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Safari A.,Vrije Universiteit Brussel | De Smedt F.,Vrije Universiteit Brussel | Moreda F.,Riverside Technologies Inc. | Moreda F.,National Oceanic and Atmospheric Administration
Journal of Hydrology

This paper describes the application of a spatially distributed hydrologic model (WetSpa) Water and Energy Transfer between Soil, Plants and Atmosphere, for the second phase of the Distributed Model Intercomparison Project (DMIP2) study. The model implementation is based on 30-m spatial resolution and 1. h time-step for all basins and interior watersheds involved in the DMIP study. Rainfall inputs are derived from Next Generation Radar (NEXRAD). The distributed model parameters are based on an extensive database of watershed characteristics available for the region, including digital maps of topography, soil type, and landuse. The model is calibrated and validated on part of the river flow records for each basin and applied to the smaller interior watersheds not used in calibration to assess the model performance in ungaged basins. The statistics improve significantly with calibration of the global model parameters but even for uncalibrated simulations, the WetSpa model reproduces flow rates of acceptable accuracy for most cases. To evaluate the model performance during calibration and validation periods, an Aggregated Measure (AM) is introduced that measures different aspects of the simulated hydrograph such as shape, size and volume. The statistics for the five calibration basins show that the model produces very good to excellent results for the calibration period. With the exception of Blue River basin, the overall model performance for the validation period remains good to very good, indicating that the model is able to simulate the relevant hydrologic processes in the basins accurately. The performance of the uncalibrated model for the subcatchments is more variable, but the hourly flow rates generally reproduced with reasonable accuracy indicating an encouraging performance of the model. © 2009 Elsevier B.V. Source

Seguin W.R.,Riverside Technologies Inc. | Smith S.B.,National Oceanic and Atmospheric Administration
Bulletin of the American Meteorological Society

A position with the NWS has long been a goal of many students graduating with bachelor's, master's, and doctoral degrees in meteorology and hydrology. This goal has been and is increasingly difficult to realize, particularly for meteorology graduates, because the average number of entry-level physical scientists the NWS hired over the last five years has averaged about 120 per year, including SCEP students converted to career conditional positions, while universities are generating between 600 and 1,000 new bachelor's degree recipients each year (Knox 2008). Having a large pool to draw on for filling the few vacancies that exist would normally be considered a good situation. However, for entry-level positions, where most applicants are coming straight out of university programs and possess little relevant job experience, distinguishing between the qualified candidates who will merely be able to do the work and those who will excel as NWS employees is challenging. The SCEP greatly mitigates this challenge. The SCEP is an excellent way for the NWS to bring in fresh ideas, experience, and knowledge through the student and to reach the faculties of universities and to tap into their research and development. It allows the NWS manager to recruit exceptional individuals into targeted positions for workforce and succession planning, and to evaluate them in real work situations. It allows the agency to noncompetitively convert the students to permanent positions upon completion of degree programs. For the students, the program is an opportunity to "get their foot in the door' and to refine their career paths. All of these opportunities expose students to working on special projects and conducting research and development, which can lead to published reports and conference presentations. Students learn about the NWS organization, its mission, and how the mission is carried out. Some students discover the work they are exposed to as a SCEP student is not what the want to do as a career. Others who had planned to attend graduate school decide they would like to enter the NWS operational world rather than immediately pursue graduate degrees or to work in a research and development position. Regardless of experiences, these students are acquiring position and life skills that will serve them well on jobs taken immediately after graduation and in future more senior positions. As one former SCEP student stated: "When I have interviewed and hired programmers, I have found that the candidates that had co-operative education experience always transitioned into an office environment easily . . . Another former SCEP student stated: "The SCEP was an invaluable experience to guide me onto my career path, and to provide me with the skills needed to help me succeed . . . ". Source

Regonda S.K.,National Oceanic and Atmospheric Administration | Regonda S.K.,Riverside Technologies Inc. | Seo D.-J.,University of Texas at Arlington | Lawrence B.,National Oceanic and Atmospheric Administration | Brown J.D.,Hydrologic Solutions Ltd
Journal of Hydrology

We present a statistical procedure for generating short-term ensemble streamflow forecasts from single-valued, or deterministic, streamflow forecasts produced operationally by the U.S. National Weather Service (NWS) River Forecast Centers (RFCs). The resulting ensemble streamflow forecast provides an estimate of the predictive uncertainty associated with the single-valued forecast to support risk-based decision making by the forecasters and by the users of the forecast products, such as emergency managers. Forced by single-valued quantitative precipitation and temperature forecasts (QPF, QTF), the single-valued streamflow forecasts are produced at a 6-h time step nominally out to 5. days into the future. The single-valued streamflow forecasts reflect various run-time modifications, or "manual data assimilation", applied by the human forecasters in an attempt to reduce error from various sources in the end-to-end forecast process. The proposed procedure generates ensemble traces of streamflow from a parsimonious approximation of the conditional multivariate probability distribution of future streamflow given the single-valued streamflow forecast, QPF, and the most recent streamflow observation. For parameter estimation and evaluation, we used a multiyear archive of the single-valued river stage forecast produced operationally by the NWS Arkansas-Red River Basin River Forecast Center (ABRFC) in Tulsa, Oklahoma. As a by-product of parameter estimation, the procedure provides a categorical assessment of the effective lead time of the operational hydrologic forecasts for different QPF and forecast flow conditions. To evaluate the procedure, we carried out hindcasting experiments in dependent and cross-validation modes. The results indicate that the short-term streamflow ensemble hindcasts generated from the procedure are generally reliable within the effective lead time of the single-valued forecasts and well capture the skill of the single-valued forecasts. For smaller basins, however, the effective lead time is significantly reduced by short basin memory and reduced skill in the single-valued QPF. © 2013 Elsevier B.V.. Source

Ondrusek M.,National Oceanic and Atmospheric Administration | Stengel E.,National Oceanic and Atmospheric Administration | Kinkade C.,National Oceanic and Atmospheric Administration | Vogel R.,S. M. Resources Corporation | And 3 more authors.
Remote Sensing of Environment

Sediment loading is one of the primary threats to the health of the Chesapeake Bay. We have developed a high resolution (250m) ocean color satellite tool to monitor sediment concentrations in the Bay. In situ optical and sediment sampling is used to develop a total suspended matter (TSM) algorithm for the Chesapeake Bay. The Coastal Optical Characterization Experiment (COCE) is part of an ongoing effort to optically characterize processes and to develop regional remote sensing ocean color algorithms in the coastal waters. The goal is to characterize sediment concentrations and to develop a tool to track plumes cascading down the Bay following heavy rainfall events. Background TSM concentrations in the Chesapeake Bay Watershed can also be characterized. The plumes can have potentially devastating effects on the Chesapeake Bay's fragile ecosystem by increasing nutrient loads, depositing sediments, and decreasing salinity and light levels. Sampling took place throughout 2006 to 2008 in the upper and mid portions of the Chesapeake Bay. Measurements of TSM, chlorophyll a (Chl), and hyperspectral optics were collected. The optical measurements included above water surface irradiance (E s(λ)), in-water downwelling irradiance (E d(λ)) and in-water upwelling radiance (L u(λ)). These optical data were used to analyze the performance and utility of the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua Band 1 (645nm) for use as a TSM monitoring tool. From the optical measurements we have derived a 3rd order polynomial regression of TSM to normalized water-leaving radiance (r 2=0.79) to form an algorithm that quantitatively relates TSM to the MODIS 250m resolution band 1 (645nm). The algorithm performance was validated (a mean percent difference of -4.2%) against 270 total suspended solids samples collected by the Chesapeake Bay Program during routine water quality monitoring of the Chesapeake Bay environment. The TSM algorithm tool is then used to demonstrate monitoring of significant runoff events that occurred in June, 2006 and March, 2008. In addition, the utility of the Chesapeake Bay TSM product is demonstrated by describing regional and seasonal variations in sediment concentrations throughout the Chesapeake Bay for 2009. Mean concentrations ranged from 11.55mg/l in the upper Chesapeake Bay winter season to 6.37mg/l in the middle Chesapeake Bay spring season. These remote sensing tools can be valuable instruments in the detection and tracking of runoff events and background concentration for monitoring the health and recovery of the Chesapeake Bay. © 2012. Source

Agency: Department of Commerce | Branch: National Oceanic and Atmospheric Administration | Program: SBIR | Phase: Phase II | Award Amount: 400.00K | Year: 2015

In Phase I, Riverside investigated the need for increased access to NCDC storm data using web Application Programming Interfaces (APIs) to connect severe weather and socioeconomic information. The focus of Phase I was to design and validate an architecture that specifies the methods through which the NCDC Storm data can be programmatically accessed, processed, and displayed in easy to use interfaces. Phase I market research verified the need for tools and software algorithms for accessing the NCDC data combined with socioeconomic data for the purpose of risk identification and assessment. We have designed a software product to move customers through the process of scoping and identifying climate-and weather-based risks to assets of interest. Through discussions with potential customers we have identified prospective markets for our proposed products. For Phase II, we have identified seven main objectives to create a commercially viable product. These objectives are: Refine Product Requirements, Develop User Interface, Develop API, Acquire Data, Incorporate Additional Datasets, Develop Workflow Framework, and Create Risk Assessment and Calculation Tools.

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