Hook Hua
Hook Hua of NASA’s Jet Propulsion Laboratory (JPL) richly deserves the Charles S. Falkenberg Award for 2017. Hook’s primary professional contributions have been in applying emerging computer science techniques and technologies to Earth science to accelerate our understanding of the Earth, its phenomena, and its processes. Hook demonstrates the spirit and dedication of Dr. Falkenberg in his tireless efforts to improve our understanding of Earth science phenomena and processes by making instrument output more useful more quickly. His hallmark effort, the Advanced Rapid Imaging and Analysis (ARIA) Project and its related Synthetic Aperture Radar (SAR) Science Data Processing Foundry, embodies his ability to think strategically and execute ideas to completion with the science community in mind.
Hook has led a brilliant and talented team addressing the science data processing of SAR data. Both his own contributions and his leadership of the team in applying workflow tools, cloud computing, and machine learning techniques to the processing of data from multiple Earth science instruments are a credit to his skill and ability to attract very smart people to work with him. Hook’s team has created and implemented innovations in science data processing that have accelerated the availability of SAR data for use by the solid Earth, hydrology, and hazard response communities. Hook and his team have transformed the discipline of science data processing by three different and important contributions: (1) workflow tools to pipeline processing, (2) automated quality control, and (3) expanding the use of cloud computing as an environment for quickly processing the high-volume output of these instruments.
The effect has been to move SAR data processing out of the realm of the artisan and into a true production capability, driving down the cost. The NASA–Indian Space Research Organisation (ISRO) SAR and Orbiting Carbon Observatory 2 instrument teams recognized the value of Hook’s strategy in that they adopted his approach for their instruments.
Another of Hook’s innovations has been the use of machine learning techniques in identifying anomalies in data to adjust the science data processing approach for a given scene, minimizing human intervention. Hook and his team were able to apply some advanced computer science techniques and then retest the scene. This reduced the labor and delays from manual rehandling of the data by scarce experts.
—Michael Little, NASA Earth Science Technology Office, Greenbelt, Md.; Chris Lynnes, NASA Earth Science Data and Information System Project, Greenbelt, Md.; Curt Tilmes, NASA Goddard Research Center, Greenbelt, Md.; and Sue Owen, Jet Propulsion Laboratory, Pasadena, Calif.
ResponseI am deeply honored by this recognition of the 2017 Charles S. Falkenberg Award. It is very humbling to be recognized along with the prior recipients, who are great role models in the use of Earth science toward improving societal benefits.
I owe this recognition to Michael Little of NASA’s Earth Science Technology Office and colleagues at JPL, other NASA centers, the Earth Science Data and Information System, Distributed Active Archive Centers, Federation of Earth Science Information Partners, Earth Science Data System Working Groups, and program management at NASA Headquarters, who all share similar passions. Particular appreciation goes to Curt Tilmes, Chris Lynnes, Steve Berrick, Sue Owen, Gerald Manipon, Brian Wilson, and Frank Lindsay, who gave me my first big break in the Advancing Collaborative Connections for Earth System Science (ACCESS) program over a decade ago. In addition, I have been blessed to work with a diverse and talented team of multidisciplinary scientists and technologists in the Advanced Rapid Imaging and Analysis (ARIA) Project at JPL/California Institute of Technology. Last but not least, I want to thank my family, who has supported these pursuits.
My roots in the late 1990s at JPL working on science data management and high-performance computing (HPC), and later applying HPC to interferometric SAR processing, exposed me to the pain points of Earth science data processing, such as long queue times and moving voluminous data to the computer. In 2009 we first proposed to do large-scale SAR processing “in the cloud.” This proposal was naturally received with skepticism and uncertainty. But through perseverance and trust from the Earth Science Technology Office’s Advanced Information Systems Technology Program, we were able to demonstrate that not only can SAR analysis be done in the cloud, but it can be more viable for addressing the computation and data volume challenges associated with large-scale SAR processing.
Six years later, NASA’s Orbiting Carbon Observatory 2 (OCO-2) mission came to our team to help port Level-2 full physics processing to the cloud. This was the pivotal moment when a tier 1 NASA mission started to take cloud computing more seriously as a viable approach beyond just research projects. From this, we pioneered the exploitation of the AWS “spot market” for low-cost operational science data processing in a volatile computing environment.
Through real-world use of cloud computing in projects such as ARIA, the SAR Science Data Processing Foundry, and Getting Ready for NISAR (GRFN) project, we also had opportunities to innovate in pay-as-you-go approaches to custom on-demand and large-scale SAR analysis. It is humbling to see our efforts being used for disaster urgent response events such as earthquakes, floods, hurricanes, and volcano monitoring, even more so when we can see how effective cloud computing has been for generating rapid response SAR data products that are being used within hours by other agencies such as the Federal Emergency Management Agency for disaster response.
After years of perseverance, we finally see cloud computing for Earth science now becoming part of the baseline plan for NASA’s upcoming large radar missions, Surface Water Ocean Topography (SWOT) and NASA-ISRO SAR (NISAR). We are finally crossing the “Valley of Death” from research to flight infusion. It is amazing to see firsthand the evolution of Earth science data systems finally transition to the paradigm of “data lakes,” where we move computers closer to the data but do so in cost-effective and science-enabling ways. Doing so will require continued innovation (e.g., applied machine learning) that bridges the gaps between the research and flight project worlds.
—Hook Hua, Jet Propulsion Laboratory, California Institute of Technology, Pasadena
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