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 CMARA 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.

An Innovative Bias-Correction Approach to CMA-GD Hourly Quantitative Precipitation 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 biascorrected 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.

A Recombination Clustering Technique for Forecasting of Tropical Cyclone Tracks Based on the CMA-TRAMS Ensemble Prediction System

Despite marked improvements in tropical cyclone(TC) track ensemble forecasting,forecasters still have difficulty in making quick decisions when facing multiple potential predictions,so it is demanding to develop post-processing techniques reducing the uncertainty in TC track forecasts,and one of such techniques is the cluster-based methods.To improve the effect and efficiency of the previous cluster-based methods,this study adopts recombination clustering(RC) by optimizing the use of limited TC variables and constructing better features that can accurately capture the good TC track forecasts from the ensemble prediction system(EPS) of the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea(CMA-TRAMS).The RC technique is further optimized by constraining the number of clusters using the absolute track bias between the ensemble mean(EM) and ensemble spread(ES).Finally,the RC-based deterministic and weighted probabilistic forecasts are compared with the TC track forecasts from traditional methods.It is found that(1) for deterministic TC track forecasts,the RC-based TC track forecasts outperform all other methods at 12–72-h lead times;compared with the skillful EM(118.6 km),the improvements introduced by the use of RC reach up to 10.8%(8.1 km),10.2%(13.7 km),and 8.7%(20.5 km) at forecast times of 24,48,and 72 h,respectively.(2) For probabilistic TC track forecasts,RC yields significantly more accurate and discriminative forecasts than traditional equal-weight track forecasts,by increasing the weight of the best cluster,with a decrease of 4.1% in brier score(BS) and an increase of 1.4% in area under the relative operating characteristic curve(AUC).(3) In particular,for cases with recurved tracks,such as typhoons Saudel(2017) and Bavi(2008),RC significantly reduces track errors relative to EM by 56.0%(125.5 km) and 77.7%(192.2 km),respectively.Our results demonstrate that the RC technique not only improves TC track forecasts but also helps to unravel skillful ensemble members,and is likely useful for feature construction in machine learning.