DEVELOPMENT OF STOCHASTICALLY PERTURBED PARAMETERIZATION SCHEME FOR THE SURFACE VARIABLES IN WRF BY OPTIMIZING THE RANDOM FORCING PARAMETERS USING THE MICRO-GENETIC ALGORITHM

The ensemble data assimilation system expresses the model uncertainties by ensemble spread, that is, the standard deviation of ensemble background error covariance. The ensemble spread generally suffers from underestimation problems due to the limited ensemble size, sampling errors, model errors, et...

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Published in18th Annual Meeting of the Asia Oceania Geosciences Society pp. 10 - 12
Main Authors Lim, S., Park, S. K., Cassardo, C.
Format Book Chapter Conference Proceeding
LanguageEnglish
Published WORLD SCIENTIFIC 01.04.2022
Subjects
Online AccessGet full text
ISBN9811260095
9789811260117
9811260109
9789811260100
9789811260094
9811260117
DOI10.1142/9789811260100_0004

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Abstract The ensemble data assimilation system expresses the model uncertainties by ensemble spread, that is, the standard deviation of ensemble background error covariance. The ensemble spread generally suffers from underestimation problems due to the limited ensemble size, sampling errors, model errors, etc. To solve this problem in terms of model errors, recent studies proposed stochastic perturbation schemes to increase the ensemble spreads by adding random forcing to model tendencies or variables. In this study, we focus on the near-surface uncertainties which are affected by interactions between the land surface (LS) and atmospheric processes. Although the LS variables are crucial to provide various fluxes as the lower boundary condition to the atmosphere, their uncertainties were not much addressed yet. We employed the stochastically perturbed parameterization (SPP) scheme for the LS variables within the Weather Research and Forecasting (WRF) ensemble system. As a testbed experiment with the newly developed WRF ensemble-SPP system, we perturbed soil temperature and moisture in the Noah Land Surface Model to improve the performance of low-level atmospheric variables. To determine the optimal random forcing parameters used in perturbation, we employed a global optimization algorithm - the micro-genetic algorithm. Our results depicted positive impacts on the ensemble spread.
AbstractList The ensemble data assimilation system expresses the model uncertainties by ensemble spread, that is, the standard deviation of ensemble background error covariance. The ensemble spread generally suffers from underestimation problems due to the limited ensemble size, sampling errors, model errors, etc. To solve this problem in terms of model errors, recent studies proposed stochastic perturbation schemes to increase the ensemble spreads by adding random forcing to model tendencies or variables. In this study, we focus on the near-surface uncertainties which are affected by interactions between the land surface (LS) and atmospheric processes. Although the LS variables are crucial to provide various fluxes as the lower boundary condition to the atmosphere, their uncertainties were not much addressed yet. We employed the stochastically perturbed parameterization (SPP) scheme for the LS variables within the Weather Research and Forecasting (WRF) ensemble system. As a testbed experiment with the newly developed WRF ensemble-SPP system, we perturbed soil temperature and moisture in the Noah Land Surface Model to improve the performance of low-level atmospheric variables. To determine the optimal random forcing parameters used in perturbation, we employed a global optimization algorithm - the micro-genetic algorithm. Our results depicted positive impacts on the ensemble spread.
Author Lim, S.
Park, S. K.
Cassardo, C.
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Satoh, Masaki
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Keywords stochastic perturbation
model error
land surface model
optimization
Ensemble data assimilation
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SubjectTerms AS - Atmospheric Sciences
Title DEVELOPMENT OF STOCHASTICALLY PERTURBED PARAMETERIZATION SCHEME FOR THE SURFACE VARIABLES IN WRF BY OPTIMIZING THE RANDOM FORCING PARAMETERS USING THE MICRO-GENETIC ALGORITHM
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