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Improved Liquid Water Equivalent Nowcasting Using the Weather Support to Deicing Decision Making System

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  • 1 Colorado State University, Fort Collins, Colorado
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Abstract

Short-term automated forecasts (nowcasts) of liquid water equivalent (LWE) values can be used to assist aviation deicing decision-making activities. Such decisions can mitigate hazards that cause losses of life and property and increase costs because of travel delays. The Weather Support to Deicing Decision Making (WSDDM) system provides LWE nowcasts and is currently deployed at several major airports in the United States. WSDDM produces these nowcasts in two steps. First, an equation relating radar reflectivity to LWE rate is calibrated by correlating radar and surface observations. Then, nowcasts of reflectivity are converted to nowcasts of LWE using this calibrated equation. This paper shows that the incorporation of the Dynamic and Adaptive Radar Tracking of Storms (DARTS) radar–based nowcasting method into WSDDM can provide more accurate and efficient nowcasts of LWE relative to the correlation-based nowcasting method currently used. Results of an evaluation considering approximately 92 h of data collected during four winter weather events show the incorporation of DARTS into WSDDM provides an approximate 14% average improvement in the accuracy of 60-min LWE nowcasts and reduces runtime by two orders of magnitude.

Current affiliation: Vaisala, Inc., Louisville, Colorado.

Corresponding author address: Evan Ruzanski, Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523. E-mail: ruzanski@engr.colostate.edu

Abstract

Short-term automated forecasts (nowcasts) of liquid water equivalent (LWE) values can be used to assist aviation deicing decision-making activities. Such decisions can mitigate hazards that cause losses of life and property and increase costs because of travel delays. The Weather Support to Deicing Decision Making (WSDDM) system provides LWE nowcasts and is currently deployed at several major airports in the United States. WSDDM produces these nowcasts in two steps. First, an equation relating radar reflectivity to LWE rate is calibrated by correlating radar and surface observations. Then, nowcasts of reflectivity are converted to nowcasts of LWE using this calibrated equation. This paper shows that the incorporation of the Dynamic and Adaptive Radar Tracking of Storms (DARTS) radar–based nowcasting method into WSDDM can provide more accurate and efficient nowcasts of LWE relative to the correlation-based nowcasting method currently used. Results of an evaluation considering approximately 92 h of data collected during four winter weather events show the incorporation of DARTS into WSDDM provides an approximate 14% average improvement in the accuracy of 60-min LWE nowcasts and reduces runtime by two orders of magnitude.

Current affiliation: Vaisala, Inc., Louisville, Colorado.

Corresponding author address: Evan Ruzanski, Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523. E-mail: ruzanski@engr.colostate.edu
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