Analyzing Weather Station Reliability During Extreme Precipitation Events

Abstract

The Global Historical Climatology Network Daily (GHCNd), maintained by NOAA’s National Centers for Environmental Information, provides archived daily climate observations from over 25,000 stations worldwide. During extreme precipitation events, accurate weather-station observations are crucial for forecasting, climate analysis, and hazard response. However, hurricanes and other severe storms can disrupt stations through power outages, communication failures, sensor malfunction, or physical damage. This study evaluates U.S. weather-station reliability during extreme precipitation events by analyzing patterns of missing precipitation observations.

Using Python, QGIS, and spatial statistical methods, station-level missing-data percentages were calculated before and during hurricane events. Individual hurricane case studies were used to visualize geographic patterns of station outages, while Global Moran's I was used to evaluate spatial clustering. To test whether these patterns generalized beyond the case studies, hurricanes were selected from NOAA's HURDAT2 best-track database, and a cross-event regression was conducted using 21,097 station observations from 38 of these hurricanes to examine associations between missing data, hurricane category, station density, and geographic region.

The case studies showed that failures were often spatially concentrated near storm-affected coastal areas, while dense networks could maintain broader observational coverage despite individual station failures. These findings demonstrate that weather-station reliability depends not only on whether individual stations fail, but also on the spatial distribution and redundancy of the surrounding monitoring network. The results support incorporating network density and spatial redundancy into the evaluation and future optimization of weather-monitoring systems.