No abstract

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.

Touch is a primary nonverbal communication channel used to communicate emotions or other social messages. A variety of social touch exists including hugging, rubbing and punching. Despite its importance, this channel is still very little explored in the affective computing field, as much more focus has been placed on visual and aural channels. In this paper, we investigate the possibility to automatically discriminate between different social touch types. We propose five distinct feature sets for describing touch behaviours captured by a grid of pressure sensors. These features are then combined together by using the Random Forest and Boosting methods for categorizing the touch gesture type. The proposed methods were evaluated on both the HAART (7 gesture types over different surfaces) and the CoST (14 gesture types over the same surface) datasets made available by the Social Touch Gesture Challenge 2015. Well above chance level performances were achieved with a 67% accuracy for the HAART and 59% for the CoST testing datasets respectively.

Summary In recent years, researchers have been trying to detect human emotions from recorded brain signals such as electroencephalogram (EEG) signals. However, due to the high levels of noise from the EEG recordings, a single feature alone cannot achieve good performance. A combination of distinct features is the key for automatic emotion detection. In this paper, we present a hybrid dimension feature reduction scheme using a total of 14 different features extracted from EEG recordings. The scheme combines these distinct features in the feature space using both supervised and unsupervised feature selection processes. Maximum Relevance Minimum Redundancy (mRMR) is applied to re‐order the combined features into max‐relevance with the labels and min‐redundancy of each feature. The generated features are further reduced with principal component analysis (PCA) for extracting the principal components. Experimental results show that the proposed work outperforms the state‐of‐art methods using the same settings in the publicly available DEAP data set.

This study investigates the roles of atmospheric moisture transport under the influence of topography for summer extreme precipitation over North China (NC) during 1979-2016. Based on rain gauge precipitation data and a reanalysis, 38 extreme precipitation days in NC during the 38 years were selected and associated moisture fluxes estimated. The results show that there is a dominant moisture influx of 311.8 kg m −1 s −1 into NC along its southern boundary from tropical oceans, and a secondary influx of 107.9 kg m −1 s −1 across its western boundary carried by mid-latitude westerlies. The outflux across the eastern boundary is 206.9 kg m −1 s −1 and across the northern boundary is 76.0 kg m −1 s −1 , giving a net moisture gain over NC of 136.8 kg m −1 s −1. During extreme precipitation days, the moisture flux convergence (MFC) was much larger, exceeding 4 × 10 −5 kg m −1 s −1. The MFC maximum core, the pronounced moisture transport, and the striking extreme precipitation zone over NC are all anchored to the east of the steep slopes of the surrounding topography. Moreover, a remarkably high humidity and strong upward motion also occur near steep slopes, indicating the critical role of the adjacent topography on the extreme precipitations. Simulations with and without the topography in NC using the Weather and Research Forecasting model for six selected out of the 38 extreme precipitation days demonstrate that the surrounding topography reinforces the MFC over NC by 16% relative to the case without terrain, primarily through enhanced wind convergence and higher moisture content, as well as stronger vertical motion induced by diabatic heating. The interactions between moisture convergence and topographic settings strengthen the extreme precipitation over NC.