Previous studies suggest that the zonally averaged Hadley circulation (ZAHC) has experienced a robust poleward expansion, and its trend in intensity displays inconsistency among different data sets. This study examines changes in regional HC intensity and poleward edge using six reanalyses, outgoing longwave radiation, and precipitation data sets. HCs in six regions, including Africa (AFHC), the Indian Ocean (IOHC), the western Pacific (WPHC), the eastern Pacific (EPHC), South America (SAHC), and the Atlantic (ATHC), are investigated. Intensity trends in the Northern Hemisphere (NH) WPHC and ATHC and the Southern Hemisphere (SH) EPHC and ATHC are in agreement with each other in the six reanalyses. Furthermore, regional HCs in these domains appear to be intensifying, although not all of the reanalyses show statistically significant trends. For the poleward edge, its trend in the NH AFHC, IOHC, EPHC, SAHC, and ATHC is significantly larger than zero, and the northern HC poleward edge exhibits uniform poleward migrations in these five regions. In the SH, only the trend in the SAHC poleward edge is significantly different from zero. Furthermore, the trend in the SH SAHC poleward edge is significantly larger than those in the SH AFHC, IOHC, and ATHC. The results indicate that the poleward migration of the southern ZAHC poleward edge during recent decades that has been identified by previous studies may be attributed mainly to the poleward migration of the southern SAHC poleward edge. Further analyses suggest that changes in regional HC poleward edges could have a significant impact on regional precipitation anomalies.

Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model’s ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful.

Haze pollution is a serious air quality issue in China. Previous studies over the North China Plain (NCP) mainly focused on analysing the haze during boreal winter. However, the variation in haze during spring and the related factors remain unclear. This study investigates inter-annual variation of the spring haze, which is represented by the humidity-corrected dry extinction coefficient (DEC) over the NCP. During high DEC years, pronounced positive DEC anomalies appear over the NCP and its surrounding regions. Correspondingly, a notable anticyclonic anomaly is observed over Northeast Asia, inducing significant southeasterly wind anomalies over the NCP. The anomalous southeasterly winds reduce wind speed and increase relative humidity, which provides favourable meteorological conditions for accumulation and growth of aerosol pollutants. Further analysis shows that the North Atlantic Oscillation (NAO) contributes to the formation of anticyclonic anomalies over Northeast Asia via downstream propagating atmospheric wave trains. Additionally, positive sea surface temperature (SST) anomalies over the subtropical northeastern Atlantic Ocean may also impact the DEC variation. The linear barotropic model experiment further confirms the important role of subtropical northeastern Atlantic SST anomalies in contributing to the anomalous anticyclone over Northeast Asia and southerly wind anomalies over the NCP region, which are triggered via eastwards propagating atmospheric wave trains. This study gives support to the idea that the NAO and the North Atlantic SST may be potential predictors for the frequently observed springtime NCP haze variation.

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model (ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-to-image translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks. Second, the ECMWF-IFS forecasts and ECMWF reanalysis data (ERA5) from 2005 to 2018 are used as training, validation, and testing datasets. The predictors and labels (ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations (ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.