Proceedings of the 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA), IEEE, Paris, France, 9-12 November 2020
Nowcasting is a field of meteorology which aims at forecasting weather on a short term of up to a few hours. In the meteorology landscape, this field is rather specific as it requires particular techniques, such as data extrapolation, where conventional meteorology is generally based on physical modeling. In this paper, we focus on cloud cover nowcasting, which has various application areas such as satellite shots optimisation and photovoltaic energy production forecast.
Following recent deep learning successes on multiple imagery tasks, we applied deep convolutionnal neural networks on Meteosat satellite images for cloud cover nowcasting. We present the results of several architectures specialized in image segmentation and time series prediction. We selected the best models according to machine learning metrics as well as meteorological metrics. All selected architectures showed significant improvements over persistence and the well-known U-Net surpasses AROME physical model.
When Does Advection-Aware Graph Nowcasting Help? A Controlled Study of Distributed Solar Ramp Forecasting with a Self-Supervised Cloud-Motion Estimator
Physically Explainable Deep Learning for Convective Initiation Nowcasting Using GOES-16 Satellite Observations
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania（气象学与大气科学系，宾夕法尼亚州立大学，大学公园，宾夕法尼亚州）；National Center for Atmospheric Research, Boulder, Colorado（国家大气研究中心，伯尔尼，科罗拉多州）
Exploring Geometric Deep Learning For Precipitation Nowcasting
Technical University of Munich (TUM)（慕尼黑工业大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Indian Institute of Technology Delhi（印度理工学院德里分校）
Bayesian Deep Learning for Convective Initiation Nowcasting Uncertainty Estimation
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania（气象学与大气科学系，宾夕法尼亚州立大学，大学公园，宾夕法尼亚州）；National Center for Atmospheric Research, Boulder, Colorado（国家大气研究中心，博尔德，科罗拉多）；Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, Pennsylvania（计算与数据科学研究所，宾夕法尼亚州立大学，大学公园，宾夕法尼亚州）；Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, Pennsylvania（土木与环境工程系，宾夕法尼亚州立大学，大学公园，宾夕法尼亚州）
LenghuSky-8: An 8-Year All-Sky Cloud Dataset with Star-Aware Masks and Alt-Az Calibration for Segmentation and Nowcasting
State Key Laboratory of Dark Matter Physics, Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University（暗物质物理国家重点实验室、李政道研究所及物理天文学院、上海交通大学）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Computer Technology and Application, Qinghai University（青海大学计算机技术与应用学院）
SolarBench: A global solar energy nowcasting benchmark
Massachusetts Institute of Technology（麻省理工学院）；European Space Agency（欧洲空间局）；University of Nottingham（诺丁汉大学）；Hong Kong Polytechnic University（香港理工大学）；Hochschule Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）；Stanford University（斯坦福大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Mines Paris, Université PSL（巴黎矿业大学（巴黎文理研究大学））；Technical University of Denmark（丹麦技术大学）
SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data
School of Information, University of California, Berkeley（加州大学伯克利分校信息学院）；EECS / BAIR, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系 / 伯克利人工智能研究所）
Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI（遥感与摄影测量系，芬兰地理研究所）；Faculty of Technology, University of Turku（技术学院，图尔库大学）；Department of Geography and Geology, University of Turku（地理与地质系，图尔库大学）；Faculty of Geological Sciences, Complutense University of Madrid（地质科学学院，马德里卡洛斯三世大学）
CloudBreaker: Breaking the Cloud Covers of Sentinel-2 Images using Multi-Stage Trained Conditional Flow Matching on Sentinel-1
Computer Science and Engineering（计算机科学与工程）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Institute of Water and Flood Management（水与洪水管理研究所）
Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（信息工程测绘与遥感国家重点实验室）；School of Computer Science and Information Engineering（计算机科学与信息工程学院）；Hangzhou International Innovation Institute（杭州国际创新研究院）；Oriental Space Port Research Institute（东方航天港研究院）；Hubei Luojia Laboratory（湖北珞珈实验室）
