LASSO

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lasso > LASSO Alpha 1 Data BundlesData Source Type(s) > PI

The Alpha 1 release contains initial data bundles from the large-eddy simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) workflow pilot project for five shallow convection days from June and August 2015 plus an interface to a NoSQL approach to querying the simulation skill scores. These bundles represent initial model configurations, evaluation metrics, and products being considered for eventual automation for ongoing LASSO LES simulations. 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.

IMPORTANT:
For most purposes, the Alpha 1 simulations have been superseded by supplemental simulations added to Alpha 2 for the five case dates in 2015. Thus, users seeking LASSO simulations for 2015 should refer to the Alpha 2 set of data bundles. However, not all sensitivity comparisons were re-run for Alpha 2, and thus some users still may consider using the Alpha 1 product. Users interested in using the Alpha 1 simulations should see Appendix D in the Alpha 2 documentation update from April 2018 for details.

Purpose

The purpose of releasing these data bundles is to share with researchers how the models are behaving, as well as to get feedback on ways to present the data and improve overall results.

Primary Measurements

Cloud base height

Cloud effective radius

Cloud fraction

Cloud optical depth

Cloud particle number concentration

Longwave broadband downwelling irradiance

Longwave broadband upwelling irradiance

Planetary boundary layer height

Precipitable water

Precipitation

Radar reflectivity

Radiative heating rate

Sea surface temperature

Sensible heat flux

Shortwave broadband diffuse downwelling irradiance

Shortwave broadband direct normal irradiance

Shortwave broadband total downwelling irradiance

Shortwave broadband total upwelling irradiance

Surface skin temperature

Vertical velocity

Virtual temperature

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Locations

Fixed
Mobile

Data Details

Developed By	William Gustafson \| Andrew Vogelmann \| Xiaoping Cheng \| Satoshi Endo \| Zhijin Li \| Tami Toto \| Heng Xiao \| Bhargavi Krishna
Contact	William Gustafson
Resource(s)	Data Directory ReadMe
Data format	netCDF, png, gif, jpeg, html, pdf, txt, Cassandra with Spark
Site	SGP
Content time range	6 June 2015 - 29 August 2015
Attribute accuracy	No formal attribute accuracy tests were conducted.
Positional accuracy	No formal positional accuracy tests were conducted.
Data Consistency and Completeness	Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. Missing values in input data sets are carried through to the final analysis.
Access Restriction	No access constraints are associated with this data.
Use Restriction	No use constraints are associated with this data.
File naming convention	Mixed--combination of metrics with ARM-like names and model output that uses WRF and SAM naming conventions
Directory Organization	Directory organization: See Section 5.1 of http://iop.archive.arm.gov/arm-iop/0eval-data/gustafson/lasso-alpha1/docs/LASSO_Alpha1_Description.pdf
Citations	Khairoutdinov, M. F., and D. A. Randall (2003), Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities, J. Atmos. Sci., 60, 607-625, doi:10.1175/1520-0469(2003)060<0607:Crmota>2.0.Co;2. Li, Z., X. Cheng, W. I. Gustafson, and A. M. Vogelmann (2016), Spectral characteristics of background error covariance and multiscale data assimilation, Int. J. Numer. Meth. Fluids, in press, doi:10.1002/fld.4253. Li, Z., S. Feng, Y. Liu, W. Lin, M. Zhang, T. Toto, A. M. Vogelmann, and S. Endo (2015), Development of fine-resolution analyses and expanded large-scale forcing properties: 1. Methodology and evaluation, J. Geophys. Res., 120, 654-666, doi:10.1002/2014jd022245. Li, Z. J., J. C. McWilliams, K. Ide, and J. D. Farrara (2015), A multiscale variational data assimilation scheme: Formulation and illustration, Mon. Wea. Rev., 143, 3804-3822, doi:10.1175/mwr-d-14-00384.1. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, W. Wang, and J. G. Powers (2008), A description of the advanced research WRF version 3, 113 pp, NCAR Technical Note, NCAR/TN-475+STR, National Center for Atmospheric Research, http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf. Xie, S. C., R. T. Cederwall, and M. H. Zhang (2004), Developing long-term single-column model/cloud system-resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations, J. Geophys. Res., 109, D01104, doi:10.1029/2003jd004045. Zhang, M. H., and J. L. Lin (1997), Constrained variational analysis of sounding data based on column-integrated budgets of mass, heat, moisture, and momentum: Approach and application to ARM measurements, J. Atmos. Sci., 54, 1503-1524, doi:10.1175/1520-0469(1997)054 1503:Cvaosd 2.0.Co;2. Zhang, M. H., J. L. Lin, R. T. Cederwall, J. J. Yio, and S. C. Xie (2001), Objective analysis of ARM IOP data: Method and sensitivity, Mon. Wea. Rev., 129, 295-311, doi:10.1175/1520-0493(2001)129 0295:Oaoaid 2.0.Co;2.