Data Assimilation Effect of Mobile Rawinsonde Observation using Unified Model Observing System Experiment during the Summer Intensive Observation Period in 2013

被引:7
|
作者
Lim, Yun-Kyu [1 ]
Song, Sang-Keun [2 ]
Han, Sang-Ok [3 ]
机构
[1] Natl Inst Meteorol Res, Appl Meteorol Res Div, Jeju 697845, South Korea
[2] Jeju Natl Univ, Dept Earth & Marine Sci, Jeju 690756, South Korea
[3] Natl Inst Meteorol Res, High Impact Weather Res Ctr, Forecast Res Div, Gangwon 210702, South Korea
来源
关键词
Unified Model; observing system experiment; data assimilation; mobile rawinsonde observation; precipitation verification;
D O I
10.5467/JKESS.2014.35.4.215
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Data assimilation effect of mobile rawinsonde observation was evaluated using Unified Model (UM) with a Three-Dimensional Variational (3DVAR) data assimilation system during the intensive observation program of 2013 summer season (rainy season: 20 June-7 July 2013, heavy rain period: 8 July-30 July 2013). The analysis was performed by two sets of simulation experiments: (1) ConTroL experiment (CTL) with observation data provided by Korea Meteorological Administration (KMA) and (2) Observing System Experiment (OSE) including both KMA and mobile rawinsonde observation data. In the model verification during the rainy season, there were no distinctive differences for 500 hPa geopotential height, 850 hPa air temperature, and 300 hPa wind speed between CTL and OSE simulation due to data limitation (0000 and 1200 UTC only) at stationary rawinsonde stations. In contrast, precipitation verification using the hourly accumulated precipitation data of Automatic Synoptic Observation System (ASOS) showed that Equivalent Threat Score (ETS) of the OSE was improved by about 2% compared with that of the CTL. For cases having a positive effect of the OSE simulation, ETS of the OSE showed a significantly higher improvement (up to 41%) than that of the CTL. This estimation thus suggests that the use of mobile rawinsonde observation data using UM 3DVAR could be reasonable enough to assess the improvement of prediction accuracy.
引用
收藏
页码:215 / 224
页数:10
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