Muutke küpsiste eelistusi

Land Surface Observation, Modeling And Data Assimilation [Kõva köide]

(Beijing Normal Univ, China), (Univ Of Maryland, Usa & Beijing Normal Univ, China), (Cold And Arid Regions Environmental & Engineering Res Inst, Chinese Academy Of Sci, China)
  • Formaat: Hardback, 492 pages
  • Ilmumisaeg: 14-Nov-2013
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814472603
  • ISBN-13: 9789814472609
Teised raamatud teemal:
Land Surface Observation, Modeling And Data AssimilationSuurem pilt
  • Formaat: Hardback, 492 pages
  • Ilmumisaeg: 14-Nov-2013
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814472603
  • ISBN-13: 9789814472609
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789814472609.html
This book is unique in its ambitious and comprehensive coverage of earth system land surface characterization, from observation and modeling to data assimilation, including recent developments in theory and techniques, and novel application cases. The contributing authors are the active research scientists, and many of them are internationally leading experts in their areas, ensuring that the text is authoritative.This book includes four parts that are logically connected from data, modeling, data assimilation that integrates data and models to applications. Land data assimilation is the key focus of the book, which encompasses both theoretical and applied aspects with various novel methodologies and applications to the water cycle, carbon cycle, crop monitoring, and yield estimation.Readers can benefit from a state-of-the-art presentation of the latest tools and their usage for understanding earth system processes. Discussions in the book present and stimulate new challenges and questions facing today's earth science and modeling communities.
Foreword v
Preface ix
Acknowledgements xiii
Part 1 Observation
1(90)
Chapter 1 Remote Sensing Data Products for Land Surface Data Assimilation System Application
3(42)
Yunjun Yao
Shunlin Liang
Tongren Xu
1.1 Introduction
3(1)
1.2 Atmospheric Forcing Data
4(10)
1.2.1 Radiation data
4(4)
1.2.2 Air temperature
8(2)
1.2.3 Water vapor
10(1)
1.2.4 Precipitation
11(3)
1.3 Land Surface Remote Sensing Data Products
14(17)
1.3.1 Land Surface Temperature
15(2)
1.3.2 Land surface albedo
17(3)
1.3.3 Leaf area index
20(4)
1.3.4 Fraction of absorbed photosynthetically active radiation
24(3)
1.3.5 Vegetation indices
27(2)
1.3.6 Soil moisture
29(2)
1.4 Data for Parameterization of Models
31(1)
1.4.1 Land cover types
31(1)
1.5 Summary
32(13)
References
33(12)
Chapter 2 Second-Generation Polar-Orbiting Meteorological Satellites of China: The Fengyun 3 Series and Its Applications in Global Monitoring
45(22)
Peng Zhang
2.1 Historical Review of Chinese Meteorological Satellites
45(1)
2.2 Mission of the Fengyun 3 Series
46(1)
2.3 The Payloads on FY-3A and FY-3B
47(3)
2.4 Ground Segment Designs for FY-3A and FY-3B
50(2)
2.5 Standard Product in Level 1 and Level 2
52(1)
2.6 Data Archives and Service
53(2)
2.7 Demonstration of Multidisciplinary Data Utilization
55(8)
2.7.1 Synoptic weather monitoring
55(1)
2.7.2 Typhoon monitoring
55(3)
2.7.3 NWP by data assimilation
58(2)
2.7.4 Ozone monitoring
60(1)
2.7.5 Air quality monitoring
61(2)
2.8 Conclusions
63(4)
Acknowledgments
64(1)
References
64(3)
Chapter 3 NASA Satellite and Model Land Data Services: Data Access Tutorial
67(24)
Suhung Shen
Gregory Leptoukh
Hongliang Fang
3.1 Introduction of NASA Land Products
67(4)
3.1.1 NASA satellite missions on land observations
67(1)
3.1.2 NASA satellite land products, processing levels, resolutions, and data format
68(2)
3.1.3 NASA land assimilation model products
70(1)
3.2 Search and Order NASA Earth Science Data Products
71(7)
3.2.1 NASA earth science data centers
71(1)
3.2.2 Find and access data from the centralized systems
71(2)
3.2.3 Find land data from data archive centers
73(1)
3.2.4 Access Landsat data
74(1)
3.2.5 Access data from GES DISC
75(3)
3.3 NASA Online Visualization Services
78(2)
3.3.1 Giovanni
78(1)
3.3.2 MODIS Rapid Response System
79(1)
3.3.3 NASA Earth Observations (NEO)
79(1)
3.3.4 NASA Earth Observatory
79(1)
3.3.5 NASA visible earth
80(1)
3.4 Support Research Projects and Sample Usage of Data and Services
80(7)
3.4.1 NASA data to support research projects: NEESPI and MAIRS
80(1)
3.4.2 Sample plots by using Giovanni
81(6)
3.5 Summary
87(4)
Acknowledgments
88(1)
References
88(3)
Part 2 Modeling
91(50)
Chapter 4 Land Surface Process Study and Modeling in Drylands and High-Elevation Regions
93(34)
Yingying Chen
Kun Yang
4.1 Brief Review of Land Surface Models
93(2)
4.2 Issues in Land Surface Modeling of Drylands and High-Elevation Regions
95(7)
4.2.1 Thermal coupling between land and atmosphere in drylands
96(4)
4.2.2 Soil stratification beneath alpine grassland
100(1)
4.2.3 Soil surface resistance for evaporation
100(2)
4.3 Parameterization Schemes for Arid and High-Elevation Regions
102(9)
4.3.1 A novel thermal roughness length scheme and its validation
102(5)
4.3.2 Inverse analysis of the role of soil vertical heterogeneity
107(3)
4.3.3 A soil surface resistance scheme for evaporation
110(1)
4.4 Land Surface Modeling Improvements
111(9)
4.4.1 Modeling improvements in drylands
111(6)
4.4.2 Improvements considering soil vertical stratification in alpine grasslands
117(2)
4.4.3 Improvements by introduction of soil surface resistance for evaporation in LSM
119(1)
4.5 Summary and Remarks
120(7)
References
120(7)
Chapter 5 Review of Parameterization and Parameter Estimation for Hydrologic Models
127(14)
Soroosh Sorooshian
Wei Chu
5.1 Overview
127(1)
5.2 Review of Hydrologic Models
128(5)
5.2.1 Basic concepts of a hydrologic model
128(2)
5.2.2 Trends of modern hydrologic modeling
130(3)
5.3 Review of Parameter Estimation Methods
133(5)
5.3.1 Automatic calibration requirements
133(1)
5.3.2 Choice of calibration criteria
134(1)
5.3.3 State-of-the-art algorithms of optimization for hydrologic models
135(3)
5.4 Conclusions
138(3)
Acknowledgments
138(1)
References
139(2)
Part 3 Data Assimilation
141(194)
Chapter 6 Assimilating Remote Sensing Data into Land Surface Models: Theory and Methods
143(28)
Xin Li
Yulong Bai
6.1 Theory of Data Assimilation
144(5)
6.1.1 Uncertainties of modeling
144(2)
6.1.2 Uncertainties of observation
146(2)
6.1.3 Rationales for land data assimilation
148(1)
6.2 Methods of Data Assimilation
149(11)
6.2.1 Classification of data assimilation methods
149(1)
6.2.2 Bayesian theoretical foundation for data assimilation
150(6)
6.2.3 EnKF
156(4)
6.3 Case Studies of Land Data Assimilation
160(6)
6.3.1 Retrieving soil temperature profile by assimilating MODIS land surface temperature products with EnKF
160(3)
6.3.2 Assimilation of passive microwave remote sensing data for active layer soil temperature estimation
163(3)
6.4 Summary
166(5)
Acknowledgments
167(1)
References
168(3)
Chapter 7 Estimating Model and Observation Error Covariance Information for Land Data Assimilation Systems
171(36)
Wade T. Crow
7.1 Introduction
171(2)
7.2 Background
173(5)
7.3 Application to a Modern LSM
178(5)
7.4 Remaining Challenges
183(11)
7.4.1 Auto-correlated observation errors
185(4)
7.4.2 Uncertainty in the source and structure of model error
189(4)
7.4.3 Speed of adaptive filter convergence
193(1)
7.5 Potential Solutions
194(5)
7.5.1 Use of triple collocation to estimate R
194(4)
7.5.2 Robust filtering strategies
198(1)
7.6 Summary
199(8)
Appendix A Innovation Properties in an Optimal KF
200(3)
References
203(4)
Chapter 8 Inflation Adjustment on Error Covariance Matrices for Ensemble Kalman Filter Assimilation
207(28)
Xiaogu Zheng
Guocan Wu
Xiao Liang
Shupeng Zhang
8.1 Introduction
207(3)
8.2 Inflation Adjustment on Error Covariance Matrices in EnKF
210(6)
8.2.1 Ensemble Kalman filter
210(2)
8.2.2 Inflation adjustment on error covariance matrices in the case of linear observation operator
212(1)
8.2.3 Inflation adjustment on error covariance matrices in the case of nonlinear observation operator
213(2)
8.2.4 Statistics to verify assimilation methods
215(1)
8.3 Introduction of Simplified Ideal Models for Verification
216(2)
8.3.1 Lorenz-96 model
216(1)
8.3.2 Two-dimensional SWE model
217(1)
8.4 Verification Results Using Linear Observation
218(7)
8.4.1 The case of time-dependent inflation
218(2)
8.4.2 The case of time-independent inflation
220(1)
8.4.3 The case of inaccurate observation error covariance matrix
221(3)
8.4.4 The case of time-dependent inflation factor of observation error covariance matrix
224(1)
8.5 Verification Results Using Nonlinear Observation
225(4)
8.5.1 Sensitivity analysis on the degree of tangent linearity of observation operator
226(1)
8.5.2 Comparison of several inflation adjustment schemes in the case of tangent linear observation operator
227(2)
8.6 Discussion and Main Conclusion
229(6)
Appendix A Li et al.'s Estimation of the Inflation Factor of Forecast Error Covariance Matrix λt
230(1)
Appendix B A Calculation Method of Determinant det(HtλtPft HTt + Rt)
231(1)
References
232(3)
Chapter 9 A Review of Error Estimation in Land Data Assimilation Systems
235(40)
Yulong Bai
Xin Li
Qianlong Chai
9.1 Introduction
235(3)
9.2 Error Problems in Modern DA Methods
238(7)
9.2.1 Error definitions and their sources
238(2)
9.2.2 Error definitions in sequential DA methods
240(4)
9.2.3 Error definitions in variational DA methods
244(1)
9.3 Error Estimation Issues
245(4)
9.3.1 Model error
245(3)
9.3.2 Observation error
248(1)
9.3.3 Algorithm errors in ensemble DA
249(1)
9.3.4 Summary for error estimation
249(1)
9.4 Error Handling Methods in Ensemble DA
249(15)
9.4.1 Multiplicative inflation methods
250(1)
9.4.2 Additive inflation methods
251(1)
9.4.3 The relaxation-to-prior method
251(1)
9.4.4 Evolutionary algorithm-based error parameterization methods
252(2)
9.4.5 Experiments designed with crossover error parameterization methods
254(10)
9.5 Summary and Discussions
264(11)
Acknowledgments
267(1)
References
268(7)
Chapter 10 An Introduction to Multi-scale Kalman Smoother-Based Framework and Its Application to Data Assimilation
275(60)
Daniel E. Salas
Xu Liang
10.1 Introduction
275(3)
10.2 Traditional Kalman Filter
278(1)
10.3 MKS and Its Extension
279(25)
10.3.1 Upward sweep
285(15)
10.3.2 Downward sweep
300(4)
10.4 EM Algorithm for Parameter Estimation
304(2)
10.4.1 E step
305(1)
10.4.2 M Step
305(1)
10.5 Application of the MKS-Based Framework with EM Method for Data Assimilation
306(2)
10.5.1 Algorithm complexity
306(2)
10.6 Example
308(22)
10.7 Symbols
330(5)
Acknowledgments
331(1)
References
331(4)
Part 4 Application
335(130)
Chapter 11 Overview of the North American Land Data Assimilation System (NLDAS)
337(42)
Youlong Xia
Brian A. Cosgrove
Michael B. Ek
Justin Sheffield
Lifeng Luo
Eric F. Wood
Kingtse Mo
the NLDAS team
11.1 Introduction
338(5)
11.1.1 Background of LDAS
339(1)
11.1.2 NOAA-NASA-University collaborations and the development of NLDAS
340(3)
11.1.3 Other LDAS activities around the world
343(1)
11.2 NLDAS History
343(25)
11.2.1 NLDAS-1
343(8)
11.2.2 NLDAS-2
351(17)
11.3 Summary and Concluding Remarks
368(11)
Acknowledgments
370(1)
References
370(9)
Chapter 12 Soil Moisture Data Assimilation for State Initialization of Seasonal Climate Prediction
379(26)
Wenge Ni-Meister
12.1 Introduction
380(1)
12.2 Brief History of Soil Moisture Data Assimilation
381(2)
12.3 Basic Concepts of Soil Moisture Data Assimilation
383(3)
12.4 Soil Moisture Assimilation --- A Case Study
386(11)
12.4.1 Data assimilation algorithm development
386(2)
12.4.2 Assimilation of SMMR data into CLSM
388(9)
12.5 Conclusions and Discussion
397(8)
Acknowledgments
399(1)
References
399(6)
Chapter 13 Assimilation of Remote Sensing Data and Crop Simulation Models for Agricultural Study: Recent Advances and Future Directions
405(36)
Hongliang Fang
Shunlin Liang
Gerrit Hoogenboom
13.1 Introduction
406(1)
13.2 Crop Growth Modeling
407(2)
13.3 Data Assimilation Methods
409(5)
13.3.1 Direct input approach
410(1)
13.3.2 Sequential assimilation approach
411(2)
13.3.3 Variational assimilation approach
413(1)
13.4 Remote Sensing Data and Preprocessing
414(6)
13.4.1 Visible and near-infrared (NIR) information
415(2)
13.4.2 Microwave information
417(2)
13.4.3 Thermal infrared information
419(1)
13.5 Corn Yield Estimation at a Regional Level
420(9)
13.5.1 Sensitivity study
422(1)
13.5.2 Cost function construction
423(1)
13.5.3 Corn yield estimation
424(2)
13.5.4 Water balance studies
426(3)
13.6 Challenges and Future Studies
429(1)
13.7 Conclusions
430(11)
References
431(10)
Chapter 14 Simultaneous State-Parameter Estimation for Hydrologic Modeling Using Ensemble Kalman Filter
441(24)
Xianhong Xie
14.1 Introduction
441(2)
14.2 EnKF with State-Augmentation Technique
443(2)
14.3 Case Study for a Simple Rainfall-Runoff Model
445(3)
14.4 Application to a Distributed Hydrologic Model
448(8)
14.4.1 SWAT model
448(1)
14.4.2 Data assimilation procedure
449(2)
14.4.3 Result
451(5)
14.5 Discussion
456(4)
14.6 Conclusion
460(5)
Acknowledgments
461(1)
References
462(3)
Index 465