S2S模式对四川汛期候尺度降水预测技巧评估和误差订正

庞轶舒; 马振峰; 郑然; 肖颖; 马晓慧

doi:10.11676/qxxb2022.068

S2S模式对四川汛期候尺度降水预测技巧评估和误差订正

doi: 10.11676/qxxb2022.068

庞轶舒^1,,
马振峰^1, ,,
郑然¹,
肖颖²,
马晓慧³

1.
四川省气候中心/高原与盆地暴雨旱涝灾害四川省重点实验室，成都，610072
2.
成都信息工程大学，成都，610103
3.
青岛航空股份有限公司，青岛，266000

基金项目: 中国气象局创新发展专项（CXFZ2021J018）、国家自然科学基金面上项目（41772173）、四川省科技计划项目（2019YJ0620）、高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金项目（SCQXKJQN202214）

详细信息

作者简介:
庞轶舒，主要从事短期气候预测等研究。 E-mail：pangyishu@126.com

通讯作者:
马振峰，主要从事短期气候预测研究。 E-mail：Mzf616@aliyun.com

中图分类号: P466
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- 被引次数: 0
出版历程
- 收稿日期: 2021-09-06
- 修回日期: 2022-07-15
- 网络出版日期: 2022-07-25

Evaluation of forecast skills and error correction of S2S models for pentad precipitation anomaly in Sichuan province during rainy season

1.
Sichuan Climate Center/Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province，Chengdu 610072，China
2.
Chengdu University of Information Technology，Chengdu 610103，China
3.
Qingdao Airlines，Qingdao 266000，China

摘要

摘要: 针对四川汛期候降水距平百分率（PAP），采用距平相关系数（ACC）、时间相关系数（TCC）、符号一致率（SCR）和趋势异常综合评分（PS）4种预测评分方法对S2S计划中10个模式的预测技巧进行检验评估，并在误差分析的基础上提出“正负概率异常订正”方案对各模式候降水距平百分率预测结果进行订正。结果表明，随着预测时效延长，多数模式的预测技巧快速降低，模式间预测技巧的差距缩小。至第10天左右，各模式进入低技巧时段，预测技巧随时效变化的幅度减小，各模式仅对降水趋势异常有一定预测能力，其中BoM模式明显高于其他模式。除BoM模式外的其他模式对降水年际变化幅度都存在低估，降水距平百分率异常偏差为−33%—−18%，不随预测时效发生太大变化，但空间分布不均。经过误差订正各模式的距平相关系数和符号一致率有所提高，趋势异常综合评分有效提高，并且对次季节尺度的订正效果优于天气尺度。订正后，各模式在次季节尺度的平均趋势异常综合评分均高于76.8， 66.7%的模式评分为79.2—80.2，超过业务评分标准（72.0）近8分。订正效果在4 a独立样本检验中也得到验证。
- S2S模式 /
- 预测技巧评估 /
- 误差订正 /
- 候降水趋势预测
Abstract: In this paper, anomaly correlation coefficient (ACC), time correlation coefficient (TCC), symbol consistency rate (SCR) and trend anomaly comprehensive score (PS) are used to evaluate the prediction skills of ten models in the S2S plan for pentad precipitation anomaly percentage (PAP) in Sichuan province during rainy season. Based on model bias analysis, a "correction of positive and negative probability anomaly" scheme was proposed to improve the model forecast. The results show the prediction skills of most models decrease rapidly, and the gap of prediction skills among these models narrows with increasing forecast lead-time. At around the 10th day, all the models enter the low skill period with little changes with lead-time. The models only have a certain ability to predict the abnormal trend of precipitation, and the BoM model has the best skill among these models. All the models except the BoM underestimate the anomaly degree of precipitation trend. The anomaly deviations of predicted PAP by these models are from −33% to −18% and invariant with the lead-time, but they are unevenly distributed in space. After error correction, ACC, SCR and PS of each model are improved, especially PS. The correction effect on the sub-seasonal scale is better than that on the synoptic scale. After modification, the average PS scores of all models on the sub-seasonal scale are higher than 76.8, of which 66.7% are within the range 79.2—80.2, nearly 8 points higher than the business-scoring standard (72.0). This correction effect has also been verified in 4-year independent sample tests.
- S2S models /
- Prediction skill evaluation /
- Error correction /
- Pentad precipitation trend prediction

HTML全文

图 1 四川省156个气象台站分布

Figure 1. Distribution of 156 meteorological stations in Sichuan province

下载: 全尺寸图片幻灯片

图 2 S2S计划模式对四川省汛期候降水距平百分率的预测技巧（a. ACC，b. PS评分，c. TCC通过0.1显著性t检验的站点比例（单位：%），d. SCR≥60%的站点比例（单位：%））

Figure 2. Forecast skills of models in the S2S plan （a. ACC，b. PS score，c. percentage of stations for TCC passing the significance t-test at 0.1 level （unit：%），d. percentage of stations for SCR≥60% （unit：%） of S2S models for pentad precipitation anomaly percentage in Sichuan province during the flood season）

下载: 全尺寸图片幻灯片

图 3 S2S计划模式在不同时间尺度下对四川汛期候降水距平百分率的预测技巧（a. ACC，b. PS评分，c. TCC通过0.1显著性t检验的站点比例（单位：%），d. SCR≥60%的站点比例（单位：%））

Figure 3. Forecast skills of models in the S2S plan on different time scales （a. ACC，b. PS score，c. percentage of stations for TCC passing the significance t-test at 0.1 level （unit：%），d. percentage of stations for SCR≥60% （unit：%） of S2S models for pentad precipitation anomaly percentage in Sichuan province during the flood season）

下载: 全尺寸图片幻灯片

图 4 S2S计划模式（a—j）在次季节尺度内对四川汛期候降水距平百分率PS评分的概率分布

Figure 4. Probability density distribution （PDF） for PS scores of the S2S models （a—j） for pentad precipitation anomaly percentage in Sichuan province during the flood season on the sub-seasonal scale

下载: 全尺寸图片幻灯片

图 5 S2S计划模式不同起报时间对四川省次季节尺度候降水距平百分率的PS评分

Figure 5. PS scores of the S2S models in different forecast months for pentad precipitation anomaly percentage in Sichuan province during the flood season on the sub-seasonal scale

下载: 全尺寸图片幻灯片

图 6 S2S计划模式在不同预测时段内对四川汛期候降水距平百分率的预测技巧（a） N₀，（b） N₁，（c） N₂，（d） M的站点比例

Figure 6. Forecast skills on different time scales percentages of （a） N₀，（b） N₁，（c） N₂ and （d） M to the sum of stations of the S2S models for pentad precipitation anomaly percentage in Sichuan province during the flood season

下载: 全尺寸图片幻灯片

图 7 S2S计划模式预测四川汛期候平均降水量的误差（a）和降水距平百分率的异常偏差（b）

Figure 7. Errors of pentad mean precipitation （a） and anomaly deviations of pentad precipitation anomaly percentage （b） of the S2S forecasts verified against observations in Sichuan province during the flood season

下载: 全尺寸图片幻灯片

图 8 S2S计划各模式在预测时效为1—26 d时对四川汛期候降水距平百分率的异常偏差（a. ECWMF，b. UKMO，c. KMA，d. CNR-ISAC，e. CMA，f. JMA，g. ECCC，h. BoM，i. NCEP，j. CNRM）

Figure 8. Anomaly deviations of S2S models for pentad precipitation anomaly percentage in Sichuan province during the flood season in the lead-time from 1 to 26 days （a. ECWMF，b. UKMO，c. KMA，d. CNR-ISAC，e. CMA，f. JMA，g. ECCC，h. BoM，i. NCEP，j. CNRM）

下载: 全尺寸图片幻灯片

图 9 误差订正前后S2S各模式在月内（a）、天气尺度（b）和次季节尺度（c）对四川省汛期候降水距平百分率的PS评分及其订正率

Figure 9. PS scores and PS correction rates of S2S models on pentad precipitation anomaly percentage during flood season in Sichuan province on monthly scale （a）， synoptic scale （b） and sub-seasonal scale （c） before and after error correction

下载: 全尺寸图片幻灯片

图 10 独立样本检验中误差订正前后S2S各模式在月内（a）、天气尺度（b）和次季节尺度（c）对四川省汛期候降水距平百分率的PS评分及其订正率

Figure 10. PS scores and PS correction rates of S2S models on pentad precipitation anomaly percentage during flood season in Sichuan province on monthly scale （a），synoptic scale （b） and sub-seasonal scale （c） before and after error correction in the independent sample test

下载: 全尺寸图片幻灯片

表 1 模式资料简介

Table 1. Profiles of model data

预报中心	预报形式	回报时段	预报时效	频率	海洋耦合	海冰耦合	水平分辨率	评估样本量
ECMWF	动态	1999—2019年	第0—46天	2次/周	是	否	1.5°×1.5°	44/a
CNR-ISAC	固定	1990—2010年	第0—31天	1次/（5 d）	否	否	1.5°×1.5°	31/a
UKMO	动态	1999—2021年	第0—60天	4次/月	是	是	1.5°×1.5°	20/a
CMA	固定	1994—2014年	第0—60天	1次/日	是	是	1.5°×1.5°	153/a
NCEP	固定	1999—2010年	第0—44天	1次/日	是	是	1.5°×1.5°	153/a
KMA	动态	1991—2010年	第0—60天	4次/月	是	是	1.5°×1.5°	20/a
JMA	固定	1981—2010年	第0—33天	3次/月	否	否	1.5°×1.5°	15/a
ECCC	固定	1995—2014年	第0—32天	1次/周	否	否	1.5°×1.5°	22/a
BoM	固定	1981—2013年	第0—62天	6次/月	是	否	2.5°×2.5°	30/a
CNRM	固定	1993—2014年	第0—61天	4次/月	是	是	1.5°×1.5°	20/a

下载: 导出CSV

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