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1. Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present

   https://doi.org/10.1038/s41597-021-01003-9  

   Singer, Michael Bliss; Asfaw, Dagmawi Teklu; Rosolem, Rafael; Cuthbert, Mark O.; Miralles, Diego G.; MacLeod, David; Quichimbo, Edisson Andres; Michaelides, Katerina (December 2021, Scientific Data)

   Abstract Challenges exist for assessing the impacts of climate and climate change on the hydrological cycle on local and regional scales, and in turn on water resources, food, energy, and natural hazards. Potential evapotranspiration (PET) represents atmospheric demand for water, which is required at high spatial and temporal resolutions to compute actual evapotranspiration and thus close the water balance near the land surface for many such applications, but there are currently no available high-resolution datasets of PET. Here we develop an hourly PET dataset (hPET) for the global land surface at 0.1° spatial resolution, based on output from the recently developed ERA5-Land reanalysis dataset, over the period 1981 to present. We show how hPET compares to other available global PET datasets, over common spatiotemporal resolutions and time frames, with respect to spatial patterns of climatology and seasonal variations for selected humid and arid locations across the globe. We provide the data for users to employ for multiple applications to explore diurnal and seasonal variations in evaporative demand for water. 

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2. Novel Insights to Be Gained From Applying Metacommunity Theory to Long-Term, Spatially Replicated Biodiversity Data

   https://doi.org/10.3389/fevo.2020.612794  

   Record, Sydne; Voelker, Nicole M.; Zarnetske, Phoebe L.; Wisnoski, Nathan I.; Tonkin, Jonathan D.; Swan, Christopher; Marazzi, Luca; Lany, Nina; Lamy, Thomas; Compagnoni, Aldo; et al (January 2021, Frontiers in Ecology and Evolution)

   null (Ed.)

   Global loss of biodiversity and its associated ecosystem services is occurring at an alarming rate and is predicted to accelerate in the future. Metacommunity theory provides a framework to investigate multi-scale processes that drive change in biodiversity across space and time. Short-term ecological studies across space have progressed our understanding of biodiversity through a metacommunity lens, however, such snapshots in time have been limited in their ability to explain which processes, at which scales, generate observed spatial patterns. Temporal dynamics of metacommunities have been understudied, and large gaps in theory and empirical data have hindered progress in our understanding of underlying metacommunity processes that give rise to biodiversity patterns. Fortunately, we are at an important point in the history of ecology, where long-term studies with cross-scale spatial replication provide a means to gain a deeper understanding of the multiscale processes driving biodiversity patterns in time and space to inform metacommunity theory. The maturation of coordinated research and observation networks, such as the United States Long Term Ecological Research (LTER) program, provides an opportunity to advance explanation and prediction of biodiversity change with observational and experimental data at spatial and temporal scales greater than any single research group could accomplish. Synthesis of LTER network community datasets illustrates that long-term studies with spatial replication present an under-utilized resource for advancing spatio-temporal metacommunity research. We identify challenges towards synthesizing these data and present recommendations for addressing these challenges. We conclude with insights about how future monitoring efforts by coordinated research and observation networks could further the development of metacommunity theory and its applications aimed at improving conservation efforts. 

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3. Griddle: Effective Query Support over Voluminous Gridded Spatial Datasets

   https://doi.org/10.1109/BigData55660.2022.10020796  

   Smith, Pierce; Pallickara, Sangmi Lee; Pallickara, Shrideep (December 2022, 2022 IEEE International Conference on Big Data (Big Data))

   Gridded datasets occur in several domains. These datasets comprise (un)structured grid points, where each grid point is characterized by XY(Z) coordinates in a spatial referencing system. The data available at individual grid points are high-dimensional encapsulating multiple variables of interest. This study has two thrusts. The first targets supporting effective management of voluminous gridded datasets while reconciling challenges relating to colocation and dispersion. The second thrust is to support sliding (temporal) window queries over the gridded dataset. Such queries involve sliding a temporal window over the data to identify spatial locations and chronological time points where the specified predicate evaluates to true. Our methodology includes support for a space-efficient data structure for organizing information within the data, query decomposition based on dyadic intervals, support for temporal anchoring, query transformations, and effective evaluation of query predicates. Our empirical benchmarks are conducted on representative voluminous high dimensional datasets such as gridMET (historical meteorological data) and MACA (future climate datasets based on the RCP 8.5 greenhouse gas trajectory). In our benchmarks, our system can handle throughputs of over 3000 multi-predicate sliding window queries per second. 

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4. Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis

   Mao, Hanzi; Liu, Xi; Duffield, Nick; Yuan, Hao; Ji, Shuiwang Ji; Mohanty, Binayak (January 2020, International Conference on Data Mining)

   null (Ed.)

   Abstract—The emergence of remote sensing technologies cou- pled with local monitoring workstations enables us the un- precedented ability to monitor the environment in large scale. Information mining from multi-channel geo-spatiotemporal data however poses great challenges to many computational sustainability applications. Most existing approaches adopt various dimensionality reduction techniques without fully taking advantage of the spatiotemporal nature of the data. In addition, the lack of labeled training data raises another challenge for modeling such data. In this work, we propose a novel semi-supervised attention-based deep representation model that learns context-aware spatiotemporal representations for prediction tasks. A combination of convolutional neural networks with a hybrid attention mechanism is adopted to extract spatial and temporal variations in the geo-spatiotemporal data. Recognizing the importance of capturing more complete temporal dependencies, we propose the hybrid attention mechanism which integrates a learnable global query into the classic self-attention mechanism. To overcome the data scarcity issue, sampled spatial and temporal context that naturally reside in the largely-available unlabeled geo-spatiotemporal data are exploited to aid meaningful representation learning. We conduct experiments on a large-scale real-world crop yield prediction task. The results show that our methods significantly outperforms existing state-of-the-art yield prediction methods, especially under the stress of training data scarcity. 

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5. Empowering Time Series Analysis with Large Language Models: A Survey

   https://doi.org/10.24963/ijcai.2024/895  

   Jiang, Yushan; Pan, Zijie; Zhang, Xikun; Garg, Sahil; Schneider, Anderson; Nevmyvaka, Yuriy; Song, Dongjin (August 2024, International Joint Conferences on Artificial Intelligence Organization)

   Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks. However, completely training a large general-purpose model from the scratch is challenging for time series analysis, due to the large volumes and varieties of time series data, as well as the non-stationarity that leads to concept drift impeding continuous model adaptation and re-training. Recent advances have shown that pre-trained LLMs can be exploited to capture complex dependencies in time series data and facilitate various applications. In this survey, we provide a systematic overview of existing methods that leverage LLMs for time series analysis. Specifically, we first state the challenges and motivations of applying language models in the context of time series as well as brief preliminaries of LLMs. Next, we summarize the general pipeline for LLM-based time series analysis, categorize existing methods into different groups (\textit{i.e.}, direct query, tokenization, prompt design, fine-tune, and model integration), and highlight the key ideas within each group. We also discuss the applications of LLMs for both general and spatial-temporal time series data, tailored to specific domains. Finally, we thoroughly discuss future research opportunities to empower time series analysis with LLMs. 

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