Application of the WRF-LETKF Data Assimilation System over Southern South America: Sensitivity to Model Physics
Autores

Resumen
Improving the initial conditions of short-range numerical weather prediction (NWP) models is one of the main goals of the meteorological community. Development of data assimilation and ensemble forecast systems is essential in any national weather service (NWS). In this sense, the local ensemble transform Kalman filter (LETKF) is a methodology that can satisfy both requirements in an efficient manner. The Weather Research and Forecasting (WRF) Model coupled with the LETKF, developed at the University of Maryland, College Park, have been implemented experimentally at the NWS of Argentina [Servicio Meteorológico Nacional (SMN)], but at a somewhat lower resolution (40 km) than the operational Global Forecast System (GFS) at that time (27 km). The purpose of this work is not to show that the system presented herein is better than the higher-resolution GFS, but that its performance is reasonably comparable, and to provide the basis for a continued improved development of an independent regional data assimilation and forecasting system. The WRF-LETKF system is tested during the spring of 2012, using the prepared or quality controlled data in Binary Universal Form for Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) and lateral boundary conditions from the GFS. To assess the effect of model error, a single-model LETKF system (LETKF-single) is compared with a multischeme implementation (LETKF-multi), which uses different boundary layer and cumulus convection schemes for the generation of the ensemble of forecasts. The performance of both experiments during the test period shows that the LETKF-multi usually outperforms the LETKF-single, evidencing the advantages of the use of the multischeme approach. Both data assimilation systems are slightly worse than the GFS in terms of the synoptic environment representation, as could be expected given their lower resolution. Results from a case study of a strong convective system suggest that the LETKF-multi improves the location of the most intense area of precipitation with respect to the LETKF-single, although both systems show an underestimation of the total accumulated precipitation. These preliminary results encourage continuing the development of an operational data assimilation system based on WRF-LETKF at the SMN.
Descripción
Artículo publicado en la Revista Weather and Forecasting, Volume 31 No. 1
February 2016, páginas 217–236.
Colecciones
Fecha
2016-02-10Metadatos
Mostrar el registro completo del ítemUtilice este identificador (URI) para citar o enlazar este item
http://hdl.handle.net/20.500.12160/124El ítem tiene asociados los siguientes ficheros de licencia:
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
High-Resolution NWP Forecast Precipitation Comparison over Complex Terrain of the Sierras de Cordoba during RELAMPAGO-CACTI
Autores
Casaretto, Gimena
Dillon, María Eugenia
Salio, Paola
García Skabar, Yanina
Nesbitt, Stephen
Schumacher, Russ S.
García, Carlos Marcelo
Catalini, Carlos
(American Meteorological Society, 2022-02)Resumen
Sierras de Cordoba (Argentina) is characterized by the occurrence of extreme precipitation events during the austral warm season. Heavy precipitation in the region has a large societal impact, causing flash floods. This ... -
Sensitivity of WRF short-term forecasts to different soil moisture initializations from the GLDAS database over South America in March 2009
Autores
Dillon, María Eugenia
Collini, Estela Ángela
Ferreira, Lorena Judith
(Elsevier, 2016-01-01)Resumen
In Numerical Weather Prediction models it is essential to properly describe both the atmosphere and the surface initial conditions. With respect to the last, a major issue is the difficulty to attain a correct representation ... -
Modelado de la humedad en el suelo con HRLDAS
Autores
Righetti, Silvina Andrea
García Skabar, Yanina
(Servicio Meteorológico Nacional. Gerencia de Investigación Desarrollo y Capacitación. Departamento de Investigación y Desarrollo, 2015-05-26)