Satellite Precipitation Measurement: Volume 1 (Advances in Global Change Research, 67)

Preface
Acknowledgments
Contents of Volume 1
Contents of Volume 2
List of Figures
List of Tables
Contributors
Acronyms
Part I: Status of Observations and Satellite Programs
	Chapter 1: The Global Precipitation Measurement (GPM) Mission
		1.1 Introduction
		1.2 Satellite Sensors and Characteristics
		1.3 Products
		1.4 Validation
		1.5 Advancing Precipitation Science
			1.5.1 Snowfall and Cold-Season Precipitation
			1.5.2 Drop Size Distributions (DSDs)
			1.5.3 Latent Heating Products
		1.6 Applications and Outreach
			1.6.1 Precipitation Extremes, Food Security, and Health
			1.6.2 Assimilation and Numerical Modelling
			1.6.3 Outreach Activities
		1.7 Beyond GPM
		References
	Chapter 2: Status of the CloudSat Mission
		2.1 CloudSat Instrument and Measurements
		2.2 Limitations and Benefits of CloudSat for Precipitation Sensing
		2.3 CloudSat Mission Operations History
		2.4 CloudSat Data Products
			2.4.1 Precipitation Identification and Classification
			2.4.2 Quantifying Snowfall
			2.4.3 Quantifying Rainfall
		References
	Chapter 3: The Megha-Tropiques Mission After Seven Years in Space
		3.1 Introduction
		3.2 The Status of the Mission
			3.2.1 Orbital Aspects
			3.2.2 The MADRAS Radiometer
			3.2.3 The SAPHIR Sounder
		3.3 Addressing the Scientific Objectives
			3.3.1 Precipitation Related Remote Sensing Products from MT Payloads
			3.3.2 Tropical Science
				3.3.2.1 Hydrometeorology
				3.3.2.2 Deep Convection
		3.4 Addressing the Operational Objective
			3.4.1 Upstream Investigations
			3.4.2 Operational Applications
		3.5 Conclusions and Outlook
		References
	Chapter 4: Microwave Sensors, Imagers and Sounders
		4.1 Introduction
		4.2 Characteristics of Microwave Imagers
			4.2.1 The Electrically Scanning Microwave Radiometers (ESMRs)
			4.2.2 The Scanning Multichannel Microwave Radiometer (SMMR)
			4.2.3 The Special Sensor Microwave Imager (SSM/I)
			4.2.4 The TRMM Microwave Imager (TMI)
			4.2.5 WindSat
			4.2.6 Advanced Microwave Scanning Radiometer (AMSR) Series
			4.2.7 GPM Microwave Imager (GMI)
		4.3 Characteristics of Microwave Sounders
			4.3.1 Microwave Sounding Unit (MSU)
			4.3.2 Special Sensor Microwave Temperature and Temperature-2 (SSM/T and SSM/T2)
			4.3.3 Special Sensor Microwave Imager Sounder (SSMIS)
			4.3.4 Advanced Microwave Sounding Unit-A and -B (AMSU-A and AMSU-B) and the Microwave Humidity Sounder (MHS)
			4.3.5 Sondeur Atmosphérique du Profil d´Humidité Intertropicale par Radiométrie (SAPHIR)
			4.3.6 Advanced Technology Atmospheric Sounder (ATMS)
		4.4 Summary and Future
		References
	Chapter 5: Microwave and Sub-mm Wave Sensors: A European Perspective
		5.1 Introduction
			5.1.1 EPS-SG Microwave Imaging (MWI) Mission
			5.1.2 EPS-SG Ice Cloud Imaging (ICI) Mission
		5.2 MWI and ICI Data Processing and Products
		5.3 Applications
			5.3.1 Numerical Weather Prediction
			5.3.2 Climate Monitoring
			5.3.3 Nowcasting
		5.4 Copernicus Imaging Microwave Radiometry (CIMR) Mission
		5.5 Summary
		References
	Chapter 6: Plans for Future Missions
		6.1 Requirements of Future Global Precipitation Measurement
		6.2 Technical Developments
			6.2.1 Radar
			6.2.2 Microwave Radiometer
			6.2.3 Infrared Radiometer
		6.3 Proposed Mission Concepts
			6.3.1 Missions and Sensors Moving Ahead
			6.3.2 Missions in Planning Stages
		References
Part II: Retrieval Techniques, Algorithms and Sensors
	Chapter 7: Introduction to Passive Microwave Retrieval Methods
		7.1 Theory
		7.2 Sensors and Algorithms
			7.2.1 The ESMR Era
			7.2.2 The SMMR Era
			7.2.3 The SSM/I Era
			7.2.4 The TRMM and GPM Era
			7.2.5 The NOAA AMSU/ATMS Sensor Era
		References
	Chapter 8: The Goddard Profiling (GPROF) Precipitation Retrieval Algorithm
		8.1 Introduction
		8.2 GPROF a priori Database
			8.2.1 Hydrometeor Profiles and Surface Precipitation
			8.2.2 Ancillary Datasets
		8.3 Satellite Sensor Pixel Preparation: GPROF Preprocessor
		8.4 The GPROF Bayesian Retrieval Algorithm
		8.5 Conclusions
		References
	Chapter 9: Precipitation Estimation from the Microwave Integrated Retrieval System (MiRS)
		9.1 Background
		9.2 Algorithm Description
		9.3 Algorithm Components
		9.4 Treatment of Hydrometeors
		9.5 Retrieval Examples
		9.6 Validation Results
		9.7 Planned Operational Improvements
		9.8 Conclusions and Future Work
		References
	Chapter 10: Introduction to Radar Rain Retrieval Methods
		10.1 Introduction
		10.2 Formulation of Radar Measurement of Rain
		10.3 Rain Retrieval Algorithm
		10.4 Surface Reference Technique (SRT)
		10.5 Errors in Retrievals
		10.6 Summary
		References
	Chapter 11: Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Mission´s Core Observatory
		11.1 Dual-Frequency Precipitation Radar
		11.2 Outline of the DPR Data Processing Algorithm
		11.3 Outline of the DPR L2 Algorithm Modules
		11.4 Special Features in the DPR Algorithm
		11.5 Future of the DPR Algorithm
		References
	Chapter 12: DPR Dual-Frequency Precipitation Classification
		12.1 Introduction
		12.2 Precipitation Type Classification
		12.3 Melting Layer Detection
		12.4 Evaluation of the Dual-Frequency Classification Module
			12.4.1 Comparison Between Dual-Frequency and TRMM Legacy Single Frequency Methods
			12.4.2 Surface Snowfall Identification
			12.4.3 Ground Validation for the Surface Snowfall Identification Algorithm
		References
	Chapter 13: Triple-Frequency Radar Retrievals
		13.1 Introduction
			13.1.1 Why Triple-Frequency Radars?
				13.1.1.1 Why a Triple-Frequency Approach for Rain?
				13.1.1.2 Why a Triple-Frequency Approach for Ice?
		13.2 Triple-Frequency Datasets
		13.3 Triple-Frequency Retrievals
		13.4 Critical Issues and Open Questions
		13.5 Recommendations for Future Work
		References
	Chapter 14: Precipitation Retrievals from Satellite Combined Radar and Radiometer Observations
		14.1 Introduction
		14.2 The GPM Combined Algorithm
			14.2.1 Formulation
			14.2.2 Areas Requiring Improvement
		14.3 Brightness Temperature - PIA Relationships, Revisited
		14.4 Summary and Conclusions
		References
	Chapter 15: Scattering of Hydrometeors
		15.1 Scattering Methods
			15.1.1 Rayleigh, Mie, and T-Matrix Methods
			15.1.2 Effective Medium Approximation
			15.1.3 Rayleigh Gans and Self-Similar Rayleigh Gans Approximation
			15.1.4 Discrete Dipole Approximation (DDA)
			15.1.5 Generalized Multiparticle Mie-Solution (GMM)
		15.2 Hydrometeor Models
			15.2.1 Liquid Hydrometeors
			15.2.2 Ice and Snow
			15.2.3 Melting Ice
		15.3 Scattering Properties and Scattering Databases
			15.3.1 Liquid Hydrometeors
			15.3.2 Ice Crystals, Aggregates, and Rimed Particles
			15.3.3 Melting Ice
			15.3.4 Future Directions
		References
	Chapter 16: Radar Snowfall Measurement
		16.1 Introduction
		16.2 Radar Snowfall Retrieval Method
			16.2.1 Factors Impacting Z - S Relations
			16.2.2 A Z-S Relation
			16.2.3 Issues Related to Detectability and Attenuation
		16.3 Results from CloudSat Measurements
			16.3.1 First Global Snowfall Map
			16.3.2 Snow Cloud Structures
		16.4 Guiding Passive Sensors for Snowfall Estimation
		16.5 Concluding Remarks
		References
	Chapter 17: A 1DVAR-Based Snowfall Rate Algorithm for Passive Microwave Radiometers
		17.1 Introduction
		17.2 Data and Models
			17.2.1 Instruments and Data
			17.2.2 Logistic Regression
			17.2.3 Radiative Transfer Model and 1DVAR
			17.2.4 Ice Particle Terminal Velocity
		17.3 Snowfall Detection
			17.3.1 Satellite Module
			17.3.2 Weather Module
			17.3.3 Hybrid Algorithm
			17.3.4 SD Filters
		17.4 Snowfall Rate
			17.4.1 Methodology
			17.4.2 Calibration
		17.5 Validation
			17.5.1 SD Validation
			17.5.2 SFR Validation
		17.6 Summary and Conclusions
		References
	Chapter 18: X-Band Synthetic Aperture Radar Methods
		18.1 Introduction
		18.2 Evidence of Precipitation Signatures on X-SAR Imagery
		18.3 Forward Model of SAR Response to Rainfall
			18.3.1 SAR Observing Geometry and Response Model
			18.3.2 Example of Precipitation-Affected SAR Scene
		18.4 SAR Precipitation Retrieval Techniques
			18.4.1 Data Pre-processing
			18.4.2 Regressive Empirical Algorithm (REA)
			18.4.3 Probability Matching Algorithm (PMA)
		18.5 Applications
			18.5.1 Improving SAR Retrieval Using Background Estimation
			18.5.2 Statistical Approaches for Retrieval Validation
			18.5.3 Case Study
		18.6 Conclusion
		References
Part III: Merged Precipitation Products
	Chapter 19: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG)
		19.1 Introduction
		19.2 Input Data Sets
		19.3 IMERG Processing
		19.4 IMERG Data Set Status
		19.5 IMERG Performance and Examples
		19.6 Status for Version 06 and Concluding Remarks
		References
	Chapter 20: Global Satellite Mapping of Precipitation (GSMaP) Products in the GPM Era
		20.1 Introduction
		20.2 GSMaP Product List in the GPM Era
		20.3 Algorithm Description
			20.3.1 Overall Algorithm Framework
			20.3.2 Outline of the PMW Algorithm
			20.3.3 Methodology in the PMW Algorithm
			20.3.4 Orographic/Non-orographic Rainfall Classification Scheme
			20.3.5 Modifications Due to Sensor Specifications
			20.3.6 Snowfall Estimation Method
			20.3.7 PMW-IR Combined Algorithm
			20.3.8 Gauge-Adjustment Algorithm
			20.3.9 Brief Summary of Evolutions from V6 to V7
		20.4 Validation Results of the GSMaP Products
			20.4.1 Comparisons of the GSMaP Products Around Japan
			20.4.2 Validation Using the US Radar Network
		20.5 Conclusions
		References
	Chapter 21: Improving PERSIANN-CCS Using Passive Microwave Rainfall Estimation
		21.1 Introduction
		21.2 Re-calibration of PERSIANN-CCS
			21.2.1 PERSIANN-CCS
			21.2.2 Passive Microwave Adjustment of PERSIANN-CCS Estimation
		21.3 Evaluation of Re-calibrated PERSIANN-CCS Estimation
		21.4 Improving Warm Rain Estimation
		21.5 Conclusions and Future Directions
		References
	Chapter 22: TAMSAT
		22.1 The History of TAMSAT
		22.2 TAMSAT Products
		22.3 The TAMSAT Rainfall Estimation Approach
			22.3.1 Overview
			22.3.2 Calibration Method
			22.3.3 Strengths and Limitations
		22.4 Usage and Applications
		References
	Chapter 23: Algorithm and Data Improvements for Version 2.1 of the Climate Hazards Center´s InfraRed Precipitation with Statio...
		23.1 Context - Increasing Food Insecurity and the CHIRPS2.0 Dataset
		23.2 Description of the CHIRPS2.1 Methods
			23.2.1 The CHIRPS2.1 Modeling Process
			23.2.2 The CHPclim 2.1 Climatology
				23.2.2.1 Localized Correlation Estimates
				23.2.2.2 Interpolation of Model Residuals
				23.2.2.3 Adjusting the CHTclim Climatology
		23.3 Experimental Results for the CHIRPS 2.1 Redistribution Process
			23.3.1 CHIRP2.0 Systematic Bias Analysis
			23.3.2 CHIRP2.1 Systematic Bias Corrections
			23.3.3 Changes in the Ability to Detect Low Precipitation Events
		23.4 Conclusions
		References
	Chapter 24: Merging the Infrared Fleet and the Microwave Constellation for Tropical Hydrometeorology (TAPEER) and Global Clima...
		24.1 Introduction
		24.2 Merging Satellite Observations for Accumulation and Uncertainty Estimation
			24.2.1 Estimation of the Accumulated Precipitation
				24.2.1.1 Background
				24.2.1.2 Performance Sensitivity
				24.2.1.3 Sensitivity to the Configuration of the Microwave Constellation
			24.2.2 Estimation of the Uncertainty
				24.2.2.1 Background
				24.2.2.2 The Sampling Uncertainty
				24.2.2.3 Bias Correction Scheme
				24.2.2.4 Summary and the 1 x 1 x 1 Day Optimum
		24.3 Implementations for Tropical Water Cycle
			24.3.1 Data
				24.3.1.1 The Geostationary Data
				24.3.1.2 The BRAIN Data
			24.3.2 The TAPEER Implementation
				24.3.2.1 Common Aspects
				24.3.2.2 TAPEER 1.0 with MADRAS
				24.3.2.3 TAPEER 1.5 with SAPHIR
			24.3.3 TAPEER-GPROFv5-PRPS
				24.3.3.1 The GPROF and PRPS Data
				24.3.3.2 TAPEER 2.0
				24.3.3.3 Future Evolution
		24.4 Implementation for Climate Monitoring
			24.4.1 GIRAFE 1.0 - GPROFv5
				24.4.1.1 First Results
				24.4.1.2 Sensitivity to the Constellation Configuration
			24.4.2 Future Evolution
				24.4.2.1 The Evolution of the HOAPS Instantaneous Precipitation Rate Estimate
				24.4.2.2 Use of Sounders
				24.4.2.3 Extension to the Poles and to Snow
				24.4.2.4 The Time Dependent Uncertainty Estimation
		24.5 Conclusions
		References
	Correction to: TAMSAT
		Correction to: Chapter 22 in: V. Levizzani et al. (eds.), Satellite Precipitation Measurement, Advances in Global Change Resea...