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Prospects for Advancing Drought Understanding, Monitoring, and Prediction
  • Published Date:
    2015
  • Source:
    Journal of Hydrometeorology, 16(4), 1636-1657.
Filetype[PDF-4.28 MB]


Details:
  • Description:
    This paper summarizes and synthesizes the research carried out under the NOAA Drought Task Force (DTF) and submitted in this special collection. The DTF is organized and supported by NOAA's Climate Program Office with the National Integrated Drought Information System (NIDIS) and involves scientists from across NOAA, academia, and other agencies. The synthesis includes an assessment of successes and remaining challenges in monitoring and prediction capabilities, as well as a perspective of the current understanding of North American drought and key research gaps. Results from the DTF papers indicate that key successes for drought monitoring include the application of modern land surface hydrological models that can be used for objective drought analysis, including extended retrospective forcing datasets to support hydrologic reanalyses, and the expansion of near-real-time satellite-based monitoring and analyses, particularly those describing vegetation and evapotranspiration. In the area of drought prediction, successes highlighted in the papers include the development of the North American Multimodel Ensemble (NMME) suite of seasonal model forecasts, an established basis for the importance of La Nina in drought events over the southern Great Plains, and an appreciation of the role of internal atmospheric variability related to drought events. Despite such progress, there are still important limitations in our ability to predict various aspects of drought, including onset, duration, severity, and recovery. Critical challenges include (i) the development of objective, science-based integration approaches for merging multiple information sources; (ii) long, consistent hydro-meteorological records to better characterize drought; and (iii) extending skillful precipitation forecasts beyond a 1-month lead time.
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