Machine Learning and Data Mining Approaches to Climate Science

This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is headed. Given its interdisciplinary nature, Climate Informatics offers insights, tools and methods that are increasingly needed in order to understand the climate system, an aspect which in turn has become crucial because of the threat of climate change. There has been a veritable explosion in the amount of data produced by satellites, environmental sensors and climate models that monitor, measure and forecast the earth system. In order to meaningfully pursue knowledge discovery on the basis of such voluminous and diverse datasets, it is necessary to apply machine learning methods, and Climate Informatics lies at the intersection of machine learning and climate science. This book grew out of the fourth workshop on Climate Informatics held in Boulder, Colorado in Sep. 2014.

State of the art application in a new and rapidly expanding field Includes review articles by acknowledged experts Presents novel research in climate informatics

Auteur
Dr. Valliappa Lakshmanan is an expert in machine intelligence for meteorological applications, and in designing and developing large-scale software systems. He is skilled in communicating technical and non-technical material to diverse audiences. He has studied at the Indian Institute of Technology in Madras, the Ohio State University in Columbus and the University of Oklahoma. Dr. Lakshmanan is currently employed as a Research Scientist at CIMMS, being the technical lead on several software projects and research groups. He also develops automated real-time pattern recognition algorithms and visualization techniques for weather phenomena. He has (co-)written many journal articles.

Contenu
From the Contents: Machine learning, statistics, or data mining, applied to climate science.- Management and processing of large climate datasets.- Long and short-term climate prediction.- Ensemble characterization of climate model projections.- Past (paleo) climate reconstruction.