Kao-Shen Chung;
Ching-Yin Ke
National Central University, Taiwan
The 8th EnKF Data Assimilation Workshop
Impact of assimilating temperature and humidity information with radar data at convective scale: An observing System Simulation Experiments
Talk:
EnKF_workshop_2018_kaoshen-Chung.pptx
By using WRF-LETKF Radar Assimilation System (WLRAS), the OSSE experiments with heavy rainfall event are conducted to evaluate the impact of assimilating temperature and water vapor with Radar observations. To alleviate the identical-twin problem in perfect-model experiments, the nature run of initial and boundary conditions is generated from the reanalysis of ECMWF, and the experiments of data assimilation are generate from the NCEP final analysis data. The assimilation frequency is every 15-min and the assimilation window is 1-h.
Results show that the observations of radial wind and reflectivity are more useful in the convective area, and assimilating radar observations would trigger the convection in the right time and right place easily. On the other hand, the information of temperature is more important in the stratiform area where the convergence is relative weak compared to the convective region. In addition, assimilating water vapor can also improve the stratiform area via the error correlation between temperature and humidity. Furthermore, if there is position error of the precipitation, the information of temperature and humidity plays an important role to correct position error in analysis and improve the very short-term forecast. According to this study, high-density information of temperature and water vapor in three-dimension is the key to improve the QPF for severe weather system.
By using WRF-LETKF Radar Assimilation System (WLRAS), the OSSE experiments with heavy rainfall event are conducted to evaluate the impact of assimilating temperature and water vapor with Radar observations. To alleviate the identical-twin problem in perfect-model experiments, the nature run of initial and boundary conditions is generated from the reanalysis of ECMWF, and the experiments of data assimilation are generate from the NCEP final analysis data. The assimilation frequency is every 15-min and the assimilation window is 1-h.
Results show that the observations of radial wind and reflectivity are more useful in the convective area, and assimilating radar observations would trigger the convection in the right time and right place easily. On the other hand, the information of temperature is more important in the stratiform area where the convergence is relative weak compared to the convective region. In addition, assimilating water vapor can also improve the stratiform area via the error correlation between temperature and humidity. Furthermore, if there is position error of the precipitation, the information of temperature and humidity plays an important role to correct position error in analysis and improve the very short-term forecast. According to this study, high-density information of temperature and water vapor in three-dimension is the key to improve the QPF for severe weather system.
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