Wildfires are a major issue facing the United States, a matter further exacerbated by an ever-changing climate. In California alone, wildfires are responsible for billions of dollars in damages and take lives each year. Accurately predicting fire danger conditions allows preparation awareness before wildfires start. Transformers are a class of deep learning models designed to identify patterns in sequential datasets. In recent years, transformers have gained popularity through their impressive performance in natural language processing and other applications of signal recognition. This analysis demonstrates the ability of a transformer with a residual connection to forecast fire danger potential over the state of California. Wildland fire potential index (WFPI) maps collected from the US Geological Survey database from January 1st 2020 to December 31st 2023 were used to tune, train and evaluate the transformer. Meteorological inputs (provided by Daymet daily weather and climatological summaries), the normalized difference vegetation index (NDVI) (calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS)), and outputs from the Scott and Burgman fire behavior fuel models (to characterize maps of fuel types), were used as inputs. Our results show that a transformer can effectively emulate the US Forest Service modeled WFPI maps of California USA for four week long forecasts over the month of July, 2023, with correlations ranging from 0.85 – 0.98.

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like the Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.

Abstract Inter‐comparison and similarity analysis to gauge consensus among multiple simulation models is a critical visualization problem for understanding climate change patterns. Climate models, specifically, Terrestrial Biosphere Models (TBM) represent time and space variable ecosystem processes, like, simulations of photosynthesis and respiration, using algorithms and driving variables such as climate and land use. While it is widely accepted that interactive visualization can enable scientists to better explore model similarity from different perspectives and different granularity of space and time, currently there is a lack of such visualization tools. In this paper we present three main contributions. First, we propose a domain characterization for the TBM community by systematically defining the domain‐specific intents for analyzing model similarity and characterizing the different facets of the data. Second, we define a classification scheme for combining visualization tasks and multiple facets of climate model data in one integrated framework, which can be leveraged for translating the tasks into the visualization design. Finally, we present SimilarityExplorer, an exploratory visualization tool that facilitates similarity comparison tasks across both space and time through a set of coordinated multiple views. We present two case studies from three climate scientists, who used our tool for a month for gaining scientific insights into model similarity. Their experience and results validate the effectiveness of our tool.

Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses additional challenges. In this paper we define visual reconciliation as the problem of reconciling multiple alternative similarity spaces through visualization and interaction. We derive this problem from our work on model comparison in climate science where climate modelers are faced with the challenge of making sense of alternative ways to describe their models: one through the output they generate, another through the large set of properties that describe them. Ideally, they want to understand whether groups of models with similar spatio-temporal behaviors share similar sets of criteria or, conversely, whether similar criteria lead to similar behaviors. We propose a visual analytics solution based on linked views, that addresses this problem by allowing the user to dynamically create, modify and observe the interaction among groupings, thereby making the potential explanations apparent. We present case studies that demonstrate the usefulness of our technique in the area of climate science.

We investigate the potential of geospatiotemporal data mining of multi-year land surface phenology data (250 m Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study) for the conterminous United States as part of an early warning system to identify threats to forest ecosystems. Cluster analysis of this massive data set, using high-performance computing, provides a basis for several possible approaches to defining the bounds of “normal” phenological patterns, indicating healthy vegetation in a given geographic location. We demonstrate the applicability of such an approach, using it to identify areas in Colorado, USA, where an ongoing mountain pine beetle outbreak has caused significant tree mortality.

The authors present a metacomputing application of multivariate, nonhierarchical statistical clustering to geographic environmental data from the 48 conterminous United States in order to produce maps of regions of ecological similarity, called ecoregions. These maps represent finer scale regionalizations than do those generated by the traditional technique: an expert with a marker pen. Several variables (e.g., temperature, organic matter, rainfall etc.) thought to affect the growth of vegetation are clustered at resolutions as fine as one square kilometer (1 km 2). These data can represent over 7.8 million map cells in an n–dimensional (n = 9 to 25) data space. A parallel version of the iterative statistical clustering algorithm is developed by the authors using the MPI (Message Passing Interface) message passing routines. The parallel algorithm uses a classical, self–scheduling, single–program, multiple data (SPMD) organization; performs dynamic load balancing for reasonable performance in heterogeneous metacomputing environments; and provides fault tolerance by saving intermediate results for easy restarts in case of hardware failure. The parallel algorithm was tested on various geographically distributed heterogeneous metacomputing configurations involving an IBM