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You searched for subject:(specific differential phase). Showing records 1 – 2 of 2 total matches.

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Colorado State University

1. Chen, Haonan. Quantitative precipitation estimation for an X-band weather radar network.

Degree: MS(M.S.), Electrical and Computer Engineering, 2013, Colorado State University

Currently, the Next Generation (NEXRAD) radar network, a joint effort of the U.S. Department of Commerce (DOC), Defense (DOD), and Transportation (DOT), provides radar data with updates every five-six minutes across the United States. This network consists of about 160 S-band (2.7 to 3.0 GHz) radar sites. At the maximum NEXRAD range of 230 km, the 0.5 degree radar beam is about 5.4 km above ground level (AGL) because of the effect of earth curvature. Consequently, much of the lower atmosphere (1-3 km AGL) cannot be observed by the NEXRAD. To overcome the fundamental coverage limitations of today's weather surveillance radars, and improve the spatial and temporal resolution issues, the National Science Foundation Engineering Center (NSF-ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) was founded to revolutionize weather sensing in the lower atmosphere by deploying a dense network of shorter-range, low-power X-band dual-polarization radars. The distributed CASA radars are operating collaboratively to adapt the changing atmospheric conditions. Accomplishments and breakthroughs after five years operation have demonstrated the success of CASA program. Accurate radar quantitative precipitation estimation (QPE) has been pursued since the beginning of weather radar. For certain disaster prevention applications such as flash flood and landslide forecasting, the rain rate must however be measured at a high spatial and temporal resolution. To this end, high-resolution radar QPE is one of the major research activities conducted by the CASA community. A radar specific differential propagation phase (Kdp)-based QPE methodology has been developed in CASA. Unlike the rainfall estimation based on the power terms such as radar reflectivity (Z) and differential reflectivity (Zdr), Kdp-based QPE is less sensitive to the path attenuation, drop size distribution (DSD), and radar calibration errors. The CASA Kdp-based QPE system is also immune to the partial beam blockage and hail contamination. The performance of the CASA QPE system is validated and evaluated by using rain gauges. In CASA's Integrated Project 1 (IP1) test bed in Southwestern Oklahoma, a network of 20 rainfall gauges is used for cross-comparison. 40 rainfall cases, including severe, multicellular thunderstorms, squall lines and widespread stratiform rain, that happened during years 2007 - 2011, are used for validation and evaluation purpose. The performance scores illustrate that the CASA QPE system is a great improvement compared to the current state-of-the-art. In addition, the high-resolution CASA QPE products such as instantaneous rainfall rate map and hourly rainfall amount measurements can serve as a reliable input for various distributed hydrological models. The CASA QPE system can save lived and properties from hazardous flash floods by incorporating hydraulic and hydrologic models for flood monitoring and warning. Advisors/Committee Members: Chandrasekar, V. (advisor), Notaros, Branislav M. (committee member), Mielke, Paul W. (committee member).

Subjects/Keywords: polarimetric radar; specific differential phase (KDP) estimation; radar network; quantitative precipitation estimation

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APA (6th Edition):

Chen, H. (2013). Quantitative precipitation estimation for an X-band weather radar network. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/79026

Chicago Manual of Style (16th Edition):

Chen, Haonan. “Quantitative precipitation estimation for an X-band weather radar network.” 2013. Masters Thesis, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/79026.

MLA Handbook (7th Edition):

Chen, Haonan. “Quantitative precipitation estimation for an X-band weather radar network.” 2013. Web. 27 Feb 2021.

Vancouver:

Chen H. Quantitative precipitation estimation for an X-band weather radar network. [Internet] [Masters thesis]. Colorado State University; 2013. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/79026.

Council of Science Editors:

Chen H. Quantitative precipitation estimation for an X-band weather radar network. [Masters Thesis]. Colorado State University; 2013. Available from: http://hdl.handle.net/10217/79026


Colorado State University

2. Ruzanski, Evan. Nowcasting for a high-resolution weather radar network.

Degree: PhD, Electrical and Computer Engineering, 2010, Colorado State University

Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings and advisories and is of great practical importance. Nowcasting using weather radar reflectivity data has been shown to be particularly useful. The Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network provides high-resolution reflectivity data amenable to producing valuable nowcasts. The high-resolution nature of CASA data requires the use of an efficient nowcasting approach, which necessitated the development of the Dynamic Adaptive Radar Tracking of Storms (DARTS) and sinc kernel-based advection nowcasting methodology. This methodology was implemented operationally in the CASA Distributed Collaborative Adaptive Sensing (DCAS) system in a robust and efficient manner necessitated by the high-resolution nature of CASA data and distributed nature of the environment in which the nowcasting system operates. Nowcasts up to 10 min to support emergency manager decision-making and 1-5 min to steer the CASA radar nodes to better observe the advecting storm patterns for forecasters and researchers are currently provided by this system. Results of nowcasting performance during the 2009 CASA IP experiment are presented. Additionally, currently state-of-the-art scale-based filtering methods were adapted and evaluated for use in the CASA DCAS to provide a scale-based analysis of nowcasting. DARTS was also incorporated in the Weather Support to Deicing Decision Making system to provide more accurate and efficient snow water equivalent nowcasts for aircraft deicing decision support relative to the radar-based nowcasting method currently used in the operational system. Results of an evaluation using data collected from 2007-2008 by the Weather Service Radar-1988 Doppler (WSR-88D) located near Denver, Colorado, and the National Center for Atmospheric Research Marshall Test Site near Boulder, Colorado, are presented. DARTS was also used to study the short-term predictability of precipitation patterns depicted by high-resolution reflectivity data observed at microalpha (0.2-2 km) to mesobeta (20-200 km) scales by the CASA radar network. Additionally, DARTS was used to investigate the performance of nowcasting rainfall fields derived from specific differential phase estimates, which have been shown to provide more accurate and robust rainfall estimates compared to those made from radar reflectivity data. Advisors/Committee Members: Chandrasekar, V. (advisor), Jayasumana, Anura P. (committee member), Mielke, Paul W. (committee member), Notaros, Branislav M. (committee member).

Subjects/Keywords: weather radar; weather forecasting; nowcasting; specific differential phase; prediction; Nowcasting (Meteorology); Meteorological satellites; Weather forecasting; Weather radar networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ruzanski, E. (2010). Nowcasting for a high-resolution weather radar network. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/45965

Chicago Manual of Style (16th Edition):

Ruzanski, Evan. “Nowcasting for a high-resolution weather radar network.” 2010. Doctoral Dissertation, Colorado State University. Accessed February 27, 2021. http://hdl.handle.net/10217/45965.

MLA Handbook (7th Edition):

Ruzanski, Evan. “Nowcasting for a high-resolution weather radar network.” 2010. Web. 27 Feb 2021.

Vancouver:

Ruzanski E. Nowcasting for a high-resolution weather radar network. [Internet] [Doctoral dissertation]. Colorado State University; 2010. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10217/45965.

Council of Science Editors:

Ruzanski E. Nowcasting for a high-resolution weather radar network. [Doctoral Dissertation]. Colorado State University; 2010. Available from: http://hdl.handle.net/10217/45965

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