北半球夏季热带季节内振荡影响我国夏季降水的规律和预测方法

利用1979—2018年夏季逐日观测和再分析数据，对北半球夏季热带季节内振荡影响我国夏季降水的规律和预测方法开展了研究。首先，利用非传统滤波即异常相对倾向（Anomalous Relative Tendency，ART）方法获取了气象要素的次季节变化分量，并采用EOF分析方法提取了北半球夏季热带主要季节内振荡信号，结果表明向外长波辐射（Outgoing Longwave Radiation，OLR）异常相对倾向EOF前两个模态共同反映了北半球夏季起源于印度洋并向东和向北传播的、具有30~60 d周期的季节内振荡（Boreal Summer Intraseasonal Oscillation，BSISO）信号。回归分析表明，该季节内振荡信号能够导致当地及其北面地区低层风场和位势高度场异常，影响该地区及其北面地区的水汽辐合辐散，从而能引起我国尤其是我国南方地区季节内旱涝变化，并一定程度上反映了我国异常雨带的向北推进过程。而后，将提取的热带主要季节内振荡信号作为预测因子，将降水异常相对倾向作为先行预板对象，利用多元线性回归方法构建了我国夏季旬降水异常相对倾向的预报模型，将预报的旬降水异常相对倾向加上观测已知的降水近期背景距平，从而得到旬降水距平的预报结果。通过历史回报和交叉检验，评估了该模型对梅雨期我国江淮流域降水（包括2020年梅汛期异常降水）的次季节预测能力。

Regularity and prediction method of the influence of boreal summer tropical intraseasonal oscillation on summer rainfall of China

Based on the daily observational and reanalysis data for 1979-2018, regularity and prediction method of the influence of boreal summer tropical intraseasonal oscillations on summer rainfall over China were investigated. The Anomalous Relative Tendency (ART) method was used to extract the subseasonal components of meteorological elements, then the EOF analysis was used to extract main boreal summer intraseasonal oscillations in the tropics. Results show that the first two EOF modes of the ART for OLR are orthogonal in time and space, which reflect the Boreal Summer Intraseasonal Oscillation (BSISO). Originated from Indian Ocean, the BSISO travels eastward as well as northward with a period of about 30-60 days. The results of regression analysis show that the BSISO leads to anomalies in the low-level wind field and geopotential height field in the local and its north area, consequently affects the convergences and divergences of water vapor in the local and its north area, which as a result, have impacts on the intraseasonal droughts and floods over China, especially the southern China, and to some extent reflect the northward propagation of anomalous rain belt in China. On the basis of the regularity, a subseasonal forecast model of summer precipitation over China has been constructed with the prediction of precipitation anomaly converted into the prediction of ART for precipitation by linear regression using the BSISO captured. The forecast of intraseasonal anomalous precipitation is output by combining the predicted ART for precipitation with observed recent background anomaly for precipitation. Through hindcast and cross validation, the intraseasonal rainfall skills of subseasonal forecast models have been evaluated in the Yangtze-Huaihe region during the Meiyu period (including the record-breaking Meiyu rainfall around the Yangtze River in 2020).