Radar Rainfall Data Estimation and Use, MOP 139, provides a detailed look at the basic philosophy and principles for estimating radar rainfall data and analyzing data. Radar-derived rainfall estimation is one of the most significant recent advances in hydrologic engineering and practice. Manual of Practice 139 provides a framework for researchers and practicing engineers working in hydrologic engineering to develop radar rainfall data sets and analyze them according to their varied goals and resources. This manual will be a valuable resource for government agencies, engineering firms, and practicing engineers working in the hydrologic engineering field.
|Radar Rainfall Data Estimation and Use, MOP 139, provides a detailed look at the basic philosophy and principles for estimating radar rainfall data and analyzing data. Radar-derived rainfall estimation is one of the most significant recent advances in hydrologic engineering and practice. Manual of Practice 139 provides a framework for researchers and practicing engineers working in hydrologic engineering to develop radar rainfall data sets and analyze them according to their varied goals and resources. This manual will be a valuable resource for government agencies, engineering firms, and practicing engineers working in the hydrologic engineering field.|Prepared by the Standard Practice on Radar Rainfall Estimation Task Committee of the Environmental and Water Resources Institute of ASCE
Radar Rainfall Data Estimation and Use, MOP 139, provides a detailed look at the basic philosophy and principles for estimating and analyzing radar rainfall data and analyzing data. Radar-derived rainfall estimation is one of the most significant recent advances in hydrologic engineering and practice. Rain gauges provide point values of rainfall depth and intensity but are not cost effective in providing information about the spatial distribution of rainfall, whereas radar-derived rainfall data provides a density of measurements that are not obtainable by rain gauges alone. Combining these two sensor systems produces better rainfall estimates that more accurately characterize rainfall across a watershed.
This Manual of Practice provides a framework for researchers and practicing engineers working in hydrologic engineering to develop radar rainfall data sets and analyze them according to their varied goals and resources. Topics include
An introduction to and examples of radar rainfall estimation,
Temporal and spatial characteristics of radar rainfall data,
Methodology for radar rainfall data quality evaluation and improvement,
Use of radar rainfall data in hydrologic modeling,
Rainfall data augmentation and design of rainfall monitoring networks, and
Examples of radar rainfall data analysis and data applications.
MOP 139 will be a valuable resource for government agencies, a framework for engineering firms, practicing engineers, researchers, and students working in the hydrologic engineering field.
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Höhe: 229 mm
Breite: 152 mm
ISBN-13
978-0-7844-1511-5 (9780784415115)
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Schweitzer Klassifikation
1. RADAR RAINFALL ESTIMATION 1.1 Introduction
1.2 Background
1.2 Scope
1.3 Availability of Radar Rainfall Data within the USA
2. RADAR RAINFALL DATA: TEMPORAL AND SPATIAL CHARACTERISTICS
2.1 Native Radar Data Resolution
2.2 Radar Rainfall Data Mosaics
2.3 Data Formats and Resolutions
2.3.1 Hydrologic Rainfall Analysis Project Grid
2.3.2 Standard Hydrologic Grid
2.4 Radar Rainfall Data QA/QC and Data Management
2.5 Gauge-Adjusted Radar Rainfall Estimates
2.6 Tool for Radar Rainfall Data Analysis (HEC-MetVue)
2.7 Use of Radar Rainfall Data
2.7.1 Real-time Hydrologic Monitoring, Flood Forecasting, and Disaster Management
2.7.2 Probable Maximum Precipitation Estimation
2.8 Radar Rainfall Data Issues and Future Perspectives
2.8.1 Dual Polarization
2.8.2 Phased Array
2.8.3 Archived Radar Rainfall Data
2.9 Conclusions
3. RADAR-BASED RAINFALL DATA PROCESSING
3.1. Background
3.1.1 Programmatic Background and Organization of This Chapter
3.1.2. Limitations on the Content of This Chapter
3.2. Data Acquisition and Processing
3.2.1 Stages in preparation of Radar and Gauge-radar products
3.2.2 Physical Principles for Single-polarization Radar Precipitation Estimates
3.2.3. Processing for Radar Quality Control
3.2.4. Hydrometeor Identification
3.3. Reflectivity-Precipitation Rate Relationships
3.4 Radar QPE Products from the WSR-88D Radar Product Generator
3.5 Error Distribution of Radar Rainfall Estimates
3.5.1. Errors Caused by Sampling Differences between Gauge and Radar
3.5.2 Numerical Truncation Errors in RPG Products before 2004
3.6. Approaches to Gauge-Radar Adjustment
3.6.1. Mean Field Bias Correction
3.6.2. Local Bias Correction
3.7 Approaches to Gauge-Radar Observation Merging
3.8. Applicability of the Gauge-Radar Approaches
3.9. Use of Daily Precipitation Reports in Combination with Radar QPE
3.10. Access to Precipitation Observations and Estimates
3.10.1. Products from Individual Radar Sites
3.10.2. Gridded Multi-Radar and Multi-Sensor Products
3.10.3. Rain Gauge Reports
3.11 Conclusions
4. EVALUATION AND IMPROVEMENT OF RADAR-BASED RAINFALL DATA
4.1 Rainfall Measurement Methods, Errors, and Accuracy
4.2 Rain Gauge and Radar-based Measurements
4.3 Improving Radar-based Estimation: Optimal Z-R Relationships
4.4 Conclusions
5. USE OF RADAR RAINFALL DATA IN HYDROLOGIC MODELING
5.1 Data Requirements for Hydrologic Modeling and Design
5.2 Radar-based Rainfall Data for Hydrologic Modeling
5.2.1 Hydrologic Model Calibration
5.2.2 Data for Hydrologic Design
5.3 Conclusions
6. EXAMPLES IN RADAR RAINFALL DATA, ANALYSES, AND APPLICATIONS
6.1 Radar Rainfall Estimation-South Florida Water Management District
6.2 Radar Rainfall Data Analyses
6.2.1 Rainfall Frequency Analysis
6.2.2 Depth Area Reduction Factors
6.3 Other Radar Rainfall Data Applications
6.3.1 Near Real-time Flood Warning System
6.3.2 Sewer System Modeling
6.3.3 Groundwater Recharge Modeling
6.3.4 Rain Gauge Network Design
6.4 Conclusions
7. Advanced Topic: Framework for Bias Analysis of Radar Data
7.2. Ideal Performance Measures and Skill Scores
7.3. Utility of Assessment Indexes and Performance Measures
7.4. Bias Corrections
7.5. Bias Corrections with Limited Rain Gauge Data
7.6. Bias Corrections: Temporal Resolution Issues
7.7. Conclusions
8. ADVANCED TOPIC: RAIN GAUGE RAINFALL DATA AUGMENTATION AND RADAR RAINFALL DATA ANALYSIS
8.1 Spatial and Temporal Analysis of Rainfall
8.1.1 Spatial Interpolation Methods for Rainfall
8.2 Missing Data Estimation
8.3 Use of Radar Data for Infilling Rainfall Data
8.3.1 Functional Forms Linking Radar and Rain Gauge Data
8.4 Geospatial Grid-based Transformations of Radar-based Rainfall Data
8.5 Issues with Filled Precipitation Data Series
8.6 Conclusions
9. ADVANCED TOPIC: DESIGN OF RAINFALL MONITORING NETWORKS
9.1 Design of Rainfall Monitoring Networks
9.2 Rain Gauge Network Density
9.3 Optimal Rain Gauge Monitoring Networks
9.4 Optimal Density and Monitoring Networks
9.5 Objectives for Monitoring Network Design
9.6 Optimal Monitoring Network Design
9.6 Optimal Network Design using Radar Data
9.6.1 Variable Density Analysis Block Approach
9.7 Post Network Design Recommendations for Rain Gauge Placements
9.8 Identification of Meteorological Homogeneous Areas
9.9 Conclusions
