Ensemble sensitivity localization

Ensemble sensitivity is a tool to quantitatively determine which initial conditions influence a forecast quantity of choice. This information can then be used to understand the sources and dynamics of forecast uncertainty, quantify the impact of observations (e.g., E-FSOI), and determine where to best deploy observations to improve the forecast (e.g., observation targetting and network design). The ensemble sensitivity is calculated from the covariances of the initial ensemble to the forecast ensemble. Unfortunately, these covariances are prone to sampling errors due to the limited ensemble size. The most common approach in data assimilation to mitigate sampling errors is to apply distance-based damping, i.e., localization. This poster explores how to localize the sensitivity correctly and how it differs from analysis localization. Using simplified problems, we highlight the benefits and drawbacks of sensitivity localization and discuss its usefulness to numerical weather prediction applications.