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http://dx.doi.org/10.14191/Atmos.2014.24.1.001

Predictability Study of Snowfall Case over South Korea Using TIGGE Data on 28 December 2012  

Lee, Sang-Min (Forecast Research Laboratory, National Institute of Meteorological Research, KMA)
Han, Sang-Un (Forecast Technology Division, Forecast Bureau, KMA)
Won, Hye Young (Forecast Research Laboratory, National Institute of Meteorological Research, KMA)
Ha, Jong-Chul (Forecast Research Laboratory, National Institute of Meteorological Research, KMA)
Lee, Jeong-Soon (Forecast Research Laboratory, National Institute of Meteorological Research, KMA)
Sim, Jae-Kwan (Forecast Research Laboratory, National Institute of Meteorological Research, KMA)
Lee, Yong Hee (Forecast Research Laboratory, National Institute of Meteorological Research, KMA)
Publication Information
Atmosphere / v.24, no.1, 2014 , pp. 1-15 More about this Journal
Abstract
This study compared ensemble mean and probability forecasts of snow depth amount associated with winter storm over South Korea on 28 December 2012 at five operational forecast centers (CMA, ECMWF, NCEP, KMA, and UMKO). And cause of difference in predicted snow depth at each Ensemble Prediction System (EPS) was investigated by using THe Observing system Research and Predictability EXperiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data. This snowfall event occurred due to low pressure passing through South Sea of Korea. Amount of 6 hr accumulated snow depth was more than 10 cm over southern region of South Korea In this case study, ECMWF showed best prediction skill for the spatio-temporal distribution of snow depth. At first, ECMWF EPS has been consistently enhancing the indications present in ensemble mean snow depth forecasts from 7-day lead time. Secondly, its ensemble probabilities in excess of 2~5 cm/6 hour have been coincided with observation frequencies. And this snowfall case could be predicted from 5-day lead time by using 10-day lag ensemble mean 6 hr accumulated snow depth distribution. In addition, the cause of good performances at ECMWF EPS in predicted snow depth amounts was due to outstanding prediction ability of forming inversion layer with below $0^{\circ}C$ temperature in low level (below 850 hPa) according to $35^{\circ}N$ at 1-day lead time.
Keywords
TIGGE; ensemble prediction system; winter storm; snow depth;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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