High Accuracy Surface Modeling Method: The Robustness

✍ Scribed by Na Zhao, TianXiang Yue

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 200
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book focuses on the robustness analysis of high accuracy surface modeling method (HASM) to yield good performance of it. Understanding the sensitivity and uncertainty is important in model applications. The book aims to advance an integral framework for assessing model error that can demonstrate robustness across sets of possible controls, variable definitions, standard error, algorithm structure, and functional forms. It is an essential reference to the most promising numerical models. In areas where there is less certainty about models, but also high expectations of transparency, robustness analysis should aspire to be as broad as possible. This book also contains a chapter at the end featuring applications in climate simulation illustrating different implementations of HASM in surface modeling. The book is helpful for people involved in geographical information science, ecological informatics, geography, earth observation, and planetary surface modeling.

✦ Table of Contents

Preface
Contents
List of Figures
List of Tables
1 Introduction
1.1 Research Background and Significance
1.1.1 Surface Modelling
1.1.2 HASM of Climatic Elements
1.2 Research Background
1.2.1 Common Surface Reconstruction Methods
1.2.2 High Accuracy Surface Modelling (HASM)
1.2.3 Main Spatial Interpolation Methods of Climatic Elements
1.3 Research Objectives, Contents, and Methods
1.3.1 Research Objectives
1.3.2 Research Contents and Methods
References
2 Modern HASM Method
2.1 Improvements in Traditional HASM Methods
2.1.1 Numerical Simulation
2.1.2 Case Study
2.2 Reasons for Accuracy Improvement
2.3 Boundary Value Problem
2.4 Stopping Criteria for Iteration in HASM
2.5 Summary
References
3 Sensitivity of HASM to the Selection of the Driving Field
3.1 Sensitivity of Traditional HASM to the Selection of the Driving Field
3.2 Comparison of the Sensitivity of HASM and HASM.MOD to the Selection of the Driving Field
3.2.1 Numerical Simulation
3.2.2 Case Study
3.3 Summary
4 Influence of Sampling Information on the Performance of HASM.MOD
4.1 Influence of the Sampling Ratio on Simulation Accuracy of HASM.MOD
4.1.1 Numerical Simulation
4.1.2 Case Study
4.2 Influence of the Sampling Error on the Simulation Accuracy of HASM.MOD
4.2.1 Numerical Simulation
4.2.2 Case Study
4.3 Summary
References
5 Fast Computation and Parallel Computing of HASM.MOD
5.1 Fast Computation Method for HASM.MOD
5.1.1 Incomplete Cholesky Conjugate Gradient (ICCG)
5.1.2 Ssymmetric Successive Over Relaxation-Preconditioned Conjugate Gradient (SSORCG)
5.1.3 Numerical Simulation
5.2 Parallelization of HASM.MOD
5.3 Summary
References
6 High Accuracy Surface Modelling of Average Seasonal Precipitation in China Over a Recent Period of 60 Years
6.1 Introduction
6.2 Data
6.3 Methods
6.3.1 Polynomial Regression
6.3.2 HASM
6.4 Model Verification and Result Analysis
6.4.1 Model Verification
6.4.2 Results
6.5 Results and Discussion
References
7 HASM of the Percentage of Sunshine in China
7.1 Introduction
7.2 Data and Study Area
7.3 Simulation of Average Monthly Percentage of Sunshine
7.4 Conclusion
References
8 Simulation of Potential Evapotranspiration in the Heihe River Basin by HASM
8.1 Introduction
8.2 Study Area and Data Source
8.3 Methods
8.3.1 Polynomial Regression and Stepwise Regression
8.3.2 HASM
8.4 Results
8.4.1 Accuracy
8.4.2 Comparison of the Potential ET in the Heihe River Basin Simulated by HASM and Kriging
8.5 Results and Discussion
References
9 HASM-Based Downscaling Simulation of Temperature and Precipitation and Scenario Prediction in the Heihe River Basin
9.1 Introduction
9.2 Data and Methods
9.2.1 Data
9.2.2 Methods
9.3 Comparison of the CMIP5 Baseline Data and Meteorological Observation Data
9.4 Downscaling Simulation of Future Temperature and Precipitation in the Heihe River Basin Under Different Scenarios
9.5 Conclusion
References