Precipitation data collected at sub-hourly resolution represents specific challenges for missing data recovery by being largely stochastic in nature and highly unbalanced in the duration of rain vs non-rain. Here we present a two-step analysis utilising current machine learning techniques for imputing precipitation data sampled at 30-minute intervals by devolving the task into (a) the classification of rain or non-rain samples, and (b) regressing the absolute values of predicted rain samples. Investigating 37 weather stations in the UK, this machine learning process produces more accurate predictions for recovering precipitation data than an established surface fitting technique utilising neighbouring rain gauges. Increasing available features for the training of machine learning algorithms increases performance with the integration of weather data at the target site with externally sourced rain gauges providing the highest performance. This method informs machine learning models by utilising information in concurrently collected environmental data to make accurate predictions of missing rain data. Capturing complex non-linear relationships from weakly correlated variables is critical for data recovery at sub-hourly resolutions. Such pipelines for data recovery can be developed and deployed for highly automated and near instantaneous imputation of missing values in ongoing datasets at high temporal resolutions.
Research Institute for Infrastructure Paradigm Shift, Yachiyo Engineering Co., Ltd.（基础设施范式转换研究所，八千代工程株式会社）
最近，由于气候变化引起的前所未有的天气条件，洪水灾害已成为一个社会问题。对暴雨立即作出反应对于减轻经济损失和迅速恢复都很重要。时空降水预报可以提高大坝入库流量预测的准确性，提前6小时以上减轻洪水损失。然而，由于目标预测值与地面实况值之间存在不可约的偏差，普通的ConvLSTM在实际降水预报中存在着可预报范围大于3个时间步的局限性。本文提出了一种雨码的时空降水代码到代码的预测方法。我们提出了一种新的雨的功能，表示一个时间雨的过程，使用多帧融合的时间步长减少。我们基于标准ConvLSTM对各种术语范围进行了雨码研究。我们应用了一个坝区内的日本雨季小时降水数据，2006年至2019年下约12.7万小时，每年5月至10月。我们将雷达分析每小时的数据应用于面积为136 x 148 km 2的中央更广泛的区域。最后，我们在几个预报范围内提供了雨码大小和每小时准确度之间的敏感性研究。
Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Georgia Institute of Technology（佐治亚理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Memphis（孟菲斯大学）
RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling
IVADO；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；connAIx Research School, Tübingen AI Center, University of Tübingen（蒂宾根大学connAIx研究学院，蒂宾根AI中心）；Cluster of Excellence Machine Learning, University of Tübingen（蒂宾根大学机器学习卓越集群）；Mila - Quebec AI Institute（Mila-魁北克人工智能研究所）
Investigating the Robustness of Extreme Precipitation Super-Resolution Across Climates
Expertise Center for Climate Extremes, University of Lausanne（极端气候专家中心，洛桑大学）；Faculty of Geosciences and Environment, University of Lausanne（地球科学与环境学院，洛桑大学）；Faculty of Business and Economics (HEC), University of Lausanne（商业经济学院（HEC），洛桑大学）
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling
International Research Organization for Advanced Science and Technology, Kumamoto University（熊本大学先进科学技术国际研究机构）；Center for Water Cycle, Marine Environment, and Disaster Management, Kumamoto University（熊本大学水循环、海洋环境与灾害管理研究中心）；Department of Civil and Environmental Engineering, University of California, Davis（加州大学戴维斯分校土木与环境工程系）；Graduated School of Science and Technology, Kumamoto University（熊本大学科学与技术研究生院）；Faculty of Advanced Science and Technology, Kumamoto University（熊本大学先进科学技术学部）
National Taiwan University（台湾大学）；Chinese Culture University（中国文化大学）；National Science and Technology Center for Disaster Reduction（国家灾害防救科技中心）；DataQualia Lab. Co. Ltd.（DataQualia实验室有限公司）
Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations
Bochum Institute of Technology（波恩技术学院）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Okeanos Smart Data Solutions GmbH（Okeanos智能数据解决方案有限公司）
Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography
Federal Office of Meteorology and Climatology MeteoSwiss（瑞士联邦气象与气候办公室（MeteoSwiss））；Swiss Data Science Center (SDSC), ETH Zürich（苏黎世联邦理工学院瑞士数据科学中心（SDSC））；Center for Climate Systems Modeling (C2SM), ETH Zürich（苏黎世联邦理工学院气候系统建模中心（C2SM））；European Centre for Medium-Range Weather Forecasts (ECMWF)（欧洲中期天气预报中心（ECMWF））；Norwegian Meteorological Institute (MET Norway)（挪威气象研究所（MET Norway））；Royal Netherlands Meteorological Institute (KNMI)（荷兰皇家气象研究所（KNMI））；Institute for Atmospheric and Climate Science (IAC), ETH Zürich（苏黎世联邦理工学院大气与气候科学研究所（IAC））
A Lightweight DL Model for Smart Grid Power Forecasting with Feature and Resolution Mismatch
Center for Automotive Research (CAR), The Ohio State University（汽车研究中心（CAR），俄亥俄州立大学）；Informatics Institute, Istanbul Technical University（信息学院，伊斯坦布尔技术大学）；Computer Engineering Department, Konkuk University（计算机工程系，韩国康 kuk 大学）；Computer Science and Information Technology Department, Maulana Azad National Urdu University（计算机科学与信息技术系，莫拉尔·阿扎德国家乌尔都语大学）
