Climate and Environmental Database Systems contains the papers presented at the Second International Workshop on Climate and Environmental Database Systems, held November 21-23, 1995, in Hamburg, Germany. Climate and environmental data may be separated into two classes, large amounts of well structured data and smaller amounts of less structured data. The large amounts are produced by numerical climate models and by satellites, handling data in the order of magnitude of 100 Tbytes for the climate modelling sites and 1000 Tbytes for the recording and processing of satellite data. Smaller amounts of poorly structured data are the environmental data, which come mainly from observations and measurements. Present-day problems in data management are connected with a variety of data types. Climate and Environmental Database Systems addresses the state of the art, practical experience, and future perspectives for climate and environmental database systems, and may be used as a text for a graduate level course on the subject or as a reference for researchers or practitioners in industry.

The report outlines key elements to consider in designing a program to create climate-quality data from satellites. It examines historical attempts to create climate data records, provides advice on steps for generating, re-analyzing, and storing satellite climate data, and discusses the importance of partnering between agencies, academia, and industry. NOAA will use this report-the first in a two-part study-to draft an implementation plan for climate data records.

Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy.

Environmental Economics and Policy is a best-selling text for environmental economics courses. Offering a policy-oriented approach, it introduces economic theory, empirical fieldwork, and case studies that show how underlying economic principles provided the foundation for environmental policies. Key features include: Introductions to the theory and method of environmental economics, including externalities, benefit-cost analysis, valuation methods, and ecosystem goods and services. Extensive coverage of the major issues including climate change mitigation and adaptation, air and water pollution, and environmental justice. Boxed "Examples" and "Debates" throughout the text, which highlight global examples and major talking points. This text will be of use to undergraduate students of economics. Students will leave the course with a global perspective of how environmental economics has played and can continue to play a role in promoting fair and efficient environmental management. The text is fully supported with end-of-chapter summaries, discussion questions, and self-test exercises in the book. Additional online resources include references, as well as PowerPoint slides for each chapter.

The first comprehensive treatment of data science as a new and powerful way to understand and manage human-environment interactions.

"Environmental Data Analysis with MatLab" is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. It is well written and outlines a clear learning path for researchers and students. It uses real world environmental examples and case studies. It has MatLab software for application in a readily-available software environment. Homework problems help user follow up upon case studies with homework that expands them.