RT Journal Article
T1 A new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm
A1 Insua Costa, Damián
A1 Míguez Macho, Gonzalo
AB A new moisture tagging tool, usually known as water vapor tracer (WVT) method or online Eulerianmethod, has been implemented into the Weather Research and Forecasting (WRF) regional meteorologicalmodel, enabling it for precise studies on atmospheric moisture sources and pathways.We present here the methodand its formulation, along with details of the implementation into WRF. We perform an in-depth validation witha 1-month long simulation over North America at 20 km resolution, tagging all possible moisture sources: lateralboundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as themoisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that themethod exhibits high precision, with errors considerably lower than 1% during the entire simulation period,for both precipitation and total precipitable water. We apply the method to the Great Lake-effect snowstorm ofNovember 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulationsobserved in the event. We perform simulations in a nested domain at 5 km resolution with the tagging technique,demonstrating that about 30–50% of precipitation in the regions immediately downwind, originated fromevaporated moisture in the Great Lakes. This contribution increases to between 50 and 60% of the snow waterequivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes havea fundamental role in producing the most extreme accumulations in these episodes, resulting in the highestsocioeconomic impacts.
PB Copernicus Publications
SN 2190-4979
YR 2018
FD 2018
LK http://hdl.handle.net/10347/22802
UL http://hdl.handle.net/10347/22802
LA eng
NO Insua-Costa, D. and Miguez-Macho, G.: A new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm, Earth Syst. Dynam., 9, 167–185, https://doi.org/10.5194/esd-9-167-2018, 2018
NO Funding for this work came from the European Commission FP7 (EartH2Observe) and the Spanish Ministerio de Economía y Competitividad (CGL2017-89859-R and CGL2013-45932-R), and from contributions by the CRETUS Strategic Partnership (AGRUP2015/02)
DS Minerva
RD 24 abr 2026