SBIR Phase I: A Physics Guided Statistical Model for Weather Extremes Under Climate Change

Period of Performance: 07/01/2016 - 06/30/2017

$225K

Phase 1 SBIR

Recipient Firm

RISQ INC
404 Broadway B
Cambridge, MA 02139
Firm POC, Principal Investigator

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project extends to academics and industry stakeholders holding intellectual or financial interests that are impacted by climate change. Given growing evidence for climate change-driven increases in extreme weather events over recent years, it has become increasingly important for stakeholders to factor climate change into their resilience plans. Engineering firms must embed changes in risks into engineering design processes due to increased urbanization, coastal inhabitancy, and climate change impacts. Insurance companies need to base risk assessments, underwriting strategies, and reinsurance purchasing decisions on quantitative methods that appropriately consider credible, probabilistic projections of changes in extremes with appropriate uncertainty bounds. Public agencies, municipalities, and private organizations must implement resilience strategies for critical infrastructure that will withstand climate extremes at decadal to multidecadal scales. This project focuses on developing and translating patent-protected research to analytics and products that address the emerging needs of these industry stakeholders. The publications and software developed via this proposal will significantly advance best practices in hazard risk assessment and climate change adaptation, and a sample of the New England design storm curves will be made freely available to support educational and outreach efforts. This Small Business Innovation Research (SBIR) Phase I project aims to address the deep uncertainties in climate projections rooting from intrinsic variability and longstanding gaps in physics understanding. The project will consist of developing a Physics-Guided Statistical Modeling (PGSM) framework for probabilistically quantifying projected changes in regional precipitation extremes, and translating those projections to actionable, climate change-informed local design storm curves. The initial focus is on precipitation extremes given that theory and evidence suggest climate change-driven increases in storm severity in many regions, that these projections are needed to enhance design curves, and given they are crucial inputs to flood models that will be pursued aggressively in subsequent efforts. The project will culminate in multiple deliverables, including (1) peer reviewed scientific publications, (2) a proprietary spatio-temporal precipitation extremes database, and (3) design storm curves for a New England testbed, the last of which will be disseminated via a software prototype.