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E-grāmata: Passive Microwave Remote Sensing of the Earth: for Meteorological Applications

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Essential to the understanding of global climate change and improved weather forecasts is the gathering of basic data of variables such as temperature, pressure, wind, and the distribution of water vapor, clouds, and other active constituents.
Preface xiii
1 Introduction
1(24)
1.1 A Microwave Radiometer System
1(2)
1.2 Blackbody Emission
3(1)
1.3 Linearized Planck Function
4(1)
1.4 Stokes Vector and Its Transformation
5(2)
1.5 Microwave Spectrum
7(1)
1.6 Spectral Response Function
8(2)
1.7 Microwave Antenna Gain and Distribution Function
10(1)
1.8 Microwave Instrument Scan Geometry
11(2)
1.9 Microwave Data Records and Their Terminology
13(2)
2 Atmospheric Absorption and Scattering
15(1)
2.1 Introduction
15(1)
2.2 Microwave Gaseous Absorption
16(1)
2.2.1 Absorption Line and Shape
16(2)
2.2.2 Oxygen Absorption
18(4)
2.2.3 Water Vapor Absorption
22(1)
2.2.4 Nitrogen and Ozone Absorption
23(1)
2.2.5 Line-by-Line Radiative Transfer Model (LBLRTM)
23(1)
2.2.6 Zeeman Splitting Absorption
24(4)
2.2.7 Parameterized Transmittance Model
28(4)
2.3 Cloud Absorption and Scattering
32(12)
2.3.1 Scattering Parameters
32(2)
2.3.2 Particle Size Distribution
34(4)
2.3.3 Rayleigh Approximation
38(4)
2.3.4 Henyey--Greenstein and Rayleigh Phase Matrix
42(2)
2.4 Summary and Conclusions
44(1)
3 Radiative Transfer Modeling at Microwave Frequencies
45(46)
3.1 Introduction
45(1)
3.2 Radiative Transfer Equation
45(2)
3.3 Vector Discrete-Ordinate Method
47(6)
3.4 Radiance Gradient or Jacobians
53(2)
3.5 Benchmark Tests
55(5)
3.6 The Zeroth-Order Approximation to Radiative Transfer Solution
60(2)
3.7 The First-Order Approximation to Radiative Transfer Solution
62(1)
3.8 Ocean Emissivity Model
62(16)
3.8.1 Ocean Roughness Phenomena
62(2)
3.8.2 Approximation of Water Dielectric Constant
64(3)
3.8.3 Ocean Roughness Heights and Spectrum
67(6)
3.8.4 Foam Coverage
73(1)
3.8.5 Surface Emissivity Vector
74(4)
3.9 Land Emissivity Model
78(10)
3.9.1 Theoretical Approach for Land Emission
78(3)
3.9.2 Optical Parameters of Vegetation Canopy
81(2)
3.9.3 Optical Parameters of Snow
83(2)
3.9.4 Surface Reflection at Layer Interfaces
85(2)
3.9.5 Soil Dielectric Constant
87(1)
3.9.6 Simulated Surface Emissivity Spectra
87(1)
3.10 Summary and Conclusions
88(3)
4 Microwave Radiance Simulations
91(32)
4.1 Introduction
91(1)
4.2 Fast Radiative Transfer Simulations
92(4)
4.3 Calculations of Antenna Brightness Temperatures
96(3)
4.4 Simulations of ATMS Sounding Channels Using Global Forecast Model Outputs
99(6)
4.5 Simulations of ATMS Sounding Channels Using GPSRO Data
105(1)
4.5.1 Collocation of GPS RO and ATMS Data
105(2)
4.5.2 ATMS Bias with Respect to GPS RO Data
107(2)
4.6 Uses of TRMM-Derived Hydrometeor Data in Radiative Transfer Simulations
109(8)
4.6.1 Collocation of ATMS and TRMM Data
109(3)
4.6.2 ATMS Biases With Respect to TRMM-Derived Simulations
112(5)
4.7 Advanced Radiative Transfer Simulations
117(3)
4.8 Summary and Conclusions
120(3)
5 Calibration of Microwave Sounding Instruments
123(28)
5.1 Introduction
123(1)
5.2 Calibration Concept
124(1)
5.3 ATMS Instrument Description
124(4)
5.4 ATMS Radiometric Calibration
128(5)
5.5 Impacts of ATMS Antenna Emission on Two-Point Calibration
133(2)
5.6 Retrieval of Reflector Emissivity Using ATMS Pitch-Over Data
135(3)
5.7 ATMS Noise-Equivalent Difference Temperature (NEDT)
138(5)
5.8 Conversion from Antenna to Sensor Brightness Temperature
143(4)
5.9 Summary and Conclusion
147(4)
6 Detection of Interference Signals at Microwave Frequencies
151(126)
6.1 Introduction
151(1)
6.2 Microwave Imaging Radiometers and Data Sets
152(2)
6.3 Radio-Frequency Interference Signals in Microwave Data
154(1)
6.4 Detection of RFI over Land
155(1)
6.4.1 Double Principal Component Analysis (DPCA)
155(5)
6.4.2 Spectral Difference Method
160(2)
6.5 RFI Detection over Oceans
162(3)
6.6 Summary and Conclusions
175(2)
7 Microwave Remote Sensing of Surface Parameters
177(1)
7.1 Introduction
177(1)
7.2 Remote Sensing of Ocean Surface Parameters
178(1)
7.2.1 Retrievals of Surface Wind Vector
178(5)
7.2.2 Simultaneous Retrieval of Sea Surface Temperature and Wind Speed
183(7)
7.3 Remote Sensing of Land Surface Parameters
190(15)
7.3.1 Retrievals of Land Surface Temperature
190(5)
7.3.2 Retrieval of Land Surface Emissivity
195(3)
7.3.3 Error Sensitivity of Land Surface Emissivity
198(4)
7.3.4 Fast Land Emissivity Algorithms
202(3)
7.4 Summary and Conclusions
205(2)
8 Remote Sensing of Clouds from Microwave Sounding Instruments
207(28)
8.1 Introduction
207(1)
8.2 Remote Sensing of Cloud Liquid Water
208(5)
8.2.1 Principle of Microwave Remote Sensing of Clouds
208(2)
8.2.2 Cloud Liquid Water Algorithm
210(3)
8.3 Remote Sensing of Cloud Ice Water
213(11)
8.3.1 Microwave Scattering from Ice-Phase Cloud
213(3)
8.3.2 Cloud Ice Water Retrieval Algorithm
216(8)
8.4 Cloud Vertical Structures from Microwave Double Oxygen Bands
224(8)
8.4.1 FY-3C Microwave Sounding Instruments and Their Channel Pairing
225(2)
8.4.2 Typhoon Neoguri Observed by MWHS and MWTS
227(3)
8.4.3 The Cloud Emission and Scattering Index (CESI)
230(2)
8.5 Summary and Conclusions
232(3)
9 Microwave Remote Sensing of Atmospheric Profiles
235(24)
9.1 Introduction
235(1)
9.2 Microwave Sounding Principle
236(3)
9.3 Regression Algorithms
239(5)
9.4 One-Dimensional Variational (1DVAR) Theory
244(3)
9.5 Multiple 1DVARs for All-Weather Profiles
247(4)
9.6 Microwave Integrated Retrieval System (MIRS)
251(6)
9.7 Summary and Conclusions
257(2)
10 Assimilation of Microwave Data in Regional NWP Models
259(40)
10.1 Introduction
259(1)
10.2 NCEP GSI Analysis System
260(2)
10.3 ATMS Data Assimilation in HWRF
262(20)
10.3.1 Hurricane Weather Research and Forecast (HWRF) System
262(2)
10.3.2 Hurricane Events in 2012
264(2)
10.3.3 ATMS Data Quality Control
266(6)
10.3.4 Comparison between (O -- B) and (O -- A) Statistics
272(1)
10.3.5 Impact of ATMS Data on Forecasting Track and Intensity
272(10)
10.4 SSMIS Data Assimilation
282(14)
10.4.1 SSMIS Instrument
282(5)
10.4.2 SSMIS Data Quality Control
287(1)
10.4.3 SSMIS Bias Correction
288(5)
10.4.4 Impacts from SSMIS and AMSU-A Data Assimilation
293(3)
10.4.5 Impact of SSMIS LAS Data on GFS Operational Forecasts
296(1)
10.5 Summary and Conclusions
296(3)
11 Applications of Microwave Data in Climate Studies
299(42)
11.1 Introduction
299(1)
11.2 Climate Trend Theory
300(3)
11.3 A Long-Term Climate Data Record from SSM/I
303(17)
11.3.1 Simultaneous Conical Overpassing (SCO) Method
304(3)
11.3.2 Bias Characterization of Specific SSM/I Instrument
307(1)
11.3.3 RADCAL Beacon Interference with F15 SSM/I
308(2)
11.3.4 SSM/I Intersensor Bias Correction
310(3)
11.3.5 Impact of Cross-Calibration on SSM/I SDR
313(2)
11.3.6 Impacts of SSM/I Intersensor Calibration on TPW
315(5)
11.4 A Long-Term Climate Data Record from MSU/AMSU
320(10)
11.4.1 Impacts of Clouds and Precipitation on AMSU-A Trends
323(1)
11.4.2 Emission and Scattering Effect on AMSU-A
323(3)
11.4.3 AMSU-A Brightness Temperature Trend
326(4)
11.5 Atmospheric Temperature Trend from 1DVar Retrieval
330(7)
11.5.1 Climate Applications of 1DVar
330(1)
11.5.2 MSU and AMSU-A Cross-Calibration
331(1)
11.5.3 Cloud Detection Algorithm for MSU Applications
331(3)
11.5.4 Temperature Trend from 1DVar
334(3)
11.6 Summary and Conclusions
337(4)
References 341(18)
Index 359
Dr. Fuzhong Weng received his PhD degree in 1992 from Department of Atmospheric Science, Colorado State University (CSU), Fort Collins, Colorado, USA. He joined NOAA in 1998 as a physical scientist and then managed the US Joint Center for Satellite Data Assimilation Program (JCSDA) from 2002-2005. He served as the chief of sensor physics branch at NOAA/NESDIS from 2005-2010. From 2011 to 2017, Dr. Weng was appointed as the chief of Satellite Meteorology and Climatology of NOAA/NESDIS/Center for Satellite Applications and Research, JCSDA Senior Scientist and Joint Polar Satellite System (JPSS) Sensor Science Chair. He won a number of awards including the first winner of the 2000 NOAA David Johnson Award for his outstanding contributions to satellite microwave remote sensing fields and the utilization of satellite data in the NWP models, US Department of Commerce Gold Medal Award in 2005 for his achievement in satellite data assimilation, NOAA bronze medal for leading successful NOAA-18 instrument calibration, and NOAA Administrator's Award for developing new and powerful radiative transfer models to assimilate advanced satellite data in 2009 and NOAA Administrator's Award for leadership in developing a state-of-the art satellite instrument health monitoring system enabling corrective actions to extend instrument life. He published over 160 papers in US and other international journals.
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