• Barnston, A. G., He Y. , and Unger D. A. , 2000: A forecast product that maximizes utility for state-of-the-art seasonal climate prediction. Bull. Amer. Meteor. Soc., 81 , 12711279.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Briggs, W. M., and Wilks D. S. , 1996: Estimating monthly and seasonal distributions of temperature and precipitation using the new CPC long-range forecasts. J. Climate, 9 , 818826.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, J. M., and Garbrecht J. D. , 2002: A blueprint for the use of NOAA/CPC precipitation climate forecasts in agricultural applications. Preprints, Third Symp. on Environmental Applications, Orlando, FL, Amer. Meteor. Soc., J71–J77.

  • Schneider, J. M., and Garbrecht J. D. , 2003: Temporal disaggregation of probabilistic seasonal climate forecasts. Preprints, 14th Conf. on Global Change and Climate Variations, Long Beach, CA, Amer. Meteor. Soc., CD-ROM, 5.4.

  • Wilks, D. S., 2000: On interpretation of probabilistic climate forecasts. J. Climate, 13 , 19651971.

  • Wilks, D. S., 2002: Realizations of daily weather in forecast seasonal climate. J. Hydrometeor., 3 , 195207.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 59 30 1
PDF Downloads 35 20 0

A Heuristic Method for Time Disaggregation of Seasonal Climate Forecasts

View More View Less
  • 1 Grazinglands Research Laboratory, USDA/ARS, El Reno, Oklahoma
  • | 2 NOAA/NWS/NCEP/Climate Prediction Center, Camp Springs, Maryland
Restricted access

Abstract

To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealistic sequences of monthly forecasts. As an alternative, a heuristic method has been developed to disaggregate the NOAA/Climate Prediction Center (CPC) probability of exceedance seasonal precipitation forecasts, and tested on observed precipitation data for 1971–2000 for the 102 forecast divisions covering the contiguous United States. This simple method produces monthly values that replicate the direction and amplitude of variations on the 3-month time scale, and approach the amplitude of variations on the 1-month scale, without any unrealistic behavior. Root-mean-square errors between the disaggregated values and the actual precipitation over the 30-yr test period and all forecast divisions averaged 0.94 in., which is 39% of the mean monthly precipitation, and 58% of the monthly standard deviation. This method performs equally well across widely different precipitation regimes and does a reasonable job reproducing the sudden onset of strong seasonal variations such as the southwest U.S. monsoon.

Corresponding author address: Jeanne M. Schneider, Grazinglands Research Laboratory, USDA/ARS, 7207 West Cheyenne St., El Reno, OK 73036-2144. Email: schneide@grl.ars.usda.gov

Abstract

To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealistic sequences of monthly forecasts. As an alternative, a heuristic method has been developed to disaggregate the NOAA/Climate Prediction Center (CPC) probability of exceedance seasonal precipitation forecasts, and tested on observed precipitation data for 1971–2000 for the 102 forecast divisions covering the contiguous United States. This simple method produces monthly values that replicate the direction and amplitude of variations on the 3-month time scale, and approach the amplitude of variations on the 1-month scale, without any unrealistic behavior. Root-mean-square errors between the disaggregated values and the actual precipitation over the 30-yr test period and all forecast divisions averaged 0.94 in., which is 39% of the mean monthly precipitation, and 58% of the monthly standard deviation. This method performs equally well across widely different precipitation regimes and does a reasonable job reproducing the sudden onset of strong seasonal variations such as the southwest U.S. monsoon.

Corresponding author address: Jeanne M. Schneider, Grazinglands Research Laboratory, USDA/ARS, 7207 West Cheyenne St., El Reno, OK 73036-2144. Email: schneide@grl.ars.usda.gov

Save