{"product_id":"data-analysis-for-the-geosciences-essentials-of-uncertainty-comparison-and-visualization-paperback-softback-9781119747871","title":"Data Analysis for the Geosciences; Essentials of Uncertainty, Comparison, and Visualization (Paperback \/ softback) 9781119747871","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Analysis for the Geosciences\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eEssentials of Uncertainty, Comparison, and Visualization\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMichael W. Liemohn (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119747871, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 2 November 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e448 pages\u003cbr\u003e25.2 x 17.8 x 2.3 cm, 0.907 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eAn initial course in scientific data analysis and hypothesis testing designed for students in all science, technology, engineering, and mathematics disciplines\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eData Analysis for the Geosciences: Essentials of Uncertainty, Comparison, and Visualization \u003c\/i\u003eis a textbook for upper-level undergraduate STEM students, designed to be their statistics course in a degree program.\u003c\/p\u003e \u003cp\u003eThis volume provides a comprehensive introduction to data analysis, visualization, and data-model comparisons and metrics, within the framework of the uncertainty around the values. It offers a learning experience based on real data from the Earth, ocean, atmospheric, space, and planetary sciences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout this volume:\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eServes as an initial course in scientific data analysis and hypothesis testing\u003c\/li\u003e \u003cli\u003eFocuses on the methods of data processing\u003c\/li\u003e \u003cli\u003eIntroduces a wide range of analysis techniques\u003c\/li\u003e \u003cli\u003eDescribes the many ways to compare data with models\u003c\/li\u003e \u003cli\u003eCenters on applications rather than derivations\u003c\/li\u003e \u003cli\u003eExplains how to select appropriate statistics for meaningful decisions\u003c\/li\u003e \u003cli\u003eExplores the importance of the concept of uncertainty\u003c\/li\u003e \u003cli\u003eUses examples from real geoscience observations\u003c\/li\u003e \u003cli\u003eHomework problems at the end of chapters\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eThe American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.\u003c\/i\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xxi\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Assessment and Uncertainty: Examples and Introductory Concepts 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Chicken Little, Amateur Meteorologist 2\u003c\/p\u003e \u003cp\u003e1.2 Uncertainty Ascribes Meaning to Values 3\u003c\/p\u003e \u003cp\u003e1.3 Significant Figures 3\u003c\/p\u003e \u003cp\u003e1.4 Types of Uncertainty 7\u003c\/p\u003e \u003cp\u003e1.5 Example: Finding Saturn’s Moons 9\u003c\/p\u003e \u003cp\u003e1.6 Comparing Two Numbers: Are They Measuring the Same Value? 11\u003c\/p\u003e \u003cp\u003e1.6.1 Distributions of Number Sets 12\u003c\/p\u003e \u003cp\u003e1.6.2 The Gaussian Distribution 13\u003c\/p\u003e \u003cp\u003e1.6.3 Testing a Specific Value within a Data Set: The z Test 14\u003c\/p\u003e \u003cp\u003e1.6.4 Comparing Two Values Revisited 18\u003c\/p\u003e \u003cp\u003e1.7 Use and Misuse of Statistics 19\u003c\/p\u003e \u003cp\u003e1.8 Example: Solar Wind Density and Space Weather 20\u003c\/p\u003e \u003cp\u003e1.9 Uncertainty and the Scientific Method 22\u003c\/p\u003e \u003cp\u003e1.10 Further Reading 24\u003c\/p\u003e \u003cp\u003e1.11 Exercises in the Geosciences 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Plotting Data: Visualizing Sets of Numbers 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Plotting One- Dimensional Data 27\u003c\/p\u003e \u003cp\u003e2.1.1 What Makes a Good Plot? 29\u003c\/p\u003e \u003cp\u003e2.1.2 Exploratory Versus Explanatory Plot Styles 31\u003c\/p\u003e \u003cp\u003e2.2 Example: Earth’s Magnetic Field Strength 33\u003c\/p\u003e \u003cp\u003e2.3 Probability Distributions— The Histogram 35\u003c\/p\u003e \u003cp\u003e2.4 Plotting Two Data Sets Against Each Other 39\u003c\/p\u003e \u003cp\u003e2.4.1 Overlaid Histograms 39\u003c\/p\u003e \u003cp\u003e2.4.2 The Scatterplot 40\u003c\/p\u003e \u003cp\u003e2.4.3 The Box Plot 42\u003c\/p\u003e \u003cp\u003e2.4.4 The Box- and- Whisker Scatterplot 43\u003c\/p\u003e \u003cp\u003e2.4.5 The Running Average Plot 44\u003c\/p\u003e \u003cp\u003e2.5 Example: Temperature and Carbon Dioxide 48\u003c\/p\u003e \u003cp\u003e2.6 Scientific Visualization: A Sampling from the Literature 50\u003c\/p\u003e \u003cp\u003e2.6.1 A Very Brief History of Visualization 51\u003c\/p\u003e \u003cp\u003e2.6.2 Good Modern- Day Example Visualizations 53\u003c\/p\u003e \u003cp\u003e2.7 Visualization Best Practices 58\u003c\/p\u003e \u003cp\u003e2.7.1 Levels of Abstraction 58\u003c\/p\u003e \u003cp\u003e2.7.2 A Process for a Good Graphic 61\u003c\/p\u003e \u003cp\u003e2.7.3 Types of Colorblindness 63\u003c\/p\u003e \u003cp\u003e2.7.4 Color Scales 63\u003c\/p\u003e \u003cp\u003e2.8 Further Reading 65\u003c\/p\u003e \u003cp\u003e2.9 Exercises in the Geosciences 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Uncertainty Analysis: Techniques for Propagating Uncertainty 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Propagating Uncertainty 69\u003c\/p\u003e \u003cp\u003e3.1.1 Calculating Uncertainty with One Independent Variable 69\u003c\/p\u003e \u003cp\u003e3.1.2 Calculating Uncertainty with Two Independent Variables 70\u003c\/p\u003e \u003cp\u003e3.1.3 Calculating Uncertainty with Many Independent Variables 72\u003c\/p\u003e \u003cp\u003e3.2 Example: Atmospheric Density 72\u003c\/p\u003e \u003cp\u003e3.2.1 The Hydrostatic Equilibrium Approximation 72\u003c\/p\u003e \u003cp\u003e3.2.2 One Independent Variable 73\u003c\/p\u003e \u003cp\u003e3.2.3 Two Independent Variables 74\u003c\/p\u003e \u003cp\u003e3.2.4 Many Independent Variables 74\u003c\/p\u003e \u003cp\u003e3.3 Fractional and Percentage Uncertainties 75\u003c\/p\u003e \u003cp\u003e3.4 Special Cases of Uncertainty Propagation 77\u003c\/p\u003e \u003cp\u003e3.4.1 Addition and Subtraction 77\u003c\/p\u003e \u003cp\u003e3.4.2 Multiplication and Division 78\u003c\/p\u003e \u003cp\u003e3.4.2.1 Multiplication of Two Parameters 78\u003c\/p\u003e \u003cp\u003e3.4.2.2 Uncertainty of Air Pressure 79\u003c\/p\u003e \u003cp\u003e3.4.2.3 Division with Correlated Variables 80\u003c\/p\u003e \u003cp\u003e3.4.2.4 Multiplication and Division with Independent Variables 81\u003c\/p\u003e \u003cp\u003e3.4.3 Power Laws 82\u003c\/p\u003e \u003cp\u003e3.4.4 Exponentials and Logarithms 82\u003c\/p\u003e \u003cp\u003e3.4.4.1 Exponential Functions 83\u003c\/p\u003e \u003cp\u003e3.4.4.2 Logarithmic Functions 84\u003c\/p\u003e \u003cp\u003e3.4.5 Trigonometric Functions 84\u003c\/p\u003e \u003cp\u003e3.5 Stepwise Uncertainty Propagation 85\u003c\/p\u003e \u003cp\u003e3.6 Example: Planetary Equilibrium Temperature 87\u003c\/p\u003e \u003cp\u003e3.7 Multistep Processing 90\u003c\/p\u003e \u003cp\u003e3.8 Final Advice on Uncertainty Propagation 91\u003c\/p\u003e \u003cp\u003e3.9 Further Reading 93\u003c\/p\u003e \u003cp\u003e3.10 Exercises in the Geosciences 93\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Centroids and Spreads: Analyzing a Set of Numbers 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Quantitatively Describing a Data Set: The Centroid 95\u003c\/p\u003e \u003cp\u003e4.1.1 Three Versions of Mean 96\u003c\/p\u003e \u003cp\u003e4.1.2 More Centroids: Median and Mode 98\u003c\/p\u003e \u003cp\u003e4.1.3 Histograms and the Arithmetic Mean 99\u003c\/p\u003e \u003cp\u003e4.2 Quantitatively Describing a Data Set: Spread 100\u003c\/p\u003e \u003cp\u003e4.2.1 Measures of Spread: Standard Deviation and Mean Absolute Difference 100\u003c\/p\u003e \u003cp\u003e4.2.2 Another Measure of Spread: Quantiles 102\u003c\/p\u003e \u003cp\u003e4.2.3 Spread Via Full Width at Half Maximum 106\u003c\/p\u003e \u003cp\u003e4.2.4 Spread as an L- p Norm 107\u003c\/p\u003e \u003cp\u003e4.2.5 Sample Versus Population 108\u003c\/p\u003e \u003cp\u003e4.3 Random and Systematic Error of a Data Set 109\u003c\/p\u003e \u003cp\u003e4.4 Which Centroid and Spread to Use and Other Tidbits of Advice 111\u003c\/p\u003e \u003cp\u003e4.5 Standard Deviation of the Mean 112\u003c\/p\u003e \u003cp\u003e4.6 Counting Statistics 113\u003c\/p\u003e \u003cp\u003e4.7 Example: Galactic Cosmic Rays 116\u003c\/p\u003e \u003cp\u003e4.8 Further Reading 119\u003c\/p\u003e \u003cp\u003e4.9 Exercises in the Geosciences 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Assessing Normality: Tests for Assessing the Gaussian Nature of a Distribution 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Histogram Check 124\u003c\/p\u003e \u003cp\u003e5.2 Comparing Centroid and Spread Measures 126\u003c\/p\u003e \u003cp\u003e5.3 Skew 128\u003c\/p\u003e \u003cp\u003e5.4 Kurtosis 130\u003c\/p\u003e \u003cp\u003e5.5 The Chi- Squared Test 132\u003c\/p\u003e \u003cp\u003e5.6 The Kolmogorov–Smirnov Test 137\u003c\/p\u003e \u003cp\u003e5.7 Example: pH in a Lake 139\u003c\/p\u003e \u003cp\u003e5.8 Asymmetric Uncertainties 142\u003c\/p\u003e \u003cp\u003e5.9 Outliers— Tests for a Single Data Value 144\u003c\/p\u003e \u003cp\u003e5.10 Combining Centroid and Spread: The Weighted Average 146\u003c\/p\u003e \u003cp\u003e5.11 Example: pH in a Lake Redux 148\u003c\/p\u003e \u003cp\u003e5.12 Further Reading 149\u003c\/p\u003e \u003cp\u003e5.13 Exercises in the Geosciences 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Correlating Two Data Sets: Analyzing Two Sets of Numbers Together 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Comparing Two Number Sets 153\u003c\/p\u003e \u003cp\u003e6.1.1 Chi- Squared and Kolmogorov–Smirnov Tests 154\u003c\/p\u003e \u003cp\u003e6.1.2 The Student’s t Test 155\u003c\/p\u003e \u003cp\u003e6.1.3 The Welch’s t Test 156\u003c\/p\u003e \u003cp\u003e6.2 Linear Correlation 157\u003c\/p\u003e \u003cp\u003e6.2.1 Covariance of Two Data Sets 158\u003c\/p\u003e \u003cp\u003e6.2.2 Pearson Linear Correlation Coefficient 161\u003c\/p\u003e \u003cp\u003e6.2.3 Spearman Rank- Order Correlation 163\u003c\/p\u003e \u003cp\u003e6.2.4 Correlation with Logarithms 167\u003c\/p\u003e \u003cp\u003e6.3 Example: Atmospheric Ozone and Temperature 168\u003c\/p\u003e \u003cp\u003e6.4 Uncertainty of R 172\u003c\/p\u003e \u003cp\u003e6.4.1 The Jackknife Method 172\u003c\/p\u003e \u003cp\u003e6.4.2 The Bootstrap Method 173\u003c\/p\u003e \u003cp\u003e6.4.3 Uncertainty of R for the Ozone- Temperature Example 175\u003c\/p\u003e \u003cp\u003e6.5 Correlation and Causation 177\u003c\/p\u003e \u003cp\u003e6.6 Further Reading 178\u003c\/p\u003e \u003cp\u003e6.7 Exercises in the Geosciences 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Curve Fitting: Fitting a Line between Two Sets of Numbers 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Linear Regression 181\u003c\/p\u003e \u003cp\u003e7.1.1 Obtaining A and B 181\u003c\/p\u003e \u003cp\u003e7.1.2 Uncertainties on A and B 185\u003c\/p\u003e \u003cp\u003e7.1.3 The Zero- Intercept Special Case 186\u003c\/p\u003e \u003cp\u003e7.1.4 Weighted Linear Fitting 187\u003c\/p\u003e \u003cp\u003e7.2 Testing a Linear Fit 188\u003c\/p\u003e \u003cp\u003e7.3 Example: Human- Induced Seismicity 191\u003c\/p\u003e \u003cp\u003e7.4 Nonlinear Fitting 194\u003c\/p\u003e \u003cp\u003e7.4.1 Polynomial Fitting 194\u003c\/p\u003e \u003cp\u003e7.4.2 Generalized “Linear Coefficient” Fitting 196\u003c\/p\u003e \u003cp\u003e7.4.3 Exponential Fitting: Linearizing the Dependence on Coefficients 197\u003c\/p\u003e \u003cp\u003e7.4.4 Piecewise Linear Fitting 198\u003c\/p\u003e \u003cp\u003e7.4.5 Advice about Curve Fitting 199\u003c\/p\u003e \u003cp\u003e7.5 Example: The Ozone Hole 200\u003c\/p\u003e \u003cp\u003e7.6 Iterative Curve Fitting 203\u003c\/p\u003e \u003cp\u003e7.6.1 One- Dimensional Iterative Curve Fitting 203\u003c\/p\u003e \u003cp\u003e7.6.2 Multidimensional Iterative Curve Fitting 205\u003c\/p\u003e \u003cp\u003e7.6.3 Gradient Descent Curve Fitting 208\u003c\/p\u003e \u003cp\u003e7.7 Final Thoughts on Curve Fitting 209\u003c\/p\u003e \u003cp\u003e7.8 Further Reading 210\u003c\/p\u003e \u003cp\u003e7.9 Exercises in the Geosciences 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Data- Model Comparison Basics: Philosophies of Calculating and Categorizing Metrics 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Example Model: River Flow Rate 213\u003c\/p\u003e \u003cp\u003e8.2 What Is a Model? 214\u003c\/p\u003e \u003cp\u003e8.3 Visualizing Observed and Modeled Values Together 217\u003c\/p\u003e \u003cp\u003e8.3.1 Scatterplots of Data and Model Values 217\u003c\/p\u003e \u003cp\u003e8.3.2 The 2D Histogram Plot 219\u003c\/p\u003e \u003cp\u003e8.3.3 Overlaid Histogram Plots 221\u003c\/p\u003e \u003cp\u003e8.3.4 Cumulative Probability Distribution Plots 222\u003c\/p\u003e \u003cp\u003e8.3.5 Quantile–Quantile Plots 224\u003c\/p\u003e \u003cp\u003e8.4 Example: Total Solar Irradiance 226\u003c\/p\u003e \u003cp\u003e8.5 A Diverse Zoo of Metrics 229\u003c\/p\u003e \u003cp\u003e8.5.1 The Primary Categories of Metrics 230\u003c\/p\u003e \u003cp\u003e8.5.2 Skill 231\u003c\/p\u003e \u003cp\u003e8.5.3 Metrics Categories Based on Subsetting 234\u003c\/p\u003e \u003cp\u003e8.6 The Concept of Model “Goodness of Fit” 235\u003c\/p\u003e \u003cp\u003e8.7 Application Usability Levels 236\u003c\/p\u003e \u003cp\u003e8.8 Designing a Meaningful Data- Model Comparison 237\u003c\/p\u003e \u003cp\u003e8.9 Further Reading 239\u003c\/p\u003e \u003cp\u003e8.10 Exercises in the Geosciences 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Fit Performance Metrics: Data- Model Comparisons Based on Exact Observed and Modeled Values 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 What Is Fit Performance? 244\u003c\/p\u003e \u003cp\u003e9.2 Running Example: Dst and the O’Brien Model 245\u003c\/p\u003e \u003cp\u003e9.3 Accuracy 250\u003c\/p\u003e \u003cp\u003e9.3.1 The Big Three of Accuracy: MSE, RMSE, and MAE 251\u003c\/p\u003e \u003cp\u003e9.3.2 Neglecting Degrees of Freedom 253\u003c\/p\u003e \u003cp\u003e9.3.3 Normalizing the Accuracy Measure 256\u003c\/p\u003e \u003cp\u003e9.3.4 Percentage Accuracy Metrics 257\u003c\/p\u003e \u003cp\u003e9.3.5 Choosing the Right Accuracy Metric 261\u003c\/p\u003e \u003cp\u003e9.4 Bias 262\u003c\/p\u003e \u003cp\u003e9.4.1 Mean Error 262\u003c\/p\u003e \u003cp\u003e9.4.2 Percentage Bias 265\u003c\/p\u003e \u003cp\u003e9.5 Precision 266\u003c\/p\u003e \u003cp\u003e9.5.1 Modeling Yield 266\u003c\/p\u003e \u003cp\u003e9.5.2 Definitions of Precision Using Standard Deviation 268\u003c\/p\u003e \u003cp\u003e9.6 Association 268\u003c\/p\u003e \u003cp\u003e9.6.1 Correlation Coefficient 269\u003c\/p\u003e \u003cp\u003e9.6.2 Nonlinear Association Metrics 270\u003c\/p\u003e \u003cp\u003e9.7 Extremes 272\u003c\/p\u003e \u003cp\u003e9.7.1 Extremes of the Cumulative Probability Distribution 272\u003c\/p\u003e \u003cp\u003e9.7.2 Using Skew and Kurtosis for an Extremes Assessment 276\u003c\/p\u003e \u003cp\u003e9.8 Skill 278\u003c\/p\u003e \u003cp\u003e9.8.1 Prediction Efficiency 278\u003c\/p\u003e \u003cp\u003e9.8.2 Other Options for Fit Performance Skill 279\u003c\/p\u003e \u003cp\u003e9.9 Discrimination 281\u003c\/p\u003e \u003cp\u003e9.10 Reliability 283\u003c\/p\u003e \u003cp\u003e9.11 Summarizing the Running Example 286\u003c\/p\u003e \u003cp\u003e9.12 Summary of Fit Performance Metrics 287\u003c\/p\u003e \u003cp\u003e9.13 Further Reading 291\u003c\/p\u003e \u003cp\u003e9.14 Exercises in the Geosciences 292\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Event Detection Metrics: Comparing Observed and Modeled Number Sets When Only Event Status Matters 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Defining an Event 296\u003c\/p\u003e \u003cp\u003e10.2 Contingency Tables 299\u003c\/p\u003e \u003cp\u003e10.3 Data- Model Comparisons with Events 301\u003c\/p\u003e \u003cp\u003e10.4 Running Example: Will It Rain? 303\u003c\/p\u003e \u003cp\u003e10.5 Significance of a Contingency Table 307\u003c\/p\u003e \u003cp\u003e10.6 Accuracy 310\u003c\/p\u003e \u003cp\u003e10.7 Bias 311\u003c\/p\u003e \u003cp\u003e10.8 Precision 313\u003c\/p\u003e \u003cp\u003e10.9 Association 314\u003c\/p\u003e \u003cp\u003e10.9.1 Odds Ratio 315\u003c\/p\u003e \u003cp\u003e10.9.2 Odds Ratio Skill Score 316\u003c\/p\u003e \u003cp\u003e10.9.3 Matthews Correlation Coefficient 317\u003c\/p\u003e \u003cp\u003e10.10 Extremes 317\u003c\/p\u003e \u003cp\u003e10.11 Skill 321\u003c\/p\u003e \u003cp\u003e10.11.1 Heidke Skill Score 321\u003c\/p\u003e \u003cp\u003e10.11.2 Peirce and Clayton Skill Scores 323\u003c\/p\u003e \u003cp\u003e10.11.3 Gilbert Skill Score 324\u003c\/p\u003e \u003cp\u003e10.12 Discrimination 325\u003c\/p\u003e \u003cp\u003e10.13 Reliability 326\u003c\/p\u003e \u003cp\u003e10.14 Summarizing the Running Example 327\u003c\/p\u003e \u003cp\u003e10.15 Summary of Event Detection Metrics 328\u003c\/p\u003e \u003cp\u003e10.16 Further Reading 330\u003c\/p\u003e \u003cp\u003e10.17 Exercises in the Geosciences 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Sliding Thresholds: Event Detection Metrics with a Variable Event Identification 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Sliding the Event Identification Thresholds 334\u003c\/p\u003e \u003cp\u003e11.2 Sweeping the Modeled Threshold 337\u003c\/p\u003e \u003cp\u003e11.3 Sweeping the Data Threshold 340\u003c\/p\u003e \u003cp\u003e11.4 Sweeping Both Thresholds Simultaneously 342\u003c\/p\u003e \u003cp\u003e11.5 Metric- Versus- Metric Curves 344\u003c\/p\u003e \u003cp\u003e11.5.1 ROC Curves 344\u003c\/p\u003e \u003cp\u003e11.5.2 Alt- ROC Curves 346\u003c\/p\u003e \u003cp\u003e11.5.3 STONE Curves 347\u003c\/p\u003e \u003cp\u003e11.6 Application of Sliding Thresholds to the Geophysical Running Examples 349\u003c\/p\u003e \u003cp\u003e11.6.1 Event Definitions for the Running Examples 349\u003c\/p\u003e \u003cp\u003e11.6.2 Metric- Versus- Modeled Threshold Curves for the Running Examples 352\u003c\/p\u003e \u003cp\u003e11.6.3 Metric- Versus- Observed Threshold Curves for the Running Examples 355\u003c\/p\u003e \u003cp\u003e11.6.4 Metric- Versus- Simultaneous Threshold Sweep Curves for the Running Examples 357\u003c\/p\u003e \u003cp\u003e11.6.5 Metric- Versus- Metric Analysis for the Running Examples 359\u003c\/p\u003e \u003cp\u003e11.7 The Power of Sliding Thresholds 362\u003c\/p\u003e \u003cp\u003e11.8 Further Reading 364\u003c\/p\u003e \u003cp\u003e11.9 Exercises in the Geosciences 365\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Applications of Metrics and Uncertainty: Final Advice and Introductions to Advanced Topics 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Choosing the Right Set of Metrics 367\u003c\/p\u003e \u003cp\u003e12.1.1 Metrics for Fit Performance Assessment on Gaussian Distributions 368\u003c\/p\u003e \u003cp\u003e12.1.2 Metrics for Fit Performance Assessment on Non- Gaussian Distributions 369\u003c\/p\u003e \u003cp\u003e12.1.3 Metrics for Event Detection Assessment 372\u003c\/p\u003e \u003cp\u003e12.2 Combining Metrics for Robust Data- Model Comparisons 374\u003c\/p\u003e \u003cp\u003e12.2.1 The Accuracy–Bias–Precision Trifecta 374\u003c\/p\u003e \u003cp\u003e12.2.2 The Accuracy–Association Connection 376\u003c\/p\u003e \u003cp\u003e12.2.3 The Association–Extremes Linkage 377\u003c\/p\u003e \u003cp\u003e12.2.4 Expanding Our Understanding of Skill 378\u003c\/p\u003e \u003cp\u003e12.2.5 Using Discrimination and Reliability Together 379\u003c\/p\u003e \u003cp\u003e12.3 Uncertainty on Metrics 380\u003c\/p\u003e \u003cp\u003e12.4 Uncertainty on Fit Performance Metrics for the Dst Running Example 381\u003c\/p\u003e \u003cp\u003e12.5 A Recipe for Robust Comparisons 385\u003c\/p\u003e \u003cp\u003e12.6 Metrics and Decision- Making 387\u003c\/p\u003e \u003cp\u003e12.6.1 Choice Combination Statistics 388\u003c\/p\u003e \u003cp\u003e12.6.2 Example: Spacecraft- Charging Model 390\u003c\/p\u003e \u003cp\u003e12.7 Additional Advanced Topics 392\u003c\/p\u003e \u003cp\u003e12.7.1 Periodicity Analysis 392\u003c\/p\u003e \u003cp\u003e12.7.2 Time- Lagged Analysis 393\u003c\/p\u003e \u003cp\u003e12.7.3 Additional Tests 394\u003c\/p\u003e \u003cp\u003e12.7.4 Multidimensional Data Analysis 394\u003c\/p\u003e \u003cp\u003e12.7.5 Multidimensional Data- Model Comparisons 396\u003c\/p\u003e \u003cp\u003e12.7.6 Uncertainty Quantification 397\u003c\/p\u003e \u003cp\u003e12.7.7 Design of Experiments 398\u003c\/p\u003e \u003cp\u003e12.7.8 Geographical Information System (GIS) Analysis 398\u003c\/p\u003e \u003cp\u003e12.7.9 Machine Learning 399\u003c\/p\u003e \u003cp\u003e12.8 Uncertainty and the Scientist 400\u003c\/p\u003e \u003cp\u003e12.9 Further Reading 402\u003c\/p\u003e \u003cp\u003e12.10 Exercises in Geoscience 406\u003c\/p\u003e \u003cp\u003eIndex 407\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Earth sciences [\u003ca title=\"See our other books on Earth sciences\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Earth%20sciences%20%5BRB%5D%22\"\u003eRB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"American Geophysical Union","offers":[{"title":"Brand New","offer_id":52584781545752,"sku":"9781119747871","price":82.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119747871.jpg?v=1787966900","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/data-analysis-for-the-geosciences-essentials-of-uncertainty-comparison-and-visualization-paperback-softback-9781119747871","provider":"Freshly Printed Books","version":"1.0","type":"link"}