Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network

Current time-series forecasting problems use short-term weather attributes as exogenous inputs. However, in specific time-series forecasting solutions (e.g., demand prediction in the supply chain), seasonal climate predictions are crucial to improve its resilience. Representing mid to long-term seasonal climate forecasts is challenging as seasonal climate predictions are uncertain, and encoding spatio-temporal relationship of climate forecasts with demand is complex. We propose a novel modeling framework that efficiently encodes seasonal climate predictions to provide robust and reliable time-series forecasting for supply chain functions. The encoding framework enables effective learning of latent representations -- be it uncertain seasonal climate prediction or other time-series data (e.g., buyer patterns) -- via a modular neural network architecture. Our extensive experiments indicate that learning such representations to model seasonal climate forecast results in an error reduction of approximately 13\% to 17\% across multiple real-world data sets compared to existing demand forecasting methods.Comment: 15 page

AI Foundation Models for Weather and Climate: Applications, Design, and Implementation

Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government institutions, and meteorological agencies in building digital twins of the Earth. Recent approaches using transformers, physics-informed machine learning, and graph neural networks have demonstrated state-of-the-art performance on relatively narrow spatiotemporal scales and specific tasks. With the recent success of generative artificial intelligence (AI) using pre-trained transformers for language modeling and vision with prompt engineering and fine-tuning, we are now moving towards generalizable AI. In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks. Despite this progress, we are still in the nascent stages of a generalizable AI model for global Earth system models, regional climate models, and mesoscale weather models. Here, we review current state-of-the-art AI approaches, primarily from transformer and operator learning literature in the context of meteorology. We provide our perspective on criteria for success towards a family of foundation models for nowcasting and forecasting weather and climate predictions. We also discuss how such models can perform competitively on downstream tasks such as downscaling (super-resolution), identifying conditions conducive to the occurrence of wildfires, and predicting consequential meteorological phenomena across various spatiotemporal scales such as hurricanes and atmospheric rivers. In particular, we examine current AI methodologies and contend they have matured enough to design and implement a weather foundation model.Comment: 44 pages, 1 figure, updated Fig.

AI for climate impacts: applications in flood risk

Abstract In recent years there has been a surge of interest in the potential of Artificial Intelligence (AI) to address the global threat of climate change. Here, we consider climate change applications, and review the ability of AI technologies to better quantify climate change-induced hazards, impacts and risks, and address key challenges in this domain. We focus on three application areas: data-driven modeling, enabling uncertainty quantification, and leveraging geospatial big data. For these, we provide examples from flood-related applications to illustrate the advantages of AI, in comparison to alternative methods, whilst also considering its limitations. We conclude that by streamlining the process of translating weather and climate data into actionable information, facilitated by a suitable technology framework, AI can play a key role in building climate change resilience

A Summer School Session on Mastering Geospatial Artificial Intelligence: From Data Production to Artificial Intelligence Foundation Model Development and Downstream Applications [Technical Committees]

In collaboration with IBM Research, the NASA Interagency Implementation and Advanced Concepts Team (IMPACT) organized a specialized one-day summer school session focused on exploring the topic of data science at scale. This session was a part of the “High Performance and Disruptive Computing in Remote Sensing” summer school hosted by the University of Iceland from 29 May to 1 June 2023 in Reykjavik, Iceland. This marked the third edition of the school organised by the High Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group of the IEEE Geoscience and Remote Sensing Society’s (GRSS’s) Earth Science Informatics (ESI) Technical Committee (TC)