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Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection

Active Dates 9/1/2022-2/29/2024
Program Area Atmospheric System Research
Project Description
Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection

H. M. J. Barbosa, University of Maryland Baltimore County (Principal Investigator)

L. Xiaowen, Morgan State University (Co-Investigator)

R. Sakai, Howard University (Collaborator)

Clouds and atmospheric convection present a very challenging range of spatial and temporal scales. Understanding and modeling convection is a huge challenge for climate and regional models, particularly over the continents and in the tropics. Because of their low spatial resolution, these models must parameterize precipitation, i.e., use simple empirical equations to represent what occurs at the scale of individual clouds. A consequence is that climate models often misrepresent the diurnal cycle of convective precipitation, which leads to other errors and accounts for one of the largest uncertainties in climate sensitivity estimations.

To improve our climate models, we need to better understand the physical mechanisms relevant for making a cloud grow and precipitate. For that we can use both Large-Eddy Simulation (LES) and long-term high-resolution measurements. High-resolution here means enough to resolve the air flow inside the clouds. A LES model provides a consistent representation of the atmosphere and gives context for the observations, as well as give information on unobservable processes and properties. Observations are the truth and, although incomplete, are the key to validating hypothesis and conclusions based on models. To bridge the gap between observations and models, the Atmospheric Radiation Measurement (ARM) program of the Department of Energy (DoE) has recently developed LASSO: the LES ARM Symbiotic Simulation and Observation (LASSO). LASSO bundles LES model outputs with ARM observations of real clouds, providing a powerful tool to understand cloud processes in the atmosphere.

Having that in mind, the first objective of this project is to develop a new partnership among the proposing institutions, allowing the team to share expertise and collaborate on topics which they have been pursuing independently. We then want to establish a collaboration with the LASSO team at Pacific Northwest National Laboratory (PNNL) to achieve our second objective, which is to participate in outreach and training activities offered by the Earth and Environmental Systems Sciences Division (EESSD), such as the LASSO Tutorial in May 2021. Pursuing these two objectives will enable our team to engage in EESSD relevant research using ARM data and models. This will lead to the fulfillment of our third objective, which is to foster atmospheric science research and training capacity at our minority-serving institutions. The proposed activities include: (1) monthly gatherings to discuss the local use and implementation of LASSO, (2) a journal club where students will discuss relevant scientific publication, (3) visits to and from PNNL scientists, (4) organizing a new LASSO training tutorial. Following the training, the team will work on (5) implementing LASSO on our computer cluster, and (6) performing initial simulations. These will be used in sensitivity experiments to test scientific hypothesis about how clouds work. The proposed activities and associated objectives will help us develop our capabilities and partnerships needed to participate in future EESSD research solicitations.
Award Recipient(s)
  • University of Maryland Baltimore County (PI: deMeloJorgeBarbosa, Henrique)