61 research outputs found
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.
Graph Neural Network for spatiotemporal data: methods and applications
In the era of big data, there has been a surge in the availability of data
containing rich spatial and temporal information, offering valuable insights
into dynamic systems and processes for applications such as weather
forecasting, natural disaster management, intelligent transport systems, and
precision agriculture. Graph neural networks (GNNs) have emerged as a powerful
tool for modeling and understanding data with dependencies to each other such
as spatial and temporal dependencies. There is a large amount of existing work
that focuses on addressing the complex spatial and temporal dependencies in
spatiotemporal data using GNNs. However, the strong interdisciplinary nature of
spatiotemporal data has created numerous GNNs variants specifically designed
for distinct application domains. Although the techniques are generally
applicable across various domains, cross-referencing these methods remains
essential yet challenging due to the absence of a comprehensive literature
review on GNNs for spatiotemporal data. This article aims to provide a
systematic and comprehensive overview of the technologies and applications of
GNNs in the spatiotemporal domain. First, the ways of constructing graphs from
spatiotemporal data are summarized to help domain experts understand how to
generate graphs from various types of spatiotemporal data. Then, a systematic
categorization and summary of existing spatiotemporal GNNs are presented to
enable domain experts to identify suitable techniques and to support model
developers in advancing their research. Moreover, a comprehensive overview of
significant applications in the spatiotemporal domain is offered to introduce a
broader range of applications to model developers and domain experts, assisting
them in exploring potential research topics and enhancing the impact of their
work. Finally, open challenges and future directions are discussed
Computational socioeconomics
Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies
Towards the “Perfect” Weather Warning
This book is about making weather warnings more effective in saving lives, property, infrastructure and livelihoods, but the underlying theme of the book is partnership. The book represents the warning process as a pathway linking observations to weather forecasts to hazard forecasts to socio-economic impact forecasts to warning messages to the protective decision, via a set of five bridges that cross the divides between the relevant organisations and areas of expertise. Each bridge represents the communication, translation and interpretation of information as it passes from one area of expertise to another and ultimately to the decision maker, who may be a professional or a member of the public. The authors explore the partnerships upon which each bridge is built, assess the expertise and skills that each partner brings and the challenges of communication between them, and discuss the structures and methods of working that build effective partnerships. The book is ordered according to the “first mile” paradigm in which the decision maker comes first, and then the production chain through the warning and forecast to the observations is considered second. This approach emphasizes the importance of co-design and co-production throughout the warning process. The book is targeted at professionals and trainee professionals with a role in the warning chain, i.e. in weather services, emergency management agencies, disaster risk reduction agencies, risk management sections of infrastructure agencies. This is an open access book
Big data analytics and its role to support groundwater management in the Southern African development community
Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of
heterogeneous data through advanced analytics to drive information discovery. This paper aims to
highlight the potential role BDA can play to improve groundwater management in the Southern
African Development Community (SADC) region in Africa. Through a review of the literature,
this paper defines the concepts of big data, big data sources in groundwater, big data analytics,
big data platforms and framework and how they can be used to support groundwater management
in the SADC region. BDA may support groundwater management in SADC region by filling in
data gaps and transforming these data into useful information. In recent times, machine learning
and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big
data from collection to information delivery requires critical application of selected tools, techniques
and methods. Hence, in this paper we present a conceptual framework that can be used to manage
the implementation of BDA in a groundwater management context. Then, we highlight challenges
limiting the application of BDA which included technological constraints and institutional barriers.
In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA
exists to be used in groundwater sciences thereby providing the basis to further explore data-driven
sciences in groundwater management
Early Warning Systems and Their Role in Disaster Risk Reduction
In this chapter, we introduce early warning systems (EWS) in the context of disaster risk reduction, including the main components of an EWS, the roles of the main actors and the need for robust evaluation. Management of disaster risks requires that the nature and distribution of risk are understood, including the hazards, and the exposure, vulnerability and capacity of communities at risk. A variety of policy options can be used to reduce and manage risks, and we emphasise the contribution of early warnings, presenting an eight-component framework of people-centred early warning systems which highlights the importance of an integrated and all-society approach. We identify the need for decisions to be evidence-based, for performance monitoring and for dealing with errors and false information. We conclude by identifying gaps in current early warning systems, including in the social components of warning systems and in dealing with multi-hazards, and obstacles to progress, including issues in funding, data availability, and stakeholder engagement
Earth Observation Open Science and Innovation
geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc
Deep Learning in Mobile and Wireless Networking: A Survey
The rapid uptake of mobile devices and the rising popularity of mobile
applications and services pose unprecedented demands on mobile and wireless
networking infrastructure. Upcoming 5G systems are evolving to support
exploding mobile traffic volumes, agile management of network resource to
maximize user experience, and extraction of fine-grained real-time analytics.
Fulfilling these tasks is challenging, as mobile environments are increasingly
complex, heterogeneous, and evolving. One potential solution is to resort to
advanced machine learning techniques to help managing the rise in data volumes
and algorithm-driven applications. The recent success of deep learning
underpins new and powerful tools that tackle problems in this space.
In this paper we bridge the gap between deep learning and mobile and wireless
networking research, by presenting a comprehensive survey of the crossovers
between the two areas. We first briefly introduce essential background and
state-of-the-art in deep learning techniques with potential applications to
networking. We then discuss several techniques and platforms that facilitate
the efficient deployment of deep learning onto mobile systems. Subsequently, we
provide an encyclopedic review of mobile and wireless networking research based
on deep learning, which we categorize by different domains. Drawing from our
experience, we discuss how to tailor deep learning to mobile environments. We
complete this survey by pinpointing current challenges and open future
directions for research
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