Dr. David K.A. Mordecai was invited to present at the Courant Institute for Mathematical Sciences NYU Center for Atmosphere Ocean Science (CAOS) at the Atmosphere Ocean Science Colloquium on October 27, 2021.
The presentation by Dr. David Mordecai was entitled Multi-resolution Remote-Sensing and Data Fusion for Multi-Modal Estimation of Mesoscale Terrestrial Atmospheric Scattering Fields: Statistical Models and Applications to Risk Domains.
Although severe convective storms (SCSs) have been customarily classified as “secondary perils” by the insurance sector, it is particularly noteworthy that during the previous 10 years, SCSs have contributed more than half of global insured losses from secondary perils, and during 2020, such secondary perils account for more than 70% of the natural catastrophe insured losses (resulting mostly from SCSs and wildfire occurrences). Reliably modeling region-specific hazards of mesoscale climate risks related to SCSs at relevant temporal and spatial scales – among other industrial and municipal exposures to such climate-driven conditions (e.g., air quality, urban wind fields, flash flooding, drought, wildfire propagation), including impairment to energy grid stability and transportation networks – is inherently a joint-hypothesis problem. At mesoscales, e.g., within ranges greater than 5 km up to 200km and less than 1000km (Fujita 1981), predominant atmospheric instabilities (thermal, symmetric, barotropic, Kelvin-Helmholtz, etc.) contributing to storm propensities are highly state-dependent.
Given these geospatial complexities, the propensity and propagation of mesoscale severe convective storms are subject to prevailing localized spatial and temporal conditions. In the absence of robust statistical sampling, reliable estimation and classification of such storm occurrences at these temporal and spatial scales cannot be viably numerically simulated. Remote statistical measurements at relevant temporal and spatial mesoscales involve sampling and assimilation of signals characterizing atmospheric composition based upon reflectivity, propagation, attenuation and doppler signatures of complementary acoustic, optical and radar-based emissions across a range of spectral bands, and under corresponding conditions of temperature, pressure and humidity. However, the physics and geometric properties of these signals (e.g., Rayleigh, Mie and Bragg scattering) across a range of respective emission wavelengths tend to covary relative to composite atmospheric particle size and shape distributions.
Dr. Mordecai is President and Co-Founder of Risk Economics, and Visiting Scholar at Courant Institute of Mathematical Sciences NYU, advising research activities at RiskEcon® Lab for Decision Metrics @ Courant Institute.
About Center for Atmosphere Ocean Science
The Center for Atmosphere Ocean Science is a unit of the Department of Mathematics, within the Courant Institute of Mathematical Sciences NYU. Their mission is to advance the understanding of and ability to predict the coupled atmosphere, ocean and ice system through the use of mathematical and computational tools and analysis of observations; and to train the next generation of leading theoretical and computational climate scientists to face one of the most consequential problems of the 21st century. For more information, visit: https://caos.cims.nyu.edu/.
About Risk Economics
Risk Economics provides advisory services at the intersection of commercial business-process engineering and risk engineering with a particular focus on coupling commercial reinsurance and financial technology, through the rigorous application of agent-based, demographic, and statistical methodologies to microeconomic and macroeconomic analytics.