Surface Water and Climate

A resampling procedure for generating conditioned daily weather sequences

  1. Martyn P. Clark1,
  2. Subhrendu Gangopadhyay1,6,
  3. David Brandon2,
  4. Kevin Werner2,
  5. Lauren Hay3,
  6. Balaji Rajagopalan4,7,
  7. David Yates5

Article first published online: 28 APR 2004

DOI: 10.1029/2003WR002747

Issue

Water Resources Research

Water Resources Research

Additional Information

How to Cite

Clark, M. P., S. Gangopadhyay, D. Brandon, K. Werner, L. Hay, B. Rajagopalan, and D. Yates (2004), A resampling procedure for generating conditioned daily weather sequences, Water Resour. Res., 40, W04304, doi:10.1029/2003WR002747.

Author Information

  1. 1

    Center for Science and Technology Policy Research, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA

  2. 2

    Colorado Basin River Forecast Center, Salt Lake City, Utah, USA

  3. 3

    Water Resources Division, United States Geological Survey, Lakewood, Colorado, USA

  4. 4

    Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, Colorado, USA

  5. 5

    Research Applications Program, National Center for Atmospheric Research, Boulder, Colorado, USA

  6. 6

    Also at Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, Colorado, USA.

  7. 7

    Also at Center for Science and Technology Policy Research, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA.

Publication History

  1. Issue published online: 28 APR 2004
  2. Article first published online: 28 APR 2004
  3. Manuscript Accepted: 27 FEB 2004
  4. Manuscript Revised: 10 FEB 2004
  5. Manuscript Received: 7 OCT 2003

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Keywords:

  • hydroclimatology;
  • prediction;
  • stochastic hydrology

[1] A method is introduced to generate conditioned daily precipitation and temperature time series at multiple stations. The method resamples data from the historical record “nens” times for the period of interest (nens = number of ensemble members) and reorders the ensemble members to reconstruct the observed spatial (intersite) and temporal correlation statistics. The weather generator model is applied to 2307 stations in the contiguous United States and is shown to reproduce the observed spatial correlation between neighboring stations, the observed correlation between variables (e.g., between precipitation and temperature), and the observed temporal correlation between subsequent days in the generated weather sequence. The weather generator model is extended to produce sequences of weather that are conditioned on climate indices (in this case the Niño 3.4 index). Example illustrations of conditioned weather sequences are provided for a station in Arizona (Petrified Forest, 34.8°N, 109.9°W), where El Niño and La Niña conditions have a strong effect on winter precipitation. The conditioned weather sequences generated using the methods described in this paper are appropriate for use as input to hydrologic models to produce multiseason forecasts of streamflow.

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