Preliminary results of ETKF based ensemble precipitation prediction over the Korean Peninsula

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Tuesday, 4 February 2014
Hall C3 (The Georgia World Congress Center )
Jun Kyung Kay, Yonsei University, Seodaemun-gu, Seoul, South Korea; and H. M. Kim

Ensemble prediction plays an important role in quantifying the distribution of forecast uncertainties. To conduct the convective-scale short range ensemble forecasts for heavy precipitation over the Korean Peninsula for the month of July 2011, Weather and Research Forecast Model Advanced Research (WRF-ARW) modeling system is used with 24 ensemble members. The model has a configuration with a triple nesting with 3 km highest horizontal resolution. The initial ensemble perturbations generated by Ensemble Transform Kalman Filter (ETKF) are added to the National Center for Environmental Prediction (NCEP) final analysis, then run for 36 h four times a day (00 UTC, 06 UTC, 12 UTC and 18 UTC). ETKF is a family of ensemble square root filters, and initial ensemble perturbations are updated by solving Kalman Filter equations considering the error estimates of observation and forecast. Because ensemble spread underestimates the forecast error uncertainties, two different kinds of inflation method that include adaptively calculated multiplicative inflation factor and additive inflation factor method are implemented in the ensemble prediction system, and the effects of these inflation methods on the quality of ensemble prediction are investigated in terms of probabilistic forecasts. In addition, the uncertainties from unresolved topographic are considered for the ensemble prediction of precipitation, and the results are discussed in detail in the meeting.