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  • Author or Editor: Suzanne Van Cooten x
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Suzanne Van Cooten

Native American (American Indian)/Alaska Natives (AI/AN) are significantly underrepresented in the U.S. federal science and engineering (S&E) labor force. This underrepresentation extends into the leadership ranks of federal agencies responsible for designing, implementing, and maintaining resource monitoring and enforcement programs on tribal lands. Datasets documenting demographics and salaries of the federal S&E workforce show AI/AN are the smallest S&E workforce segment among minorities and receive the lowest average salaries for engineers and physical scientists. Academic statistics show AI/AN students earn significantly fewer engineering and Earth, atmospheric, and ocean science (EA&OS) bachelor's degrees than other ethnic groups and rarely earn advanced degrees in these disciplines. Additional aspects in federal and academic datasets offer clues on a spectrum of causative factors affecting the AI/AN recruitment pool for federal S&E jobs and the rarity of AI/AN ascending to leadership positions with federal scientific organizations.

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Jian Zhang
,
Kenneth Howard
,
Carrie Langston
,
Steve Vasiloff
,
Brian Kaney
,
Ami Arthur
,
Suzanne Van Cooten
,
Kevin Kelleher
,
David Kitzmiller
,
Feng Ding
,
Dong-Jun Seo
,
Ernie Wells
, and
Chuck Dempsey

The National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or “NMQ”, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verification, and aviation product development.

Through about ten years of R&D, a real-time NMQ system has been implemented (http://nmq.ou.edu). Since June 2006, the system has been generating high-resolution 3D reflectivity mosaic grids (31 vertical levels) and a suite of severe weather and QPE products in real-time for the conterminous United States at a 1-km horizontal resolution and 2.5 minute update cycle. The experimental products are provided in real-time to end users ranging from government agencies, universities, research institutes, and the private sector and have been utilized in various meteorological, aviation, and hydrological applications. Further, a number of operational QPE products generated from different sensors (radar, gauge, satellite) and by human experts are ingested in the NMQ system and the experimental products are evaluated against the operational products as well as independent gauge observations in real time.

The NMQ is a fully automated system. It facilitates systematic evaluations and advances of hydrometeorological sciences and technologies in a real-time environment and serves as a test bed for rapid science-to-operation infusions. This paper describes scientific components of the NMQ system and presents initial evaluation results and future development plans of the system.

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Suzanne Van Cooten
,
Kevin E. Kelleher
,
Kenneth Howard
,
Jian Zhang
,
Jonathan J. Gourley
,
John S. Kain
,
Kodi Nemunaitis-Monroe
,
Zac Flamig
,
Heather Moser
,
Ami Arthur
,
Carrie Langston
,
Randall Kolar
,
Yang Hong
,
Kendra Dresback
,
Evan Tromble
,
Humberto Vergara
,
Richard A Luettich Jr.
,
Brian Blanton
,
Howard Lander
,
Ken Galluppi
,
Jessica Proud Losego
,
Cheryl Ann Blain
,
Jack Thigpen
,
Katie Mosher
,
Darin Figurskey
,
Michael Moneypenny
,
Jonathan Blaes
,
Jeff Orrock
,
Rich Bandy
,
Carin Goodall
,
John G. W. Kelley
,
Jason Greenlaw
,
Micah Wengren
,
Dave Eslinger
,
Jeff Payne
,
Geno Olmi
,
John Feldt
,
John Schmidt
,
Todd Hamill
,
Robert Bacon
,
Robert Stickney
, and
Lundie Spence

The objective of the Coastal and Inland Flooding Observation and Warning (CI-FLOW) project is to prototype new hydrometeorologic techniques to address a critical NOAA service gap: routine total water level predictions for tidally influenced watersheds. Since February 2000, the project has focused on developing a coupled modeling system to accurately account for water at all locations in a coastal watershed by exchanging data between atmospheric, hydrologic, and hydrodynamic models. These simulations account for the quantity of water associated with waves, tides, storm surge, rivers, and rainfall, including interactions at the tidal/surge interface.

Within this project, CI-FLOW addresses the following goals: i) apply advanced weather and oceanographic monitoring and prediction techniques to the coastal environment; ii) prototype an automated hydrometeorologic data collection and prediction system; iii) facilitate interdisciplinary and multiorganizational collaborations; and iv) enhance techniques and technologies that improve actionable hydrologic/hydrodynamic information to reduce the impacts of coastal flooding. Results are presented for Hurricane Isabel (2003), Hurricane Earl (2010), and Tropical Storm Nicole (2010) for the Tar–Pamlico and Neuse River basins of North Carolina. This area was chosen, in part, because of the tremendous damage inflicted by Hurricanes Dennis and Floyd (1999). The vision is to transition CI-FLOW research findings and technologies to other U.S. coastal watersheds.

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