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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.

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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.