气候变化研究进展 ›› 2020, Vol. 16 ›› Issue (6): 714-724.doi: 10.12006/j.issn.1673-1719.2020.061
池艳珍1,2(), 梁潇云3(), 何芬4, 吴伟杰2, 唐振飞4
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,基金资助:
CHI Yan-Zhen1,2(), LIANG Xiao-Yun3(), HE Fen4, WU Wei-Jie2, TANG Zhen-Fei4
Received:
2020-03-30
Revised:
2020-05-14
Online:
2020-11-30
Published:
2020-12-03
Contact:
LIANG Xiao-Yun
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。
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%)
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