Ensemble Forecasts for Sub-seasonal to Seasonal Rainfall over the Economic Belt of the Northern Slope of Tianshan Mountains

Li Haiyan; Jie Weihua; Wu Tongwen; Li Qiaoping; Liang Xiaoyun; Yao Junchen; Liu Xiangwen; Lu Yixiong; Yang Tao; Liu Chun

doi:10.11898/1001-7313.20230104

Vol.34, NO.1, 2023

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Li Haiyan, Jie Weihua, Wu Tongwen, et al. Ensemble forecasts for sub-seasonal to seasonal rainfall over the economic belt of the northern slope of Tianshan Mountains. J Appl Meteor Sci, 2023, 34(1): 39-51. DOI: 10.11898/1001-7313.20230104.

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Li Haiyan, Jie Weihua, Wu Tongwen, et al. Ensemble forecasts for sub-seasonal to seasonal rainfall over the economic belt of the northern slope of Tianshan Mountains. J Appl Meteor Sci, 2023, 34(1): 39-51. DOI: 10.11898/1001-7313.20230104.

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Ensemble Forecasts for Sub-seasonal to Seasonal Rainfall over the Economic Belt of the Northern Slope of Tianshan Mountains

DOI: 10.11898/1001-7313.20230104

1.
Institute of Desert Meteorology of CMA, Urumqi 830002
2.
Xinjiang Uygur Autonomous Region Climate Center, Urumqi 830002
3.
Center for Earth System Modeling and Prediction of CMA, Beijing 100081
4.
Anhui Meteorological Station, Hefei 230002

Received Date: 2022-09-04
Rev Recd Date: 2022-10-26
Publish Date: 2023-01-31

Abstract

Abstract

The economic belt of the northern slope of Tianshan Mountains (NSTM) has important social, economic and ecological effects in Xinjiang. Thus, it is critical to improve the prediction ability of sub-seasonal to seasonal rainfall in this region. Focusing on the large terrain of Tianshan Mountains, the global high-resolution climate prediction operational system version 3 developed by China Meteorological Administration (CMA-CPSv3) is applied. The sub-seasonal to seasonal rainfall over the NSTM is predicted by using the control run, the traditional ensemble mean, and the improved optimal deterministic ensemble forecast using a probabilistic threshold (DEFPT), respectively. The DEFPT method is not intended to predict the probability of rainfall, but to forecast the occurrence (yes or no) of rainfall event in any model grid box by judging whether it exceeds a certain probabilistic threshold, and the spatial-temporal distribution characteristic is analyzed. All the predictions are evaluated by the frequency bias, equitable threat score (ETS), Hanssen and Kuipers score (HK) and anomaly correlation coefficient (ACC). The evaluation results show that the improved DEFPT method can improve the sub-seasonal to seasonal predictions of the 1-5 mm rainfall locations and persistence over the NSTM and is superior to the traditional ensemble mean and the control run. These results also indicate that it is necessary to combine numerical model prediction with proper objective ensemble prediction method, especially in regions with large terrain background and significant climatology difference. Based upon the analyses of three rainfall events (during 29 July-2 August of 2016, 7-11 June of 2017, and 8-12 July of 2020, respectively), the DEFPT method performs better in western and southern NSTM from the perspectives of rainfall locations, anomalies and persistence. However, the prediction ability is relatively low in the eastern and northern parts of NSTM, which is possibly related to the low skill of humidity prediction from each ensemble member in corresponding regions. In addition, this method can also be used in other regions by tuning the related empirical coefficients in the formula to limit forecasting biases.
- CMA-CPSv3;
- sub-seasonal to seasonal (S2S);
- ensemble rainfall prediction;
- the northern slope of Tianshan Mountains

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References(36)

Figures and Tables

Fig. 1 Location of the economic belt of the northern slope of Tianshan Mountains(NSTM)

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Fig. 2 Pentadly bias standard deviation for 1 mm threshold rainfall over the NSTM in Xinjiang calculated based on all cases initialized from May to Sep in 2006-2020

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Fig. 3 Pentadly optimal probabilistic threshold for 1 mm threshold rainfall over the NSTM in Xinjiang calculated based on all cases initialized from May to Sep in 2006-2020

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图 4 CMA-CPSv3预测系统在新疆天山北坡2006—2020年5—9月全部个例的逐候累积降水不同阈值不同预报时效技巧评分

(黑色虚线为偏差评分等于1.0标准线)

Fig. 4 Bias, ETS and HK scores for pentadly rainfall at 1-20 mm thresholds at different lead times over the NSTM from all cases in Xinjiang from May to Sep in 2006-2020 predicted by CMA-CPSv3

(the black dashed line denotes the standard line(equal to 1.0))

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图 5 CMA-CPSv3预测系统在新疆天山北坡2006—2020年5—9月全部个例的日降水发生频次与观测之间的不同预报时效异常相关系数

(黑色虚线表示0.05显著性水平)

Fig. 5 Anomaly correlations of daily rainfall event frequencies in each pentad and ten days at different lead times over the NSTM in Xinjiang from all cases from May to Sep in 2006-2020 between CMA-CPSv3 and observation

(the black dashed line denotes the level of 0.05)

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图 6 CMA-CPSv3预测系统预报2016年7月29日—8月2日的候累积降水分布

(日期标记为起报时间)
(a)观测，(b)提前0 d控制预报，(c)提前0 d集合平均预报，(d)提前0 d DEFPT集合预报, (e)提前1周控制预报，(f)提前1周集合平均预报，(g)提前1周DEFPT集合预报, (h)提前2周控制预报，(i)提前2周集合平均预报，(j)提前2周DEFPT集合预报

Fig. 6 Pentadly rainfall including the period from 29 Jul to 2 Aug in 2016 predicted by CMA-CPSv3

(the date of model initialized is marked in each figure)
(a)observations, (b)CTL run at the lead of 0 day, (c)ensemble mean at the lead of 0 day, (d)DEFPT result at the lead of 0 day (e)CTL run at the lead of one week, (f)ensemble mean at the lead of one week, (g)DEFPT result at the lead of one week, (h)CTL run at the lead of two weeks, (i)ensemble mean at the lead of two weeks, (j)DEFPT result at the lead of two weeks

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Fig. 7 The same as in Fig. 6, but for anomalous percentage of pentadly rainfall with threshold of 5 mm

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Fig. 8 The same as in Fig. 6, but for frequency of daily rainfall in each pentad

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Fig. 9 The same as in Fig. 6, but for pentadly rainfall from 7 Jun to 11 Jun in 2017

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Fig. 10 The same as in Fig. 6, but for pentadly rainfall from 8 Jul to 12 Jul in 2020

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Fig. 11 Correlation between observation and prediction from all cases for the 4th-6th pentad humidity over the NSTM in Xinjiang from May to Sep in 2006-2020

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Table 1 Empirical coefficients α and β for reasonable forecasting biases of rainfall events with different thresholds from May to Sep in 2006-2020

降水阈值/mm	5月		6月		7月		8月		9月
降水阈值/mm	α	β	α	β	α	β	α	β	α	β
1	1.0	4.0	1.5	4.0	1.5	4.0	1.0	3.0	1.0	2.5
2	3.0	5.0	3.5	5.0	3.0	5.0	2.0	5.0	2.0	5.0
3	3.5	6.0	4.0	6.0	4.0	7.0	3.0	6.0	4.0	6.0
4	3.5	6.0	4.0	6.0	4.0	8.0	4.0	8.0	5.0	8.0
5	3.5	6.0	4.5	7.0	4.5	8.5	4.5	8.5	5.0	8.5

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