587 Impact of Landfalling Hurricane on Rainfall and Flood: Prototyping Use of GTPAS on Near-Real-Time GOES-16 Data

Tuesday, 8 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Long Chiu, George Mason Univ., Fairfax, VA; and J. L. Kinter III, X. Hao, Q. Liu, Z. Chester, G. Stearn, and M. Aliani

A GOES-16 data via Terrestrial Link (G-T) Data Processing and Analysis System (GTPAS) has been developed by George Mason University and Ligado Networks. The system receives near real time GOES-16 data from a GRB/antenna reception station maintained by Ligado Networks and delivered via their Content Delivery Network (CDN). The primary purpose of the GPTAS is to evaluate the value of near real time earth and space weather data in an academic and research setting. These low latency data allows the generation of videos such as Advanced Baseline Imager (ABI) images and dissemination online at near real time. The GTPAS system is easily extensible and very flexible to allow testing new ideas for GOES-16 applications, hence it can serve as a testbed for research and education activities. The GPTAS also can be configured to monitor natural hazard events, such as hurricanes and wildland fires. In addition, GTPAS receives files that may be missed or delayed from the NOAA cloud server and hence complements the GOES-16 data that NOAA puts on the “cloud.”

There are several high impact hurricanes making landfall to the continental US in 2017 and 2018, which were absent since hurricane Wilma in 2005 (http://www.aoml.noaa.gov/hrd/tcfaq/E23.html). They include Harvey and Irma in 2017 and Florence and Michael in 2018. These events provide opportunities to evaluate the improvement of GOES -16 over its predecessor sensors. We tracked the paths of Irma, Florence and Michael using a pattern matching technique. The track error is about 20 km compared with the post-analyzed hurricane track information provided by the NOAA National Hurricane Center (NHC). Additionally, the Global Lightning Mapper (GLM) also provides lightning information at high resolution which can be used to examine hurricane intensities changes by superposing lightning activities on ABI imageries. The results further confirm the capability of lightning information in aiding the forecasting of hurricane rapid intensification. Lower resolution GOES -13 data were simulated from the full resolution GOES-16 data to evaluate the impact of the advancement in GOES sensor technology.

With higher temporal and spatial resolutions, the GOES-16 ABI data provide clear spatial structure and temporal changes of hurricane rain bands. For the hurricane events, GOES-16 ABI data were used to characterize rainfall patterns, and compared with the rainfall data at selected ground stations, as well as the IMERG precipitation product. To evaluate the potential for flood area monitoring, a Normalized Difference Water Index (NDWI) were generated using GOES-16 ABI data before and after hurricane passes for comparison and analyses of flood area.

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