SEAS5模式对黑龙江省夏季降水的预测性能评估

doi:10.11755/j.issn.1006-7639-2026-01-0159

干旱气象 ›› 2026, Vol. 44 ›› Issue (1): 159-171.DOI: 10.11755/j.issn.1006-7639-2026-01-0159

SEAS5模式对黑龙江省夏季降水的预测性能评估

王昊¹^,²(), 马浩³(), 李永生¹^,², 刘长征⁴, 姜雨函⁵

1.黑龙江省气候中心，黑龙江哈尔滨 150030
2.五营国家气候观象台，黑龙江伊春 153000
3.浙江省气候中心，浙江杭州 310052
4.国家气候中心，北京 100081
5.黑龙江省生态气象中心，黑龙江哈尔滨 150030

收稿日期:2025-07-29 修回日期:2025-12-08 出版日期:2026-02-28 发布日期:2026-03-25
通讯作者: 马浩（1984—），男，安徽合肥人，正高级工程师，主要从事多尺度气候预测和气候动力学研究。E-mail： mahao20032003@aliyun.com。
作者简介:王昊（1996—），女，黑龙江哈尔滨人，工程师，主要从事气候预测与气候变化研究。E-mail： 1005115621@qq.com。
基金资助:
中国气象局气象能力提升联合研究专项(22NLTSY001);黑龙江省自然科学基金项目(LH202D022);中国气象局青年创新团队气象灾害风险评估配套项目(CMA2023QN01);浙江省自然科学基金联合基金重大项目(LZJMD24D050002);中国气象局沈阳大气环境研究所联合开放基金项目(2024SYIAEKFMS09);松辽流域气象科技创新项目(SL2025014)

Evaluation of the predictive performance of the SEAS5 model for summer precipitation in Heilongjiang Province

WANG Hao¹^,²(), MA Hao³(), LI Yongsheng¹^,², LIU Changzheng⁴, JIANG Yuhan⁵

1. Heilongjiang Climate Centre，Harbin 150030，China
2. Wuying National Climatological Observatory，Yichun 153000，Heilongjiang，China
3. Zhejiang Climate Centre，Hangzhou 310052，China
4. National Climate Center，Beijing 100081，China
5. Heilongjiang Eco-Meteorology Center，Harbin 150030，China

Received:2025-07-29 Revised:2025-12-08 Online:2026-02-28 Published:2026-03-25

摘要/Abstract

摘要：

为明确欧洲中期天气预报中心第五代季节预测系统（the Fifth Generation Seasonal Forecast System，SEAS5）季节模式对黑龙江汛期降水的预测能力，并为提升区域季节预测与解释应用水平提供依据，基于SEAS5模式1993—2023年的历史回报与实时预报数据及黑龙江省83个气象站降水观测资料，利用距平相关系数、Ps评分等检验评估方法，对该模式的夏季降水预测性能进行系统评估。结果表明：（1）SEAS5模式对黑龙江夏季降水总量空间分布的刻画与观测较为一致，但高值中心偏南、年际变率偏小、全省以湿偏差为主，3月起报的预测技巧优于4月和5月起报。就降水距平而言，不同月份起报的多年平均距平相关系数均为负技巧，仅有个别年份Ps评分超过80分，3月起报的预测相对表现更优。（2）模式对东北冷涡相关环流的刻画能力是影响黑龙江夏季降水预测技巧的关键因素。高技巧年份能较好再现冷涡引起的环流异常及其对应的降水异常，而低技巧年份在东北及其邻近区域的低层环流预测偏差显著。（3）3月起报因对对流层高层环流空间分布的预测更为合理而表现相对较优，但中低层环流误差仍制约整体技巧提升；SEAS5对西太平洋副热带高压的预测能力与黑龙江夏季降水技巧关联较弱，东北冷涡相关环流的影响更为直接。

关键词: 黑龙江夏季降水, SEAS5, 模式评估, 低层环流, 东北冷涡

Abstract:

To clarify the predictive capability of the fifth-generation seasonal forecast system (SEAS5) of the European Centre for Medium-Range Weather Forecasts for summer precipitation in Heilongjiang and to provide a basis for improving regional seasonal prediction and its interpretative application, this study employs SEAS5 historical reforecast and real-time forecast data during 1993-2023 together with precipitation observations from 83 meteorological stations in Heilongjiang Province, and uses verification methods, including the anomaly correlation coefficient (ACC) and Ps score, to systematically evaluate the model performance and its underlying causes. The results show that: (1) In terms of total precipitation, SEAS5 generally reproduces the spatial distribution consistent with observations, but the high-value center is shifted southward, interannual variability is underestimated, and a wet bias prevails over most of the province. The forecasts initialized in March exhibit higher skill than those initialized in April and May. For precipitation anomalies, the multi-year mean ACC for different initialization months shows negative skill, and only a few years have a Ps score exceeding 80 points, with March initialization showing relatively better performance. (2) The model’s ability to represent the circulation associated with the northeast cold vortex (NCV) is a key factor affecting the prediction skill of summer precipitation in Heilongjiang. In high-skill years, the model can reasonably reproduce NCV-induced circulation and corresponding precipitation anomalies, whereas in low-skill years significant biases occur in the prediction of low-level circulation over northeast China and adjacent regions. (3) The March initialization performs relatively better due to a more reasonable prediction of the spatial distribution of upper-tropospheric circulation. However, errors in the middle- and lower-tropospheric circulation still limit further improvement of skill. In addition, the predictive capability of the western Pacific subtropical high shows weak correlation with the summer precipitation skill in Heilongjiang, while the influence of NCV-related circulation is more direct and critical.

Key words: Heilongjiang summer precipitation, SEAS5, model evaluation, low-level circulation, northeast cold vortex

中图分类号:

P456

王昊, 马浩, 李永生, 刘长征, 姜雨函. SEAS5模式对黑龙江省夏季降水的预测性能评估[J]. 干旱气象, 2026, 44(1): 159-171.

WANG Hao, MA Hao, LI Yongsheng, LIU Changzheng, JIANG Yuhan. Evaluation of the predictive performance of the SEAS5 model for summer precipitation in Heilongjiang Province[J]. Journal of Arid Meteorology, 2026, 44(1): 159-171.

图/表 10

图1 黑龙江省83个常规气象站空间分布

Fig.1 Spatial distribution of 83 conventional meteorological stations in Heilongjiang Province

图2 1993—2023年黑龙江夏季降水总量观测值（a、e）与SEAS5模式Sf3（b、f）、Sf4（c、g）、Sf5（d、h）预测值的多年平均（a、b、c、d）及其均方差（e、f、g、h）的空间分布（单位：mm）

Fig.2 Spatial distribution of the multi-year average (a, b, c, d) and the mean square error (e, f, g, h) of summer total precipitation from observations (a, e) and SEAS5 forecasts of Sf3 (b, f), Sf4 (c, g), and Sf5 (d, h) in Heilongjiang during 1993-2023 (Unit: mm)

图3 1993—2023年SEAS5模式Sf3（a、d）、Sf4（b、e）、Sf5（c、f）的黑龙江夏季降水总量预测偏差（模式预测值减去观测值）的多年平均值（a、b、c）及降水总量RMSE（d、e、f）空间分布（单位：mm）（图中数字为全省平均，下同）

Fig.3 Spatial distribution of multi-year mean bias (model values minus observation) (a, b, c) and RMSE (d, e, f) of SEAS5 forecast of summer total precipitation of Sf3 (a, d), Sf4 (b, e), and Sf5 (c, f) in Heilongjiang during 1993-2023 (Unit: mm) (The number in the figure denotes the provincial mean, the same as below)

图4 1993—2023年SEAS5模式Sf3（a）、Sf4（b）、Sf5（c）的黑龙江夏季降水总量与观测值的TCC （打点区域表示通过置信水平为90%的显著性检验）

Fig.4 The TCC of summer total precipitation between observations and SEAS5 forecasts of Sf3 （a），Sf4 （b），and Sf5 （c） in Heilongjiang during 1993-2023 （the dotted area passing the significance test at 90% confidence level）

图5 1993—2023年SEAS5模式Sf3（a、b）、Sf4（c、d）、Sf5（e、f）的黑龙江夏季降水量距平百分率的ACC（a、c、e）及Ps评分（b、d、f）年际变化（黑色虚线ACC=0.20、红色虚线Ps=60、蓝色虚线Ps=80）

Fig.5 Interannual variations of ACC (a, c, e) and Ps score (b, d, f) for percentage of anomalies of summer precipitation in Heilongjiang from SEAS5 forecasts of Sf3 (a, b), Sf4 (c, d), and Sf5 (e, f) during 1993-2023 (The black dotted line represents ACC=0.20, the red dotted line represents Ps=60, and the blue dotted line represents Ps=80)

图6 1993—2023年黑龙江夏季降水偏多年中SEAS5模式Sf3高技巧年观测（a、c、e）及模式预报（b、d、f）的200 hPa纬向风场距平（a、b，单位：m·s-1）、500 hPa位势高度场（等值线）及其距平（填色）（c、d，单位：gpm）及850 hPa水平风场（箭矢，单位：m·s-1）及涡度（填色，单位：10-6 s-1）距平（e、f）的合成分析（紫色线为5 880 gpm位势高度气候态等值线，下同）

Fig.6 Composite analysis of 200 hPa zonal wind field anomalies (a, b, Unit: m·s-1), 500 hPa geopotential height field (the contour lines) and its anomalies (the color shaded) (c, d, Unit: gpm), 850 hPa horizontal wind field (arrow vectors, Unit: m·s-1) and vorticity (the color shaded, Unit: 10-6 s-1) anomalies (e, f) from observations (a, c, e) and SEAS5 Sf3 forecasts (b, d, f) for high-skill years with above-normal summer precipitation in Heilongjiang during 1993-2023 (The purple line represents the climatological 5 880 gpm geopotential height contour, the same as below)

图7 1993—2023年黑龙江夏季降水偏多年中SEAS5模式Sf3低技巧年观测（a、c、e）及模式预报（b、d、f）的200 hPa纬向风场距平（a、b，单位：m·s-1）、500 hPa位势高度场（等值线）及其距平（填色）（c、d，单位：gpm）及850 hPa水平风场（箭矢，单位：m·s-1）及涡度（填色，单位：10-6 s-1）距平（e、f）的合成分析

Fig.7 Composite analysis of 200 hPa zonal wind field anomalies (a, b, Unit: m·s-1), 500 hPa geopotential height field (the contour lines) and its anomalies (the color shaded) (c, d, Unit: gpm), 850 hPa horizontal wind field (arrow vectors, Unit: m·s-1) and vorticity (the color shaded, Unit: 10-6 s-1) anomalies (e, f) from observations (a, c, e) and SEAS5 Sf3 forecasts (b, d, f) for low-skill years with above-normal summer precipitation in Heilongjiang during 1993-2023

图8 1993—2023年黑龙江夏季降水偏少年中SEAS5模式Sf3高技巧年观测（a、c、e）及模式预报（b、d、f）的200 hPa纬向风场距平（a、b，单位：m·s-1）、500 hPa位势高度场（等值线）及其距平（填色）（c、d，单位：gpm）及850 hPa水平风场（箭矢，单位：m·s-1）及涡度（填色，单位：10-6 s-1）距平（e、f）的合成分析

Fig.8 Composite analysis of 200 hPa zonal wind field anomalies (a, b, Unit: m·s-1), 500 hPa geopotential height field (the contour lines) and its anomalies (the color shaded) (c, d, Unit: gpm), 850 hPa horizontal wind field (arrow vectors, Unit: m·s-1) and vorticity (the color shaded, Unit: 10-6 s-1) anomalies (e, f) from observations (a, c, e) and SEAS5 Sf3 forecasts (b, d, f) for high-skill years with below-normal summer precipitation in Heilongjiang during 1993-2023

图9 1993—2023年黑龙江夏季降水偏少年中SEAS5模式Sf3低技巧年观测（a、c、e）及模式预报（b、d、f）的200 hPa纬向风场距平（a、b，单位：m·s-1）、500 hPa位势高度场（等值线）及其距平（填色）（c、d，单位：gpm）及850 hPa水平风场（箭矢，单位：m·s-1）及涡度（填色，单位：10-6 s-1）距平（e、f）的合成分析

Fig.9 Composite analysis of 200 hPa zonal wind field anomalies (a, b, Unit: m·s-1), 500 hPa geopotential height field (the contour lines) and its anomalies (the color shaded) (c, d, Unit: gpm), 850 hPa horizontal wind field (arrow vectors, Unit: m·s-1) and vorticity (the color shaded, Unit: 10-6 s-1) anomalies (e, f) from observations (a, c, e) and SEAS5 Sf3 forecasts (b, d, f) for low-skill years with below-normal summer precipitation in Heilongjiang during 1993-2023

图10 1993—2023年黑龙江夏季降水偏多（a、b、c、d）、偏少（e、f、g、h）年观测（a、e）与SEAS5模式Sf3（b、f）、Sf4（c、g）、Sf5（d、h）200 hPa纬向风场距平合成（单位：m·s-1）

Fig.10 The composite anomalies of 200 hPa zonal wind field of observations (a, e) and SEAS5 forecasts of Sf3 (b, f), Sf4 (c, g), and Sf5 (d, h) for years with above-normal (a, b, c, d) and below-normal (e, f, g, h) summer precipitation in Heilongjiang during 1993-2023 (Unit: m·s-1)

参考文献 34

[1]	陈明升, 宋敏红, 梁潇云, 等, 2024. CMA-CPSv3对夏季南亚高压和西太副高的预测能力评估[J]. 高原气象, 43(5):1 138-1 151.
[2]	丁婷, 陈丽娟, 崔大海, 2015. 东北夏季降水的年代际特征及环流变化[J]. 高原气象, 34(1):220-229. DOI
[3]	顾伯辉, 郑志海, 封国林, 等, 2017. 季节预测模式对东亚夏季环流的预测能力及其对热带海洋的响应分析[J]. 大气科学, 41(1):91-105.
[4]	郭渠, 刘向文, 吴统文, 等, 2017. 基于BCC CSM模式的中国东部夏季降水预测检验及订正[J]. 大气科学, 41(1):71-90.
[5]	何慧根, 李巧萍, 吴统文, 等, 2014. 月动力延伸预测模式业务系统DERF2.0对中国气温和降水的预测性能评估[J]. 大气科学, 38(5):950-964.
[6]	何金海, 吴志伟, 祁莉, 等, 2006. 北半球环状模和东北冷涡与我国东北夏季降水关系分析[J]. 气象与环境学报, 22(1):1-5.
[7]	李海燕, 颉卫华, 吴统文, 等, 2023. 天山北坡次季节-季节尺度降水集合预测[J]. 应用气象学报, 34(1):39-51.
[8]	李维京, 1999. 1998年大气环流异常及其对中国气候异常的影响[J]. 气象, 25(4):20-25.
[9]	李永生, 张健, 于梅, 等, 2014. 2013年黑龙江省夏季洪涝灾害成因分析[J]. 气象与环境学报, 30(3):31-37.
[10]	刘长征, 杜良敏, 柯宗建, 等, 2013. 国家气候中心多模式解释应用集成预测[J]. 应用气象学报, 24(6):677-685.
[11]	刘玉莲, 李秀芬, 康恒元, 等, 2025. 1961—2023年黑龙江省多尺度干旱时空特征[J]. 干旱气象, 43(2):186-194. DOI
[12]	马浩, 于翡, 葛敬文, 等, 2022. CFSv2对2018年浙江梅雨降水多尺度预测的性能评估[J]. 气象科学, 42(5):690-702.
[13]	沈柏竹, 林中达, 陆日宇, 等, 2011. 影响东北初夏和盛夏降水年际变化的环流特征分析[J]. 中国科学:地球科学, 41(3):402-412.
[14]	孙力, 安刚, 丁立, 等, 2000. 中国东北地区夏季降水异常的气候分析[J]. 气象学报, 58(1):70-82.
[15]	孙照渤, 曹蓉, 倪东鸿, 2016. 东北夏季降水分型及其大气环流特征[J]. 大气科学学报, 39(1):18-27.
[16]	汪栩加, 郑志海, 封国林, 等, 2017. 延伸期预报中大气初值与海温边值的相对作用[J]. 气象学报, 75(1):111-122.
[17]	吴黎, 解文欢, 张有智, 等, 2022. 基于温度植被干旱指数的黑龙江省20年干旱时空特征研究[J]. 水土保持研究, 29(5):358-363.
[18]	吴遥, 唐红玉, 蒋兴文, 等, 2024. NCEP CFSv2模式对中国西南月尺度降水预测能力评估[J]. 高原山地气象研究, 44(3):67-75.
[19]	肖颖, 庞轶舒, 马振峰, 等, 2023. NCEP CFSv2模式对川渝夏季降水次季节预测技巧评估及预报偏差分析[J]. 高原气象, 42(6):1 576-1 588.
[20]	张庆云, 陶诗言, 2003. 夏季西太平洋副热带高压异常时的东亚大气环流特征[J]. 大气科学, 27(3):369-380.
[21]	赵俊虎, 熊开国, 陈丽娟, 2020. 东北夏季降水预测技巧偏低的原因探讨[J]. 大气科学, 44(5):913-934.
[22]	赵珊珊, 高歌, 黄大鹏, 等, 2017. 2004—2013年中国气象灾害损失特征分析[J]. 气象与环境学报, 33(1):101-107.
[23]	郑嘉雯, 蔡宏珂, 吴捷, 等, 2021. 三个气候系统模式对500 hPa高度场预报的检验对比分析[J]. 高原山地气象研究, 41(2):115-124.
[24]	周秀杰, 那济海, 潘华盛, 2011. 黑龙江省夏季干旱气候特征及成因分析[J]. 自然灾害学报, 20(5):131-135.
[25]	GUBLER S, SEDLMEIER K, BHEND J, et al, 2019. Assessment of ECMWF SEAS5 seasonal forecast performance over south America[J]. Weather and Forecasting, 35(2): 561-584. DOI URL
[26]	KALNAY E, KANAMITSU M, KISTLER R, et al, 1996. The NCEP/NCAR 40-year reanalysis project[J]. Bulletin of the American Meteorological Society, 77(3): 437-471. DOI URL
[27]	LIANG L Q, LI L J, LIU Q, 2011. Precipitation variability in Northeast China from 1961 to 2008[J]. Journal of Hydrology, 404(1/2): 67-76. DOI URL
[28]	LIU B, YANG K, LIU X W, et al, 2023. Improved Indian Ocean dipole seasonal prediction in the new generation of CMA prediction system[J]. Geoscience Letters, 10: 60. DOI:10.1186/s40562-023-00315-5.
[29]	LIU X W, WU T W, YANG S, et al, 2015. Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m)[J]. Advances in Atmospheric Sciences, 32(8):1 156-1 172. DOI URL
[30]	SUN Y M, SIMPSON I, WEI H L, et al, 2024. Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models[J]. Meteorological Applications, 31: e2178. DOI: 10.1002/met.2178.
[31]	TAKEMURA K, MAEDA S, YAMADA K, et al, 2023. Improved predictability of summertime rossby wave breaking frequency near Japan in JMA/MRI-CPS3 seasonal forecasts[J]. Weather and Forecasting, 38(6): 999-1 010. DOI URL
[32]	WANG B, LEE J Y, KANG I S, et al, 2009. Advance and prospectus of seasonal prediction: Assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004)[J]. Climate Dynamics, 33(1): 93-117. DOI URL
[33]	WANG Q J, SHAO Y W, SONG Y, et al, 2019. An evaluation of ECMWF SEAS5 seasonal climate forecasts for Australia using a new forecast calibration algorithm[J]. Environmental Modelling & Software, 122: 104550. DOI: 10.1016/j.envsoft.2019.104550.
[34]	WU J, REN H L, WAN J H, et al, 2024. Verification of seasonal prediction by the upgraded China multi-model ensemble prediction system (CMMEv2.0)[J]. Journal of Meteorological Research, 38(5): 880-900. DOI

SEAS5模式对黑龙江省夏季降水的预测性能评估

Evaluation of the predictive performance of the SEAS5 model for summer precipitation in Heilongjiang Province

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 34

相关文章 7

编辑推荐

Metrics

[1]	任丽, 卜文惠, 于震宇, 白俊杰, 李瑶. 一次伴有极端降水的东北冷涡过程分析[J]. 干旱气象, 2025, 43(6): 939-952.
[2]	李淑萍, 全文杰, 王正, 陈奕卓, 苏涛, 颜鹏程. BCC-CSM2-MR全球气候模式对东亚地区降水和气温的模拟评估[J]. 干旱气象, 2023, 41(6): 984-996.
[3]	马阳, 崔洋, 张雯, 李欣. 基于CMIP6模式的黄河流域宁夏段未来气温变化预估研究[J]. 干旱气象, 2023, 41(1): 43-53.
[4]	任丽, 杨艳敏. 东北冷涡底部一次MCC暴雨动力热力特征分析[J]. 干旱气象, 2021, 39(1): 65-75.
[5]	李爽，丁治英，戴萍，刘云桦，韩颖. 东北冷涡的最新研究进展[J]. 干旱气象, 2016, 34(1): 13-19.
[6]	王亚华1，何宏让1，臧增亮1，潘晓滨1，何绵忠2，刘哲辉2. GRAPES全球模式的动能谱分析[J]. 干旱气象, 2015, 33(2): 220-226.
[7]	叶更新，刘壮华，刘国禹. 一次罕见的左移超级单体风暴的特征[J]. 干旱气象, 2014, 32(3): 431-438.