Releases

 

The Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) datastream includes data bundles for shallow convection days at the Southern Great Plains atmospheric observatory for the period 2015–2019.

Bundles for 2015 and 2016 were produced as part of the LASSO pilot phase and are preliminary versions of the final product. Collectively, the Alpha 1 and Alpha 2 bundles serve to demonstrate the overall LASSO concept to researchers, provide a means for facilitating community discussion and feedback, and make available initial sets of simulations and accompanying observations for research purposes. From 2017 onward, a standardized model configuration and data bundle format has been used. The history of releases and differences between them can be found in the LASSO change log within the LASSO technical document.

Each release is a collection of data files, which entails a collection of what ARM calls “data bundles.” Such bundles consist of:

  • LES input and outputs
  • ARM observations co-registered on the model grid
  • model diagnostics and skill scores
  • data plots of various fields.

For routine operations, simulations from the Weather Research and Forecasting (WRF) model are available for each case day that have been driven using eight forcing options. This selection of forcings forms an ensemble that increases the likelihood of obtaining realistic LES simulations.

More complete details about the LES configuration and data included in the bundles are available in the LASSO technical document.

The simulations within LASSO represent the typical behavior expected from LES using best-practice configurations and are valid for use in various research applications. We recommend users contact the LASSO team to ensure the details of the simulations are understood and that the simulations are used appropriately for a given application.

LASSO Bundle Browser

The Bundle Browser interface has been designed to help users find simulations of relevance for their needs. The browser is a tool based on the Cassandra NoSQL database methodology that permits search by value queries of the skill scores and dynamically creates data plots or “quicklooks” of selected simulations for selected metrics.

The Bundle Browser returns results for the operational and Alpha 2 shallow convection cases and is the easiest method for accessing the data bundles. See the LASSO technical document for details on the browser.

Use of Alpha Products for Shallow Convection

The LASSO pilot phase produced two sets of “alpha” releases. In addition to the large-scale forcing ensemble available for all available shallow convection data bundles, the alpha releases include sensitivity tests for multiple configuration choices, such as domain size, grid spacing, model selection, and microphysics choice.

When using Alpha 1 results, note that Alpha 2 corrected some deficiencies in Alpha 1. To make the 2015 cases available, select Alpha 1 simulations have been reprocessed using Alpha 2 code and included as a supplemental release to Alpha 2.

Developing New Scenarios

A new scenario focusing on deep convection is under development and expected to be available in late 2021. This scenario will focus on cases from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in the Sierras de Córdoba mountain range of north-central Argentina.

Development of a maritime scenario focused on the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign will begin during 2021 as well, with release timing undetermined at this point.

Looking for Feedback

Efforts are ongoing to improve LASSO and its associated model configuration, forcings, and analysis tools. These will evolve based on user feedback and continued effort.

We encourage users to explore the available simulations and tools and to share their experience and ideas for improvement with the LASSO team at lasso@arm.gov. Users may also contact William Gustafson, lead principal investigator, or Andrew Vogelmann, co-principal investigator, directly.