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Abstract. Quantitative precipitation nowcasting (QPN) may assist to mitigate the tremendous socioeconomic harm caused by severe weather. Because of the rapid atmospheric variability, the QPN has been a challenging problem to solve. Recent QPN research has presented data-driven models that make use of deep learning (DL) and ground weather radar. Previous research has mostly concentrated on constructing DL models, while other elements for DL-QPN have received less attention. This research looks at four crucial aspects in the DL-QPN and their impact on predicting performance. The prediction strategy (single, recursive, and multiple predictions), deep learning model (U-Net and convolutional long short term memory; ConvLSTM), input past sequence length (60 and 120 min), and output future sequence length were the four key factors (60 and 120 min). Using weather radar data from South Korea, twelve schemes have been developed to assess the influence of each factor. A long-term operational study for 2018–2020 was conducted, and a summer high rainfall event was examined to investigate the extreme case. In both situations, U-Net outperformed the critical score index (CSI) using a multiple prediction design. While ConvLSTM did not show a definite CSI difference across input sequence length, U-Net performed better with shorter input sequences (i.e., 60 min) than with longer input sequences (i.e., 120 min). The length of future sequences has little influence on model performance. As the lead time increased, all of the DL-QPN schemes showed underestimation and blurry outcomes. U-Net was shown to be significantly reliant on the most recent input time (i.e., 0 previous minutes) in sensitivity analysis, while ConvLSTM was more responsive to multiple time steps. This work may give a modeling technique and contingency plan for future DL-QPN employing weather radar data by explicitly comparing critical elements.

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<p>The accurate forecasting of the intensity of tropical cyclones (TCs) is able to effectively reduce the overall costs of disaster management. In this study, we proposed a deep learning-based model for TC forecasting with the lead time of 24, 48, and72 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 268 TCs which developed in the Northwest Pacific from 2011 to 2019 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of TCs, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract atmosphere and ocean forecasting data. In this study, we suggested hybrid convolutional neural network (hybrid-CNN)-based TC forecasting models. It enables to efficiently consider not only the physical but also the spatial characteristics of variables. The Joint Typhoon Warning Center (JTWC) was used for validating the suggested model, and Korea Meteorological Administrator (KMA)-based operational TC predictions were utilized for evaluating the performance of the model. A hybrid-CNN-based prediction model obtained mean absolute errors (MAE) of 13.58, 16.48, and 21.64 kts and skill scores (SS) of 29%, 19%, and 1.6% for 24h, 48h, and 72h forecasts, respectively. Since the rapid intensification (RI) is one of the challenging tasks in the TC intensity prediction, the performance of suggested model for all RIs in 2019 were additionally evaluated. Compared to KMA-based predictions, the suggested models achieved average SS of 66%. Furthermore, using an explainable artificial intelligence (XAI) approach, it is possible to verify how the suggested model works for forecasting TC intensity and propose the feasibility of the suggested model in the meteorology field.</p><p> </p>

Tropical cyclones (TCs) are destructive natural disasters. Accurate prediction and monitoring are important to mitigate the effects of natural disasters. Although remarkable efforts have been made to understand TCs, operational monitoring information still depends on the experience and knowledge of forecasters. In this study, a fully automated geostationary-satellite-based TC center estimation approach is proposed. The proposed approach consists of two improved methods: the setting of regions of interest (ROI) using a score matrix (SCM) and a TC center determination method using an enhanced logarithmic spiral band (LSB) and SCM. The former enables prescreening of the regions that may be misidentified as TC centers during the ROI setting step, and the latter contributes to the determination of an accurate TC center, considering the size and length of the TC rainband in relation to its intensity. Two schemes, schemes A and B, were examined depending on whether the forecasting data or real-time observations were used to determine the initial guess of the TC centers. For each scheme, two models were evaluated to discern whether SCM was combined with LSB for TC center determination. The results were investigated based on TC intensity and phase to determine the impact of TC structural characteristics on TC center determination. While both proposed models improved the detection performance over the existing approach, the best-performing model (i.e., LSB combined with SCM) achieved skill scores (SSs) of +17.4% and +20.8% for the two schemes. In particular, the model resulted in a significant improvement for strong TCs (categories 4 and 5), with SSs of +47.8% and +72.8% and +41.2% and +72.3% for schemes A and B, respectively. The research findings provide an improved understanding of the intensity- and phase-wise spatial characteristics of TCs, which contributes to objective TC center estimation.