S. Businger | USGS Science

USGS2013

The utility of atmospheric analyses for the mitigation of artifacts in InSAR

The numerical weather models (NWMs) developed by the meteorological community are able to provide accurate analyses of the current state of the atmosphere in addition to the predictions of the future state. To date, most attempts to apply the NWMs to estimate the refractivity of the atmosphere at the time of satellite synthetic aperture radar (SAR) data acquisitions have relied on predictive models. We test the hypothesis that performing a final assimilative routine, ingesting all available meteorological observations for the times of SAR acquisitions, and generating customized analyses of the atmosphere at those times will better mitigate atmospheric artifacts in differential interferograms. We find that, for our study area around Mount St. Helens (Amboy, Washington, USA), this approach is unable to model the refractive changes and provides no mean benefit for interferogram analysis. The performance is improved slightly by ingesting atmospheric delay estimates derived from the limited local GPS network; however, the addition of water vapor products from the GOES satellites reduces the quality of the corrections. We interpret our results to indicate that, even with this advanced approach, NWMs are not a reliable mitigation technique for regions such as Mount St. Helens with highly variable moisture fields and complex topography and atmospheric dynamics. It is possible, however, that the addition of more spatially dense meteorological data to constrain the analyses might significantly improve the performance of weather modeling of atmospheric artifacts in satellite radar interferograms.

Journal of Geophysical Research B: Solid Earth↗

USGS2000

Hydrological Aspects of Weather Prediction and Flood Warnings: Report of the Ninth Prospectus Development Team of the U.S. Weather Research Program

Among the many natural disasters that disrupt human and industrial activity in the United States each year, including tornadoes, hurricanes, extreme temperatures, and lightning, floods are among the most devastating and rank second in the loss of life. Indeed, the societal impact of floods has increased during the past few years and shows no sign of abating. Although the scientific questions associated with flooding and its accurate prediction are many and complex, an unprecedented opportunity now exists - in light of new observational and computing systems and infrastructures, a much improved understanding of small-scale meteorological and hydrological processes, and the availability of sophisticated numerical models and data assimilation systems - to attack the flood forecasting problem in a comprehensive manner that will yield significant new scientific insights and corresponding practical benefits. The authors present herein a set of recommendations for advancing our understanding of floods via the creation of natural laboratories situated in a variety of local meteorological and hydrological settings. Emphasis is given to floods caused by convection and cold season events, fronts and extratropical cyclones, orographic forcing, and hurricanes and tropical cyclones following landfall. Although the particular research strategies applied within each laboratory setting will necessarily vary, all will share the following principal elements: (a) exploitation of those couplings important to flooding that exist between meteorological and hydrological processes and models; (b) innovative use of operational radars, research radars, satellites, and rain gauges to provide detailed spatial characterizations of precipitation fields and rates, along with the use of this information in hydrological models and for improving and validating microphysical algorithms in meteorological models; (c) comparisons of quantitative precipitation estimation algorithms from both research (especially multiparameter) and operational radars against gauge data as well as output produced by meso- and storm-scale models; (d) use of data from dense, temporary river gauge networks to trace the fate of rain from its starting location in small basins to the entire stream and river network; and (e) sensitivity testing in the design and implementation of separate as well as coupled meteorological and hydrologic models, the latter designed to better represent those nonlinear feedbacks between the atmosphere and land that are known to play an important role in runoff prediction. Vital to this effort will be the creation of effective and sustained linkages between the historically separate though scientifically related disciplines of meteorology and hydrology, as well as their observational infrastructures and research methodologies.

Conference Paper↗