Challenges and opportunities for data assimilation in mountainous environments

Abstract

This contribution aims to summarize the current state of data assimilation research as applied to land and atmosphere simulation and prediction in mountainous environments. It identifies and explains critical challenges, and offers opportunities for productive research based on both models and observations. Though many of the challenges to optimal data assimilation in the mountains are also challenges in flatter terrain, the complex land-atmosphere interactions and increased surface heterogeneity in the mountains violate key assumptions and methods in the data assimilation algorithms and the underlying models. The effects of model inadequacy are particularly acute in complex terrain. Recent research related to some of the key challenges suggest opportunities to make gains in both land and atmospheric data assimilation in the mountains. Research directions are suggested, focusing on model improvement in a data assimilation context, and design of field programs aimed at data assimilation.