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Machine Learning and Data Mining Approaches to Climate Science

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Machine Learning and Data Mining Approaches to Climate Science
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Combining Analog Method and Ensemble Data Assimilation: Application to the Lorenz-63 Chaotic System
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    Chapter 2 Machine Learning Methods for ENSO Analysis and Prediction
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    Chapter 3 Teleconnections in Climate Networks: A Network-of-Networks Approach to Investigate the Influence of Sea Surface Temperature Variability on Monsoon Systems
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    Chapter 4 Comparison of Linear and Tobit Modeling of Downscaled Daily Precipitation over the Missouri River Basin Using MIROC5
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    Chapter 5 Unsupervised Method for Water Surface Extent Monitoring Using Remote Sensing Data
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    Chapter 6 A Bayesian Multivariate Nonhomogeneous Markov Model
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    Chapter 7 Extracting the Climatology of Thunderstorms
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    Chapter 8 Predicting Crop Yield via Partial Linear Model with Bootstrap
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    Chapter 9 A New Distribution Mapping Technique for Climate Model Bias Correction
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    Chapter 10 Evaluation of Global Climate Models Based on Global Impacts of ENSO
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    Chapter 11 Using Causal Discovery Algorithms to Learn About Our Planet’s Climate
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    Chapter 12 SCI-WMS: Python-Based Web Mapping Service for Visualizing Geospatial Data
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    Chapter 13 Multilevel Random Slope Approach and Nonparametric Inference for River Temperature, Under Haphazard Sampling
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    Chapter 14 Kernel and Information-Theoretic Methods for the Extraction and Predictability of Organized Tropical Convection
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    Chapter 15 A Complex Network Approach to Investigate the Spatiotemporal Co-variability of Extreme Rainfall
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    Chapter 16 Evaluating the Impact of Climate Change on Dynamics of House Insurance Claims
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    Chapter 17 Change Detection in Climate Time Series Based on Bounded-Variation Clustering
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    Chapter 18 Developing an Event Database for Cutoff Low Climatology over Southwestern North America
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    Chapter 19 Detecting Extreme Events from Climate Time Series via Topic Modeling
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    Chapter 20 Identifying Developing Cloud Clusters Using Predictive Features
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    Chapter 21 Comparison of the Main Features of the Zonally Averaged Surface Air Temperature as Represented by Reanalysis and AR4 Models
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    Chapter 22 Investigation of Precipitation Thresholds in the Indian Monsoon Using Logit-Normal Mixed Models
Attention for Chapter 5: Unsupervised Method for Water Surface Extent Monitoring Using Remote Sensing Data
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