앙상블 칼만 필터를 이용한 태풍 우쿵 (200610) 예측과 앙상블 민감도 분석
Typhoon Wukong (200610) Prediction Based on The Ensemble Kalman Filter and Ensemble Sensitivity Analysis
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초록
An ensemble Kalman filter (EnKF) with Weather Research and Forecasting (WRF)Model is applied for Typhoon Wukong (200610) to investigate the performance of ensemble forecasts depending on experimental configurations of the EnKF. In addition, the ensemble sensitivity analysis is applied to the forecast and analysis ensembles generated in EnKF, to investigate the possibility of using the ensemble sensitivity analysis as the adaptive observation guidance. Various experimental configurations are tested by changing model error, ensemble size,assimilation time window, covariance relaxation, and covariance localization in EnKF. First of all,experiments using different physical parameterization scheme for each ensemble member show less root mean square error compared to those using single physics for all the forecast ensemble members, which implies that considering the model error is beneficial to get better forecasts. A larger number of ensembles are also beneficial than a smaller number of ensembles. For the assimilation time window, the experiment using less frequent window shows better results than that using more frequent window, which is associated with the availability of observational data in this study. Therefore, incorporating model error, larger ensemble size, and less frequent assimilation window into the EnKF is beneficial to get better prediction of Typhoon Wukong (200610). The covariance relaxation and localization are relatively less beneficial to the forecasts compared to those factors mentioned above. The ensemble sensitivity analysis shows that the sensitive regions for adaptive observations can be determined by the sensitivity of the forecast measure of interest to the initial ensembles. In addition, the sensitivities calculated by the ensemble sensitivity analysis can be explained by dynamical relationships established among wind, temperature, and pressure.
Abstract
An ensemble Kalman filter (EnKF) with Weather Research and Forecasting (WRF)Model is applied for Typhoon Wukong (200610) to investigate the performance of ensemble forecasts depending on experimental configurations of the EnKF. In addition, the ensemble sensitivity analysis is applied to the forecast and analysis ensembles generated in EnKF, to investigate the possibility of using the ensemble sensitivity analysis as the adaptive observation guidance. Various experimental configurations are tested by changing model error, ensemble size,assimilation time window, covariance relaxation, and covariance localization in EnKF. First of all,experiments using different physical parameterization scheme for each ensemble member show less root mean square error compared to those using single physics for all the forecast ensemble members, which implies that considering the model error is beneficial to get better forecasts. A larger number of ensembles are also beneficial than a smaller number of ensembles. For the assimilation time window, the experiment using less frequent window shows better results than that using more frequent window, which is associated with the availability of observational data in this study. Therefore, incorporating model error, larger ensemble size, and less frequent assimilation window into the EnKF is beneficial to get better prediction of Typhoon Wukong (200610). The covariance relaxation and localization are relatively less beneficial to the forecasts compared to those factors mentioned above. The ensemble sensitivity analysis shows that the sensitive regions for adaptive observations can be determined by the sensitivity of the forecast measure of interest to the initial ensembles. In addition, the sensitivities calculated by the ensemble sensitivity analysis can be explained by dynamical relationships established among wind, temperature, and pressure.
참고문헌 (32)
- Ancell, B., "Comparing adjoint- and ensemble- sensitivity analysis with application to observation targeting", Mon. Wea. Rev., 135, pp.4117~4134 (2007)
- Baker, D. M., "A three- dimensional variational data assimilation system for MM5: Implementation and Initial Results", Mon. Wea. Rev., 132, pp.897~914 (2004)
- Barker, T. W., "The relationship between spread and forecast error in extended-range forecasts", J. Climate, 4, pp.733~742 (1991)
- Bowler, N. E., "The MOGREPS short-range ensemble prediction system", Quart. J. Roy. Meteor. Soc., 134, pp.703~722 (2008)
- Daley, R., "Atmospheric Data Analysis", pp.457~ (1991), Cambridge University Press
- Russell L. Elsberry, "Tropical Cyclone Structure (TCS08) Field ExperimentScience Basis, Observational Platforms, and Strategy", 한국기상학회지, 44(3), pp.209~231 (2008), 한국기상학회
- Evensen, G., "Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics", J. Geophys. Res., 99, pp.10143~10162 (1994)
- Gaspari, G., "Construction of correlation functions in two and three dimensions", Q. J. R. Meteorol. Soc., 125, pp.723~758 (1999)
- Grell, G. A., "A generalized approach to parameterizing convection combining ensemble and data assimilation techniques", Geophys. Res. Lett., 29, pp.1693~ (2002)
- Hakim G. J., "Ensemble sensitivity Analysis", Mon. Wea. Rev., 136, pp.663~677 (2008)
- Hamill, T. M., "A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme", Mon. Wea. Rev., 128, pp.2905~2919 (2000)
- Kalman, R. E., "A new approach to linear filtering and prediction problems", T.ASME - J. Basic Eng., 82D, pp.35~45 (1960)
- Kalnay, E., "Atmospheric Modeling, Data Assimilation and Predictablity", pp.341~ (2003), Cambridge University Press
- Kim, H. M., "Evolution of analysis error and adjoint-based sensitivities: Implications for adaptive observations", J. Atmos. Sci., 61, pp.795~812 (2004)
- 김현미, "Adaptive Observation Guidance Applied to Typhoon Rusa: Implications for THORPEX-PARC 2008", 한국기상학회지, 44(3), pp.297~312 (2008), 한국기상학회
- Krishnamurti, T. N., "Multimodel ensemble forecasts for weather and seasonal climate", J. Climate, 13, pp.4196~4216 (2000)
- Meng, Z., "Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments", Mon. Wea. Rev., 135, pp.1403~1423 (2007)
- Molteni, F., "The ECMWF ensemble prediction system: Methodology and validation", Quart. J. Roy. Meteor. Soc., 122, pp.73~119 (1996)
- Miyoshi, T., "Local Ensemble Transform Kalman Filtering with an AGCM at a T159", L48 resolution, 2007, pp.135, 3841~3861
- Palmer, T. N., "A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parameterization in weather and climate prediction models", Quart. J. Roy. Meteor. Soc., 127, pp.279~304 (2001)
- Park, J. I., "Predictability study of Typhoon Wukong (2006) using Ensemble Kalman Filter", 석사, pp.2010
- Park, Y. Y., "TIGGE : Preliminarly results on comparing and combining ensembles", Quart. J. Roy. Meteor. Soc., 134, pp.2029~2050 (2008)
- A description of the advanced research WRF version 2, "Skamarock, W. C.", NCAR, ~, pp.NCAR (2005), TN-468+STR
- Snyder, C., "Assimilation of Simulated Doppler Radar Observations with an Ensemble Kalman Filter", Mon. Wea. Rev., 131, pp.1663~1677 (2003)
- Stensrud, D. J., "Using initial condition and model physics perturbations in shortrange ensemble simulations of mesoscale convective systems", Mon. Wea. Rev., 128, pp.2077~2107 (2000)
- Talagrand, O., "Assimilation of observations, an introduction", J. Meteor. Soc. Japan, 75, pp.191~209 (1997)
- Torn, R. D., "Ensemble Data Assimilation Applied to RAINEX Observations of Hurricane Katrina (2005)", Mon. Wea. Rev., 137, pp.2817~2829 (2009)
- Torn, R. D., "Initial condition sensitivity of western Pacific extratropical transitions determined using ensemble-based sensitivity analysis", Mon. Wea. Rev., 137, pp.3388~3406 (2009)
- Whitaker, J. S., "Ensemble data assimilation without perturbed observations", Mon. Wea. Rev., 130, pp.1913~1924 (2002)
- Zhang, F., "Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter", Mon. Wea. Rev., 132, pp.1238~1253 (2004)
- Zhang, F., "Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I : Perfect model experiments", Mon. Wea. Rev., 134, pp.722~736 (2006)
- Zhang, F., "Cloudresolving hurricane initialization and prediction through assimilation of Doppler radar observations with an Ensemble Kalman filter", Mon. Wea. Rev., 137, pp.2105~2125 (2009)