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contributor authorTong, Mingjing
contributor authorXue, Ming
date accessioned2017-06-09T16:21:01Z
date available2017-06-09T16:21:01Z
date copyright2008/05/01
date issued2008
identifier issn0027-0644
identifier otherams-66252.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4207568
description abstractThe ensemble Kalman filter method is applied to correct errors in five fundamental microphysical parameters that are closely involved in the definition of drop/particle size distributions of microphysical species in a commonly used single-moment ice microphysics scheme, for a model-simulated supercell storm, using radar data. The five parameters include the intercept parameters for rain, snow, and hail/graupel and the bulk densities of hail/graupel and snow. The ensemble square root Kalman filter (EnSRF) is employed for simultaneous state and parameter estimation. The five microphysical parameters are estimated individually or in different combinations starting from different initial guesses. A data selection procedure based on correlation information is introduced, which combined with variance inflation, effectively avoids the collapse of the spread of parameter ensemble, hence filter divergence. Parameter estimation results demonstrate, for the first time, that the ensemble-based method can be used to correct model errors in microphysical parameters through simultaneous state and parameter estimation, using radar reflectivity observations. When error exists in only one of the microphysical parameters, the parameter can be successfully estimated without exception. The estimation of multiple parameters is less reliable, mainly because the identifiability of the parameters becomes weaker and the problem might have no unique solution. The parameter estimation results are found to be very sensitive to the realization of the initial parameter ensemble, which is mainly related to the use of relatively small ensemble sizes. Increasing ensemble size generally improves the parameter estimation. The quality of parameter estimation also depends on the quality of observation data. It is also found that the results of state estimation are generally improved when simultaneous parameter estimation is performed, even when the estimated parameter values are not very accurate.
publisherAmerican Meteorological Society
titleSimultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part II: Parameter Estimation Experiments
typeJournal Paper
journal volume136
journal issue5
journal titleMonthly Weather Review
identifier doi10.1175/2007MWR2071.1
journal fristpage1649
journal lastpage1668
treeMonthly Weather Review:;2008:;volume( 136 ):;issue: 005
contenttypeFulltext


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