Computational Geosciences

8.

Software Development and Model Validation

8.1.

Scientific Programming Practices

8.1.1.

Languages for Scientific Computing

8.1.1.1.

8.1.1.1.1.

8.1.1.1.1.1.

Array Operations

8.1.1.1.1.2.

8.1.1.1.1.3.

8.1.1.1.2.

8.1.1.1.2.1.

8.1.1.1.2.2.

8.1.1.1.2.3.

Signal Processing

8.1.1.1.3.

8.1.1.1.3.1.

Data Structures

8.1.1.1.3.2.

Data Manipulation

8.1.1.1.3.3.

Time Series Analysis

8.1.1.2.

8.1.1.2.1.

Modern Fortran Features

8.1.1.2.2.

Array Programming

8.1.1.2.3.

Parallel Programming

8.1.1.3.

8.1.1.3.1.

Memory Management

8.1.1.3.2.

Performance Optimization

8.1.1.3.3.

Library Development

8.1.2.

Version Control

8.1.2.1.

8.1.2.1.1.

Repository Management

8.1.2.1.2.

8.1.2.1.3.

Remote Repositories

8.1.2.2.

Branching and Merging

8.1.2.2.1.

Feature Branches

8.1.2.2.2.

Merge Strategies

8.1.2.2.3.

Conflict Resolution

8.1.3.

Debugging and Profiling

8.1.3.1.

Debugging Tools and Techniques

8.1.3.1.1.

8.1.3.1.2.

8.1.3.1.3.

Testing Strategies

8.1.3.2.

Code Profiling and Optimization

8.1.3.2.1.

Performance Analysis

8.1.3.2.2.

Bottleneck Identification

8.1.3.2.3.

Optimization Techniques

8.2.

Verification, Validation, and Uncertainty Quantification

8.2.1.

Code Verification

8.2.1.1.

Analytical Solutions

8.2.1.1.1.

Method of Manufactured Solutions

8.2.1.1.2.

Benchmark Problems

8.2.1.1.3.

Convergence Testing

8.2.1.2.

8.2.1.2.1.

Standard Test Cases

8.2.1.2.2.

Code Comparison

8.2.1.2.3.

Performance Metrics

8.2.2.

Model Validation

8.2.2.1.

Comparison with Observational Data

8.2.2.1.1.

Statistical Measures

8.2.2.1.2.

Goodness of Fit

8.2.2.1.3.

Residual Analysis

8.2.2.2.

Validation Metrics

8.2.2.2.1.

Root Mean Square Error

8.2.2.2.2.

Correlation Coefficients

8.2.2.2.3.

8.2.3.

Sensitivity Analysis

8.2.3.1.

Local Sensitivity Analysis

8.2.3.1.1.

Finite Difference Methods

8.2.3.1.2.

Adjoint Methods

8.2.3.1.3.

Parameter Ranking

8.2.3.2.

Global Sensitivity Analysis

8.2.3.2.1.

8.2.3.2.2.

8.2.3.2.3.

Variance-Based Methods

8.2.4.

Uncertainty Quantification

8.2.4.1.

Sources of Uncertainty

8.2.4.1.1.

Parameter Uncertainty

8.2.4.1.2.

Model Structure Uncertainty

8.2.4.1.3.

Input Data Uncertainty

8.2.4.2.

Propagation of Uncertainty

8.2.4.2.1.

Monte Carlo Methods

8.2.4.2.2.

Polynomial Chaos

8.2.4.2.3.

Stochastic Collocation

8.2.5.

Model Intercomparison Projects

8.2.5.1.

Purpose and Structure

8.2.5.1.1.

Standardized Protocols

8.2.5.1.2.

Common Datasets

8.2.5.1.3.

Evaluation Metrics

8.2.5.2.

Examples in Geosciences

8.2.5.2.1.

Climate Model Intercomparison

8.2.5.2.2.

Hydrological Model Comparison

8.2.5.2.3.

Seismic Wave Propagation Studies

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7. Data-Driven Methods and Machine Learning

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1. Foundations of Computational Geosciences