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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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Suzanne Van Cooten, Kimberly L. Elmore, Donald E. Barbé, J. Alex McCorquodale, and Denise J. Reed

Abstract

This study quantifies the spatial distribution of precipitation patterns on an annual basis for southeast Louisiana. To compile a long-term record of 24-h rainfall, rainfall reports collected by National Weather Service (NWS) cooperative observers were gathered from National Climatic Data Center (NCDC) archives, private collections of observational data held at regional and local libraries, NWS offices, and local utility providers. The reports were placed into a digital database in which each station’s record was subjected to an extensive quality control process. This process produced a database of daily rainfall reports for 59 south Louisiana stations for the period 1836–2002, with extensive documentation for each site outlining the differences between the study’s data and the data available from the NCDC Web page. A statistical methodology was developed to determine if the four NCDC climate divisions for southeast Louisiana accurately depict average monthly rainfall for the area. This method employs cluster analysis, using Euclidean distance as the measure of dissimilarity for the clustering technique. To resolve missing rainfall observations, an imputation scheme was developed that uses the two most similar stations (based on Euclidean distance) to determine appropriate values for missing rainfall observations. Results from this testing structure show statistical evidence of precipitation microclimates across south Louisiana at higher spatial scales than those of the NCDC climate zones. Quantifying the spatial extent of daily precipitation and documenting historical trends of precipitation provides critical design information for regional infrastructure within this highly vulnerable area of the central Gulf Coast region.

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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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David Kitzmiller, Suzanne Van Cooten, Feng Ding, Kenneth Howard, Carrie Langston, Jian Zhang, Heather Moser, Yu Zhang, Jonathan J. Gourley, Dongsoo Kim, and David Riley

Abstract

This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated ZR selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of ZR selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

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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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