We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting （WRF） three-dimensional variational assimilation （3DVAR） system. In particular, we s...We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting （WRF） three-dimensional variational assimilation （3DVAR） system. In particular, we studied the effect of this parameter on the assimilation of high-resolution surface data for heavy rainfall forecasts associated with mesoscale convective systems over the Korean Peninsula. In the assimilation of high-resolution surface data, the National Meteorological Center method tended to exaggerate the length scale that determined the shape and extent to which observed information spreads out. In this study, we used the difference between observation and background data to tune the length scale in the assimilation of high-resolution surface data. The resulting assimilation clearly showed that the analysis with the tuned length scale was able to reproduce the small-scale features of the ideal field effectively. We also investigated the effect of a double-iteration method with two different length scales, representing large and small-length scales in the WRF-3DVAR. This method reflected the large and small-scale features of observed information in the model fields. The quantitative accuracy of the precipitation forecast using this double iteration with two different length scales for heavy rainfall was high; results were in good agreement with observations in terms of the maximum rainfall amount and equitable threat scores. The improved forecast in the experiment resulted from the development of well-identified mesoscale convective systems by intensified low-level winds and their consequent convergence near the rainfall area.展开更多
A heavy rainfall case related to Mesoscale Convective Systems （MCSs） over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and...A heavy rainfall case related to Mesoscale Convective Systems （MCSs） over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting （WRF） three-dimensional variational （3DVAR） data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reffectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences （RMSDs） of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.展开更多
基金supported by International S&T Cooperation Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education,Science and Technology(MEST)(2011-00265)the BK21 program of the Korean Government Ministry of Education
文摘We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting （WRF） three-dimensional variational assimilation （3DVAR） system. In particular, we studied the effect of this parameter on the assimilation of high-resolution surface data for heavy rainfall forecasts associated with mesoscale convective systems over the Korean Peninsula. In the assimilation of high-resolution surface data, the National Meteorological Center method tended to exaggerate the length scale that determined the shape and extent to which observed information spreads out. In this study, we used the difference between observation and background data to tune the length scale in the assimilation of high-resolution surface data. The resulting assimilation clearly showed that the analysis with the tuned length scale was able to reproduce the small-scale features of the ideal field effectively. We also investigated the effect of a double-iteration method with two different length scales, representing large and small-length scales in the WRF-3DVAR. This method reflected the large and small-scale features of observed information in the model fields. The quantitative accuracy of the precipitation forecast using this double iteration with two different length scales for heavy rainfall was high; results were in good agreement with observations in terms of the maximum rainfall amount and equitable threat scores. The improved forecast in the experiment resulted from the development of well-identified mesoscale convective systems by intensified low-level winds and their consequent convergence near the rainfall area.
基金supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2006–2303 and by the Brain Korea 21 Project in 2007
文摘A heavy rainfall case related to Mesoscale Convective Systems （MCSs） over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting （WRF） three-dimensional variational （3DVAR） data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reffectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences （RMSDs） of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.
