Cross-Scale Land-Atmosphere Experiment (CSLAEX)

Funding Program: 

It is now well recognized that land-atmosphere interactions play an important role in both weather and climate prediction. These interactions take place over a wide range of spatial and temporal scales. Most current formulations of surface turbulent exchange of momentum, heat, and moisture are informed by observations with a limited temporal and spatial scale and hence may have limited applicability. The objective of this work is to improve the multi-scale representation of the near-surface heat exchange using observations based on high-frequency fiber optic cables.

Our knowledge and predictive capacity in land-atmosphere coupling is limited by our ability to observe the full-scale spectrum in action. The first long-term Cross-Scale Land-Atmosphere Experiment (CSLAEX) will bridge this fundamental gap in observation capacity by measuring components of the surface energy balance at a spatial scale relevant to meteorological applications and remote sensing (~2 kilometers) along with recording observations of the nested subscale processes using fiber optic measurements based on novel Distributed Temperature Sensors (DTS) at 20-centimeter spatial resolution and 1-Hertz temporal frequency.

The CSLAEX experiment will be deployed at the Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site near Lamont, Oklahoma. The data recorded from the Surface Energy Balance and simultaneous observations of the atmospheric state (boundary layer, cloud, convection) available at the DOE ARM SGP site will be used to frame and evaluate new transport laws and parameterizations for the boundary layer and the land surface that can then be implemented into the Weather and Research Forecasting (WRF) model and Community Atmospheric Model (CAM). The results of this study will impact multiple fields including meteorology, climate, hydrology, and remote sensing.


Project Term: 
2015 to 2020
Project Type: 
University Project

Research Highlights:

Deep Learning to Represent Subgrid Processes in Climate Models Highlight Presentation