Precipitation has an important impact on people’s daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Furthermore, existing rainfall nowcasting methods based on machine learning and deep learning cannot provide large-area rainfall nowcasting with high spatiotemporal resolution. This paper proposes a dual-input dual-encoder recurrent neural network, namely Rainfall Nowcasting Network (RN-Net), to solve this problem. It takes the past grid rainfall data interpolated by automatic weather stations and doppler radar mosaic data as input data, and then forecasts the grid rainfall data for the next 2 h. We conduct experiments on the Southeastern China dataset. With a threshold of 0.25 mm, the RN-Net’s rainfall nowcasting threat scores have reached 0.523, 0.503, and 0.435 within 0.5 h, 1 h, and 2 h. Compared with the Weather Research and Forecasting model rainfall nowcasting, the threat scores have been increased by nearly four times, three times, and three times, respectively.
Reliable near-real-time precipitation estimation is crucial for scientific research and resistance to natural disasters such as floods. Compared with ground-based precipitation measurements, satellite-based precipitation measurements have great advantages, but precipitation estimation based on satellite is still a challenging issue. In this paper, we propose a deep learning model named Attention-Unet for precipitation estimation. The model utilizes the high temporal, spatial and spectral resolution data of the FY4A satellite to improve the accuracy of precipitation estimation. To evaluate the effectiveness of the proposed model, we compare it with operational near-real-time satellite-based precipitation products and deep learning models which proved to be effective in precipitation estimation. We use classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR), Critical success index (CSI), and regression metrics including Root Mean Square Error (RMSE) and Pearson correlation coefficient (CC) to evaluate the performance of precipitation identification and precipitation amounts estimation, respectively. Furthermore, we select an extreme precipitation event to validate the generalization ability of our proposed model. Statistics and visualizations of the experimental results show the proposed model has better performance than operational precipitation products and baseline deep learning models in both precipitation identification and precipitation amounts estimation. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. This study suggests that applying an appropriate deep learning algorithm may provide an opportunity to improve the quality of satellite-based precipitation products.
Multi-source meteorological data can reflect the development process of single meteorological elements from different angles. Making full use of multi-source meteorological data is an effective method to improve the performance of weather nowcasting. For precipitation nowcasting, this paper proposes a novel multi-input multi-output recurrent neural network model based on multimodal fusion and spatiotemporal prediction, named MFSP-Net. It uses precipitation grid data, radar echo data, and reanalysis data as input data and simultaneously realizes 0–4 h precipitation amount nowcasting and precipitation intensity nowcasting. MFSP-Net can perform the spatiotemporal-scale fusion of the three sources of input data while retaining the spatiotemporal information flow of them. The multi-task learning strategy is used to train the network. We conduct experiments on the dataset of Southeast China, and the results show that MFSP-Net comprehensively improves the performance of the nowcasting of precipitation amounts. For precipitation intensity nowcasting, MFSP-Net has obvious advantages in heavy precipitation nowcasting and the middle and late stages of nowcasting.
The spatial resolution of precipitation predicted by general circulation models is too coarse to meet current research and operational needs. Downscaling is one way to provide finer resolution data at local scales. The single-image super-resolution method in the computer vision field has made great strides lately and has been applied in various fields. In this article, we propose a novel reference-based and gradient-guided deep learning model (RBGGM) to downscale daily precipitation considering the discontinuity of precipitation and ill-posed nature of downscaling. Global Precipitation Measurement Mission (GPM) precipitation data, variables in ERA5 re-analysis data, and topographic data are selected to perform the downscaling, and a residual dense attention block is constructed to extract features of them. By exploring the discontinuous feature of precipitation, we introduce gradient feature to reconstruct precipitation distribution. We also extract the feature of high-resolution monthly precipitation as a reference feature to resolve the ill-posed nature of downscaling. Extensive experimental results on benchmark data sets demonstrate that our proposed model performs better than other baseline methods. Furthermore, we construct a daily precipitation downscaling data set based on GPM precipitation data, ERA5 re-analysis data and topographic data.
Numerical weather prediction (NWP) provides the future state of the atmosphere and is a major tool for weather forecasting. However, NWP has inevitable errors and requires bias correction to obtain more accurate forecasts. NWP is based on discrete numerical calculations, which inevitably result in a loss in resolution, and downscaling provides important support for obtaining detailed weather forecasts. In this paper, based on the spatio-temporal modeling approach, the Spatio-Temporal Transformer U-Net (ST-UNet) is constructed based on the U-net framework using the swin transformer and convolution to perform bias correction and temporal downscaling. The encoder part extracts features from the multi-time forecasts, and the decoder part uses the features from the encoder part and the constructed query vector for feature reconstruction. Besides, the query builder block generates different query vectors to accomplish different tasks. Multi-time bias correction was conducted for the 2-m temperature and the 10-m wind component. The results showed that the deep learning model significantly outperformed the anomaly numerical correction with observations, and ST-UNet also outperformed the U-Net model for single-time bias correction and the 3-dimensional U-Net (3D-UNet) model for multi-time bias correction. Forecasts from ST-UNet obtained the smallest root mean square error and the largest accuracy and correlation coefficient on both the 2-m temperature and 10-m wind component experiments. Meanwhile, temporal downscaling was performed to obtain hourly forecasts based on ST-UNet, which increased the temporal resolution and reduced the root mean square error by 0.78 compared to the original forecasts. Therefore, our proposed model can be applied to both bias correction and temporal downscaling tasks and achieve good accuracy.
Bladder cancer (BC) is the most common urologic neoplasms with a high rate of recurrence. The development of BC is a complex process, resulting from somke, drugs or bladder inflammation. However, the uinderlying mechanisms of BC tumorigenesis remained unclear. In this study, we performed RNA sequencing of three paired para-tumor and tumor tissuses from BC patients. Compared with para-tumor tissuses, BC tissues displayed 1136 upregulated and 1199 downregualted lncRNAs, and 1347 upregulated and 953 downregualted genes. Additionally, the common upregulated genes were mainly enriched in SNARE interactions, amino sugar and nucleotide sugar metabolism and antigen processing pathways. The common downregulated genes were mainly focused amino acid metabolism and drug meabolism signalings. More importantly, cytokine related genes were highly expressed in BC, which suggest that the dysregulation of immunoregulatory process and amino acid metabolism played a crucial role in the oncogenesis of BC.
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