Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/20.500.11765/10162
Numerical simulations of snowfall events: sensitivity analysis of physical parameterizations
Título : Numerical simulations of snowfall events: sensitivity analysis of physical parameterizations
Autor : Fernández-González, Sergio ORCID RESEARCHERID SCOPUSID Valero Rodríguez, FranciscoSánchez Gómez, José LuisGascón, EstíbalizLópez Campano, LauraGarcía Ortega, EduardoMerino Suances, Andrés
Palabras clave : Snowfall; Physical parameterizations; Numerical weather prediction models; Precipitation forecasting; Ensemble prediction
Fecha de publicación : 2015
Editor: American Geophysical Union
Citación : Journal of Geophysical Research: Atmospheres. 2015, 120(19), p. 10131-10148
Versión del editor: https://dx.doi.org/10.1002/2015JD023793
Resumen : Accurate estimation of snowfall episodes several hours or even days in advance is essential to minimize risks to transport and other human activities. Every year, these episodes cause severe traffic problems on the northwestern Iberian Peninsula. In order to analyze the influence of different parameterization schemes, 15 snowfall days were analyzed with the Weather Research and Forecasting (WRF) model, defining three nested domains with resolutions of 27, 9, and 3 km. We implemented four microphysical parameterizations (WRF Single‐Moment 6‐class scheme, Goddard, Thompson, and Morrison) and two planetary boundary layer schemes (Yonsei University and Mellor‐Yamada‐Janjic), yielding eight distinct combinations. To validate model estimates, a network of 97 precipitation gauges was used, together with dichotomous data of snowfall presence/absence from snowplow requests to the emergency service of Spain and observatories of the Spanish Meteorological Agency. The results indicate that the most accurate setting of WRF for the study area was that using the Thompson microphysical parameterization and Mellor‐Yamada‐Janjic scheme, although the Thompson and Yonsei University combination had greater accuracy in determining the temporal distribution of precipitation over 1 day. Combining the eight deterministic members in an ensemble average improved results considerably. Further, the root mean square difference decreased markedly using a multiple linear regression as postprocessing. In addition, our method was able to provide mean ensemble precipitation and maximum expected precipitation, which can be very useful in the management of water resources. Finally, we developed an application that allows determination of the risk of snowfall above a certain threshold.
Patrocinador: This paper was supported by the following grants: TEcoAgua, METEORISK PROJECT(RTC‐2014‐1872‐5), Granimetro(CGL2010‐15930) and MINECO(CGL2011‐25327, RTC‐2014‐1872‐5 and ESP2013‐47816‐C4‐4P), and LE220A11‐2 and LE003B009 awarded by the Junta de Castilla and León.
URI : http://hdl.handle.net/20.500.11765/10162
ISSN : 2169-897X
Colecciones: Artículos científicos 2015-2018

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