John L. Schnase

A tamarisk habitat suitability map for the continental United States

This paper presents a national-scale map of habitat suitability for tamarisk (Tamarix spp, salt cedar), a high-priority invasive species. We successfully integrate satellite data and tens of thousands of field sampling points through logistic regression modeling to create a habitat suitability map that is 90% accurate. This interagency effort uses field data collected and coordinated through the US Geological Survey and nationwide environmental data layers derived from NASAs MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrate the use of the map by ranking the 48 continental US states (and the District of Columbia) based on their absolute, as well as proportional, areas of “highly likely” and “moderately likely” habitat for Tamarix. The interagency effort and modeling approach presented here could be used to map other harmful species, in the US and globally.

A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce

ABSTRACT Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (~10× speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328%). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.

Information technology challenges of biodiversity and ecosystems informatics

Computer scientists, biologists, and natural resource managers recently met to examine the prospects for advancing computer science and information technology research by focusing on the complex and often-unique challenges found in the biodiversity and ecosystem domain. The workshop and its final report reveal that the biodiversity and ecosystem sciences are fundamentally information sciences and often address problems having distinctive attributes of scale and socio-technical complexity. The paper provides an overview of the emerging field of biodiversity and ecosystem informatics and demonstrates how the demands of biodiversity and ecosystem research can advance our understanding and use of information technologies.

Biological invasions: a challenge in ecological forecasting

The spread of invasive species is one of the most daunting environmental, economic, and human-health problems facing the United States and the World today. It is one of several grand challenge environmental problems being considered by NASAs Earth Science Vision for 2025. The invasive species problem is complex and presents many challenges. Developing an invasive species predictive capability could significantly advance the science and technology of ecological forecasting.

Biodiversity and ecosystem informatics

The field of Biodiversity and Ecosystem Informatics (BDEI) brings together computer scientists, biologists, natural resource managers, and others who wish to solve real-world challenges while advancing the underlying ecological, computer, and information sciences. The potential for synergies among these disciplines is high, because our need to understand complex, ecosystem-scale processes requires the solution to many groundbreaking technological problems. Fortunately, we are beginning to see increased support for applied computer science and information technology research in the context of environmental problem-solving. In July, 2001, the National Science Foundation (NSF), in collaboration with the United States Geological Survey (USGS), and the National Aeronautics and Space Administration (NASA), invited proposals for high-risk, small-scale planning and incubation activities to catalyze innovation and rapid advances in this new research community. The papers included in this special issue are selected, peer-reviewed summaries from principal investigators involved in this first NSF BDEI effort. These papers provide an overview of this emerging area and remind us that computer and information science and engineering play a crucial role in creating the technologies from which advances in the natural sciences evolve.

Big Data Challenges in Climate Science: Improving the next-generation cyberinfrastructure

The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Some examples of the types of activities that increasingly require an improved cyberinfrastructure for dealing with large amounts of critical scientific data are climate model intercomparison (CMIP) experiments; the integration of observational data and climate reanalysis data with climate model outputs, as seen in the Observations for Model Intercomparison Projects (Obs4MIPs), Analysis for Model Intercomparison Projects (Ana4MIPs), and Collaborative Reanalysis Technical Environment-Intercomparison Project (CREATE-IP) activities; and the collaborative work of the Intergovernmental Panel on Climate Change (IPCC). This article provides an overview of some of climate sciences big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), which is the primary cyberinfrastructure currently supporting global climate research activities.

The NASA Center for Climate Simulation Data Management System

The NASA Center for Climate Simulation (NCCS) plays a lead role in meeting the computational and data management requirements of climate modeling and data assimilation. Scientific data services are becoming an important part of the NCCS mission. The NCCS Data Management System (DMS) is a key element of NCCSs technological response to this expanding role. In DMS, we are using the Integrated Rule-Oriented Data System (iRODS) to combine disparate data collections into a federated platform upon which various data services can be implemented. Work to date has demonstrated the effectiveness of iRODS in managing a large-scale collection of observational data, managing model output data in a cloud computing context, and managing NCCS-hosted data products that are published through community-defined services such as the Earth System Grid (ESG). Plans call for staged operational adoption of iRODS in the NCCS.

Climatespark: an In-Memory Distributed Computing Framework for Big Climate Data Analytics

Abstract The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark , to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple-dimensional, array-based datasets in various geoscience domains.

Climate Analytics as a Service

Exascale computing, big data, and cloud computing are driving the evolution of large-scale information systems toward a model of data-proximal analysis. In response, we are developing a concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management. With this approach, high-performance compute–storage implemented as an analytic system is part of a dynamic archive comprising both static and computationally realized objects. It is a system the capabilities of which are framed as behaviors over a static data collection, but in which queries cause results to be created, rather than found and retrieved. Those results can be the product of a complex analysis, but, importantly, they can also be tailored responses to the simplest of requests. NASAs MERRA Analytic Service and associated Climate Data Services Application Programming Interface provide a real-world example of climate analytics delivered as a service in this way. Our experiences reveal several advantages to this approach, not the least of which is orders-of-magnitude time reduction in the data-assembly task common to many scientific workflows.

Use and benefits of NASA’s RECOVER for post-fire decision support

Today’s extended fire seasons and large fire footprints have prompted state and federal land-management agencies to devote increasingly large portions of their budgets to wildfire management. As fire costs continue to rise, timely and comprehensive fire information becomes increasingly critical to response and rehabilitation efforts. The NASA Rehabilitation Capability Convergence for Ecosystem Recovery (RECOVER) post-fire decision support system is a server-based application designed to rapidly provide land managers with the information needed to develop a comprehensive rehabilitation plan. This study evaluated the efficacy of RECOVER through structured interviews with land managers (n = 19) who used RECOVER and were responsible for post-fire rehabilitation efforts on over 715 000 ha of fire-affected lands. Although the benefit of better-informed decisions is difficult to quantify, the results of this study illustrate that RECOVER’s decision support capabilities provided information to land managers that either validated or altered their decisions on post-fire treatments estimated at over US