Precipitation is the primary driver of the regional/global hydrological cycle. Accurate precipitation estimation is essential for disaster management and climate change studies. Geostationary meteorological satellites provide a wealth of temporal and spatial information for quantitative precipitation estimation (QPE), but how to extract useful precipitation-related information from raw satellite data has always been a challenge. As a result, there is still a gap between the current satellite precipitation products and ground-based precipitation observations. Recent advancements in deep learning (DL) techniques now allow the development of more accurate QPE algorithms. This letter proposes a PREcipitation estimation algorithm based on Multi-task learning (PREM) using satellite observations extracted from the Geostationary Operational Environmental Satellite-R (GOES-R) series. The key design points of PREM are as follows: 1) the detection (rain/no-rain separation) and rainfall rate estimation are integrated in PREM; 2) infrared (IR) radiance data are the main input to PREM, and ground-based radar precipitation products are used as the target in the training phase; and 3) in addition, PREM integrates lightning data (called PREM-L) to investigate its impact on heavy precipitation estimation. Independent verification shows that PREM performs better than the operational GOES-R products, and PREM-L outperforms PREM when precipitation is heavy.

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.

The correct distinction between precipitation echo (PE) and non-precipitation echo (NPE) of weather radar is one of the key steps in the quantitative precipitation estimation (QPE). Based on the high spatiotemporal resolution data of the Himawari-8 satellite and the precipitation data of the rain gauge, this paper proposes a fuzzy logic algorithm (FLA) to remove NPE in weather radar data through statistically analyzing the probability density distribution of the satellite temperature of black body (TBB) according to different precipitation scenarios. Two methods, i.e. the FLA and the threshold method (TM), are compared and analyzed by using the data in August 2019. The critical success index (CSI) score of the FLA is 0.056 higher than that of the TM (0.796 vs. 0.74), and the probability of detection (POD) score is 0.058 higher than that of the TM (0.802 vs. 0.744). The results show that the proposed method can effectively improve the quality of weather radar data.

Detection of convective initiation (CI) is an important step of early warning of strong convective weather. This study proposes a machine learning approach for CI detection using 18 interesting fields extracted from weather radar, Himawari-8 Advanced Himawari Imager (AHI), and the variational Doppler radar analysis system (VDRAS). The collected features are used to train the machine learning model for CI detection. Therein, the support vector machine (SVM) is used to identify the CI and non-CI. It is concluded that a better result can be achieved by using multiple-source data compared to using satellite data only. The atmospheric boundary layer thermal dynamic information retrieved by VDRAS is proved to be useful for CI detection.

Radar quantitative precipitation estimation (QPE) is one of important applications of Doppler weather radar. The use of the rain gauge data to correct and improve the accuracy of radar QPE is important for weather forecasting. Based on observation data of rain gauges, this study proposes two machine learning methods to correct radar QPE product. The Support Vector Regression (SVR) and Random Forest (RF) models are used as the regression model. The rain gauge data is used as the ground truth. The radar QPE data is divided into three intervals: 0–10 mm/h, 10–30 mm/h, and> 30 mm/h. One traditional method, i.e., the PDF method, is selected for comparison. The experimental results show that the machine learning methods achieve better performance than the PDF method. The scatter plot shows that SVR effectively mitigate the overestimation problem which is a main problem of the original radar QPE product.

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.