The short-term convective storm forecasting (i.e., nowcasting) mainly relies on weather radar, which can resolve the 3-D structure of convective storms. With the rapid development of numerical models, modern models can produce 3-D reanalysis data, which gives atmospheric background information of convective storms. Current deep learning nowcasting models use only 2-D radar images for nowcasting and often require massive historical data for training. But, it may not be operationally feasible to collect long-term radar data to train a new model. Hence, how to establish a nowcasting model using only a small dataset has become an important issue. In addition, the existing models do not effectively use the state-of-the-art model reanalysis data, which is a shortcoming of these models. To tackle these problems, this article develops a pixelwise convolutional-recurrent neural network (Pixel-CRN) for precipitation nowcasting. It has three key designs: 1) through a concise pixelwise sampling and oversampling technique, Pixel-CRN can be trained using only a small dataset; 2) in spatial learning, Pixel-CRN embeds a spatial convolution subnet into the recurrent unit, which can input raw 3-D radar and model reanalysis data; thus, valuable atmospheric background information can be learned to assist nowcasting; and 3) in spatiotemporal learning, according to the information bottleneck principle, Pixel-CRN builds a heterogeneous encoder–decoder structure to squeeze multichannel 3-D input data into latent space and recurrently generates 30- and 60-min nowcasts. Compared with the existing deep learning nowcasting methods, the experimental results show that the Pixel-CRN can provide skillful results with a rather small training dataset.

Numerical weather prediction models inevitably exhibit systematic bias when forecasting winds. In the present era of big data, model improvements are essential. We subjected wind data to deep learning and built an efficient data-driven model. We devised the "Multi-Objective Optimization ResTrajGRU" (MOO-ResTrajGRU) model to handle the three-dimensional (3D) spatiotemporal sequences of the U- and V- components of the wind field. The model has an encoder-forecaster architecture, and a residual connection mechanism that effectively extracts spatiotemporal features at different scales. The periodic characteristics of directional data are solved by modeling the U- and V-components; an additive loss function simultaneously optimizes wind speed and wind direction accuracy. We forecast wind fields during the four seasons of the western North Pacific (WNP) at scales of 12–120 h. The forecast data were more accurate than those of other models; the multi-objective mode of our model halves the training time and storage space requirements with little effect on performance.

Precipitation nowcasting is an important operational service for protecting public property losses and people’s safety. Short-Term Ensemble Prediction System (STEPS) is a probabilistic nowcasting system which has been widely used in the research community (commonly referred as PySTEPS). This study investigates the predictability of PySTEPS during different precipitation systems, i.e., convective and stratiform events. In particular, two study domains, namely, Dallas-Fort Worth (DFW) area in northern Texas and San Francisco Bay Area in northern California, are selected to represent these two typical precipitation patterns, respectively. The experimental nowcasting results show that PySTEPS works well in both areas, especially during stratiform rainfall events in the Bay Area. In addition, PySTEPS exhibits different performance for different precipitation patterns. For convective cases in the DFW area, PySTEPS tends to underestimate rain rate for high-intensity precipitation regions. For stratiform cases in the Bay Area, PySTEPS can predict the precipitation intensity more accurately. With the increase of nowcasting lead time, the qualitative evaluation scores (POD - Probability of Detection, and CSI - Critical Success Index) of PySTEPS decrease slowly during stratiform events compared with convective events, which is also in line with the quantitative evaluation results.

Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

Convective precipitation nowcasting remains challenging due to the fast change of convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The input of the model is five consecutive radar images; the output is 30-min prediction of radar reflectivity image. The model consists of three parts: upsampling, downsampling and skip-connection. Two models, U-Net and TrajGRU, are used for comparison in the experiments. Different from U-Net, which is a CNN-based (Convolution Neural Network) model, TrajGRU is an RNN-based (Recurrent Neural Network) model, which is good at time-series processing and has been widely used in precipitation research community. The experimental results show that the CNN-based U-Net can achieve almost the same performance as TrajGRU. This result shows the great potential of CNN-based models in handling time-series applications.

Convective storm nowcasting has long been an important issue and has attracted substantial interest. 3D radar images and 3D re-analysis data contain spatiotemporal information of the convective processes. This paper proposes a multi-channel 3D convolutional recurrent neural network (3D-CRN) for convective storm nowcasting, which aims to learn spatiotemporal information directly from these 3D radar and reanalysis data. 3D-CRN is composed of two sub-networks: three multi-channel 3D convolutional networks are used as the front-end spatial sub-networks, and the convLSTM encoder-decoder is constructed as a back-end temporal sub-network. By combing two subnets into one unified network, 3D-CRN can be jointly trained effectively. In order to give forecasts at different lead time simultaneously, we construct a many-to-many encoder-decoder structure to avoid tedious need to train several models respectively. Experimental results show the effectiveness of the proposed method.

The goal of convective storm nowcasting is local prediction of severe and imminent convective storms. Here, we consider the convective storm nowcasting problem from the perspective of machine learning. First, we use a pixel-wise sampling method to construct spatiotemporal features for nowcasting, and flexibly adjust the proportions of positive and negative samples in the training set to mitigate class-imbalance issues. Second, we employ a concise two-stream convolutional neural network to extract spatial and temporal cues for nowcasting. This simplifies the network structure, reduces the training time requirement, and improves classification accuracy. The two-stream network used both radar and satellite data. In the resulting two-stream, fused convolutional neural network, some of the parameters are entered into a single-stream convolutional neural network, but it can learn the features of many data. Further, considering the relevance of classification and regression tasks, we develop a multi-task learning strategy that predicts the labels used in such tasks. We integrate two-stream multi-task learning into a single convolutional neural network. Given the compact architecture, this network is more efficient and easier to optimize than existing recurrent neural networks.

Convective storms are one of the severe weather hazards found during the warm season. Doppler weather radar is the only operational instrument that can frequently sample the detailed structure of convective storm which has a small spatial scale and short lifetime. For the challenging task of short-term convective storm forecasting (i.e., nowcasting), 3-D radar images contain information about the processes in convective storm. However, effectively extracting such information from multisource raw data has been problematic due to a lack of methodology and computation limitations. Recent advancements in deep learning techniques and graphics processing units (GPUs) now make it possible. This article investigates the feasibility and performance of an end-to-end deep learning nowcasting method. The nowcasting problem was transformed into a classification problem first, and then, a deep learning method that uses a convolutional neural network (CNN) was presented to make predictions. On the first layer of CNN, a cross-channel 3-D convolution was proposed to fuse 3-D raw data. The CNN method eliminates the handcrafted feature engineering, i.e., the process of using domain knowledge of the data to manually design features. Operationally produced historical data of the Beijing-Tianjin-Hebei region in China was used to train the nowcasting system and evaluate its performance; 3 737 332 samples were collected in the training data set. The experimental results show that the deep learning method improves nowcasting skills compared with traditional machine learning methods.

very short-term weather forecasting or nowcasting has attracted substantial attention in various fields. Existing methods can nowcast storm advection based on radar data. Due to the limitations of the radar observations, it is still challenging to nowcast storm initiation and growth. However, as the real-time re-analysis meteorological data can now provide valuable atmospheric boundary layer thermal dynamic information, which is essential to predict storm initiation and growth. It is of great importance to leverage these re-analysis data.This paper describes our first attempt to nowcast storm initiation, growth, and advection simultaneously under the framework of convolutional neural network using the very large multi-source meteorological data. To this end, we construct a multi-channel 3D-cube successive convolution network which leveraging both raw 3D radar and re-analysis data directly without any handcraft feature engineering. These data are formulated as multi-channel 3D cubes, to be fed into our network, which are convolved by cross-channel 3D convolutions. By stacking successive convolutional layers without pooling, we build an end-to-end trainable model for nowcasting. Experimental results show that deep learning methods achieve better performance than traditional extrapolation methods. The qualitative analyses of our approach show encouraging results of nowcasting of storm initiation, growth, and advection.

To automatically build from scratch the language processing component for a speech synthesis system in a new language a purified text corpora is needed where any words and phrases from other languages are clearly identified or excluded. When using found data and where there is no inherent linguistic knowledge of the language/languages contained in the data, identifying the pure data is a difficult problem. We propose an unsupervised language identification ap-proach based on Latent Dirichlet Allocation where we take the raw n-gram count as features without any smoothing, pruning or interpolation. The Latent Dirichlet Allocation topic model is reformulated for the language identification task and Collapsed Gibbs Sampling is used to train an unsupervised language iden-tification model. We show that such a model is highly capable of identifying the primary language in a corpus and filtering out other languages present.