This study investigates the application of deep-learning diffusion models for the super-resolution of weather data, a novel approach aimed at enhancing the spatial resolution and detail of meteorological variables. Leveraging the capabilities of diffusion models, specifically the SR3 and ResDiff architectures, we present a methodology for transforming low-resolution weather data into high-resolution outputs. Our experiments, conducted using the WeatherBench dataset, focus on the super-resolution of the two-meter temperature variable, demonstrating the models' ability to generate detailed and accurate weather maps. The results indicate that the ResDiff model, further improved by incorporating physics-based modifications, significantly outperforms traditional SR3 methods in terms of Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). This research highlights the potential of diffusion models in meteorological applications, offering insights into their effectiveness, challenges, and prospects for future advancements in weather prediction and climate analysis.
The ability to analyze and forecast stratospheric weather conditions is fundamental to addressing climate change. However, our capacity to collect data in the stratosphere is limited by sparsely deployed weather balloons. We propose a framework to collect stratospheric data by releasing a contrail of tiny sensor devices as a weather balloon ascends. The key machine learning challenges are determining when and how to deploy a finite collection of sensors to produce a useful data set. We decide when to release sensors by modeling the deviation of a forecast from actual stratospheric conditions a…
Technical University of Munich（慕尼黑工业大学）；Artisense；Audi Electronics Venture（奥迪电子创投）
Even though end-to-end supervised learning has shown promising results for sensorimotor control of self-driving cars, its performance is greatly affected by the weather conditions under which it was trained, showing poor generalization to unseen conditions. In this paper, we show how knowledge can be transferred using semantic maps to new weather conditions without the need to obtain new ground truth data. To this end, we propose to divide the task of vehicle control into two independent modules: a control module which is only trained on one weather condition for which labeled steering data is…
Enhancing Robustness of Human Detection Algorithms in Maritime SAR through Augmented Aerial Images to Simulate Weather Conditions
Jakarta Intercultural School（雅加达国际学校）；Busan Foreign School（釜山外国语学校）；SMPK 8 Penabur Tanjung Duren（丹戎杜伦佩纳布尔8号初中（基督教学校））；Avon High School（雅芳高中）；Algoverse AI Research（Algoverse人工智能研究院）
Interpretable Machine Learning for Weather and Climate Prediction: A Survey
Public Meteorological Service Center, China Meteorological Administration（中国气象局公共气象服务中心）；University of Bristol（布里斯托大学）；Zhejiang University（浙江大学）；Institute of Atmospheric Physics, Chinese Academy of Science（中国科学院大气物理研究所）；Baidu Inc.（百度公司）；New Jersey Institute of Technology（新泽西理工学院）
ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong University of Science and Technology（香港科技大学）
TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State
Sun Yat-Sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；National Supercomputing Center in Shenzhen（深圳国家超算中心）；Huawei Technologies Co Ltd（华为技术有限公司）
Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning
The School of Natural and Computing Sciences and the Interdisciplinary Institute at the University of Aberdeen（阿伯丁大学自然科学与计算科学学院及跨学科研究所）；UiT The Arctic University of Norway（挪威北极大学）
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning
Shanghai AI Laboratory（上海人工智能实验室）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Meteorological Service（上海市气象局）
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）
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…
Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network
Faculty of Electronics and Telecommunications University of Engineering and Technology, VNU Hanoi, Vietnam（电子电信学院 工程技术大学 巴黎越南河内分校）；Faculty of Information Technology University of Engineering and Technology, VNU Hanoi, Vietnam（信息技术学院 工程技术大学 巴黎越南河内分校）
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution limits. We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weather and generate medium-range forecasts five orders-of-magnitude faster than NWP while approaching state-of-the-art accuracy. FourCast-Net is optimized and scales efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS Bo…
Politecnico di Milano（米兰理工大学）；University of New South Wales（新南威尔士大学）
This study introduces a framework for quality control of measured weather data, including anomaly detection, and infilling missing values. Weather data is a fundamental input to building performance simulations, in which anomalous values defect the results while missing data lead to an unexpected termination of the simulation process. Traditionally, infilling missing values in weather data is performed through periodic or linear interpolations. However, when missing values exceed many consecutive hours, the accuracy of traditional methods is subject to debate. This study demonstrates how Neura…
Dept. ECE, Technical University of Munich (TUM)（慕尼黑工业大学（TUM）电气工程与计算机科学系（ECE））
This report first provides a brief overview of a number of supervised learning algorithms for regression tasks. Among those are neural networks, regression trees, and the recently introduced Nexting. Nexting has been presented in the context of reinforcement learning where it was used to predict a large number of signals at different timescales. In the second half of this report, we apply the algorithms to historical weather data in order to evaluate their suitability to forecast a local weather trend. Our experiments did not identify one clearly preferable method, but rather show that choosin…
Lancaster University（兰卡斯特大学）；Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）
Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, time-consuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source to increase the amount of training data. However, just directly using synthetic data may actually harm the model's performance under normal weather conditions while getting only small gains in adverse situations. Therefore, we present a no…
Computer Science Georgia State University Atlanta, USA（计算机科学 乔治亚州立大学 奥斯汀, 美国）；Computer Science Texas Christian University Texas, USA（计算机科学 德克萨斯基督教大学 德克萨斯, 美国）
