Session 2.1 Keynote: QPE in NWS hydrologic applications: present and future

Monday, 6 August 2007: 10:30 AM
Hall A (Cairns Convention Center)
Geoffrey M. Bonnin, NOAA/NWS, Silver Spring, MD; and D. Kitzmiller, D. Seo, M. Smith, and P. Restrepo

Presentation PDF (310.6 kB)

The U.S. National Weather Service (NWS) produces quantitative precipitation estimates (QPE) for use in flood, streamflow and water resource forecasting. Currently, QPE is produced from multisensor analysis of radar, rain gauge and satellite observations. The production process uses a mix of automated and human processes and is performed by different units and systems within the organization. Radar and multisensor QPE is generated for most of the United States over a broad range of hydroclimatologies at high spatial and temporal resolutions. Those resolutions will increase in the near future. NWS is beginning to provide its users with estimates of the uncertainty associated with its hydrologic and hydrometeorological forecasts. To this end, NWS is developing and introducing ensemble and probabilistic analyses, as well as statistical data assimilation and forecast verification techniques. These techniques require detailed understanding of the error characteristics of QPE as well as the propagation of those errors through the hydrologic modeling systems and their impact on the hydrologic forecasts. The operational techniques and user operations concepts for producing and using QPE have evolved, and continue to evolve, based on operational experience with new and existing observing systems. For example, NWS has recently evaluated the incorporation of the vertical profile of reflectivity into range correction techniques. In the coming years NWS will implement dual polarization in the current WSR-88D Doppler radars and will begin to routinely utilize data from non-NWS radar and new satellite systems. Research and development activities are currently addressing the algorithmic and effectiveness issues associated with the new techniques and data sources, and their role in the hydrologic forecast process. It is not sufficient to demonstrate a change or improvement in QPE itself. Effectiveness of QPE techniques for hydrologic applications must be evaluated in terms of the impact on the ultimate outputs of the hydrologic forecast process.
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