Impact of Wind Profiler Data on Regional Model Prediction in South China

Deng Hua; Liao Fei; Zhang Xubin; Shi Yang

doi:10.11898/1001-7313.20170508

Vol.28, NO.5, 2017

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Article Navigation > Journal of Applied Meteorological Science > 2017 > 28(5): 600-610

Deng Hua, Liao Fei, Zhang Xubin, et al. Impact of wind profiler data on regional model prediction in South China. J Appl Meteor Sci, 2017, 28(5): 600-610. DOI: 10.11898/1001-7313.20170508.

Citation:

Deng Hua, Liao Fei, Zhang Xubin, et al. Impact of wind profiler data on regional model prediction in South China. J Appl Meteor Sci, 2017, 28(5): 600-610. DOI: 10.11898/1001-7313.20170508.

Deng Hua, Liao Fei, Zhang Xubin, et al. Impact of wind profiler data on regional model prediction in South China. J Appl Meteor Sci, 2017, 28(5): 600-610. DOI: 10.11898/1001-7313.20170508.

Citation:

Deng Hua, Liao Fei, Zhang Xubin, et al. Impact of wind profiler data on regional model prediction in South China. J Appl Meteor Sci, 2017, 28(5): 600-610. DOI: 10.11898/1001-7313.20170508.

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Impact of Wind Profiler Data on Regional Model Prediction in South China

DOI: 10.11898/1001-7313.20170508

Deng Hua^{1,2
,
,},
Liao Fei^1,3,
Zhang Xubin¹,
Shi Yang⁴

1.
Key Laboratory of Regional Numerical Weather Prediction, Guangzhou 510640
2.
Guangdong Ecological Meteorology Center, Guangzhou 510640
3.
Guangzhou Meteorological Observatory of Guangdong Province, Guangzhou 511430
4.
Guangdong Meteorological Observatory, Guangzhou 510640

Received Date: 2017-01-09
Rev Recd Date: 2017-06-12
Publish Date: 2017-09-30

Abstract

Abstract

Weather analysis demonstrates that upper-level jet, low-level jet, and wind shear are closely related with rainstorms and severe convections in South China. Wind profiler radar can continuously observe wind, making it the most direct resource of upper wind observation comparing with conventional observations. A network of observation stations with 18 wind profiler radars is built in Guangdong, data of which are assimilated every 3 hours in GRAPES_Meso model in real time, and the influence of wind profiler data is evaluated. A precipitation process in the pre-flood season of South China from 28 March to 9 April in 2014 is simulated through three designed experiments by GRAPES_Meso model. Results of assimilation trials show that wind profile data contribute a lot to analysis increment of zonal wind at levels from 1000 hPa to 850 hPa, especially at 850 hPa, and this effect rapidly diminishes above 700 hPa level. The root mean square error (RMSE) of forecasted variables at radiosonde stations are calculated in terms of sounding observations and the outcome of three experiments. Results show that profiler data mostly improve RMSE at 850 hPa, which announces a 0.7 m·s^-1 reduce of forecasted wind speed error, for 700 hPa level there's no evident improvement, and for 925 hPa level it becomes even worse. The same RMSE analysis is done at 12 wind profiler stations. The result is in accordance with radiosonde stations, which shows that the RMSE decreases at 850 hPa as well and the improvement is not evident at 925 hPa. The analysis indicates that the quality of wind profiler data is relatively better at 850 hPa. Results of two sensitive experiments for 1200 UTC 30 March 2014 is examined, revealing that the RMSE of the forecasted wind speed is even greater when wind profiler data are used in assimilation at locations of heavy rain grade or above. Besides, it seems that the RMSE of the forecasted wind speed also increases in the downstream direction of this heavy rain location. Causes of these two phenomena still need to be analyzed.
- wind profiler;
- data assimilation;
- regional model in South China

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References(21)

Figures and Tables

Fig. 1 Distribution of wind profiler radar stations and radiosonde stations in Guangdong Province

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Fig. 2 Numbers of wind profiler stations and the sum of wind field vertical levels of every staion after quality control from 28 Mar to 8 Apr in 2014

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图 3 2014年3月29日—4月8日探空站纬向风分析增量随高度变化

(a)敏感性试验1，(b)敏感性试验2与敏感性试验1差值

Fig. 3 Analysis increments of zonal wind at mandatory pressure levels for radiosonde stations from 29 Mar to 8 Apr in 2014

(a)sensitive experiment 1, (b)sensitive experiment 2 relative to sensitive experiment 1

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Fig. 4 Ratio (r) of wind speed root mean square error to mean wind at mandatory pressure levels from 29 Mar to 8 Apr in 2014 for radiosonde stations

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图 5 2014年3月29日—4月8日敏感性试验1与敏感性试验2模拟探空站预报要素均方根误差的差值随高度分布

(a)风速(ΔV)，(b)温度(ΔT)，(c)位势高度(ΔH)

Fig. 5 Difference between sensitive experiment 1 and sensitive experiment 2 for the forecast root mean square error between observation and forecast at mandatory pressure levels from 29 Mar to 8 Apr in 2014 for radiosonde stations

(a)wind speed (ΔV), (b)temperature (ΔT), (c)potential height (ΔH)

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Fig. 6 Ratio (r) of wind speed root mean square error to mean wind at mandatory pressure levels from 29 Mar to 8 Apr in 2014 for profiler stations

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Fig. 7 Difference between sensitive experiment 1 and sensitive experiment 2 for the wind speed root mean square error at lower levels from 29 Mar to 8 Apr in 2014 for profiler stations

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Fig. 8 Differences of 850 hPa zonal wind(a) and meridional wind(b) analysis increments between sensitive experiment 1 and sensitive experiment 2 at 0600 UTC 30 Mar 2014

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Table 1 850 hPa wind speed forecast error at 1200 UTC 30 Mar 2014 for profiler stations

站名	敏感性试验1风速均方根误差/(m·s^-1)	敏感性试验2风速均方根误差/(m·s^-1)	同化本站风廓线雷达资料层数
潮州	4.40	0.94	9
连州	2.20	1.21	9
五华	8.02	7.09	10
新会	19.53	18.08	10
珠海	9.29	6.90	6
阳江	4.59	0.56	0
南雄	0.45	2.33	0
龙门	3.84	6.73	10
从化	6.03	7.12	10
增城	8.28	8.99	7
南沙	14.72	15.74	0
罗定	7.07	9.09	0

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