Atmospheric Satellite Observations: Variation Assimilation and Quality Assurance provides an invaluable reference for satellite data assimilation. Topics covered include linear algebra, frequently used statistical methods, the interpolation role of function fitting, filtering when dealing with real observations, minimization in data assimilation systems, 3D-Var and the inverse problem it solves, 4D-Var and adjoint techniques, and much more. The book concludes with satellite observation of hurricanes.
1. Chapter 1. Introduction to data assimilation - p. 1 2. Chapter 2. Linear algebra and statistics - p. 13 3. Chapter 3. Interpolation - p. 23 4. Abstract - p. 23 5. 3.1 Introduction - p. 23 6. 3.2 Polynomial function fitting - p. 23 7. 3.3 Local fitting - p. 26 8. 3.4 Regional fitting - p. 31 9. 3.5 Adding background fields - p. 37 10. 3.6 Successive correction - p. 40 11. 3.7 Optimal interpolation - p. 41 12. 3.8 Background error covariance of geopotential field - p. 44 13. 3.9 Concluding remarks - p. 46 14. Chapter 4. Filtering - p. 49 15. Chapter 5. Minimization - p. 63 16. Chapter 6. Adjoint model - p. 77 17. Abstract - p. 77 18. 6.1 Introduction - p. 77 19. 6.2 Adjoint in ordinary differential equation - p. 77 20. 6.3 Nonlinear model - p. 78 21. 6.4 Tangent linear model - p. 79 22. 6.5 Adjoint model - p. 81 23. 6.6 Equivalence between adjoint variables and Lagrangian multipliers - p. 82 24. 6.7 Analytic adjoint equations - p. 83 25. 6.8 Computer programming of adjoint models - p. 89 26. 6.9 Adjoint sensitivity and relative sensitivity - p. 93 27. 6.10 Concluding remarks - p. 96 28. Chapter 7. Microwave temperature sounding observations - p. 97 29. Abstract - p. 97 30. 7.1 Introduction - p. 97 31. 7.2 Polar-Orbiting Operational Environmental Satellites missions carrying microwave temperature sounders - p. 97 32. 7.3 Polar-Orbiting Operational Environmental Satellites orbital characteristics - p. 99 33. 7.4 Absorption and emission of radiation - p. 101 34. 7.5 Two-point calibration equation and observation errors - p. 104 35. 7.6 Absorption and emission weighting functions - p. 106 36. 7.7 A fast radiative transfer model - p. 108 37. 7.8 Channel characteristics, scan pattern, field-of-view, and weighting function - p. 112 38. 7.9 Advanced Technology Microwave Sounder striping noise analysis and mitigation - p. 118 39. 7.10 Fengyun-3 microwave temperature sounders - p. 124 40. 7.11 Relative sensitivity of Advanced Microwave Sounding Unit-A brightness temperatures - p. 128 41. 7.12 Concluding remarks - p. 132 42. Chapter 8. Three-dimensional variational data assimilation - p. 135 43. Abstract - p. 135 44. 8.1 Introduction - p. 135 45. 8.2 Deterministic mathematical formulation - p. 135 46. 8.3 Statistic formulation - p. 137 47. 8.4 The National Meteorological Center method for constructing B matrix - p. 140 48. 8.5 Recursive filters for constructing B matrix - p. 143 49. 8.6 Comparison between 3D-Var and the Kalman filter - p. 146 50. 8.7 AMSU-A cloud liquid water path retrieval and cloud detection - p. 147 51. 8.8 Bias estimate and bias correction - p. 148 52. 8.9 Impacts of AMSU-A data assimilation on quantitative precipitation forecasts - p. 155 53. 8.10 Additional remarks - p. 161 54. Chapter 9. Four-dimensional variational data assimilation - p. 163 55. Chapter 10. Global positioning system radio occultation observations - p. 175 56. Chapter 11. Geostationary Operational Environmental Satellite imagers - p. 207 57. Chapter 12. Satellite observations for tropical cyclones - p. 235 58. Bibliography - p. 295 59. Index - p. 309
Atmospheric Satellite Observations: Variation Assimilation and Quality Assurance provides an invaluable reference for satellite data assimilation. Topics covered include linear algebra, frequently used statistical methods, the interpolation role of function fitting, filtering when dealing with real observations, minimization in data assimilation systems, 3D-Var and the inverse problem it solves, 4D-Var and adjoint techniques, and much more. The book concludes with satellite observation of hurricanes.
1. Chapter 1. Introduction to data assimilation - p. 1 2. Chapter 2. Linear algebra and statistics - p. 13 3. Chapter 3. Interpolation - p. 23 4. Abstract - p. 23 5. 3.1 Introduction - p. 23 6. 3.2 Polynomial function fitting - p. 23 7. 3.3 Local fitting - p. 26 8. 3.4 Regional fitting - p. 31 9. 3.5 Adding background fields - p. 37 10. 3.6 Successive correction - p. 40 11. 3.7 Optimal interpolation - p. 41 12. 3.8 Background error covariance of geopotential field - p. 44 13. 3.9 Concluding remarks - p. 46 14. Chapter 4. Filtering - p. 49 15. Chapter 5. Minimization - p. 63 16. Chapter 6. Adjoint model - p. 77 17. Abstract - p. 77 18. 6.1 Introduction - p. 77 19. 6.2 Adjoint in ordinary differential equation - p. 77 20. 6.3 Nonlinear model - p. 78 21. 6.4 Tangent linear model - p. 79 22. 6.5 Adjoint model - p. 81 23. 6.6 Equivalence between adjoint variables and Lagrangian multipliers - p. 82 24. 6.7 Analytic adjoint equations - p. 83 25. 6.8 Computer programming of adjoint models - p. 89 26. 6.9 Adjoint sensitivity and relative sensitivity - p. 93 27. 6.10 Concluding remarks - p. 96 28. Chapter 7. Microwave temperature sounding observations - p. 97 29. Abstract - p. 97 30. 7.1 Introduction - p. 97 31. 7.2 Polar-Orbiting Operational Environmental Satellites missions carrying microwave temperature sounders - p. 97 32. 7.3 Polar-Orbiting Operational Environmental Satellites orbital characteristics - p. 99 33. 7.4 Absorption and emission of radiation - p. 101 34. 7.5 Two-point calibration equation and observation errors - p. 104 35. 7.6 Absorption and emission weighting functions - p. 106 36. 7.7 A fast radiative transfer model - p. 108 37. 7.8 Channel characteristics, scan pattern, field-of-view, and weighting function - p. 112 38. 7.9 Advanced Technology Microwave Sounder striping noise analysis and mitigation - p. 118 39. 7.10 Fengyun-3 microwave temperature sounders - p. 124 40. 7.11 Relative sensitivity of Advanced Microwave Sounding Unit-A brightness temperatures - p. 128 41. 7.12 Concluding remarks - p. 132 42. Chapter 8. Three-dimensional variational data assimilation - p. 135 43. Abstract - p. 135 44. 8.1 Introduction - p. 135 45. 8.2 Deterministic mathematical formulation - p. 135 46. 8.3 Statistic formulation - p. 137 47. 8.4 The National Meteorological Center method for constructing B matrix - p. 140 48. 8.5 Recursive filters for constructing B matrix - p. 143 49. 8.6 Comparison between 3D-Var and the Kalman filter - p. 146 50. 8.7 AMSU-A cloud liquid water path retrieval and cloud detection - p. 147 51. 8.8 Bias estimate and bias correction - p. 148 52. 8.9 Impacts of AMSU-A data assimilation on quantitative precipitation forecasts - p. 155 53. 8.10 Additional remarks - p. 161 54. Chapter 9. Four-dimensional variational data assimilation - p. 163 55. Chapter 10. Global positioning system radio occultation observations - p. 175 56. Chapter 11. Geostationary Operational Environmental Satellite imagers - p. 207 57. Chapter 12. Satellite observations for tropical cyclones - p. 235 58. Bibliography - p. 295 59. Index - p. 309
