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.

Note: Intro -- Preface -- Acknowledgments -- Contents of Volume 2 -- Contents of Volume 1 -- List of Figures -- List of Tables -- Contributors -- Acronyms -- Part IV: Validation -- Chapter 25: The IPWG Satellite Precipitation Validation Effort -- 25.1 Introduction -- 25.2 Current Validation Work: Data and Methodological Approach -- 25.3 Examples of IPWG Validation -- 25.3.1 Regional Analysis Over Japan Region -- 25.3.2 Seasonal Studies Over South America -- 25.3.3 Examples of IPWG-Related Studies -- 25.4 Future Validation Efforts -- 25.5 Conclusions -- References -- Chapter 26: The GPM Ground Validation Program -- 26.1 Overview -- 26.1.1 GV Measurement Synergy -- 26.2 Validation Instruments, Data, and Examples -- 26.2.1 Primary Datasets -- 26.2.2 Example Applications of VN Datasets -- 26.2.3 Validation Using GV-MRMS -- 26.2.4 Application to Verification of GPM L1SRs -- 26.3 Physical Validation Activities -- 26.4 Validation of the GPM IMERG Product -- 26.4.1 Examples of IMERG Validation Over South Korea -- 26.4.2 Selected Examples of IMERG Validation Over CONUS -- 26.5 Summary and Moving Forward -- References -- Chapter 27: The GPM DPR Validation Program -- 27.1 Introduction -- 27.2 Calibration -- 27.3 Ground Ka-Radar Experiment -- 27.3.1 Measurements -- 27.3.2 Results -- 27.4 Comparisons of DPR Products with Ground Observations -- 27.4.1 GPM/DPR Ground Validation Comparing with Rain Gauge Data Over Japan -- 27.4.2 GPM/DPR Ground Validation with MRMS/NMQ Data Over the US -- 27.4.3 Surface Snow Flag in the Latest DPR Product -- 27.5 Summary -- References -- Chapter 28: Error and Uncertainty Characterization -- 28.1 Uncertainty Sources of Satellite Precipitation Products -- 28.2 Methods for Assessing Satellite Precipitation Products -- 28.2.1 The Benchmark -- 28.2.2 Verification Metrics -- 28.2.3 Triple Collocation Analysis. , 28.3 Error and Uncertainty Models -- 28.4 Summary of the Performance of the Main Satellite Precipitation Products -- References -- Chapter 29: Multiscale Evaluation of Satellite Precipitation Products: Effective Resolution of IMERG -- 29.1 Introduction -- 29.2 Data -- 29.2.1 IMERG -- 29.2.2 MRMS Gauge-Adjusted Radar QPE -- 29.3 Method: Spectral Analysis in the Wavelet Domain -- 29.3.1 Rationale -- 29.3.2 Implementation -- 29.3.3 Illustrative Case Study -- 29.4 Results -- 29.5 Conclusions -- Appendix: Two-Dimensional Discrete Orthogonal Decomposition with the Haar Wavelet -- Wavelets Functions in One Dimension and N Dimensions -- The Haar Discrete Orthogonal Wavelets in One and Two Dimensions -- References -- Chapter 30: Remote Sensing of Orographic Precipitation -- 30.1 Introduction -- 30.2 Orographic Precipitation Measurement -- 30.3 Ground-Validation -- 30.4 Physical-Basis of Retrieval Errors -- 30.5 Summary -- References -- Chapter 31: Integrated Multi-satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on S... -- 31.1 Introduction -- 31.2 Spaceborne and Ground-Based Precipitation Datasets -- 31.2.1 Dual-Frequency Phased Array Radar -- 31.2.2 GPM Microwave Imager -- 31.2.3 Integrated Multi-satellitE Retrievals for GPM -- 31.2.4 Ground-Based Reference Precipitation -- 31.3 Impact of Precipitation Typology on Satellite-Based Active, Passive and Merged Precipitation Estimation -- 31.3.1 DPR QPE and Precipitation Typology -- 31.3.2 GMI QPE and Precipitation Typology -- 31.3.3 IMERG and Precipitation Typology -- 31.4 Conclusion -- References -- Chapter 32: Hydrologic Validation and Flood Analysis -- 32.1 Introduction -- 32.2 Space-Time Validation Framework -- 32.2.1 Point Based Evaluation -- 32.2.2 River Network Based Multiscale Valuation -- 32.2.3 River Network Based Error Dependence -- 32.3 Conclusions. , References -- Chapter 33: Global-Scale Evaluation of 22 Precipitation Datasets Using Gauge Observations and Hydrological Modeling -- 33.1 Introduction -- 33.2 Data and Methods -- 33.2.1 P Datasets -- 33.2.2 Performance Evaluation Using Gauge Observations -- 33.2.3 Performance Evaluation Using Hydrological Modeling -- 33.3 Results and Discussion -- 33.3.1 Performance in Terms of Temporal Dynamics -- 33.3.2 Performance in Terms of Climate Indices -- 33.3.3 Performance Evaluation Using Hydrological Modeling -- 33.4 Conclusions -- References -- Chapter 34: OceanRAIN - The Global Ocean Surface-Reference Dataset for Characterization, Validation and Evaluation of the Wate... -- 34.1 Introduction -- 34.2 The OceanRAIN Optical Disdrometer ODM470 -- 34.3 Data Ingest and Data Set Construction -- 34.4 OceanRAIN Data Sets -- 34.5 Applications and Validation -- 34.5.1 Precipitation Characterization -- 34.5.2 The Point-to-Area Representativeness -- 34.5.3 IMERG Validation -- 34.6 Conclusions and Future Outlook -- References -- Part V: Observed Characteristics of Precipitation -- Chapter 35: GPCP and the Global Characteristics of Precipitation -- 35.1 Introduction -- 35.2 GPCP Monthly Analysis Technique -- 35.3 Global Precipitation Climatology -- 35.4 Variations in Global Mean Precipitation (1979-2017) -- 35.5 Patterns of Precipitation Variation and Trends -- 35.6 Trends in Precipitation Intensity at the Monthly Scale -- 35.7 Summary -- References -- Chapter 36: Global Snowfall Detection and Measurement -- 36.1 Introduction -- 36.2 Global Snowfall: CloudSat´s Recent Contributions -- 36.3 CloudSat Snowfall Detection and QPE Algorithm -- 36.4 Global Snowfall Characteristics -- 36.4.1 CloudSat Sampling -- 36.4.2 Snowfall Occurrence Statistics -- 36.4.3 CPR Reflectivity Variability: Snowfall Events -- 36.4.4 Snowfall QPE -- 36.5 Concluding Remarks -- References. , Chapter 37: Snowfall Detection by Spaceborne Radars -- 37.1 Introduction -- 37.2 Classical Methods to Determine Surface Precipitation Types -- 37.3 Vertical Phase Distribution from Spaceborne Radars -- 37.4 Use of Dual-Frequency Observations -- 37.5 Future Directions -- References -- Chapter 38: On the Duration and Life Cycle of Precipitation Systems in the Tropics -- 38.1 Introduction -- 38.2 The Climatology and Morphology of the MCS -- 38.2.1 Background and Robust Features -- 38.2.2 TOOCAN Specific Features of Tropical MCS -- 38.3 Precipitation and MCS Duration -- 38.3.1 The Precipitation Totals and the Degree of Organization of Convection -- 38.3.2 The Precipitation Totals and the System Duration -- 38.4 Precipitation and the MCS Life Cycle -- 38.4.1 The Linear Growth/Decay Model for the Cold Cloud Shield Life Cycle -- 38.4.2 Compositing GEO and LEO Along the Life Cycle -- 38.5 Conclusions -- References -- Chapter 39: Observational Characteristics of Warm-Type Heavy Rainfall -- 39.1 Introduction -- 39.2 Data and Analysis Method -- 39.3 Comparison Between Korea and Oklahoma -- 39.4 General Features Found Over the East Asian Monsoon Area -- 39.5 Role of Collision and Coalescence Processes -- 39.6 Conclusions -- References -- Chapter 40: Satellite Precipitation Measurement and Extreme Rainfall -- 40.1 Introduction -- 40.1.1 Physical Principles of Satellite Rainfall Measurements -- 40.1.2 Selected Satellite QPE Products -- 40.2 Statistical Distribution of Rainfall Extremes -- 40.2.1 Average and Maximum Rainfall -- 40.2.2 Thresholds, Percentiles and Spatial Distribution of Daily Extremes -- 40.3 Application of Satellite QPE to Extreme Events -- 40.3.1 Rainfall Extremes at the Daily Scale -- 40.3.2 Daily Rainfall Extremes in Relation to Cyclonic Activity -- 40.3.3 Other Applications of SPPs with Respect to Rainfall Extremes. , 40.4 Verification of Satellite QPE Extremes with Respect to In-Situ Rain Gauge Observations -- 40.5 Conclusions and Future Directions in Satellite QPEs for Extreme Events Measurements -- References -- Chapter 41: Rainfall Trends in East Africa from an Ensemble of IR-Based Satellite Products -- 41.1 Introduction -- 41.2 Data and Methods -- 41.2.1 Satellite Products -- 41.2.2 Rainfall Indices and Trend Analysis Methodology -- 41.3 Rainfall Seasonality and Variability -- 41.4 Trend Results -- 41.4.1 Trend Analysis of Annual Rainfall Indices -- 41.4.2 Trend Analysis of Seasonal Rainfall Indices -- 41.4.2.1 January-February (JF) Season -- 41.4.2.2 March-April-May (MAM) Season -- 41.4.2.3 June-July-August-September (JJAS) Season -- 41.4.2.4 October-November-December (OND) season -- 41.5 Conclusions -- References -- Chapter 42: Heavy Precipitation Systems in the Mediterranean Area: The Role of GPM -- 42.1 Introduction -- 42.2 Satellite Precipitation Products -- 42.3 Isolated Deep-Convective Systems -- 42.4 Mesoscale Convective Systems -- 42.5 Medicanes -- 42.6 Final Remarks -- References -- Chapter 43: Dryland Precipitation Climatology from Satellite Observations -- 43.1 Introduction -- 43.2 Background on Dryland Climate -- 43.3 Accuracy of Satellite Precipitation Estimates for Dryland Regions -- 43.4 Satellite-Derived Climatological Properties of Dryland Precipitation -- 43.5 Quasi-Global Dryland Precipitation Climatology from TRMM Observations -- 43.6 Conclusions and Recommendations -- References -- Chapter 44: Hailfall Detection -- 44.1 Physical Basis -- 44.2 Microwave Imager and Active Radar Retrievals -- 44.3 Microwave Sounder Retrievals -- 44.3.1 MicroWave Cloud Classification (MWCC) -- 44.3.2 Hail Detection with MWCC Method -- 44.3.3 NOAA AMSU-b/MHS -- 44.4 Summary -- References. , Chapter 45: Improving High-Latitude and Cold Region Precipitation Analysis.