Evaluating Discrepancies between Hurricane Forecast Models and Observational Data in Louisiana: Implications for Agricultural Insurance Payments

Keywords: tropical cyclones, hurricanes, insurance premiums, natural hazard risk, Louisiana
Abstract Type: Paper Abstract

Abstract

Accurate and precise hurricane and tropical storm forecasting and monitoring are critical for mitigating agricultural losses, particularly in vulnerable regions where crop production plays a significant economic role. Forecasting guides storm preparation efforts, and in the post-storm recovery period discrepancies between forecasted wind speeds and observational records can affect compensation for crop damage, particularly for high-value crops like sugarcane, which are sensitive to storm conditions. This study investigates the discrepancies between predicted and observed hurricane variables— including wind speed, rainfall, and storm path—and their influence on agricultural insurance payments. Using geospatial analysis tools within ArcGIS Pro, we compare forecast model outputs with observational data across several storm events impacting key agricultural regions in Louisiana. We quantify prediction errors through spatial difference maps and statistical analysis, evaluating how these discrepancies correlate with insurance claims filed by farmers. By overlaying forecast data, actual storm observations, and insurance payout records, the spatial and temporal dynamics of storm impact and claim filings can be explored. The findings highlight critical gaps between meteorological model accuracy and real-world outcomes, offering insights into how insurance frameworks may need to adapt to better manage risk and payouts in agricultural systems. Our results provide a roadmap for improving both storm forecasting and agricultural risk management strategies, ultimately reducing the economic vulnerability of farmers to extreme weather events.