Correction of Precipitation Forecast Predicted by DERF2.0 During the Pre-flood Season in South China

Wang Juanhuai; Li Qingquan; Wang Fang; Yang Shoumao; Hu Yamin

doi:10.11898/1001-7313.20210110

Vol.32, NO.1, 2021

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Wang Juanhuai, Li Qingquan, Wang Fang, et al. Correction of precipitation forecast predicted by DERF2.0 during the pre-flood season in South China. J Appl Meteor Sci, 2021，32(1)：115-128. DOI: 10.11898/1001-7313.20210110.

Citation:

Wang Juanhuai, Li Qingquan, Wang Fang, et al. Correction of precipitation forecast predicted by DERF2.0 during the pre-flood season in South China. J Appl Meteor Sci, 2021，32(1)：115-128. DOI: 10.11898/1001-7313.20210110.

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Correction of Precipitation Forecast Predicted by DERF2.0 During the Pre-flood Season in South China

DOI: 10.11898/1001-7313.20210110

Wang Juanhuai¹,
Li Qingquan^{2, 3
,
,},
Wang Fang^{2, 3},
Yang Shoumao⁴,
Hu Yamin¹

1.
Guangdong Climate Center, Guangzhou 510641
2.
Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081
3.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044
4.
Baiyun District Meteorological Bureau of Guangdong, Guangzhou 510080

Received Date: 2020-08-16
Rev Recd Date: 2020-10-15
Publish Date: 2021-01-31

Abstract

Abstract

There are two main types of precipitation during the pre-flood season in South China, frontal precipitation in early period and monsoon precipitation in late period. It is related to not only the tropical system, but also the cold air in the middle and high latitudes. The extended range forecasting skills of the precipitation in the pre-flood season which depend on the atmosphere-ocean interaction and the internal changes of the atmosphere are still very low. There are biases in models compared to the observations, which makes it hard to directly use model in operational forecast. Therefore, in order to better apply model forecast data to extended period forecasts, the precipitation biases are corrected during the pre-flood season from 1983 to 2019 produced by the Dynamic Extended Range Forecast Operational System version 2.0 (DERF2.0) based on a non-parameter Quantile-Mapping (QM) correction method. Daily precipitation observation data from 261 stations in South China from 1983 to 2019 are selected for evaluation. On the basis of probability forecast of the original model outputs, the model biases are then corrected using monotone cubic spline interpolation combined with the observation. The models are established by cross samples and independent samples to validate the correction method's performance by absolute difference/percentage difference, root mean square error, temporal correlation coefficient and pattern correlation coefficient. It is found that the QM correction method can improve the model forecasting skills by effectively eliminating the systematic deviation of the model. It shows that the improvement of the method remains stable with different lead times and magnitudes of model biases. Further analysis shows that the main locations and average intensities of precipitation show better consistency with observation after correction. The QM correction method can generally capture the trend difference between the model and the observation, and effectively improve the inter-annual variability of model, but it has a poor ability for extreme events. On the other hand, the revised effect of the statistical scheme according to different percentile intervals is also significant. In addition, it shows that the correction performances of prediction are more consistent with the hindcast result.
- Quantile-Mapping;
- probability;
- bias correction;
- stability;
- precipitation

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References(31)

Figures and Tables

Fig. 1 Transfer function and bias corrected precipitation at grid point near Guangzhou (23.12°N, 113.28°E) in Apr-Jun during 1983-2000

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Fig. 2 Cross validation of mean precipitation rate over South China in Apr-Jun during 1983-2000

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图 3 1983—2000年4—6月华南地区LD10订正前后模式回报平均降水率的交叉检验

(打点区域均达到0.05显著性水平)

Fig. 3 Cross validation of mean precipitation rate over South China in Apr-Jun during 1983-2000

(the dotted regions denote passing the test of 0.05 level)

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Fig. 4 The same as in Fig. 2, but for independent samples validation during 2001-2014

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图 5 2001—2014年4—6月华南地区订正前后模式回报平均降水率的偏差百分比和时间相关系数

(打点区域均达到0.05显著性水平)

Fig. 5 Verification of mean precipitation rate before and after correction over South China in Apr-Jun during 2001-2014

(the dotted regions denote passing the test of 0.05 level)

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Fig. 6 Differences between the model prediction and observation before and after correction for precipitation percentiles over South China in Apr-Jun during 2001-2014

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Fig. 7 Mean precipitation rate over South China in Apr-Jun during 2015-2019

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图 8 同图 7，但为降水距平百分率

(气候态为2001—2014年平均值)

Fig. 8 The same as in Fig. 7, but for precipitation anomalous percentage

(the climate is average from 2001 to 2014)

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Fig. 9 The percentage difference of mean precipitation rate before and after correction at LD10 and LD20 compared to observation over South China in Apr-Jun during 2015-2019

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Table 1 Comparison of mean precipitation rate over South China in Apr-Jun during 2001-2014

统计量	订正前	订正后	观测值
平均值/(mm·d^-1)	8.99	4.19	7.67
偏差绝对值/(mm·d^-1)	1.32	3.48

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Table 2 Comparison of mean precipitation rate over South China in Apr-Jun during 2015-2019

统计量	LD10		LD20		观测值
统计量	订正前	订正后	订正前	订正后	观测值
平均值/(mm·d^-1)	4.176	6.39	3.92	6.95	7.21
偏差绝对值/(mm·d^-1)	3.04	0.82	3.29	0.26
空间相关系数	0.31	0.41	0.29	0.36

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