BCC_CSM1.1m模式对福建前汛期降水预测的误差订正

doi:10.12006/j.issn.1673-1719.2020.061

气候变化研究进展 ›› 2020, Vol. 16 ›› Issue (6): 714-724.doi: 10.12006/j.issn.1673-1719.2020.061

BCC_CSM1.1m模式对福建前汛期降水预测的误差订正

池艳珍¹^,²(), 梁潇云³(), 何芬⁴, 吴伟杰², 唐振飞⁴

1 福建省灾害天气重点实验室,福州 350001
2 海峡气象开放实验室,厦门 361012
3 中国气象局国家气候中心,北京 100081
4 福建省气候中心,福州 350001

收稿日期:2020-03-30 修回日期:2020-05-14 出版日期:2020-11-30 发布日期:2020-12-03
通讯作者: 梁潇云
作者简介:池艳珍,女,正研级高工,cyz0605@126.com
基金资助:
国家重点研发计划项目(2018YFC1505805);国家重点研发计划项目(2016YFA0602100);国家重点研发计划项目(2018YFE0196000);福建省气象局开放式基金(2018K03)

Verification and preliminary correction of the precipitation prediction in the pre-flood season over Fujian province by BCC_CSM1.1m climate model

CHI Yan-Zhen¹^,²(), LIANG Xiao-Yun³(), HE Fen⁴, WU Wei-Jie², TANG Zhen-Fei⁴

1 Disasters Weather Key Laboratory of Fujian Province, Fuzhou 350001, China
2 Laboratory of Straits Meteorology, Xiamen 361012, China
3 National Climate Center, China Meteorological Administration, Beijng 100081, China
4 Fujian Climate Center, Fuzhou 350001, China

Received:2020-03-30 Revised:2020-05-14 Online:2020-11-30 Published:2020-12-03
Contact: LIANG Xiao-Yun

摘要/Abstract

摘要：

采用1991—2017年BCC_CSM1.1m季节预测模式的月降水预测数据及福建省前汛期（4—6月）66个国家气象站降水资料,利用距平相关系数(ACC)、时间相关系数(TCC)、平均方差技巧评分(MSSS)和趋势异常综合评分(Ps)等评估方法,检验评估了提前0、1、2和5个月模式对福建省前汛期降水的预测能力。采用系统偏差、一元线性回归和EOF-相似误差（EOFL和EOFNL）等4种统计方法对回报结果进行订正,并进行效果检验。BCC_CSM1.1m在不同起报时间对福建省前汛期降水的预测均能抓住降水的前两个主模态：全省一致和南北反向分布的空间特征,但预测的气候平均值较实况存在负偏差。模式在不同起报时间对前汛期降水预测的TCC高技巧区主要位于福建省北部,ACC技巧和Ps评分存在比较大的年际差异,负系统偏差的存在使得MSSS技巧不高。经订正后,模式的预测能力得到明显提升。系统偏差、线性回归、EOF相似误差线性和非线性订正方法提前2个月起报的2011—2017年平均Ps评分分别提高5.9、3.5、6.7和7.8分;不同起报时间线性回归订正的2011—2017年平均ACC技巧分别提高0.02、0.21、0.12和0.11;上述4种方法订正的MSSS评分均有了显著提高,其中系统偏差和线性回归订正后达正技巧。综合而言,线性回归订正较其他3种订正方法表现出更为稳定的订正技巧。

关键词: 降水预测, 检验, 统计订正, BCC_CSM1.1m

Abstract:

Based on the re-forecast and operational data from the second-generation seasonal prediction model of Beijing Climate Center (BCC_CSM1.1m), and the monthly observational precipitation of 66 stations over Fujian province in 1991-2017, the precipitation prediction ability of the Model during the pre-flood season at different lead time was assessed. The metrics of verification used in this study were anomaly correlation coefficient (ACC), temporal correlation coefficient (TCC), mean square skill score (MSSS) and the prediction score (Ps). The system bias correction (BC), the linear regression (LR) and Empirical Orthogonal Function-analogue correction were used to revise the forecast. Results show that: (1) Although there is always a systematic negative bias in the prediction of the climatological precipitation for the pre-flood season over Fujian province at different leading time, the Model can predict the first and second typical modes of the precipitation in the pre-flood period: the uniform distribution in the whole province and the decrease from south to north; (2) The inter-annual variation of ACC skill and Ps scores of the precipitation prediction are prominent, and the MSSS scores are negative due to the systematic negative bias. The high TCC skill can be found in the northern Fujian province; (3) The prediction ability of the model for precipitation improved significantly after being corrected. The average Ps scores in 2011-2017 are 5.9, 3.5, 6.7 and 7.8 points higher than the raw ensemble at LM2 (leading two months) after the BC, LR, EOFL and EOFNL correction, respectively. The average ACC skill scores in 2011-2017 are 0.02, 0.21, 0.12 and 0.11 points higher than raw predictions after the LR correction. There are significant improvements of MSSS scores for the four correction methods, of which the bias correction and the linear regression correction receive positive techniques; (4) In general, the linear regression correction shows more advantages than the other three correction methods.

Key words: Precipitation prediction, Verification, Statistical correction, BCC_CSM1.1m

池艳珍, 梁潇云, 何芬, 吴伟杰, 唐振飞. BCC_CSM1.1m模式对福建前汛期降水预测的误差订正[J]. 气候变化研究进展, 2020, 16(6): 714-724.

CHI Yan-Zhen, LIANG Xiao-Yun, HE Fen, WU Wei-Jie, TANG Zhen-Fei. Verification and preliminary correction of the precipitation prediction in the pre-flood season over Fujian province by BCC_CSM1.1m climate model[J]. Climate Change Research, 2020, 16(6): 714-724.

图/表 11

图1 观测和模式预测的福建前汛期降水气候态注:观测记为OBS,提前0、1、2和5个月起报分别记为LMO、Ll和LM5,下同。

Fig. 1 Rainfall in pre-flood season over Fujian province from observations (a) and BCC_CSM1.1m predictions during 1991-2010

图2 1991—2010年观测及LM0起报的福建前汛期降水EOF分解前三个模态空间分布

Fig. 2 Spatial distributions of the first (EOF1, left column), second (EOF2, middle column) and third (EOF3, right column) EOF modes from observations (a-c) and BCC_CSM1.1m predictions (d-f) at LM0 during 1991-2010

图3 1991—2017年不同预报时效的前汛期降水绝对误差、均方根误差分布及其全省平均

Fig. 3 Spatial distributions of the mean absolute error (MAE, a-d), root mean square error (RMSE, e-h) and the domain-averaged (i) at different lead time predictions during 1991-2017

图4 1991—2017年不同起报时间距平相关系数（ACC）技巧的时间序列

Fig. 4 Time series of anomaly correlation coefficient (ACC) at different lead time predictions during 1991-2017

图5 同图4,但为Ps评分

Fig. 5 Same as Fig. 4, but for Ps skill

图6 不同起报时间福建前汛期降水预报的TCC技巧注:阴影为达到90%信度检验的区域。

Fig. 6 Spatial distributions of the temporal correlation coefficient (TCC) at different lead time predictions during 1991-2017 (shading denotes the confidence above 90%)

图7 同图6,但为LM0的平均方差技巧（MSSS）及其子项

Fig. 7 Same as Fig. 6, but for the mean square skill score and its components at LM0 prediction

图8 同图4,但为LM0集合平均（ENS）及4种方法误差订正ACC对比

Fig. 8 Same as Fig. 4, but for the ACC skill of raw ensemble prediction and error corrections at LM0

图9 同图8,但为LM2的Ps评分

Fig. 9 Same as Fig. 8, but for Ps skill at LM2

图10 同图6,但为LM0起报的4种误差订正结果

Fig. 10 Same as Fig. 6, but for four corrections at LM0

图11 同图10,但为LM0的4种误差订正结果的MSSS分布

Fig. 11 Same as the Fig. 10, but for MSSS at LM0

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BCC_CSM1.1m模式对福建前汛期降水预测的误差订正

Verification and preliminary correction of the precipitation prediction in the pre-flood season over Fujian province by BCC_CSM1.1m climate model

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