DMCA
Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability (2008)
| Venue: | 1630 MONTHLY WEATHER REVIEW VOLUME |
| Citations: | 50 - 26 self |
BibTeX
@MISC{Tong08simultaneousestimation,
author = {Mingjing Tong and Ming Xue},
title = {Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability},
year = {2008}
}
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Abstract
The possibility of estimating fundamental parameters common in single-moment ice microphysics schemes using radar observations is investigated for a model-simulated supercell storm by examining parameter sensitivity and identifiability. These parameters include the intercept parameters for rain, snow, and hail/graupel, and the bulk densities of snow and hail/graupel. These parameters are closely involved in the definition of drop/particle size distributions of microphysical species but often assume highly uncertain specified values. The sensitivity of model forecast within data assimilation cycles to the parameter values, and the issue of solution uniqueness of the estimation problem, are examined. The ensemble square root filter (EnSRF) is employed for model state estimation. Sensitivity experiments show that the errors in the microphysical parameters have a larger impact on model microphysical fields than on wind fields; radar reflectivity observations are therefore preferred over those of radial velocity for microphysical parameter estimation. The model response time to errors in individual parameters are also investigated. The results suggest that radar data should be used at about 5-min intervals for parameter estimation. The response functions calculated from ensemble mean forecasts for all five individual parameters show concave shapes, with unique
Keyphrases
microphysical parameter sensitivity analysis parameter identifiability ensemble square root kalman filter atmospheric state simulated radar data simultaneous estimation individual parameter hail graupel model forecast model microphysical field parameter estimation estimation problem model-simulated supercell storm wind field uncertain specified value radar data radar observation microphysical parameter estimation radar reflectivity observation radial velocity bulk density intercept parameter ensemble mean forecast ensemble square root filter concave shape response function single-moment ice microphysics scheme microphysical specie model state estimation solution uniqueness parameter value fundamental parameter 5-min interval data assimilation cycle drop particle size distribution parameter sensitivity model response time
