2022 Proceedings
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, GIS Analyst, Synoptic Data PBC
, President & Meteorologist, Monarch Weather & Climate Intelligence
, METOC Officer, United States Navy
, VP & CCM, Monarch Weather & Climate Intelligence
Extreme sea levels near the coast can cause severe risk to life and infrastructure while understanding and planning for these events remains a challenge. Sea level variability (SLV) is controlled by complex local and remote multiresolution forces that interact under the influence of climatic and non-climatic factors in the ocean and atmosphere. Magnitude and strength of connection between forcing allows for development of foundational environmental knowledge of processes driving SLV. This study developed a methodology that identifies distinct spatial patterns of SLV at small scale and depicts sea-level response to atmospheric teleconnection parents using normalized characterization process of SLV through technology resources and information already available. Verification of the methodology using San Diego Bay as proof-of-concept, revealed characterization processes that are unique to each location. Findings also suggest that an analysis of high-resolution altimetry contrasted with local measures allows for identification of distinct spatial patterns of water levels at coastal and deep-ocean regions with representation of SLV response to climatic-driven processes on a global scale.
One problem with trying to assess real time weather conditions is the spatial coverage of in-situ weather measurements. Amassing weather stations from public sources into one map (or layer) is a way to readily have an accurate view of conditions in the field. One important application is monitoring fire weather conditions in real time to keep track of the hot, dry and windy conditions that could lead to destructive and rapid fire growth. Monitoring the real time conditions in a dashboard, we can keep track of relative humidity, wind gust, and air temperature, measuring and mapping their daily extremes. In addition to keeping track of current extremes, we can also formulate warnings based on the prolonged duration of these conditions within ArcGIS, similar to how fire departments in California formulate red flag warnings. This is just one example of how real time weather can provide situational awareness to mitigate disaster risk and extent for safety and the environment.
Our climate is changing rapidly and the impacts associated with our weather have never been greater. Two Esri StartUp companies have come together to apply their expertise and unique offerings to address these challenges through improved risk assessment within the insurance industries. By utilizing Esri mapping tools, Monarch Weather & iMitig8 assist clients navigating weather extremes by adding critical value to problem solving on a national and global level. Business leaders can then access the data via cloud-based risk management workflow patterns for engineering analytics. Monarch measures exposure to our changing climate by combining historical observations, calibrated model scenarios and bias-corrected forecast projections (eg. rainfall, wildfires, humidity, wind, temp, earthquakes, sea-level rise, and other threats) with data on a company’s assets and their surrounding environment to determine future risk. Underwriters are able to manage project risks with powerful efficiency. Insurers and Reinsurers save time and money for streamlined workflow, and important analytics are delivered to the stakeholders to help build strong client relationships.