The National Science Foundation (NSF) Geosciences program has awarded a $236,421 EAGER grant to the University of Utah to develop new approaches for representing land-atmosphere interactions in numerical weather prediction and climate models. The goal is to generalize the Monin-Obukhov Similarity Theory (MOST) to better account for turbulence anisotropy and facilitate its application across diverse surface configurations, such as mountainous terrain and forests. This research aims to improve the accuracy of weather and climate forecasts, which impact sectors ranging from agriculture and energy to emergency preparedness. The project will leverage a turbulence-resolving Earth system model to compute turbulence anisotropy, and test the new framework against experimental data and across different spatial resolutions. This work represents an important first step before integrating the anisotropy-based MOST approach into non-resolving turbulence Earth system models. No sub-awards are planned under this grant.