WRF model sensitivity to choice of parameterization over South America: Validation against surface variables

The Weather and Research Forecast model is tested over South America in different configurations to identify the one that gives the best estimates of observed surface variables. Systematic, nonsystematic, and total errors are computed for 48-h forecasts initialized with the NCEP Global Data Assimila...

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Detalles Bibliográficos
Autores principales:	Ruiz, Juan Jose, Saulo, Andrea Celeste
Publicado:	2010
Materias:	Best estimates Best fit Convergence zones Day-to-day variability Dewpoint temperature FORECAST model Global data assimilation system Highly sensitive Land model Land surface models Local error Low level jet Model design Model performance Moisture advection Nonsystematic errors Parameterizations Positive bias Regional circulation Regional model South America South Atlantic Stable stratification Subgrid scale Surface process Surface temperatures Surface variables Surface winds Wind errors WRF Model Climatology Data processing Errors Moisture determination Parameterization Rain Soil moisture Surface measurement Systematic errors Turbulent flow Weather forecasting Geologic models climate prediction climatology data assimilation ensemble forecasting land surface parameterization precipitation (climatology) soil moisture stratification surface temperature weather forecasting
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00270644_v138_n8_p3342_Ruiz http://hdl.handle.net/20.500.12110/paper_00270644_v138_n8_p3342_Ruiz
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_00270644_v138_n8_p3342_Ruiz
record_format	dspace
spelling	paper:paper_00270644_v138_n8_p3342_Ruiz2023-06-08T14:54:09Z WRF model sensitivity to choice of parameterization over South America: Validation against surface variables Ruiz, Juan Jose Saulo, Andrea Celeste Best estimates Best fit Convergence zones Day-to-day variability Dewpoint temperature FORECAST model Global data assimilation system Highly sensitive Land model Land surface models Local error Low level jet Model design Model performance Moisture advection Nonsystematic errors Parameterizations Positive bias Regional circulation Regional model South America South Atlantic Stable stratification Subgrid scale Surface process Surface temperatures Surface variables Surface winds Wind errors WRF Model Climatology Data processing Errors Moisture determination Parameterization Rain Soil moisture Surface measurement Systematic errors Turbulent flow Weather forecasting Geologic models climate prediction climatology data assimilation ensemble forecasting land surface parameterization precipitation (climatology) soil moisture stratification surface temperature weather forecasting South America The Weather and Research Forecast model is tested over South America in different configurations to identify the one that gives the best estimates of observed surface variables. Systematic, nonsystematic, and total errors are computed for 48-h forecasts initialized with the NCEP Global Data Assimilation System (GDAS). There is no unique model design that best fits all variables over the whole domain, and nonsystematic errors for all configurations differ little from one another; such differences are in most cases smaller than the observed day-to-day variability. An ensemble mean consisting of runs with different parameterizations gives the best skill for the whole domain. Surface variables are highly sensitive to the choice of land surface models. Surface temperature is well represented by the Noah land model, but dewpoint temperature is best estimated by the simplest land surface model considered here, which specifies soil moisture based on climatology. This underlines the need for better understanding of humid processes at the subgrid scale. Surface wind errors decrease the intensity of the low-level jet, reducing expected heat and moisture advection over southeast South America (SESA), with negative precipitation errors over SESA and positive biases over the South Atlantic convergence zone (SACZ). This pattern of errors suggests feedbacks between wind errors, precipitation, and surface processes as follows: an increase of precipitation over the SACZ produces compensating descent in SESA, with more stable stratification, less rain, less soil moisture, and decreased rain. This is a clear example of how local errors are related to regional circulation, and suggests that improvement of model performance requires not only better parameterizations at the subgrid scales, but also improved regional models. © 2010 American Meteorological Society. Fil:Ruiz, J.J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Saulo, C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2010 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00270644_v138_n8_p3342_Ruiz http://hdl.handle.net/20.500.12110/paper_00270644_v138_n8_p3342_Ruiz
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Best estimates Best fit Convergence zones Day-to-day variability Dewpoint temperature FORECAST model Global data assimilation system Highly sensitive Land model Land surface models Local error Low level jet Model design Model performance Moisture advection Nonsystematic errors Parameterizations Positive bias Regional circulation Regional model South America South Atlantic Stable stratification Subgrid scale Surface process Surface temperatures Surface variables Surface winds Wind errors WRF Model Climatology Data processing Errors Moisture determination Parameterization Rain Soil moisture Surface measurement Systematic errors Turbulent flow Weather forecasting Geologic models climate prediction climatology data assimilation ensemble forecasting land surface parameterization precipitation (climatology) soil moisture stratification surface temperature weather forecasting South America
spellingShingle	Best estimates Best fit Convergence zones Day-to-day variability Dewpoint temperature FORECAST model Global data assimilation system Highly sensitive Land model Land surface models Local error Low level jet Model design Model performance Moisture advection Nonsystematic errors Parameterizations Positive bias Regional circulation Regional model South America South Atlantic Stable stratification Subgrid scale Surface process Surface temperatures Surface variables Surface winds Wind errors WRF Model Climatology Data processing Errors Moisture determination Parameterization Rain Soil moisture Surface measurement Systematic errors Turbulent flow Weather forecasting Geologic models climate prediction climatology data assimilation ensemble forecasting land surface parameterization precipitation (climatology) soil moisture stratification surface temperature weather forecasting South America Ruiz, Juan Jose Saulo, Andrea Celeste WRF model sensitivity to choice of parameterization over South America: Validation against surface variables
topic_facet	Best estimates Best fit Convergence zones Day-to-day variability Dewpoint temperature FORECAST model Global data assimilation system Highly sensitive Land model Land surface models Local error Low level jet Model design Model performance Moisture advection Nonsystematic errors Parameterizations Positive bias Regional circulation Regional model South America South Atlantic Stable stratification Subgrid scale Surface process Surface temperatures Surface variables Surface winds Wind errors WRF Model Climatology Data processing Errors Moisture determination Parameterization Rain Soil moisture Surface measurement Systematic errors Turbulent flow Weather forecasting Geologic models climate prediction climatology data assimilation ensemble forecasting land surface parameterization precipitation (climatology) soil moisture stratification surface temperature weather forecasting South America
description	The Weather and Research Forecast model is tested over South America in different configurations to identify the one that gives the best estimates of observed surface variables. Systematic, nonsystematic, and total errors are computed for 48-h forecasts initialized with the NCEP Global Data Assimilation System (GDAS). There is no unique model design that best fits all variables over the whole domain, and nonsystematic errors for all configurations differ little from one another; such differences are in most cases smaller than the observed day-to-day variability. An ensemble mean consisting of runs with different parameterizations gives the best skill for the whole domain. Surface variables are highly sensitive to the choice of land surface models. Surface temperature is well represented by the Noah land model, but dewpoint temperature is best estimated by the simplest land surface model considered here, which specifies soil moisture based on climatology. This underlines the need for better understanding of humid processes at the subgrid scale. Surface wind errors decrease the intensity of the low-level jet, reducing expected heat and moisture advection over southeast South America (SESA), with negative precipitation errors over SESA and positive biases over the South Atlantic convergence zone (SACZ). This pattern of errors suggests feedbacks between wind errors, precipitation, and surface processes as follows: an increase of precipitation over the SACZ produces compensating descent in SESA, with more stable stratification, less rain, less soil moisture, and decreased rain. This is a clear example of how local errors are related to regional circulation, and suggests that improvement of model performance requires not only better parameterizations at the subgrid scales, but also improved regional models. © 2010 American Meteorological Society.
author	Ruiz, Juan Jose Saulo, Andrea Celeste
author_facet	Ruiz, Juan Jose Saulo, Andrea Celeste
author_sort	Ruiz, Juan Jose
title	WRF model sensitivity to choice of parameterization over South America: Validation against surface variables
title_short	WRF model sensitivity to choice of parameterization over South America: Validation against surface variables
title_full	WRF model sensitivity to choice of parameterization over South America: Validation against surface variables
title_fullStr	WRF model sensitivity to choice of parameterization over South America: Validation against surface variables
title_full_unstemmed	WRF model sensitivity to choice of parameterization over South America: Validation against surface variables
title_sort	wrf model sensitivity to choice of parameterization over south america: validation against surface variables
publishDate	2010
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00270644_v138_n8_p3342_Ruiz http://hdl.handle.net/20.500.12110/paper_00270644_v138_n8_p3342_Ruiz
work_keys_str_mv	AT ruizjuanjose wrfmodelsensitivitytochoiceofparameterizationoversouthamericavalidationagainstsurfacevariables AT sauloandreaceleste wrfmodelsensitivitytochoiceofparameterizationoversouthamericavalidationagainstsurfacevariables
_version_	1768542209436549120

WRF model sensitivity to choice of parameterization over South America: Validation against surface variables

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