Evaluation of The Ensemble Transform Sensitivity Technique and Flight Path Design For Adaptive Observations Using An OSSE Framework

Abstract

One of the main objectives of the National Oceanic and Atmospheric Administration (NOAA)/Sensing Hazards with Operational Unmanned Technology (SHOUT) project was to conduct Observing System Simulation Experiments (OSSEs) in an effort to evaluate and test the effectiveness of targeted measurements from Unmanned Aircraft Systems (UAS) on improving the prediction of high-impact weather (HIW) events. In contrast to observing system experiments, which assimilate real data, OSSEs use simulated observations and can be employed to investigate the potential impact of a particular observing system platform. In this study, we performed nine OSSE experiments from two extratropical winter storms found in a realistic nature run using simulated Global Hawk (GH) dropsonde data. The objective of this study is to determine optimal flight patterns for the NASA GH for improving weather forecasts. For each storm, a set of OSSE-flight patterns are created that take into account meteorological features, such as jet streaks, atmospheric rivers, low-pressure systems, and frontal boundaries, as well as data-sensitive regions found by the Ensemble Transform Sensitivity (ETS) technique. Results show that sampling regions of ETS sensitivity reduce forecast error and increase forecast skill in pre-defined verification regions. This study also found that it is essential to connect the ETS sensitivity with dynamical and thermodynamical regions, as sampling the upper-level jet exit regions provide similar or better forecast benefit, not only in the verification region, but also over the continental United States.