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范可, 田宝强, 戴海霞. 2024. 中国夏季月际—季节平均降水动力和统计结合实时预测模型[J]. 大气科学, 48(1): 359−375. doi: 10.3878/j.issn.1006-9895.2308.23312
引用本文: 范可, 田宝强, 戴海霞. 2024. 中国夏季月际—季节平均降水动力和统计结合实时预测模型[J]. 大气科学, 48(1): 359−375. doi: 10.3878/j.issn.1006-9895.2308.23312
FAN Ke, TIAN Baoqiang, DAI Haixia. 2024. Hybrid Downscaling Models for Real-Time Predictions of Summer Precipitation in China on a Monthly–Seasonal Scale [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 48(1): 359−375. doi: 10.3878/j.issn.1006-9895.2308.23312
Citation: FAN Ke, TIAN Baoqiang, DAI Haixia. 2024. Hybrid Downscaling Models for Real-Time Predictions of Summer Precipitation in China on a Monthly–Seasonal Scale [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 48(1): 359−375. doi: 10.3878/j.issn.1006-9895.2308.23312

中国夏季月际—季节平均降水动力和统计结合实时预测模型

Hybrid Downscaling Models for Real-Time Predictions of Summer Precipitation in China on a Monthly–Seasonal Scale

  • 摘要: 中国夏季降水大幅度月际尺度变化往往造成极端旱涝事件交替或转折,但其月际异常会被季节平均掩盖,影响季节尺度气候预测准确度,因此亟需考虑月际气候预测,提升月际—季节尺度气候预测准确度。本文首先采用年际增量和场信息耦合型预测方法研制中国夏季6~8月月际尺度降水动力和统计结合气候预测模型,之后根据月际尺度降水预测,开展季节平均降水预测。首先,基于前期观测信息和美国第二代气候预测系统(CFSv2)预测结果,选取前期12月观测的南太平洋中高纬关键区海温、1月北极关键区海冰密集度以及CFSv2 预测系统2月起报的夏季同期关键区海温作为月际尺度降水预测因子,分别研制以上具有物理意义的单预测因子预测模型,并采用奇异值分解(SVD)误差订正方法对其改进;之后,利用多因子择优集合方案,研制预测效能较高且稳定的中国160站夏季月际尺度降水动力和统计结合预测模型,进而基于月际尺度预测开展夏季季节平均气候预测。1983~2022年夏季(6~8月)中国160站逐月降水预测模型的交叉检验结果表明:逐月回报与观测降水距平百分率的时间相关系数通过90%置信水平的站点占比分别为90%,88%,82%,多年平均的空间相关系数分别为0.39、0.40和0.39,均通过99%置信水平。针对2020~2022年连续三年同样拉尼娜背景下但不同中国夏季降水形势,开展月际—季节独立回报检验,其结果显示,2020~2022年6、7、8月预测降水距平百分率的趋势异常综合检验(PS)平均分分别为75、75和70分;夏季季节平均降水的PS评分分别为72、76和73分,均高于多年业务预测平均分。由此,考虑月际异常开展季节尺度气候预测是提升月际—季节尺度气候预测准确度的一个有效途径。

     

    Abstract: Large intermonth variations in summer precipitation tend to cause alternations or transitions of extreme drought and flood in China; however, seasonal averages may cover alternations on a monthly scale and influence prediction skills on a seasonal scale. Therefore, improving monthly climate forecasts is imperative, contributing to prediction enhancement on the seasonal scale. This study focuses on real-time predictions of monthly precipitation at 160 stations in China during the summer season (June, July, and August) using the year-to-year increment method and field information coupled pattern method, and seasonal precipitation is calculated using monthly predictions. Information from preceding observations and simultaneous predictions from the second version of the Climate Forecast System (CFSv2) are considered. Consequently, the observed sea surface temperature (SST) over the mid-high latitude of the South Pacific in December, the observed sea ice concentration in the critical region of the Arctic in January, and the simultaneous SST from CFSv2 released in February are selected as predictors to develop the downscaling model. First, prediction models based on individual predictors are established to evaluate the prediction skills of different predictors, and subsequently, the singular value decomposition error correction method is employed to reduce errors in downscaling models. Additionally, the optimized ensemble scheme is utilized to synthesize hybrid downscaling models for summer precipitation over China on monthly scale with higher stability, and further seasonal prediction is conducted with results on monthly scale. The re-forecast results during the period 1983−2022 showed that the hybrid downscaling models derived from the optimized ensemble scheme exhibit comprehensive prediction skills compared with single-predictor models. The percentages of stations, at which the time anomaly correlation coefficients of re-forecast results are larger than the 90% confidence level, count for 90%, 88%, and 82% respectively for June, July, and August. The mean values of the spatial anomaly correlation coefficients are 0.39, 0.40, and 0.39, respectively, passing the 99% confidence level. In terms of real-time prediction, the hybrid downscaling models perform well on the monthly and seasonal scales during 2020–2022, when summer precipitation situations are anomalous and different from each other under similar La Niña events. The averaged PS scores of real-time predictions are 75, 75, and 70 for precipitation in June, July, and August, respectively. The PS scores for summer precipitation derived from monthly predictions are 72, 76, and 73 for 2020, 2021 and 2022, which are higher than the multiyear-averaged PS scores of real-time forecasts. These results reveal that seasonal predictions derived from effective monthly forecasts would improve the prediction skills of climate predictions on the monthly and seasonal scales.

     

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