Description

The LASSO-CACTI scenario focuses on deep convection during the Cloud, Aerosol, and Complex Interactions (CACTI) field campaign during 2018–2019 in Argentina. In particular, the library of cloud simulations focuses on days selected from CACTI that exhibit convective initiation near the ARM Mobile Facility site with subsequent advection of the convection within visibility of ARM’s radar network. A hierarchy of simulations is available. A larger number of case days are simulated with an ensemble of mesoscale simulations using grid spacings of 7.5 and 2.5 km. Each mesoscale ensemble has up to 33 members based on different (re)analysis products used to drive the model. From these ensembles, a subset of case dates are simulated at large-eddy simulation (LES) scales with 500 and 100 m grid spacings. The number of LES simulations per case date varies depending on the behavior of the model and the number of runs attempted to achieve a simulation close to observations. All simulations for LASSO-CACTI use the Weather Research and Forecasting (WRF) model with some minor enhancements for this application (code repository). Further details about this scenario can be found in the accompanying technical description.

Accessing data

The LASSO Bundle Browser provides quick-look plots and skill score evaluations of the simulations to assist in selecting data most useful to the user. After identifying the simulations to use, they can then be selected to download via the Browser. Additionally, a selection of the LASSO-CACTI data is staged on ARM’s Cumulus cluster, which can then be used either via ARM’s Jupyter Notebooks server or via the Cumulus compute nodes—request an account via the ARM Computing Resources web page. Note that many of the LASSO-CACTI files are large, and it is best practice to use the Globus transfer protocol for downloading the files. Additionally, consider using the subset files that group related variables into smaller variable groupings instead of downloading the original raw model output—this will greatly reduce the amount of data downloaded and likely meets most users’ needs.
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