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Stochastic downscaling of hourly precipitation series from climate change projections
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  • Ziwen Yu,
  • Franco A. Montalto,
  • stefan Jacobson,
  • Upmanu Lall,
  • Daniel Bader,
  • Radley Horton
Ziwen Yu
University of Florida

Corresponding Author:[email protected]

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Franco A. Montalto
Drexel University
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stefan Jacobson
Philadelphia Water Department
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Upmanu Lall
Columbia University
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Daniel Bader
Center for Climate Systems Research, Columbia University
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Radley Horton
Center for Climate Systems Research, Columbia University
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Abstract

Stochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for non-stationary conditions informed by the Global Climate Model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly Pressure Change Events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multi-variable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100-year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast US. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the northeast US. This SPG provides physically plausible weather ensembles for water resource studies involving climate change.