Note: Intro -- 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.