Clarifying the role of soil moisture on low-level jet dynamics and the impact of SMAP data assimilation on forecast skill

The Great Plains low-level jet (LLJ) critically supports the region's agricultural and wind energy production. It may also play a major role in dictating the evolution-and particularly, cessation- of regional drought. Climate models simulate a future northerly shift in the LLJ due to large-scale (SST) forcing. However, a substantial fraction of LLJs are terrain-linked, uncoupled to the upper-level flow, and interact strongly with regional surface conditions including topography, temperature, and soil moisture. Such uncoupled LLJs may be underrepresented in coarse grid climate models. Furthermore, it is unclear how this class of LLJs will vary when they interact with northern landscapes.

In this study, we first investigate land-LLJ interactions through the lens of idealized Weather Research and Forecasting (WRF) experiments. We then apply insight gleaned from these experiments to select a set of historical LLJ events with significant potential for land-driven variability. And finally, we quantify for these events, the incremental surface wind forecast skill gains (or losses) attributable to the assimilation of NASA Soil Moisture Active Passive (SMAP) soil moisture. This work contributes to 1) improved understanding of LLJ dynamics and 2) identification of conditions when land data assimilation may be most impactful to weather prediction and correspondingly, beneficial to society.