Not only can discovering patterns and insights from atmospheric data enable more accurate weather predictions, but it may also provide valuable information to help tackle climate change. Weather4cast is an open competition that aims to evaluate machine learning algorithms' capability to predict future atmospheric states. Here, we describe our third-place solution to Weather4cast. We present a novel Variational U-Net that combines a Variational Autoencoder's ability to consider the probabilistic nature of data with a U-Net's ability to recover fine-grained details. This solution is an evolution from our fourth-place solution to Traffic4cast 2020 with many commonalities, suggesting its applicability to vastly different domains, such as weather and traffic.
Deep learning-based method for weather forecasting: A case study in Itoshima
Joint Graduate School of Mathematics for Innovation, Kyushu University（九州大学创新数学联合研究生院）；International Institute for Carbon-Neutral Energy Research, Kyushu University（九州大学国际碳中和能源研究所）；Institute of Tropical Agriculture, Kyushu University（九州大学热带农业研究所）；Faculty of Agriculture, Kyushu University（九州大学农学院）
Comparative Evaluation of Weather Forecasting using Machine Learning Models
University of California Riverside（加利福尼亚大学河滨分校）；Ahsanullah University of Science and Technology（阿赫桑乌拉科技大学）；Texas A&M University（德克萨斯农工大学）；University of Central Florida（中佛罗里达大学）；University of Florida（佛罗里达大学）
QGAPHEnsemble : Combining Hybrid QLSTM Network Ensemble via Adaptive Weighting for Short Term Weather Forecasting
Georgia Institute of Technology（佐治亚理工学院）；Indian Institute of Engineering Science and Technology, Shibpur（印度工程科学技术学院（希布普尔））；IIT Roorkee（鲁基印度理工学院）；National Institute of Technology, Rourkela（罗尔克拉国家理工学院）；National Institute of Technology Karnataka（卡纳塔克邦国家理工学院）；Jadavpur University（贾达沃普尔大学）
Technical University of Munich（慕尼黑工业大学）；ClimateAi, Inc（ClimateAi公司）
WeatherBench is a benchmark dataset for medium-range weather forecasting of geopotential, temperature and precipitation, consisting of preprocessed data, predefined evaluation metrics and a number of baseline models. WeatherBench Probability extends this to probabilistic forecasting by adding a set of established probabilistic verification metrics (continuous ranked probability score, spread-skill ratio and rank histograms) and a state-of-the-art operational baseline using the ECWMF IFS ensemble forecast. In addition, we test three different probabilistic machine learning methods -- Monte Carl…
Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models. The present paper compares two different deep learning architectures to perform weather prediction on daily data gathered from 18 cities across Europe and spanned over a period of 15 years. We propose the Deep Attention Unistream Multistream (DAUM) networks that investigate different types of input representations (i.e. tensorial unistream vs. multistream ) as well as the incorporation of the attention mechanism. In particular, we show that adding a self-attention…
Weather forecast plays an essential role in multiple aspects of the daily life of human beings. Currently, physics based numerical weather prediction is used to predict the weather and requires enormous amount of computational resources. In recent years, deep learning based models have seen wide success in many weather-prediction related tasks. In this paper we describe our experiments for the Weather4cast 2021 Challenge, where 8 hours of spatio-temporal weather data is predicted based on an initial one hour of spatio-temporal data. We focus on SmaAt-UNet, an efficient U-Net based autoencoder.…
Long Short-Term Memory (LSTM) is a well-known method used widely on sequence learning and time series prediction. In this paper we deployed stacked LSTM model in an application of weather forecasting. We propose a 2-layer spatio-temporal stacked LSTM model which consists of independent LSTM models per location in the first LSTM layer. Subsequently, the input of the second LSTM layer is formed based on the combination of the hidden states of the first layer LSTM models. The experiments show that by utilizing the spatial information the prediction performance of the stacked LSTM model improves i…
University of Corsica（科西嘉大学）；Hospital of Castelluccio（卡斯泰卢乔医院）；Galatasaray University（加拉塔萨雷大学）；University HouaryBoumédiène（胡阿里·布迈丁大学）
Numerous methods exist and were developed for global radiation forecasting. The two most popular types are the numerical weather predictions (NWP) and the predictions using stochastic approaches. We propose to compute a parameter noted constructed in part from the mutual information which is a quantity that measures the mutual dependence of two variables. Both of these are calculated with the objective to establish the more relevant method between NWP and stochastic models concerning the current problem.
Washington University in St. Louis（圣路易斯华盛顿大学）；NVIDIA（英伟达）
Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however, is based on supervision on mesh-gridded weather data without the utilization of raw climate observational data, the gold standard ground truth. In this work we develop a methodology to correct, remap, and fine-tune gridded uniform forecasts of FCN so it can be directly compared against observational ground truth, which is sparse and non-uniform in space and time. This is akin to bias correction and post-processing of numerical weather predict…
National University of Singapore（新加坡国立大学）；Institute of High Performance Computing（高性能计算研究院）
Numerical weather prediction (NWP) and machine learning (ML) methods are popular for solar forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. This is further complicated when regional NWP models are used with global climate models with different possible parameterizations. In this study, an alternative approach is proposed and evaluated for four radiation models. Weather Research and Forecasting (WRF) model is run in both global and regional mode to provide an estimate for solar irradiance. This estimate is then po…
Traditionally, weather predictions are performed with the help of large complex models of physics, which utilize different atmospheric conditions over a long period of time. These conditions are often unstable because of perturbations of the weather system, causing the models to provide inaccurate forecasts. The models are generally run on hundreds of nodes in a large High Performance Computing (HPC) environment which consumes a large amount of energy. In this paper, we present a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine l…
Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and impact-based auto-labeling strategy. CapsNets are trained on mid-tropospheric large-scale circulation patterns (Z500) labeled $0-4$ depending on the existence and geographical region of surface temperature extremes over North…
University of Cambridge（剑桥大学）；Vector Institute（矢量研究所）；University of Toronto（多伦多大学）；Microsoft Research（微软研究院）；British Antarctic Survey（英国南极调查局）；The Alan Turing Institute（阿兰·图灵研究所）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；Google Deepmind（谷歌DeepMind）
PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
