Editorial Type:
Article
Article Type:
Research Article

Forecasting Reference Evapotranspiration Using Retrospective Forecast Analogs in the Southeastern United States

Di Tian Department of Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida

Search for other papers by Di Tian in
Current site
Google Scholar
PubMed Close
and
Christopher J. Martinez Department of Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida

Search for other papers by Christopher J. Martinez in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Dec 2012
DOI:
https://doi.org/10.1175/JHM-D-12-037.1
Page(s):
1874–1892
Restricted access

Abstract

Accurate estimation of reference evapotranspiration (ET0) is needed for determining agricultural water demand and reservoir losses and driving hydrologic simulation models. This study was conducted to explore the application of the National Centers for Environmental Prediction’s (NCEP’s) Global Forecast System (GFS) retrospective forecast (reforecast) dataset combined with the NCEP–U.S. Department of Energy (DOE) Reanalysis 2 dataset (R2) to forecast ET0 in the southeastern United States using a forecast analog approach. Seven approaches of estimating ET0 using the Penman–Monteith (PM) and Thornthwaite equations were evaluated by substitution of climatological mean values of variables or by bias correcting variables including solar radiation, maximum temperature, and minimum temperature using the R2 dataset. The skill of both terciles and extremes (10th and 90th percentiles) were evaluated. Overall, for the ET0 forecast approaches that combined R2 solar radiation with temperature, relative humidity, and wind speed from GFS, the reforecasts produced higher skill than methods that estimated parameters using GFS the reforecasts data only. The primary increase in skill was due to the use of relative humidity from the GFS reforecasts and long-term climatological mean values of solar radiation from the R2 dataset, indicating its importance in forecasting ET0 in the region. While the five categorical forecasts were skillful, the skill of upper and lower tercile forecasts was greater than that of lower and upper extreme forecasts and middle tercile forecasts. Most of the forecasts were skillful in the first 5 lead days.

Corresponding author address: Di Tian, P.O. Box 110570, Gainesville, FL 32611. E-mail: tiandi@ufl.edu

Abstract

Accurate estimation of reference evapotranspiration (ET0) is needed for determining agricultural water demand and reservoir losses and driving hydrologic simulation models. This study was conducted to explore the application of the National Centers for Environmental Prediction’s (NCEP’s) Global Forecast System (GFS) retrospective forecast (reforecast) dataset combined with the NCEP–U.S. Department of Energy (DOE) Reanalysis 2 dataset (R2) to forecast ET0 in the southeastern United States using a forecast analog approach. Seven approaches of estimating ET0 using the Penman–Monteith (PM) and Thornthwaite equations were evaluated by substitution of climatological mean values of variables or by bias correcting variables including solar radiation, maximum temperature, and minimum temperature using the R2 dataset. The skill of both terciles and extremes (10th and 90th percentiles) were evaluated. Overall, for the ET0 forecast approaches that combined R2 solar radiation with temperature, relative humidity, and wind speed from GFS, the reforecasts produced higher skill than methods that estimated parameters using GFS the reforecasts data only. The primary increase in skill was due to the use of relative humidity from the GFS reforecasts and long-term climatological mean values of solar radiation from the R2 dataset, indicating its importance in forecasting ET0 in the region. While the five categorical forecasts were skillful, the skill of upper and lower tercile forecasts was greater than that of lower and upper extreme forecasts and middle tercile forecasts. Most of the forecasts were skillful in the first 5 lead days.

Corresponding author address: Di Tian, P.O. Box 110570, Gainesville, FL 32611. E-mail: tiandi@ufl.edu
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Abatzoglou, J. T., and Brown T. J. , 2012: A comparison of statistical downscaling methods suited for wildfire applications. Int. J. Climatol., 32, 772–780.

    • Search Google Scholar
    • Export Citation

  • Allen, R. G., Pereira L. S. , Raes D. , and Smith M. , 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp. [Available online at http://www.fao.org/docrep/X0490E/X0490E00.htm.]

  • Barsugli, J., Anderson C. , Smith J. , and Vogel J. , 2009: Options for improving climate modeling to assist water utility planning for climate change. Water Utility Climate Alliance White Paper, 146 pp.

  • Cai, J., Liu Y. , Lei T. , and Pereira L. S. , 2007: Estimating reference evapotranspiration with the FAO Penman–Monteith equation using daily weather forecast messages. Agric. For. Meteor., 145, 22–35.

    • Search Google Scholar
    • Export Citation

  • Cai, J., Liu Y. , Xu D. , Paredes P. , and Pereira L. , 2009: Simulation of the soil water balance of wheat using daily weather forecast messages to estimate the reference evapotranspiration. Hydrol. Earth Syst. Sci., 13, 1045–1059.

    • Search Google Scholar
    • Export Citation

  • Chattopadhyay, S., Jain R. , and Chattopadhyay G. , 2009: Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: A neurocomputing approach. Meteor. Appl., 16, 403–411.

    • Search Google Scholar
    • Export Citation

  • Chiew, F. H. S., Kamaladasa N. N. , Malano H. M. , and McMahon T. A. , 1995: Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric. Water Manage., 28, 9–21.

    • Search Google Scholar
    • Export Citation

  • Clark, M. P., and Hay L. E. , 2004: Use of medium-range numerical weather prediction model output to produce forecasts of streamflow. J. Hydrometeor., 5, 15–32.

    • Search Google Scholar
    • Export Citation

  • Dai, X., Shi H. , Li Y. , Ouyang Z. , and Huo Z. , 2009: Artificial neural network models for estimating regional reference evapotranspiration based on climate factors. Hydrol. Processes, 23, 442–450.

    • Search Google Scholar
    • Export Citation

  • Fowler, H., Blenkinsop S. , and Tebaldi C. , 2007: Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. Int. J. Climatol., 27, 1547–1578.

    • Search Google Scholar
    • Export Citation

  • Garcia, M., Raes D. , Allen R. , and Herbas C. , 2004: Dynamics of reference evapotranspiration in the Bolivian highlands (Altiplano). Agric. For. Meteor., 125, 67–82.

    • Search Google Scholar
    • Export Citation

  • Hagedorn, R., Hamill T. M. , and Whitaker J. S. , 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part I: Two-meter temperatures. Mon. Wea. Rev., 136, 2608–2619.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and Juras J. , 2006: Measuring forecast skill: Is it real skill or is it the varying climatology? Quart. J. Roy. Meteor. Soc., 132, 2905–2923.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and Whitaker J. S. , 2006: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Mon. Wea. Rev., 134, 3209–3229.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and Whitaker J. S. , 2007: Ensemble calibration of 500-hPa geopotential height and 850-hPa and 2-m temperatures using reforecasts. Mon. Wea. Rev., 135, 3273–3280.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., Whitaker J. S. , and Wei X. , 2004: Ensemble reforecasting: Improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132, 1434–1447.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., Whitaker J. S. , and Mullen S. L. , 2006: Reforecasts: An important dataset for improving weather predictions. Bull. Amer. Meteor. Soc., 87, 33–46.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., Hagedorn R. , and Whitaker J. S. , 2008: Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part II: Precipitation. Mon. Wea. Rev., 136, 2620–2632.

    • Search Google Scholar
    • Export Citation

  • Hwang, S., Graham W. , Hernández J. L. , Martinez C. , Jones J. W. , and Adams A. , 2011: Quantitative spatiotemporal evaluation of dynamically downscaled MM5 precipitation predictions over the Tampa Bay region, Florida. J. Hydrometeor., 12, 1447–1464.

    • Search Google Scholar
    • Export Citation

  • Ines, A. V. M., and Hansen J. W. , 2006: Bias correction of daily GCM rainfall for crop simulation studies. Agric. For. Meteor., 138, 44–53.

    • Search Google Scholar
    • Export Citation

  • Ishak, A. M., Bray M. , Remesan R. , and Han D. , 2010: Estimating reference evapotranspiration using numerical weather modelling. Hydrol. Processes, 24, 3490–3509.

    • Search Google Scholar
    • Export Citation

  • Kanamitsu, M., Ebisuzaki W. , Woollen J. , Yang S. K. , Hnilo J. , Fiorino M. , and Potter G. , 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1644.

    • Search Google Scholar
    • Export Citation

  • Kumar, M., Raghuwanshi N. S. , and Singh R. , 2011: Artificial neural networks approach in evapotranspiration modeling: A review. Irrig. Sci., 29, 11–25.

    • Search Google Scholar
    • Export Citation

  • Landeras, G., Ortiz-Barredo A. , and López J. J. , 2009: Forecasting weekly evapotranspiration with ARIMA and artificial neural network models. J. Irrig. Drain. Eng., 135, 323–334.

    • Search Google Scholar
    • Export Citation

  • López-Urrea, R., Martín de Santa Olalla F. , Fabeiro C. , and Moratalla A. , 2006: Testing evapotranspiration equations using lysimeter observations in a semiarid climate. Agric. Water Manage., 85, 15–26.

    • Search Google Scholar
    • Export Citation

  • Markovic, M., Jones C. G. , Winger K. , and Paquin D. , 2009: The surface radiation budget over North America: Gridded data assessment and evaluation of regional climate models. Int. J. Climatol., 29, 2226–2240.

    • Search Google Scholar
    • Export Citation

  • Marshall, C. H., Pielke R. A. , Steyaert L. T. , and Willard D. A. , 2004: The impact of anthropogenic land-cover change on the Florida peninsula sea breezes and warm season sensible weather. Mon. Wea. Rev., 132, 28–52.

    • Search Google Scholar
    • Export Citation

  • Maurer, E., and Hidalgo H. , 2008: Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Sci., 12, 551–563.

    • Search Google Scholar
    • Export Citation

  • Maurer, E., Hidalgo H. , Das T. , Dettinger M. , and Cayan D. , 2010: The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol. Earth Syst. Sci., 14, 1125–1138.

    • Search Google Scholar
    • Export Citation

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360.

  • Misra, V., Moeller L. , Stefanova L. , Chan S. , O’Brien J. J. , Smith T. J. III, and Plant N. , 2011: The influence of the Atlantic warm pool on the Florida panhandle sea breeze. J. Geophys. Res., 116, D00Q06, doi:10.1029/2010JD015367.

    • Search Google Scholar
    • Export Citation

  • Muluye, G. Y., 2011: Implications of medium-range numerical weather model output in hydrologic applications: Assessment of skill and economic value. J. Hydrol., 400, 448–464.

    • Search Google Scholar
    • Export Citation

  • Ozkan, C., Kisi O. , and Akay B. , 2011: Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration. Irrig. Sci., 29, 431–441.

    • Search Google Scholar
    • Export Citation

  • Pal, M., and Deswal S. , 2009: M5 model tree based modelling of reference evapotranspiration. Hydrol. Processes, 23, 1437–1443.

  • Panofsky, H. A., and Brier G. W. , 1958: Some Applications of Statistics to Meteorology. The Pennsylvania State University, 224 pp.

  • Plummer, D. A., and Coauthors, 2006: Climate and climate change over North America as simulated by the Canadian RCM. J. Climate, 19, 3112–3132.

    • Search Google Scholar
    • Export Citation

  • Potts, J., Folland C. , Jolliffe I. , and Sexton D. , 1996: Revised “LEPS” scores for assessing climate model simulations and long-range forecasts. J. Climate, 9, 34–53.

    • Search Google Scholar
    • Export Citation

  • Silva, D., Meza F. J. , and Varas E. , 2010: Estimating reference evapotranspiration (ET0) using numerical weather forecast data in central Chile. J. Hydrol., 382, 64–71.

    • Search Google Scholar
    • Export Citation

  • Sobash, R. A., Kain J. S. , Bright D. R. , Dean A. R. , Coniglio M. C. , and Weiss S. J. , 2011: Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convection-allowing model forecasts. Wea. Forecasting, 26, 714–728.

    • Search Google Scholar
    • Export Citation

  • Thornthwaite, C. W., 1948: An approach toward a rational classification of climate. Geogr. Rev., 38, 55–94.

  • Timbal, B., and McAvaney B. , 2001: An analogue-based method to downscale surface air temperature: Application for Australia. Climate Dyn., 17, 947–963.

    • Search Google Scholar
    • Export Citation

  • van den Dool, H. M., 1994: Searching for analogues, how long must we wait? Tellus, 46A, 314–324.

  • Vicente-Serrano, S. M., Begueria S. , and Lopez-Moreno J. I. , 2010: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Climate, 23, 1696–1718.

    • Search Google Scholar
    • Export Citation

  • Vivoni, E. R., Moreno H. A. , Mascaro G. , Ridriguez J. C. , Watts C. J. , Garatuza-Payan J. , and Scott R. L. , 2008: Observed relation between evapotranspiration and soil moisture in the North American monsoon season. Geophys. Res. Lett., 35, L22403, doi:10.1029/2008GL036001.

    • Search Google Scholar
    • Export Citation

  • Wang, Y.-M., Traore S. , Kerh T. , and Leu J.-M. , 2011: Modelling reference evapotranspiration using feed forward backpropagation algorithm in arid regions of Africa. Irrig. Drain., 60, 404–417.

    • Search Google Scholar
    • Export Citation

  • Werner, K., Brandon D. , Clark M. , and Gangopadhyay S. , 2005: Incorporating medium-range numerical weather model output into the ensemble streamflow prediction system of the National Weather Service. J. Hydrometeor., 6, 101–114.

    • Search Google Scholar
    • Export Citation

  • Whitaker, J. S., Wei X. , and Vitart F. , 2006: Improving week-2 forecasts with multimodel reforecast ensembles. Mon. Wea. Rev., 134, 2279–2284.

    • Search Google Scholar
    • Export Citation

  • Wilby, R., Charles S. , Zorita E. , Timbal B. , Whetton P. , and Mearns L. , 2004: Guidelines for use of climate scenarios developed from statistical downscaling methods. Intergovernmental Panel on Climate Change, 27 pp. [Available online at http://www.narccap.ucar.edu/doc/tgica-guidance-2004.pdf.]

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 467 pp.

  • Wilks, D. S., and Hamill T. M. , 2007: Comparison of ensemble-MOS methods using GFS reforecasts. Mon. Wea. Rev., 135, 2379–2390.

  • Wood, A. W., Leung L. R. , Sridhar V. , and Lettenmaier D. P. , 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189–216.

    • Search Google Scholar
    • Export Citation

  • Yoder, R. E., Odhiambo L. O. , and Wright W. C. , 2005: Evaluation of methods for estimating daily reference crop evapotranspiration at a site in the humid Southeast United States. Appl. Eng. Agric., 21, 197–202.

    • Search Google Scholar
    • Export Citation

  • Zhang, H., and Casey T. , 2000: Verification of categorical probability forecasts. Wea. Forecasting, 15, 80–89.

  • Zhu, C., and Lettenmaier D. P. , 2007: Long-term climate and derived hydrology and energy flux data for Mexico: 1925–2004. J. Climate, 20, 1936–1946.

    • Search Google Scholar
    • Export Citation

  • Zorita, E., and von Storch H. , 1999: The analog method as a simple statistical downscaling technique: Comparison with more complicated methods. J. Climate, 12, 2474–2489.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1584 497 60
PDF Downloads 456 83 11