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.
Synopsis 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 Co-Managing Member of Numerati® Partners, 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.
About Numerati® Partners LLC
Numerati® Partners LLC coordinates a data analytics and technology development ecosystem, with the mission of advancing and fostering the next generation of scalable data-intensive risk and liability management enterprises. The firm provides resources fundamental to advancing the development of nascent leading-edge inferential surveillance, monitoring, and predictive analytics technologies for deployment within the RiskTech domain: risk technologies associated with adaptive distributed, networked and embedded systems such as remote sensing, agent-oriented data analytics, computing and control systems. Numerati® Partners curates integrated RiskTech solutions as well as forensic and use-case applications in RiskTech sub-domains such as LitTech, RegTech, FinTech and InsurTech (litigation technology, regulation technology, financial technology and insurance technology). For more information, visit https://numeratipartnersllc.com.About RiskEcon® Lab @ Courant Institute
The mission of RiskEcon® Lab for Decision Metrics @ Courant Institute fo Mathematical Sciences NYU is the development of experimental testbeds and analytics that employ high-dimensional datasets from innovative sources by applying a range of computational and analytical methods to commercial and industrial sensor networks and edge computing embedded systems, focusing primarily on research and development (R&D) of remote- and compressed- sensing, anomaly detection, forensic analytics and statistical process control.
RiskEcon® Lab for Decision Metrics was established in 2011 at Courant Institute of Mathematical Sciences, an independent division of New York University (NYU). Courant is considered to be one of the world’s leading mathematics educational and scientific research centers, and has been ranked first in research in applied mathematics. RiskEcon® Lab is the cornerstone of the Computational Economics and Algorithmic Data Analytics (CEcADA) cooperative at New York University, established concurrently in 2011. For more information, visit https://wp.nyu.edu/riskeconlab/.
October 28, 2021 | New York, NY