Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation, modelling impacts to < 100 m is needed. Yet, the relationship between driving variables and Earth's surface at such local scales remains unresolved by current physical models. Large Earth observation datasets now enable us to create machine learning models capable of translating coarse weather information into high-resolution Earth surface forecasts. Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts. Video prediction has been tackled with deep learning models. Developing such models requires analysis-ready datasets. We introduce EarthNet2021, a new, curated dataset containing target spatio-temporal Sentinel 2 satellite imagery at 20 m resolution, matched with high-resolution topography and mesoscale (1.28 km) weather variables. With over 32000 samples it is suitable for training deep neural networks. Comparing multiple Earth surface forecasts is not trivial. Hence, we define the EarthNetScore, a novel ranking criterion for models forecasting Earth surface reflectance. For model intercomparison we frame EarthNet2021 as a challenge with four tracks based on different test sets. These allow evaluation of model validity and robustness as well as model applicability to extreme events and the complete annual vegetation cycle. In addition to forecasting directly observable weather impacts through satellite-derived vegetation indices, capable Earth surface models will enable downstream applications such as crop yield prediction, forest health assessments, coastline management, or biodiversity monitoring. Find data, code, and how to participate at www.earthnet.tech .
National Observatory of Athens（雅典国家天文台）；Universitat de València（瓦伦西亚大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support
Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（莫扎德人工智能大学）；University of Missouri（密苏里大学）；Australian National University（澳大利亚国立大学）；Linköping University（林肯大学）
Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extreme events. Although machine learning approaches have shown promising results in long-range climate forecasting, the associated model uncertainties may reduce their reliability. To address this issue, we propose a late fusion approach that systematically combines the predictions from multiple models to reduce the expected errors of the fused results.…
Monta Vista High School（蒙塔维斯塔高中）；Green Hope High School（格林霍普高中）；Bronx High School of Science（布朗克斯科学高中）
Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing preferred ecological conditions of mosquito larvae. After assembling a data set with atmospheric records and larvae observations, a neural network is trained to predict larvae counts from ecological inputs. Time series forecasting is conducted on these variables and climate projections are passed into the initial deep learning model to generate location-specific larvae abundance predictions. The results support the notion of regional ecosystem-driven …
Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting
National Center for High-Performance Computing, NARlabs（国家高性能计算中心（国家高速网络与计算中心））；Cross College Elite Program, National Cheng Kung University（成功大学跨学院精英计划）；National Synchrotron Radiation Research Center（国家同步辐射研究中心）；Imperial College London（伦敦帝国学院）
Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) and transfer learning to forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. We apply state-of-the-art interpretation techniques from the machine learning literature to attribute particular regions of int…
A Climate-Aware Deep Learning Framework for Generalizable Epidemic Forecasting
School of Engineering（工程学院）；Brown University（布朗大学）；Department of Epidemiology（流行病学系）；School of Public Health（公共卫生学院）；Institute at Brown for Environment and Society（布朗大学环境与社会研究所）
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Amazon Sustainability Science and Innovation（亚马逊可持续性科学与创新部门）
Attention-Enhanced LSTM Modeling for Improved Temperature and Rainfall Forecasting in Bangladesh
School of Science, Engineering & Technology（科学、工程与技术学院）；East Delta University（东 delta 大学）；Computer Science & Engineering（计算机科学与工程）；Islamic University of Technology（伊斯兰科技大学）
University of Minnesota, Twin Cities（明尼苏达大学双城校区）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；George Mason University（乔治梅森大学）
Sub-seasonal climate forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to ad…
Virginia Tech（弗吉尼亚理工大学）；IBM Research Africa（IBM非洲研究院）；IBM Research Europe（IBM欧洲研究院）
As acute climate change impacts weather and climate variability, there is increased demand for robust climate impact model predictions from which forecasts of the impacts can be derived. The quality of those predictions are limited by the climate drivers for the impact models which are nonlinear and highly variable in nature. One way to estimate the uncertainty of the model drivers is to assess the distribution of ensembles of climate forecasts. To capture the uncertainty in the impact model outputs associated with the distribution of the input climate forecasts, each individual forecast ensem…
University of Minnesota, Twin Cities（明尼苏达大学双城分校）；George Mason University（乔治梅森大学）；Carnegie Mellon University（卡内基梅隆大学）
Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would have immense societal value, in areas such as agricultural productivity, water resource management, transportation and aviation systems, and emergency planning for extreme weather events. However, SSF is considered more challenging than either weather prediction or even seasonal prediction. In this paper, we carefully study a variety of machine learning (ML) approaches for SSF over the US mainland. While atmosphere-lan…
Understanding seasonal climatic conditions is critical for better management of resources such as water, energy and agriculture. Recently, there has been a great interest in utilizing the power of artificial intelligence methods in climate studies. This paper presents a cutting-edge deep learning model (UNet++) trained by state-of-the-art global CMIP6 models to forecast global temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was also used for finetuning as well performance analysis in the validation dataset. Three different setups (CMIP6; CMIP6 + elevation; CMIP6 + el…
Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting
Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, India（计算机科学与工程系，Dr. B.R. 阿姆贝卡尔国立技术学院贾兰德哈尔，印度）
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
Department of Science and Engineering, Sorbonne University, Abu Dhabi, United Arab Emirates（Sorbonne大学科学与工程系）；Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India（印度统计研究院应用数学与物理单元）；Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, 75005, France（Sorbonne大学人工智能中心）；Indian Institute of Technology Gandhinagar, Gandhinagar 382355, India（印度理工学院冈丁加尔分校）；National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Vasco da Gama, 403804, India（国家极地与海洋研究中心）；Center for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Gachibowli, Hyderabad-500032, India（国际信息技术研究所计算自然科学与生物信息中心）；Department of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90–537 Lodz, Poland（Lodz技术大学动力学系）；Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata 700032, India（贾瓦德普大学数学生物学与生态中心）
Massachusetts Institute of Technology（麻省理工学院）；Argonne National Laboratory（阿贡国家实验室）
Accurately forecasting the weather is a key requirement for climate change mitigation. Data-driven methods offer the ability to make more accurate forecasts, but lack interpretability and can be expensive to train and deploy if models are not carefully developed. Here, we make use of two historical climate data sets and tools from machine learning, to accurately predict temperature fields. Furthermore, we are able to use low fidelity models that are cheap to train and evaluate, to selectively avoid expensive high fidelity function evaluations, as well as uncover seasonal variations in predicti…
Black Sigatoka disease severely decreases global banana production, and climate change aggravates the problem by altering fungal species distributions. Due to the heavy financial burden of managing this infectious disease, farmers in developing countries face significant banana crop losses. Though scientists have produced mathematical models of infectious diseases, adapting these models to incorporate climate effects is difficult. We present MR. NODE (Multiple predictoR Neural ODE), a neural network that models the dynamics of black Sigatoka infection learnt directly from data via Neural Ordin…
The Ohio State University（俄亥俄州立大学）；Potomac Research LLC（波托马克研究有限责任公司）
We explore the hyperparameter space of reservoir computers used for forecasting of the chaotic Lorenz '63 attractor with Bayesian optimization. We use a new measure of reservoir performance, designed to emphasize learning the global climate of the forecasted system rather than short-term prediction. We find that optimizing over this measure more quickly excludes reservoirs that fail to reproduce the climate. The results of optimization are surprising: the optimized parameters often specify a reservoir network with very low connectivity. Inspired by this observation, we explore reservoir design…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；IBM Almaden Research Center（IBM阿尔马登研究中心）
Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains. Most recent approaches focus on classification, regression or forecasting tasks on such data. In forecasting, it is necessary to not only forecast the right value but also to forecast when that value will occur in the irregular time series. In this work, we present an approach to forecast not only the values but also the time at which they are expected to occur.
Weather forecasting is an essential task to tackle global climate change. Weather forecasting requires the analysis of multivariate data generated by heterogeneous meteorological sensors. These sensors comprise of ground-based sensors, radiosonde, and sensors mounted on satellites, etc., To analyze the data generated by these sensors we use Graph Neural Networks (GNNs) based weather forecasting model. GNNs are graph learning-based models which show strong empirical performance in many machine learning approaches. In this research, we investigate the performance of weather forecasting using GNN…
Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes. However, these data-driven models, being over-parameterized, require a lot of training data which may not be available from reanalysis (observational data) products. Moreover, an accurate, noise-free, initial condition to start forecasting with a data-driven weather model …
We provide a machine learning solution that replaces the traditional methods for deciding the pesticide application time of Sunn Pest. We correlate climate data with phases of Sunn Pest in its life-cycle and decide whether the fields should be sprayed. Our solution includes two groups of prediction models. The first group contains decision trees that predict migration time of Sunn Pest from winter quarters to wheat fields. The second group contains random forest models that predict the nymphal stage percentages of Sunn Pest which is a criterion for pesticide application. We trained our models …
