An Innovative Bias-Correction Approach to CMA-GD Hourly QuantitativePrecipitation Forecasts

This paper proposes a simple and powerful optimal integration (OPI) method for improving hourly quantitative precipitation forecasts (QPFs, 0-24 h) of a single-model by integrating the benefits of different bias- corrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration (CMA). Three techniques are used to generate multi-method calibrated members for OPI: deep neural network (DNN), frequency-matching (FM), and optimal threat score (OTS). The results are as follows: (1) The QPF using DNN follows the basic physical patterns of CMA-GD. Despite providing superior improvements for clear-rainy and weak precipitation, DNN cannot improve the predictions for severe precipitation, while OTS can significantly strengthen these predictions. As a result, DNN and OTS are the optimal members to be incorporated into OPI. (2) Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation. Compared with the CMA-GD, OPI improves the TS by 2.5%, 5.4%, 7.8%, 8.3%, and 6.1% for QPFs from clear-rainy to rainstorms in the verification dataset. Moreover, OPI shows good stability in the test dataset. (3) It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation. Therefore, improvements in predicting severe precipitation using this method should be further realized by improving the numerical model’s forecasting capability.     

Climatology of Shear Line and Related Rainstorm over the Southern Yangtze River Valley Based on an Improved Intelligent Identification Method

Based on four reanalysis datasets including CMA-RA, ERA5, ERA-Interim, and FNL, this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear. Climatic characteristics of shear lines and related rainstorms over the Southern Yangtze River Valley (SYRV) during the summers (June-August) from 2008 to 2018 are then analyzed by using two types of unsupervised machine learning algorithm, namely the t-distributed stochastic neighbor embedding method (t-SNE) and the k-means clustering method. The results are as follows: (1) The reproducibility of the 850 hPa wind fields over the SYRV using China’s reanalysis product CMA-RA is superior to that of European and American products including ERA5, ERA-Interim, and FNL. (2) Theory and observations indicate that the introduction of a second-order zonal-wind shear criterion can effectively eliminate the continuous cyclonic curvature of the wind field and identify shear lines with significant discontinuities. (3) The occurrence frequency of shear lines appearing in the daytime and nighttime is almost equal, but the intensity and the accompanying rainstorm have a clear diurnal variation: they are significantly stronger during daytime than those at nighttime. (4) Half (47%) of the shear lines can cause short-duration rainstorms (≥20 mm (3h)-1 ), and shear line rainstorms account for one-sixth (16%) of the total summer short-duration rainstorms. Rainstorms caused by shear lines are significantly stronger than that caused by other synoptic forcing. (5) Under the influence of stronger water vapor transport and barotropic instability, shear lines and related rainstorms in the north and middle of the SYRV are stronger than those in the south.     

中国江南地区降水异常的年际变化与北极海冰密集度异常的相关分析及其机理研究

本文选取最近二十年(1998—2017年)中国江南地区降水和北极海冰为研究对象,分析了年际时间尺度上中国江南地区降水异常与北极海冰异常的关系,并进一步通过研究关键区附近罗斯贝长波(Rossby长波)的波射线路径和中国中纬度地区波作用通量的传播异常,揭示了北极海冰通过Rossby波大气桥的调制作用影响江南地区降水的可能机制。结果表明:夏、秋季东西伯利亚海与巴芬湾的海冰密集度异常与次年中国江南地区降水异常存在显著的正相关关系。其影响机制是:关键海区海冰异常和北极放大能够激发Rossby波列型遥相关从北极关键区向中国中纬度地区传播,而关键区附近的Rossby波扰动能量在夏、秋季比冬、春季更容易传播至中国中纬度地区,并最终通过上下游遥相关效应引发中纬度副热带西风急流异常和急流入口区右侧江南地区的环流和降水异常,当海冰偏多时西风急流加强,江南地区对流不稳定发展,降水偏多。