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WATER RESOURCES RESEARCH,
VOL. 40,
W04304,
doi:10.1029/2003WR002747,
2004
A resampling procedure for generating conditioned daily weather sequences
Martyn P. Clark
Center for Science and Technology Policy Research, Cooperative Institute for Research in Environmental Sciences, University
of Colorado, Boulder, Colorado, USA
Subhrendu Gangopadhyay
Center for Science and Technology Policy Research, Cooperative Institute for Research in Environmental Sciences, University
of Colorado, Boulder, Colorado, USA
David Brandon
Colorado Basin River Forecast Center, Salt Lake City, Utah, USA
Kevin Werner
Colorado Basin River Forecast Center, Salt Lake City, Utah, USA
Lauren Hay
Water Resources Division, United States Geological Survey, Lakewood, Colorado, USA
Balaji Rajagopalan
Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, Colorado, USA
David Yates
Research Applications Program, National Center for Atmospheric Research, Boulder, Colorado, USA
Abstract
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.
Received 7
October
2003;
accepted 27
February
2004;
published 28
April
2004.
Index Terms: 1833 Hydrology: Hydroclimatology; 1869 Hydrology: Stochastic processes; 1894 Hydrology: Instruments and techniques.
Read Full Article (file size: 4067002 bytes) Cited by
Citation: 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.
Copyright 2004 by the American Geophysical Union.
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