Dissertation
Exploring the use of satellite and environmental data to improve real-time NEXRAD radar quantitative precipitation estimation (QPE)
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2022
DOI: 10.17077/etd.006382
Abstract
In current real-time precipitation observation systems, weather radars play an increasingly important role in the estimation of precipitation. However, the inherent limitations of weather radars, which measure the return signals of electromagnetic waves backscattered from targets rather than directly measure precipitation, make it challenging to derive accurate quantitative precipitation estimates (QPEs). The various uncertainty sources of radar QPEs have unique multi-dimensional error signatures and require remedies specific to their error characteristics. This dissertation aims to develop real-time radar QPE algorithms to improve the accuracy of radar QPE while exploring for further improvement the use of other independent data sources provided by passive and active satellite remote sensors and numerical weather prediction (NWP) models available in real-time. The three areas of focus in this dissertation are:
1. investigation of calibration bias of ground-based weather radar and diagnosis of its causes using the Dual-frequency Precipitation Radar onboard the Global Precipitation Measurement (GPM) mission core observatory satellite
2. development of a real-time precipitation-vs.-non-precipitation classification model built upon dual-polarimetric radar measurements and Geostationary Operational Environmental Satellite-16 (GOES-16) infrared products
3. evaluation of the adaptive vertical profile of reflectivity (VPR) correction method using multiple radar observations in a network and environmental data provided by NWP models and assessment of its hydrological impacts
Details
- Title: Subtitle
- Exploring the use of satellite and environmental data to improve real-time NEXRAD radar quantitative precipitation estimation (QPE)
- Creators
- Munsung Keem
- Contributors
- Witold F. Krajewski (Advisor)Allen Bradley (Committee Member)Anton Kruger (Committee Member)Bong-Chul Seo (Committee Member)Dong-Jun Seo (Committee Member) - The University of Texas at ArlingtonGabriele Villarini (Committee Member)Osnat Stramer (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Civil and Environmental Engineering
- Date degree season
- Spring 2022
- DOI
- 10.17077/etd.006382
- Publisher
- University of Iowa
- Number of pages
- xiii, 203 pages
- Copyright
- Copyright 2022 Munsung Keem
- Language
- English
- Description illustrations
- illustrations (chiefly color), tables, maps, graphs
- Description bibliographic
- Includes bibliographical references (pages 126-138).
- Public Abstract (ETD)
- Flooding is one of the most devastating natural disasters, leading to substantial human and economic losses globally every year. The frequency and intensity of flood events have increased over the past decades, and this pattern is expected to continue due to population growth, accelerating urbanization, and climate change effects. Due to the direct relationship between precipitation and flood, it is critical to estimate precipitation accurately in real-time so as to take flood mitigation actions in a timely manner and make effective water management decisions. The ability of weather radars to observe precipitation over a large area at high resolutions makes the precipitation monitoring process more efficient. However, the indirect nature of precipitation estimation using radars, which measure the return signals of electromagnetic waves reflected from targets rather than directly measure precipitation, negatively affects their accuracy for precipitation estimation. This dissertation aims to develop real-time radar precipitation estimation algorithms to improve the measurement accuracy and investigate the use of other independent data sources provided by satellites and numerical weather prediction models for further improvement. The thesis focuses on the three major error sources: calibration bias of individual radars, radar measurement contaminations by non-precipitation targets, and range-dependent error due to non-uniform reflectivity profile in the vertical direction.
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9984271450902771
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