Event- and Feature-Based Observing System Design: Quantifying Science and Applications Benefit for Diverse Measurement Combinations

There is an increasing need for observing systems that are not only global in scope, but also flexibly responsive to emerging events. In addition, new measurements must not only advance the state of science, but also serve the needs of applications end users. Furthermore, any new measurements will be made in the context of an observing system that is ever increasing in its diversity and complexity. The key challenge is to measure the benefits of proposed new measurements in the context of the current and planned global observing system, allowing for a diversity of measurement types (spaceborne, airborne, and ground-based), enabling a focus on feature- and event-based observing, and returning quantitative estimates of benefit to science and applications. At this time, the capability to 1) quantitatively evaluate multi-agent (spaceborne, land-based, and aircraft-based) observing systems does not exist. Neither does 2) the capability to quantitatively evaluate an event-based adaptive observing system for the atmosphere. There also exists 3) no general capability to evaluate observing systems for applications use cases. In this new AIST project, we will construct a multi-agent object-based observing system simulation experiment (OSSE) framework that: 1. considers spaceborne, airborne, and ground-based fixed and adaptive measurements individually and together, 2. enables targeted observing of features of interest, and 3. quantifies the benefit of new measurements for both science and applications. It utilizes recent advances in Bayesian inverse problem theory, data fusion, and machine learning. Specifically, Spatial-Temporal Statistical Data Fusion (STDF), a generalization of optimal interpolation (OI), combines information from datasets with diverse sampling and error characteristics to produce an optimal synergistic estimate of one or more geophysical variables. STDF also propagates uncertainties from individual estimates into the merged estimate. Probabilistic measures of differences among distributions (e.g., Kullbach-Liebler divergence) can be used to provide quantitative estimates of the effects of differences in spatial and temporal sampling characteristics. Bayesian inverse (e.g., Optimal Estimation and Markov chain Monte Carlo) methods can be used to map changes in expected observation uncertainty to uncertainty in geophysical variables and thence to the ability to (dis)prove a hypothesis or provide actionable applications information. Random Forest based machine learning can be used to learn the relationships between a change in observing system configuration and a change in geophysical variable error and application utility. We will test our system on observations of the planetary boundary layer (PBL), a specific example from the 2020 Novel Observing Systems workshop. The PBL is home to nearly all of the Earth's human population and regulates the exchanges of mass, energy, and momentum between the Earth's surface and the free troposphere. The result of this project will be a multi-agent feature-based OSSE toolkit that quantifies the benefit of new observations in the context of the existing program of record for both global and regional sampling, is capable of being used for PBL observing system design, and is easily extensible to evaluation of the science and applications benefit of measurements for other observing system use cases. The timing of the end of our two year project will immediately precede the release of the 2027 Earth Science Decadal Survey. It is our hope that our system will prove useful in the initial studies that will result.

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