A Temporal Gauge Quality Control Algorithm as a Method for Identifying Potential Instrumentation Malfunctions

Abstract

Abstract There are multiple reasons as to why a precipitation gauge would report erroneous observations. Systematic errors relating to the measuring apparatus or resulting from observational limitations due to environmental factors (e.g., wind-induced undercatch or wetting losses) can be quantified and potentially corrected within a gauge dataset. Other challenges can arise from instrumentation malfunctions, such as clogging, poor siting, and software issues. Instrumentation malfunctions are challenging to quantify as most gauge quality control (QC) schemes focus on the current observation and not on whether the gauge has an inherent issue that would likely require maintenance of the gauge. This study focuses on the development of a temporal QC scheme to identify the likelihood of an instrumentation malfunction through the examination of hourly gauge observations and associated QC designations. The analyzed gauge performance resulted in a temporal QC classification using one of three categories: GOOD, SUSP, and BAD. The temporal QC scheme also accounts for and provides an additional designation when a significant percentage of gauge observations and associated hourly QC were influenced by meteorological factors (e.g., the inability to properly measure winter precipitation). Findings showed a consistent percentage of gauges that were classified as BAD through the running 7-day (2.9%) and 30-day (4.4%) analyses. Verification of select gauges demonstrated how the temporal QC algorithm captured different forms of instrumental-based systematic errors that influenced gauge observations. Results from this study can benefit the identification of degraded performance at gauge sites prior to scheduled routine maintenance. Significance Statement This study proposes a scheme that quality controls rain gauges based on its performance over a running history of hourly observational data and quality control flags to identify gauges that likely have an instrumentation malfunction. Findings from this study show the potential of identifying gauges that are impacted by issues such as clogging, software errors, and poor gauge siting. This study also highlights the challenges of distinguishing between erroneous gauge observations based on an instrumentation malfunction versus erroneous observations that were the result of an environmental factor that influence the gauge observation or its quality control classification, such as winter precipitation or virga.