Effective training of Deep Neural Networks requires massive amounts of data and compute. As a result, longer times are needed to train complex models requiring large datasets, which can severely limit research on model development and the exploitation of all available data. In this paper, this problem is investigated in the context of precipitation nowcasting, a term used to describe highly detailed short-term forecasts of precipitation and other hazardous weather. Convolutional Neural Networks (CNNs) are a powerful class of models that are well-suited for this task; however, the high resolution input weather imagery combined with model complexity required to process this data makes training CNNs to solve this task time consuming. To address this issue, a data-parallel model is implemented where a CNN is replicated across multiple compute nodes and the training batches are distributed across multiple nodes. By leveraging multiple GPUs, we show that the training time for a given nowcasting model architecture can be reduced from 59 hours to just over 1 hour. This will allow for faster iterations for improving CNN architectures and will facilitate future advancement in the area of nowcasting.
High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dramatic promise in many domains, including the geosciences, we present an application of DL to the problem of precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1 hour) predictions of precipitation. We treat forecasting as an image-to-image translation problem and leverage the power of the ubiquitous UNET convolutional neural network. We find this performs favorably when compared to three commonl…
How Effective Are Time-Series Models for Precipitation Nowcasting? A Comprehensive Benchmark for GNSS-based Precipitation Nowcasting
Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科技创新园）；Wuhan University of Technology（武汉理工大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；Interdisciplinary Artificial Intelligence Research Institute, Wuhan College（武汉学院交叉人工智能研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai（上海人工智能实验室）；School of Earth and Space Science and Technology, Wuhan University（武汉大学地球空间科学与技术学院）；School of Water Resources and Hydropower Engineering, Wuhan University（武汉大学水利水电学院）；GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）
Meteopress s.r.o（Meteopress有限责任公司）；Faculty of Information Technology, Czech Technical University in Prague（捷克技术大学布拉格信息技术学院）
Deep learning methods dominate short-term high-resolution precipitation nowcasting in terms of prediction error. However, their operational usability is limited by difficulties explaining dynamics behind the predictions, which are smoothed out and missing the high-frequency features due to optimizing for mean error loss functions. We experiment with hand-engineering of the advection-diffusion differential equation into a PhyCell to introduce more accurate physical prior to a PhyDNet model that disentangles physical and residual dynamics. Results indicate that while PhyCell can learn the intend…
Microsoft Corporation（微软公司）；Microsoft Turing（微软图灵）
We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours. By stacking a large input receptive field along the feature dimension and conditioning the model's forecaster with predictions from the physics-based High Resolution Rapid Refresh (HRRR) model, we are able to outperform optical flow and HRRR baselines by 20-25% on multiple me…
Designing early warning systems for harsh weather and its effects, such as urban flooding or landslides, requires accurate short-term forecasts (nowcasts) of precipitation. Nowcasting is a significant task with several environmental applications, such as agricultural management or increasing flight safety. In this study, we investigate the use of a UNet core-model and its extension for precipitation nowcasting in western Europe for up to 3 hours ahead. In particular, we propose the Weather Fusion UNet (WF-UNet) model, which utilizes the Core 3D-UNet model and integrates precipitation and wind …
Accurate and timely estimation of precipitation is critical for issuing hazard warnings (e.g., for flash floods or landslides). Current remotely sensed precipitation products have a few hours of latency, associated with the acquisition and processing of satellite data. By applying a robust nowcasting system to these products, it is (in principle) possible to reduce this latency and improve their applicability, value, and impact. However, the development of such a system is complicated by the chaotic nature of the atmosphere, and the consequent rapid changes that can occur in the structures of …
Institut de Recherche Technologique Saint-Exupéry（圣埃克苏佩里技术研究院）；Institut de Recherche en Informatique de Toulouse（图卢兹计算机科学研究院）；Météo-France（法国气象局）；Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique（欧洲科学计算高级研究与培训中心）；Climate, Environment, Coupling and Uncertainties（气候、环境、耦合与不确定性研究所）
Precipitation nowcasting is of great importance for weather forecast users, for activities ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. We propose the use three popular deep learning models (U-net, ConvLSTM and SVG-LP) trained on two-dimensional precipitation maps…
We propose the use of a stochastic variational frame prediction deep neural network with a learned prior distribution trained on two-dimensional rain radar reflectivity maps for precipitation nowcasting with lead times of up to 2 1/2 hours. We present a comparison to a standard convolutional LSTM network and assess the evolution of the structural similarity index for both methods. Case studies are presented that illustrate that the novel methodology can yield meaningful forecasts without excessive blur for the time horizons of interest.
Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socio-economic needs of many sectors reliant on weather-dependent decision-making. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints. While they accurately predict low-intensity rainfall, their operational…
Met Office Informatics Lab（英国气象局信息学实验室）；University of Exeter（埃克塞特大学）；DeepMind（深度思维）
A 'nowcast' is a type of weather forecast which makes predictions in the very short term, typically less than two hours - a period in which traditional numerical weather prediction can be limited. This type of weather prediction has important applications for commercial aviation; public and outdoor events; and the construction industry, power utilities, and ground transportation services that conduct much of their work outdoors. Importantly, one of the key needs for nowcasting systems is in the provision of accurate warnings of adverse weather events, such as heavy rain and flooding, for the p…
FusionCast: Enhancing Precipitation Nowcasting with Asymmetric Cross-Modal Fusion and Future Radar Priors
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Earth and Space Science and Technology, Wuhan University（武汉大学地球和空间科学与技术学院）
Deep learning has been successfully applied to precipitation nowcasting. In this work, we propose a pre-training scheme and a new loss function for improving deep-learning-based nowcasting. First, we adapt U-Net, a widely-used deep-learning model, for the two problems of interest here: precipitation nowcasting and precipitation estimation from radar images. We formulate the former as a classification problem with three precipitation intervals and the latter as a regression problem. For these tasks, we propose to pre-train the model to predict radar images in the near future without requiring g…
QWRF-Net: A Quantum-Wavelet Framework with Rectified Flow for Short-Term Precipitation Nowcasting
School of Computer Science and Technology (School of Artificial Intelligence), Yibin University（宜宾大学计算机科学与技术学院（人工智能学院））；College of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）
SynCast: Synergizing Contradictions in Precipitation Nowcasting via Diffusion Sequential Preference Optimization
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Chinese University of Hong Kong（香港中文大学）
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Kim Jaechul Graduate School of AI（金在哲人工智能研究生院）
Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i.e., predicting precipitation levels and locations in the near future). Most existing deep-learning-based approaches for precipitation nowcasting, however, consider only radar and/or satellite images as inputs, and meteorological observations collected from ground weather stations, which are sparsely located, are relatively unexplored. In this paper, we propose ASOC, a novel attentive method for effectively exploiting ground-based meteorological observatio…
Laboratory of Intelligent Collaborative Computing, University of Electronic Science（智能协同计算实验室，电子科学科技大学）；School of Computer Science（计算机科学学院）；Technology (School of Artificial Intelligence), Yibin University（技术（人工智能学院），宜宾大学）；College of Humanities（人文学院）；General Education, Chengdu Textile College（通识教育，成都纺织学院）
RainBalance: Alleviating Dual Imbalance in GNSS-based Precipitation Nowcasting via Continuous Probability Modeling
Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科技创新园）；Wuhan University of Technology（武汉理工大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Earth and Space Science and Technology, Wuhan University（武汉大学地球空间科学与技术学院）；Wuhan University（武汉大学）；School of Water Resources and Hydropower Engineering, Wuhan University（武汉大学水利水电工程学院）；GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）
Weather forecasting is dominated by numerical weather prediction that tries to model accurately the physical properties of the atmosphere. A downside of numerical weather prediction is that it is lacking the ability for short-term forecasts using the latest available information. By using a data-driven neural network approach we show that it is possible to produce an accurate precipitation nowcast. To this end, we propose SmaAt-UNet, an efficient convolutional neural networks-based on the well known UNet architecture equipped with attention modules and depthwise-separable convolutions. We eval…
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Guiyang Academy of Information Technology（贵阳信息技术研究院）；NTT DATA Corporation（NTT DATA株式会社）；NTT DATA Institute of Management Consulting Inc.（NTT DATA经营研究所）
With the goal of predicting the future rainfall intensity in a local region over a relatively short period time, precipitation nowcasting has been a long-time scientific challenge with great social and economic impact. The radar echo extrapolation approaches for precipitation nowcasting take radar echo images as input, aiming to generate future radar echo images by learning from the historical images. To effectively handle complex and high non-stationary evolution of radar echoes, we propose to decompose the movement into optical flow field motion and morphologic deformation. Following this id…
Natural disasters caused by heavy rainfall often cost huge loss of life and property. To avoid it, the task of precipitation nowcasting is imminent. To solve the problem, increasingly deep learning methods are proposed to forecast future radar echo images and then the predicted maps have converted the distribution of rainfall. The prevailing spatiotemporal sequence prediction methods apply ConvRNN structure which combines the Convolution and Recurrent neural network. Although improvements based on ConvRNN achieve remarkable success, these methods ignore capturing both local and global spatial …
The accuracy and explainability of data-driven nowcasting models are of great importance in many socio-economic sectors reliant on weather-dependent decision making. This paper proposes a novel architecture called Small Attention Residual UNet (SAR-UNet) for precipitation and cloud cover nowcasting. Here, SmaAt-UNet is used as a core model and is further equipped with residual connections, parallel to the depthwise separable convolutions. The proposed SAR-UNet model is evaluated on two datasets, i.e., Dutch precipitation maps ranging from 2016 to 2019 and French cloud cover binary images from …
DYffCast: Regional Precipitation Nowcasting Using IMERG Satellite Data. A case study over South America
Imperial College London（帝国理工学院）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）；University of California San Diego（加利福尼亚大学圣迭戈分校）；Grantham Institute for Climate Change and the Environment（格兰瑟姆气候与环境研究所）；National Center for Artificial Intelligence CENIA, Chile（智利国家人工智能中心）
Stable Attention Response for Reliable Precipitation Nowcasting
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；School of Life and Environmental Science, The University of Sydney（悉尼大学生命与环境科学学院）；School of Computer Science and Information Technology, The University of Adelaide（阿德莱德大学计算机科学与信息技术学院）；Digital Agriculture, Orange Agricultural Institute（数字农业，橙色农业研究所）；School of Science, Edith Cowan University（埃迪斯科文大学科学学院）
McCast: Memory-Guided Latent Drift Correction for Long-Horizon Precipitation Nowcasting
School of Computer Science, The University of Sydney, Australia（悉尼大学计算机科学学院，澳大利亚）；School of Life and Environmental Science, The University of Sydney, Australia（悉尼大学生命与环境科学学院，澳大利亚）
