Capsule Summary

The complexity and value of the Environmental Prediction Enterprise suggests application of a formal systems engineering perspective to optimize our approaches for further advancing its scope and capabilities.

Abstract

Our success with environmental prediction could be considered among humankind’s most remarkable developments over the past fifty years (Bauer et al. 2015). It protects lives and property, and helps us advance the well-being of society. Vast changes have occurred recently to the complexity and scope of our weather enterprise. Given its importance to society, there is reason to optimize our approaches for advancing its scope and capabilities. This essay highlights three points that may help facilitate this optimization, with an overarching suggestion to more overtly embrace a systems (engineering) approach:

  1. Continued emphasis should be placed on advancing Earth System Science as the foundational knowledge that advances weather prediction as well as the more holistic scope of Environmental Prediction (EP).

  2. The complexity and coupling of the social, programmatic, observation, modeling, analytic and interdisciplinary landscapes within theEP Enterprisesuggests adding asystem engineeringperspective/approach to further optimize outcomes and limit vulnerabilities.

  3. The consideration of the enterprise as adata to information flowproblem highlights opportunities and focal points to leverage that could help to advance thesocietal benefitsderived from theEP Enterprise.

A generalized, highly simplified, systems perspective on the advancement of Earth science and environmental prediction is offered by framing a simple equation involving the synthesis of observations, models and programmatics, that in turn yield science and applications benefits. Simplifications and derivatives of this equation are used to distill challenges and opportunities for further advancing enterprise benefits, and to motivate considerations of scope and priorities related to our community’s decadal survey(s) (e.g. ESAS, 2017).

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