimates of fluxes and co-variability of values, and test relationships without having to run the model yourself. For a modeler, LASSO will help one know ahead of time which days have good forcing, have co-registered observations at high-resolution scales, and have simulation inputs and corresponding outputs to test parameterizations. Further details on the overall LASSO project are available at http://www.arm. gov/science/themes/lasso.

54 ENVIRONMENTAL SCIENCES↗

Alpha1 LASSO data bundles Lamont, OK

A data bundle is a unified package consisting of LASSO LES input and output, observations, evaluation diagnostics, and model skill scores. LES input includes model configuration information and forcing data. LES output includes profile statistics and full domain fields of cloud and environmental variables. Model evaluation data consists of LES output and ARM observations co-registered on the same grid and sampling frequency. Model performance is quantified by skill scores and diagnostics in terms of cloud and environmental variables.

54 ENVIRONMENTAL SCIENCES↗

Spectral characteristics of background error covariance and multiscale data assimilation

The spatial resolutions of numerical atmospheric and oceanic circulation models have steadily increased over the past decades. Horizontal grid spacing down to the order of 1 km is now often used to resolve cloud systems in the atmosphere and sub-mesoscale circulation systems in the ocean. These fine resolution models encompass a wide range of temporal and spatial scales, across which dynamical and statistical properties vary. In particular, dynamic flow systems for small scales can become spatially localized and temporarily intermittent. An analysis shows that the background correlation length scale is larger than 75 km for streamfunctions, even for a 2-km resolution model, and larger than 25 km for water vapor mixing ratios. The theoretical analyses suggest that such correlation length scales prevent the currently used data assimilation schemes from constraining spatial scales smaller than 150 km for streamfunctions and 50 km for water vapor mixing ratios. These results highlight the necessity of fundamentally modifying the currently used data assimilation algorithm for assimilating high-resolution observations into the aforementioned fine resolution models. A multiscale methodology based on scale decomposition is suggested, and challenges are discussed.

Li, Zhijin↗