Access to reliable weather data is critical for designing resilient infrastructure responsive to climates subjected to extreme weather events. For remote regions, housing and essential structures (i.e., schools, community centers and hospitals) must withstand extremely harsh climates where such locations experience extreme challenges resulting from limited and unreliable data. Many remote regions also have short or incomplete weather records, increasing uncertainty and complicating design procedures. This study develops a record length coefficient implemented in the Up-Crossing Rate (UCR) analysis to predict long-term return periods for extreme wind velocities. This study emphasizes regions with limited wind record datasets by implementing larger record length coefficients for lower record lengths. UCR analysis has been recognized as a reliable approach for providing sufficient accuracy with minimal error when forecasting peak wind velocities for long return periods from short record lengths. All weather station wind data are accessed from the Environmental and Climate Change Canada (ECCC) database. The proposed improvement is achieved by first validating the up-crossing rate (UCR) method against full-record datasets across 204 stations, then systematically reducing record lengths to simulate data-scarce conditions (5–20 years). A record-length coefficient is developed by directly comparing reduced-record and full-record UCR predictions, computing the ratio, and calibrating this factor to conservatively exceed 95% of the corresponding full-record extreme wind estimates. This approach mitigates systematic underprediction associated with limited wind data. Improvement is evaluated by comparing deviations from the full-record reference and by assessing exceedance consistency across stations and return periods. Results show that incorporating the record length coefficient implemented in the UCR analysis provides conservative predictions of peak wind velocities for large return periods when applied to datasets of 5 to 10 years. Results show that incorporating the record length coefficient implemented in the UCR analysis provides conservative predictions of peak wind velocities for large return periods when applied to datasets of 5 to 10 years. This can reduce uncertainties and mitigate underpredictions of extreme wind speeds. The UCR approach offers a practical solution for assessing extreme wind conditions in regions with constrained weather data, such as limited or missing hourly wind datasets.
Brown et al. (Sun,) studied this question.