Radar Hydrology : Principles, Models, and Applications
Radar Hydrology: Principles, Models, and Applications provides graduate students, operational forecasters, and researchers with a theoretical framework and practical knowledge of radar precipitation estimation. The only text on the market solely devoted to radar hydrology, this comprehensive reference: * Begins with a brief introduction to radar * Focuses on the processing of radar data to arrive at accurate estimates of rainfall * Addresses advanced radar sensing principles and applications * Covers radar technologies for observing each component of the hydrologic cycle * Examines state-of-the-art hydrologic models and their inputs, parameters, state variables, calibration procedures, and outputs * Discusses contemporary approaches in data assimilation * Concludes with methods, case studies, and prediction system design * Includes downloadable MATLAB(R) content Flooding is the #1 weather-related natural disaster worldwide. Radar Hydrology: Principles, Models, and Applications aids in understanding the physical systems and detection tools, as well as designing prediction systems.
- 22 Jul 2017
- Taylor & Francis Ltd
- London, United Kingdom
Table of contents
Preface About the Authors Introduction to Basic Radar Principles Radar Components The Radar Beam The Radar Pulse Signal Processing References Radar Quantitative Precipitation Estimation Radar Calibration Quality Control Signal Processing Fuzzy Logic Precipitation Rate Estimation Vertical Profile of Reflectivity Rain Gauge Adjustment Space-Time Aggregation Remaining Challenges Uncertainty Estimation References Polarimetric Radar Quantitative Precipitation Estimation Polarimetric Radar Variables Polarimetric Radar Data Quality Control Noise Effect and Reduction Clutter Detection and Removal Attenuation Correction Calibration Self-Consistency Check Hydrometeor Classification Polarimetric Characteristics of Radar Echoes Classification Algorithms Polarimetric Radar-Based QPE Microphysical Retrievals Raindrop Size Distribution Model DSD Retrieval Snowfall and Hail Estimation Validation References Multi-Radar Multi-Sensor (MRMS) Algorithm Single-Radar Processing Dual-Polarization Quality Control Vertical Profile of Reflectivity Correction Product Generation Precipitation Typology Precipitation Estimation Verification Discussion References Advanced Radar Technologies for Quantitative Precipitation Estimation Mobile and Gap-Filling Radars ARRC's Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) NSSL's X-Band Polarimetric Mobile Radar (NOXP) ARRC's Atmospheric Imaging Radar (AIR) ARRC's Polarimetric X-Band 1000 (PX-1000) Collaborative Adaptive Sensing of the Atmosphere (CASA) Spaceborne Radars Precipitation Radar aboard TRMM Dual-Frequency Precipitation Radar aboard NASA GPM Phased-Array Radar Design Aspects and Product Resolution Dual Polarization Impact on Hydrology References Radar Technologies for Observing the Water Cycle The Hydrologic Cycle Surface Water Streamflow Radar Surface Water Altimetry Synthetic Aperture Radar Subsurface Water L-Band Radar C-Band Radar Ground-Penetrating Radar Subsurface Water References Radar QPE for Hydrologic Modeling Overview of Hydrological Models Model Classes Model Parameters Model State Variables and Data Assimilation Hydrological Model Evaluation Hydrological Evaluation of Radar QPE Case Study in Ft. Cobb Basin, Oklahoma Evaluation with a Hydrologic Model Calibrated to a Reference QPE Evaluation with Monte Carlo Simulations from a Hydrologic Model Evaluation with a Hydrologic Model Calibrated to Individual QPEs References Flash Flood Forecasting Flash Flood Guidance Flash Flood Guidance: History Lumped Flash Flood Guidance Flash Flood Potential Index Gridded Flash Flood Guidance Comments on the Use of Flash Flood Guidance Threshold Frequency Approach References
"This is the first book on radar hydrology written by hydrologists. Whereas the excellent knowledge of radar technology by the authors permits an adequate coverage of the principles of rainfall rate estimation by radar, their hydrological background allows them to provide a unique message on the benefits (and on the remaining challenges) in exploiting radar techniques in hydrology. ... In a clear and concise manner, the book combines topics from different scientific disciplines into a unified approach aiming to guide the reader through the requirements, strengths, and pitfalls of the application of radar technology in hydrology-mostly for flood prediction. Chapters include excellent discussion of theory, data analysis, and applications, along with several cross references for further review and useful conclusions." -Marco Borga, University of Padova, Italy
About Yang Hong
Yang Hong is a professor of hydrometeorology and remote sensing in the School of Civil Engineering and Environmental Sciences, adjunct faculty member with the School of Meteorology, co-director of the WaTER Center, faculty member with the Advanced Radar Research Center, and affiliated member of the Center for Analysis and Prediction of Storms at the University of Oklahoma. Dr. Hong also directs the HyDROS Lab at the National Weather Center. Previously, he was a research scientist at NASA's Goddard Space Flight Center and postdoctoral researcher at University of California, Irvine. He holds a BS and MS from Peking (Beijing) University, China and Ph.D from the University of Arizona. Jonathan J. Gourley is a research hydrologist with the NOAA/National Severe Storms Laboratory and affiliate associate professor with the School of Meteorology at the University of Oklahoma. His research interests include hydrologic prediction across scales ranging from water resources management to early warning of extreme events. Dr. Gourley was the principal inventor of a multisensor rainfall algorithm that was expanded to encompass all radars in North America and deployed to several foreign countries for operational use. He also assembled a comprehensive database that is being used to develop FLASH-a real-time flash flood forecasting system. He holds a BS, MS, and Ph.D from the University of Oklahoma.