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

✍ Scribed by Vincenzo Levizzani (editor), Christopher Kidd (editor), Dalia B. Kirschbaum (editor), Christian D. Kummerow (editor), Kenji Nakamura (editor), F. Joseph Turk (editor)

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 502
• Category: Library

✦ Synopsis

This book offers a complete overview of the measurement of precipitation from space, which has made considerable advancements during the last two decades. This is mainly due to the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission, CloudSat and a carefully maintained constellation of satellites hosting passive microwave sensors. The book revisits a previous book, Measuring Precipitation from Space, edited by V. Levizzani, P. Bauer and F. J. Turk, published with Springer in 2007. The current content has been completely renewed to incorporate the advancements of science and technology in the field since then. This book provides unique contributions from field experts and from the International Precipitation Working Group (IPWG).

The book will be of interest to meteorologists, hydrologists, climatologists, water management authorities, students at various levels and many other parties interested in making use of satellite precipitation data sets.

Chapter “TAMSAT” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

✦ Table of Contents

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...