Wintertime precipitation has profound impacts on surface transportation. Surface transportation ramifications include decreased public safety, compromised traveler mobility, diminished productivity of roadway users, and adverse environmental effects, owing to the need to chemically treat icy roads. An improved depiction of precipitation can be gained by fusing observations from multiple platforms together in a precipitation estimation system.

At the University of North Dakota, we have been developing such a system to estimate precipitation occurrence, type, and rate from multiple data sources, including surface observations (METARs, AWOS/ASOS and RWIS/ESS), the WSR-88D radar network, and, to a lesser extent, geostationary satellites (for cloud top pressure). Here we present an overview of our system, known as the Pavement Precipitation Accumulation Estimation Algorithm (PPAES), including its design philosophy, basic components, and recent testing of an improved blending of surface and radar datasets. Performance of the system is demonstrated using a combination of summary contingency table-based statistics and selected case studies.