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Table of contents
CHAPTER 1 INTRODUCTION
LAND SURFACE MODELS. OBJECTIVES AND ORGANIZATION OF THE THESIS
1.1 INTRODUCTION
1.2 COMPONENTS OF LAND SURFACE MODELS
1.2.1 WATER PROCESSES
1.2.2 SOIL THERMODYNAMICS
1.3 IMPORTANCE OF REPRESENTING THE PHYSICS OF THE SOIL SURFACE CORRECTLY
1.4 CHALLENGES IN LSM REPRESENTATION
1.4.1. SURFACE HETEROGENEITY
1.4.2 NUMERICAL REPRESENTATION
1.4.3. MATHEMATICAL REPRESENTATION AND MODEL CALIBRATION
1.5 THESIS CHALLENGES
1.5.1. STATE OF THE ART IN THE USE OF LST TO CONSTRAIN LSM
1.5.2 GENERAL OBJECTIVES
1.5.3 ORGANIZATION
CHAPTER 2
DESCRIPTION OF THE LAND SURFACE MODEL ORCHIDEE AND DATASETS
2.1. ORCHIDEE
2.1.1 MODULES
2.1.2 BIOSPHERE CHARACTERIZATION
2.2 SECHIBA
2.2.1 FORCING
2.2.2 ENERGY BUDGET
2.2.3 HYDROLOGICAL BUDGET
2.2.4. SECHIBA PARAMETERS
2.3 DATA
2.3.1. EDDY COVARIANCE MEASUREMENTS
2.3.2 SMOSREX
CHAPTER 3
THEORETICAL PRINCIPLES OF VARIATIONAL DATA ASSIMILATION
3.1 INTRODUCTION AND NOTATION
3.2 ADJOINT METHOD
3.3. REPRESENTING A MODEL AND ITS ADJOINT THROUGH A MODULAR GRAPH
3.3.1. DEPLOYMENT OF A MODULAR GRAPH
3.4. DIAGNOSTIC TOOLS FOR THE ASSIMILATION SYSTEM
3.4.1 TEST THE CORRECTNESS OF THE ADJOINT MODEL
3.4.2. TEST THE CORRECTNESS OF THE COST FUNCTION GRADIENTS
3.4.3. DERIVATIVE TEST
3.5. SUMMARY
CHAPTER 4
THE YAO APPROACH: THEORETICAL ASPECTS AND IMPLEMENTATION OF SECHIBA-YAO 1D
4.1 INTRODUCTION
4.2 YAO APPROACH
4.3. CREATING A PROJECT WITH YAO
4.3.1 INPUT / OUTPUT MANAGEMENT
4.3.2 DIAGNOSTIC TOOLS FOR THE GENERATED PROJECT
4.4. DEVELOPMENT OF SECHIBA-YAO 1D
4.4.1 IMPLEMENTATION OUTLINE
4.4.2 DIRECT MODEL VALIDATION
4.4.3 ADJOINT MODEL VALIDATION
CHAPTER 5
SENSITIVITY ANALYSIS OF THE SECHIBA-YAO 1D MODEL USING FLUXNET DATASET
5.1 INTRODUCTION
5.2 VARIATIONAL SENSITIVITY ANALYSIS
5.2.1. SENSITIVITY ANALYSIS WITH LAND SURFACE TEMPERATURE
CHAPTER 6
TWIN EXPERIMENTS WITH SECHIBA-YAO 1D USING FLUXNET MEASUREMENTS
6.1 INTRODUCTION
6.2 EXPERIMENT DEFINITION
6.3. RESULTS
6.3.1 EFFECT OF THE OBSERVATION SAMPLING
6.3.2 EFFECT OF RANDOM NOISE IN THE OBSERVATION
6.3.3 EFFECT OF THE CONTROL PARAMETER SET SIZE
6.4. DISCUSSION
CHAPTER 7
REAL MEASUREMENTS STUDY USING SMOSREX DATASET
7.1 INTRODUCTION
7.2 KEY PARAMETERS TO PERFORM THE OPTIMIZATION
7.3 LST DATA ASSIMILATION WITH PARAMETER STANDARD VALUES
7.3.1. SIMULATED VS. OBSERVED MEASUREMENTS
7.3.2. BRIGHTNESS TEMPERATURE SENSITIVITY ANALYSIS
7.3.3. BRIGHTNESS TEMPERATURE ASSIMILATION DURING A SINGLE DAY
7.3.4. BRIGHTNESS TEMPERATURE ASSIMILATION DURING A WEEK
7.3.5. DISCUSSION
7.4 LST VARIATIONAL DATA ASSIMILATION WITH DIFFERENT PRIOR VALUES
7.4.1. SIMULATED VS. OBSERVED MEASUREMENTS
7.4.2. BRIGHTNESS TEMPERATURE SENSITIVITY ANALYSIS
7.4.3. BRIGHTNESS TEMPERATURE ASSIMILATION DURING A SINGLE DAY
7.4.4. BRIGHTNESS TEMPERATURE ASSIMILATION DURING A WEEK
7.5. ANALYSIS OF THE ASSIMILATION SYSTEM THROUGH TWIN EXPERIMENTS
7.6 CONCLUSION
CHAPTER 8
CONCLUSION AND PERSPECTIVES
