Application of a Bias Correction Method to Meteorological Forecast for the Pyeongchang Winter Olympic Games

Zhang Yutao; Tong Hua; Sun Jian

doi:10.11898/1001-7313.20200103

Vol.31, NO.1, 2020

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Zhang Yutao, Tong Hua, Sun Jian. Application of a bias correction method to meteorological forecast for the Pyeongchang Winter Olympic Games. J Appl Meteor Sci, 2020, 31(1): 27-41. DOI: 10.11898/1001-7313.20200103.

Citation:

Zhang Yutao, Tong Hua, Sun Jian. Application of a bias correction method to meteorological forecast for the Pyeongchang Winter Olympic Games. J Appl Meteor Sci, 2020, 31(1): 27-41. DOI: 10.11898/1001-7313.20200103.

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Application of a Bias Correction Method to Meteorological Forecast for the Pyeongchang Winter Olympic Games

DOI: 10.11898/1001-7313.20200103

Zhang Yutao^1,2,
Tong Hua^{1,2
,
,},
Sun Jian^1,2

1.
National Meteorological Center, Beijing 100081
2.
Numerical Weather Prediction Center of CMA, Beijing 100081

Received Date: 2019-08-07
Rev Recd Date: 2019-09-26
Publish Date: 2020-01-31

Abstract

Abstract

The 23rd Winter Olympics Games and the 13th Winter Paralympic Games are held in Pyeongchang, South Korea during 9-25 February 2018 and 8-18 March 2018. Supported by the WMO(World Meteorological Organization) WWRP(World Weather Research Project Group), ICE-POP 2018(International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games) is organized by KMA (Korea Meteorological Administration), which aims to improve the forecasting ability of convective scale numerical models for high-impact weather system under complex terrain and to support weather forecasting and meteorological services during the Winter Olympics through international cooperation. GRAPES_3 km, a high-resolution model independently developed by CMA (China Meteorological Administration), participated in this project and provided real-time forecasting products at the specific sites during the Winter Olympics Games. In order to improve the forecasting ability of GRAPES_3 km, a bias correction method named one-order adaptive Kalman filtering is applied, which compensate for the fact that the resolution of GRAPES_3 km is not sufficient to simulate the complex terrain of the Winter Olympic Games and GRAPES_3 km doesn't assimilate Korea's observations due to some objective reasons such as data transmission. 1-24-hour calibration products are provided twice a day, including 2 m temperature, 2 m relative humidity, 10 m wind speed, and 10 m wind direction on sixteen sites. The verification and evaluation are carried out in two aspects. First, it is to check whether the bias correction improves the accuracy of GRAPES_3 km, examined from the model's diurnal cycle, daily variation and the forecast ability of complex terrain. Second, to the performance of calibrated GRAPES_3 km during the Winter Olympic Games is and examined compared to LDAPS model from KMA and NU-WRF from NASA, which shows that the high-resolution GRAPES_3 km model has abilities to simulate the near-ground elements in this service and the bias correction technology makes model products better and effective. After bias correction, the root mean square error of model products is reduced to about 2℃ for the temperature, about 2 m·s^-1 for the wind speed, and less than 20% for the relative humidity is reduced to. Compared with, NU-WRF and LDAPS models, the corrected GRAPES_3 km has the best wind speed forecast capability and root mean square error of the wind speed is significantly reduced. For the wind direction forecast of complex terrain, although all numerical models have limited capabilities, and their wind direction forecasting of all the stations are almost dominated by westerly during the Winter Games, the corrected GRAPES_3 km perform relatively well in some non-westerly dominated stations. However, GRAPES_3 km has no significant advantage in temperature and humidity compared with the other two models although they are improved more obviously than wind after the calibration. In addition, the bias correction can attenuate the diurnal variation characteristics of GRPAES_3 km, and to some extent, improve the simulation capability for complex terrain. In short, the application of the bias correction in the Pyeongchang Winter Olympic Games is an effective way for GRAPES_3 km.
- GRAPES_3 km model;
- the Pyeongchang Winter Olympic Games;
- ICE-POP 2018;
- bias correction

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References(29)

Figures and Tables

Fig. 1 Location of sites

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Fig. 2 Diurnal variation of root mean square error (the line) before and after calibration with improvement rate (the bar) averaged from 9 Feb to 18 Mar in 2018 (a)2 m temperature, (b)10 m wind speed, (c)2 m relative humidity

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Fig. 3 Daily variation of root mean square error (the line) before and after calibration with improvement rate (the bar) of 1-24 h forecast mean from 9 Feb to 18 Mar in 2018 (a)2 m temperature, (b)10 m wind speed, (c)2 m relative humidity

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Fig. 4 Variation of root mean square error (the line) before and after calibration and improvement rate (the bar) averaged from 9 Feb to 18 Mar in 2018 (a)2 m temperature, (b)10 m wind speed, (c)2 m relative humidity

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Fig. 5 Comparison of root mean square error (the line) before and after calibration at stations with different height (the bar, color of bar denotes venues) (a)2 m temperature, (b)10 m wind speed, (c)2 m relative humidity

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Fig. 6 Comparison of 2 m temperature (the line) before and after calibration with altitudes of different sites (the bar, the color denotes venue) on 12 Feb 2018 (a)6 h forecast, (b)12 h forecast, (c)24 h forecast

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Fig. 7 Comparison of 2 m temperature's root mean square error of different models (a)diurnal variation, (b)daily variation, (c)station variation

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Fig. 8 Comparison of 2 m relative humidity's root mean square error of different models (a)diurnal variation, (b)daily variation, (c)station variation

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Fig. 9 Comparison of 10 m wind speed's root mean square error of different models (a)diurnal variation, (b)daily variation, (c)station variation

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Fig. 10 Wind rose diagram of Station 2561 from 9 Feb to 18 Mar in 2018 (a)observation, (b)GRAPES_3 km, (c)NU-WRF, (d)LDAPS

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Table 1 Information of sites

场馆位置	站号	地形高度/m	站点描述
阿尔卑西亚	2575	785.0	跳台滑雪
	2557	760.0	冬季两项赛道起点
	2577	764.0	越野滑雪赛道起点
	2554	812.0	滑行运动赛道终点
龙坪	2560	1416.0	高山滑雪大回转赛道起点
	2579	1180.0	高山滑雪大回转赛道中点
	2561	975.0	高山滑雪大回转赛道终点
旌善	2584	1370.0	高山滑雪速降赛道
	2586	919.0	高山滑雪速降赛道中点
	2587	639.0	高山滑雪速降赛道终点
宝光	2580	856.0	越野竞速赛道起点
	2581	664.0	越野竞速赛道终点
	2588	874.0	坡道障碍赛道起点
	2583	709.0	坡道障碍赛道终点
大关岭	47100	772.6	大关岭站
江陵	47105	26.0	江陵站

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Table 2 Comparison of models

模式配置	GRAPES_3 km	LDAPS	NU-WRF
水平分辨率	3 km×3 km	1.5 km×1.5 km	1 km×1 km
更新频率	每日2次	每日4次	每日4次
预报时效	0~24 h	0~36 h	0~24 h
初始场和侧边界条件	T639模式预报场驱动	GDAPS(UM 17 km)预报场驱动	NCEP/EMC GFS预报场驱动
辐射方案	RRTM LW&Dudhia SW	通用二流方案^[26]	NASA/GSFC
微物理方案	WSM-6	混合相降水方案^[27]	NASA/GSFC 4ICE
边界层方案	MRF	一阶非局地方案^[28]	MYJ
同化方案	三维云分析(GRAPES-GCAS)	三维变分同化(FGAT, IAU)	Goddard卫星数据模拟器单元, 利用了土地信息系统LSM和同化框架

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