A hybrid statistical‐dynamical prediction scheme for summer monthly precipitation over Northeast China

Abstract

To improve the seasonal prediction of monthly precipitation in summer over Northeast China (NEC), a hybrid prediction scheme is developed to combine the advantages of statistical method with dynamical prediction information from 4 coupled general climate models (CGCMs). As the operational prediction of summer climate is performed in March or earlier, the information of CGCMs employed in this study is from the hindcast/prediction in February for June and July, and March for August. Predictors comprise observations of the preceding winter sea surface temperature and simultaneous information derived from the CGCMs. Stability and multicollinearity are fully considered in predictor selection to avoid over-fitting. For a single model, the monthly precipitation reconstructed from the predicted time series and the observed spatial load of the leading Empirical Orthogonal Function (EOF) modes with accumulative explained covariance more than 85%. For the multi-model ensemble (MME), the predictions with the lowest correlation coefficient were removed until the performance of the MME was optimal for the crossvalidation period. This new ensemble method shows some advantages compared to a traditional MME method. The leave-one-out cross-validation for 1982-2010 and the independent validation for 2011-2016 both indicate an improvement of the new hybrid scheme in seasonal predictions of summer monthly precipitation over NEC. The observed and predicted monthly precipitation are significantly correlated with coefficients of 0.71, 0.49, and 0.73, and their hit rates are 77%, 66%, and 77% for June, July, and August precipitation, respectively for the MME over the period 1982-2016.

Hybrid forecasting: blending climate predictions with AI models

“…Ravuri et al, 2021;Neri et al, 2019), and geographical domains (from point to street level; from a single river catchment through to global approaches). Hybrid models have been applied to predict a variety of hydrometeorological variables, including extreme heat and precipitation (Najafi et al, 2021;Miao et al, 2019;Ma et al, 2022), seasonal climate variables (Golian et al, 2022;Baker et al, 2020), tropical cyclones/hurricanes (Vecchi et al, 2011;Murakami et al, 2016;Kang and Elsner, 2020;Klotzbach et al, 2020), streamflow (Wood and Schaake, 2008;Mendoza et al, 2017;Rasouli et al, 2012;Duan et al, 2020), flooding (Slater and Villarini, 2018), drought (Madadgar et al, 2016;Wu et al, 2022), sea level (Khouakhi et al, 2019), and reservoir levels (Tian et al, 2022), over a range of timescales (Table 2). Certain other examples discussed in this review are not fully hybrid (e.g.…”

Improving seasonal prediction of summer rainfall over southern China using the BCC_CSM1.1m model‐circulation increment‐based dynamic statistical technique

“…The shortcomings of dynamic model predictions are often made up for by dynamic statistical techniques, not only for seasonal predictions (e.g., Dai & Fan, 2021; Kang et al, 2014; Liu et al, 2021; Liu & Fan, 2014; Liu & Ren, 2015; Ma et al, 2022; Wang et al, 2022; Zhu et al, 2023) but also for subseasonal predictions (Wu et al, 2022). These models primarily rely on the statistical relationships and transfer‐functions between historical observations and model outputs, and then construct hybrid dynamic statistical models by employing the predictors of observed previous external forcings and model‐predicted simultaneous atmospheric factors, which have been shown to generate considerable improvements in precipitation and temperature predictions in China.…”

Improving Predictions of Tibetan Plateau Summer Precipitation Using a Sea Surface Temperature Analog-Based Correction Method

“…On the other hand, various statistical or empirical correction methods have been developed in the past decades, aiming to improve model predictions [41][42][43][44][45][46][47][48][49][50][51]. Model prediction errors are flow-dependent, which can vary with changing climate states, and they have been found to be correlated to physical predictors [52].…”