Graduate Exam Abstract

Haonan Chen

Ph.D. Final
June 14, 2017, 9:00 am - 11:00 am
B214 Conference Room Engineering
Radar and Satellite Observations of Precipitation: Space Time Variability, Cross Validation, and Fusion

Abstract: Rainfall estimation based on satellite measurements has proven to be very useful for various applications. A number of precipitation products at multiple time and space scales have been developed based upon satellite observations. For example, the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space based rainfall estimates. The CMORPH products are essentially derived based on Infrared (IR) brightness temperature information observed by geostationary satellites and passive microwave (PMW) based retrievals from low earth orbit satellite measurements. Although the space- based precipitation products provide an excellent tool for regional and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, its accuracy is restricted due to the limitations of spatial and temporal sampling as well as the parametric retrieval algorithms, particularly for extreme events such as heavy rain or light rain. On the other hand, ground-based radar is more mature for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization technique and further enhanced by urban scale radar networks. Therefore, ground radars are often critical for providing local scale rainfall estimation and a “heads-up” for operational forecasters to issue watches and warnings, as well as validation of various space measurements and products. Since 2012, the center for Collaborative Adaptive Sensing of the Atmosphere (CASA) has been operating a high- resolution dense urban radar network in Dallas-Fort Worth (DFW) Metroplex. The CASA DFW QPE system, which is based on a local S-band National Weather Service (NWS) Weather Surveillance Radar – 1988 Doppler (WSR-88DP) and dual-polarization X- band CASA radar network, has demonstrated its excellent performance during several years of operation in a variety of precipitation regimes. The real-time CASA DFW QPE products are used extensively for localized hydrometeorological applications such as urban flash flood forecasting. It also serves as a reliable dataset for validation of global precipitation measurement (GPM) satellite precipitation products. In this study, the real-time high-resolution CASA DFW QPE system is presented. The specific dual-polarization radar rainfall algorithms at S- and X-band frequencies, as well as the fusion methodology combining radar observations at different temporal resolution are detailed. Cross- comparison between radar rainfall estimates and rainfall measurements from rain gauges is conducted to demonstrate the excellent performance of this urban QPE system. In addition, a neural network based data fusion system, termed as "Deep Multi-Layer Perceptron" (DMLP), is introduced to improve satellite-based rainfall estimation using DFW radar rainfall products. Particularly, the CMORPH methodology is first applied to derive combined PMW rainfall estimates and combined IR data from multiple satellites. The combined PMW-based retrievals and IR data then serve as input to the proposed DMLP model. The high-quality DFW rainfall products are used as target to train the DMLP model. In this dissertation, prototype architecture of the DMLP model is detailed, and the DMLP-based rainfall products are evaluated using existing CMORPH products and surface rainfall measurements from gauge networks.

Adviser: V. Chandrasekar
Co-Adviser: N/A
Non-ECE Member: Prof. Paul Mielke, Statistics
Member 3: Prof. Steven C. Reising, ECE
Addional Members: Prof. Margaret Cheney, ECE

Please contact Haonan.Chen@ColoState.Edu for details.

Program of Study: