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Christopher J. Martinez, Ph.D
Use of Retrospective Weather and Climate Forecasts to Improve Hydrologic
Forecasts
Recently, the NOAA Earth System Research Laboratory/Physical Sciences Division (ESRL/PSD) produced an archive of retrospective forecasts (reforecasts) using a fixed version of the National Centers for Environmental Prediction’s Global Forecast System (GFS). The reforecasts are available for the period 1979 to the present (http://www.esrl.noaa.gov/psd/forecasts/reforecast/). Extensive sets of reforecasts are not commonly produced using the most up-to-date forecast models due to excessive computing costs. The use of a fixed model allows for the
estimation (and correction) of model biases and errors using a suitably long archive of forecasts.
Recently, the NOAA Earth System Research Laboratory/Physical Sciences Division (ESRL/PSD) produced an archive of retrospective forecasts (reforecasts) using a fixed version of the National Centers for Environmental Prediction’s Global Forecast System (GFS). The reforecasts are available for the period 1979 to the present (http://www.esrl.noaa.gov/psd/forecasts/reforecast/). Extensive sets of reforecasts are not commonly produced using the most up-to-date forecast models due to excessive computing costs. The use of a fixed model allows for the
estimation (and correction) of model biases and errors using a suitably long archive of forecasts.
Christopher J. Martinez, Ph.D
279 Frazier Rogers Hall
PO Box 110570, University of Florida
Gainesville, FL 32611
279 Frazier Rogers Hall
PO Box 110570, University of Florida
Gainesville, FL 32611
Phone: (352) 392-1864 x279
Fax: (352) 392-4092
Email: chrisjm@ufl.edu
Fax: (352) 392-4092
Email: chrisjm@ufl.edu
The objectve of this work is to evaluate the use of reforecasts to improve
short-term hydrologic forecasts by Tampa Bay Water. Tampa Bay Water uses a
suite of custom models to forecast streamflow, groundwater levels, and demand
at multiple scales. Specifically, this project will:
• Evaluate analog techniques to
generate an ensemble of 1-14 day
forecasts.
• Evaluate model output statistics (MOS) to produce bias-corrected forecasts.
• Assess the improvement in short-term forecasts of urban water demand and streamflow using reforecast archives.
• Evaluate model output statistics (MOS) to produce bias-corrected forecasts.
• Assess the improvement in short-term forecasts of urban water demand and streamflow using reforecast archives.