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. A new moisture taggingtool, usually known as water vapor tracer (WVT) method or online Eulerianmethod, has been implemented into the Weather Research and Forecasting (WRF)regional meteorological model, enabling it for precise studies on atmosphericmoisture sources and pathways. We present here the method and itsformulation, along with details of the implementation into WRF. We perform anin-depth validation with a 1-month long simulation over North America at20 km resolution, tagging all possible moisture sources: lateral boundaries,continental, maritime or lake surfaces and initial atmospheric conditions. Weestimate errors as the moisture or precipitation amounts that cannot betraced back to any source. Validation results indicate that the methodexhibits high precision, with errors considerably lower than 1 % duringthe entire simulation period, for both precipitation and total precipitablewater. We apply the method to the Great Lake-effect snowstorm ofNovember 2014, aiming at quantifying the contribution of lake evaporation tothe large snow accumulations observed in the event. We perform simulations ina nested domain at 5 km resolution with the tagging technique, demonstratingthat about 30–50 % of precipitation in the regions immediately downwind,originated from evaporated moisture in the Great Lakes. This contributionincreases to between 50 and 60 % of the snow water equivalent in the mostseverely affected areas, which suggests that evaporative fluxes from thelakes have a fundamental role in producing the most extreme accumulations inthese episodes, resulting in the highest socioeconomic impacts.

earth system dynamics

A new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm