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dc.contributor.authorInsua Costa, Damián
dc.contributor.authorMíguez Macho, Gonzalo
dc.date.accessioned2020-06-05T18:58:52Z
dc.date.available2020-06-05T18:58:52Z
dc.date.issued2018
dc.identifier.citationInsua-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
dc.identifier.issn2190-4979
dc.identifier.urihttp://hdl.handle.net/10347/22802
dc.description.abstractA new moisture tagging tool, usually known as water vapor tracer (WVT) method or online Eulerian method, has been implemented into the Weather Research and Forecasting (WRF) regional meteorological model, enabling it for precise studies on atmospheric moisture sources and pathways.We present here the method and its formulation, along with details of the implementation into WRF. We perform an in-depth validation with a 1-month long simulation over North America at 20 km resolution, tagging all possible moisture sources: lateral boundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as the moisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that the method 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 of November 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulations observed 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 from evaporated moisture in the Great Lakes. This contribution increases to between 50 and 60% of the snow water equivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes have a fundamental role in producing the most extreme accumulations in these episodes, resulting in the highest socioeconomic impacts.
dc.description.sponsorshipFunding 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)
dc.language.isoeng
dc.publisherCopernicus Publications
dc.rights© Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleA new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm
dc.typejournal article
dc.identifier.doi10.5194/esd-9-167-2018
dc.relation.publisherversionhttps://doi.org/10.5194/esd-9-167-2018
dc.type.hasVersionVoR
dc.identifier.essn2190-4987
dc.rights.accessRightsopen access
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Física de Partículas
dc.description.peerreviewedSI
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/CGL2017-89859-R/ES/ORIGEN DE LA PRECIPITACION EXTREMA EN LA REGION DEL MEDITERRANEO OCCIDENTAL
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CGL2013-45932-R/ES/RIOS ATMOSFERICOS Y TRANSPORTE DE HUMEDAD TROPICAL EN EL ATLANTICO NORTE: RELEVANCIA PARA EL CICLO HIDROLOGICO IBERICO Y LA PRECIPITACION EXTREMA


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