앙상블 기반 관측 자료에 따른 예측 민감도 모니터링 시스템 구축 및 평가
A Monitoring System of Ensemble Forecast Sensitivity to Observation Based on the LETKF Framework Implemented to a Global NWP Model
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
In this study, we analyzed and developed the monitoring system in order to confirm the effect of observations on forecast sensitivity on ensemble-based data assimilation. For this purpose, we developed the Ensemble Forecast Sensitivity to observation (EFSO) monitoring system based on Local Ensemble Transform Kalman Filter (LETKF) system coupled with Korean Integrated Model (KIM). We calculated 24 h error variance of each of observations and then classified as beneficial or detrimental effects. In details, the relative rankings were according to their magnitude and analyzed the forecast sensitivity by region for north, south hemisphere and tropics. We performed cycle experiment in order to confirm the EFSO result whether reliable or not. According to the evaluation of the EFSO monitoring, GPSRO was classified as detrimental observation during the specified period and reanalyzed by data-denial experiment. Data-denial experiment means that we detect detrimental observation using the EFSO and then repeat the analysis and forecast without using the detrimental observations. The accuracy of forecast in the denial of detrimental GPSRO observation is better than that in the default experiment using all of the GPSRO observation. It means that forecast skill score can be improved by not assimilating observation classified as detrimental one by the EFSO monitoring system.
Abstract
In this study, we analyzed and developed the monitoring system in order to confirm the effect of observations on forecast sensitivity on ensemble-based data assimilation. For this purpose, we developed the Ensemble Forecast Sensitivity to observation (EFSO) monitoring system based on Local Ensemble Transform Kalman Filter (LETKF) system coupled with Korean Integrated Model (KIM). We calculated 24 h error variance of each of observations and then classified as beneficial or detrimental effects. In details, the relative rankings were according to their magnitude and analyzed the forecast sensitivity by region for north, south hemisphere and tropics. We performed cycle experiment in order to confirm the EFSO result whether reliable or not. According to the evaluation of the EFSO monitoring, GPSRO was classified as detrimental observation during the specified period and reanalyzed by data-denial experiment. Data-denial experiment means that we detect detrimental observation using the EFSO and then repeat the analysis and forecast without using the detrimental observations. The accuracy of forecast in the denial of detrimental GPSRO observation is better than that in the default experiment using all of the GPSRO observation. It means that forecast skill score can be improved by not assimilating observation classified as detrimental one by the EFSO monitoring system.
참고문헌 (17)
- Cardinali, C., "Monitoring the observation impact on the short-range forecast", Q. J. R. Meteorol. Soc, 135, pp.239~250 (2009)
- Gelaro, R., "The THORPEX Observation Impact Intercomparison Experiment", Mon. Wea. Rev, 138, pp.4009~4025 (2010)[DOI]
- Harris, B. A., "A satellite radiance-bias correction scheme for data assimilation", Q. J. R. Meteorol. Soc, 127, pp.1453~1468 (2001)
- 홍송유, "The Korean Integrated Model (KIM) System for Global Weather Forecasting", Asia-Pacific Journal of Atmospheric Sciences, 54, pp.267~292 (2018), 한국기상학회[DOI]
- Hotta, D., "Proactive QC : a fully flow-dependent quality control scheme based on EFSO", Mon. Wea. Rev, 145, pp.3331~3354 (2017)[DOI]
- Kalnay, E., "A simpler formulation of forecast sensitivity to observations : Application to ensemble Kalman filters", Tellus A, 64, pp.18462~ (2012)[DOI]
- Kelly, G., "The value of observations. I: Data denial experiments for the Atlantic and the Pacific", Q. J. R. Meteorol. Soc., 133, pp.1803~1815 (2007)
- Langland, R. H., "Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system", Tellus A, 56, pp.189~201 (2004)
- Lorenc, A. C., "Forecast sensitivity to observations in the Met Office global numerical weather prediction system", Q. J. R. Meteorol. Soc, 140, pp.209~224 (2014)[DOI]
- Miyoshi, T., "Localizing the error covariance by physical distances within a Local Ensemble Transform Kalman Filter(LETKF)", SOLA, 3, pp.89~92 (2007)
- Ota, Y., "Ensemble-based observation impact estimates using the NCEP GFS", Tellus A, 65, pp.20038~ (2013)[DOI]
- Schulze, G. C., "Atmospheric observations and numerical weather prediction", S. Afr. J. Sci, 103, pp.318~323 (2007)
- Shin, S., "The local ensemble transform Kalman filter(LETKF)with a global NWP model on the cubed sphere", Pure Appl. Geophys, 173, pp.2555~2570 (2016)[DOI]
- 신설은, "Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere", Asia-Pacific Journal of Atmospheric Sciences, 54, pp.351~359 (2018), 한국기상학회[DOI]
- Sienkiewicz, J. M., "An example of the importance of ship observations", Wea. Forecasting, 5, pp.683~687 (1990)
- Sommer, M., "Observation impact in a convective-scale localized ensemble transform Kalman filter", Q. J. R. Meteorol. Soc, 140, pp.2672~2679 (2014)[DOI]
- Zhu, Y., "Observation sensitivity calculations using the adjoint of the Gridpoint Statistical Interpolation(GSI)analysis system", Mon. Wea. Rev, 136, pp.335~351 (2008)