Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication

TLDR

The rapid expansion of land‑surface satellite data and model sophistication in the 21st century has increased the dimensionality of models and observations, complicating data assimilation. This paper examines how satellite‑based land data assimilation can estimate multiple water‑cycle components across scales, identifies the need for advanced DA methods, and proposes future development strategies. The authors advocate using high‑resolution, coupled land‑atmosphere models and selectively assimilating observations identified through sensitivity or coupling analyses to efficiently constrain unobserved variables. Greater access to satellite observations now permits direct constraint of additional water‑cycle components that are valuable to end users.

Abstract

The beginning of the 21 st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.