Automated Precipitation Detection and Typing in Winter: A Two-Year Study

Abstract

Precipitation detection and typing estimates from four sensors are evaluated using standard operational meteorological observations as a reference. All are active remote sensors radiating into a measurement volume near the sensor, and measuring some property of the scattered radiation. Three of the sensors use optical wavelengths and one uses microwave wavelengths. A new analysis approach for comparison of the time series from the observer and instruments is presented. This approach reduces the mismatch of the different sampling intervals and response times of humans and instruments when observing precipitation events. The algorithm postprocesses the minutely output estimates from the sensors in a window of time around the nominal time of the human observation. The paper examines the relationship between the probability of detection and false alarm ratio for these sensors. In order to represent the ?trade-off? between these parameters, the Heidke skill score is used as a figure of merit in comparing performance. A statistical method is presented to test for significance in differences of this score between sensors. Each of the sensors demonstrated skill in identifying and typing the precipitation. The window analysis showed improved scores compared to simultaneous comparisons. The difference is attributed to the analysis technique that was designed to approximate the observing instructions for the standard observations. The data processing algorithm that gave the highest Heidke skill score for each sensor resulted in identification scores of 79% for the microwave sensor but only 39%?40% for the three optical sensors when rain was reported by the observer. The identification score in snow was 63% for the microwave sensor and in the range of 53%?71% for the optical sensors. Use of a multiparameter algorithm with the microwave sensor improved the identification of both snow and drizzle over the report from the sensor alone.