299
Precipitation Estimate Using NEXRAD Ground-Based Radar Images
shows that no one method can perform better than the others in terms of all evaluation coefficients for all days. For practical estimation of precipitation distribution,
implementation of multiple methods is recommended to predict spatial precipitation.
ACKNOWLEDGMENTS
The author would like to acknowledge Dr. Michael Van Liew of the Montana
Department of Environmental Quality and Dr. David Bosch of the Southeast
Watershed Research Laboratory, Agricultural Research Service, U.S. Department
of Agriculture (USDA), for providing part of the data used in this study. Dr. DongJun Seo of the National Weather Service provided valuable information on the multisensor precipitation estimator next-generation radar (MPE NEXRAD). Dr. Judi
Bradberry, Dr. David Kitzmiller, Dr. James Paul, and Dr. Ron Jones of the National
Weather Service provided the MPE NEXRAD data used in this study and valuable
discussion on the quality control and availability of NEXRAD. This work was partially supported by the U.S. Department of Energy (US DOE) Office of Biological
and Environmental Research under contract no. DE-A06-76RLO 1830.
REFERENCES
Ahmed, S. and de Marsily, G. (1987). Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity. Water Resources
Research, 23, 1727–1737.
Anagnostou, E. N. and Krajewski, W. F. (1998). Calibration of the WSR-88D precipitation
processing subsystem. Weather Forecast, 13, 396–406.
Bosch, D. D., Sheridan, J. M., and Marshall, L. K. (2007). Precipitation, soil moisture, and
climate database, Little River Experimental Watershed, Georgia, United States. Water
Resources Research, 43, W09472, doi:10.1029/2006WR005834.
Ciach, G. J. and Krajewski, W. F. (1999). Radar-rain gauge comparisons under observational
uncertainties. Journal of Applied Meteorology, 38, 1519–1525.
Ciach, G. J., Krajewski, W. F., and Villarini, G. (2007). Product-error-driven uncertainty model
for probabilistic quantitative precipitation estimation with NEXRAD data. Journal of
Hydrometeorology, 8, 1325–1347.
Cressie, N. (1985). Fitting variogram models by weighted least squares. Mathematical
Geology, 17, 563–586.
Crum, T. D., Saffle, R. E., and Wilson, J. W. (1998). An update on the NEXRAD program and
future WSR-88D support to operations. Weather Forecast, 13, 253–262.
Diak, G. R., Anderson, M. C., Bland, W. L., Norman, J. M., Mecikalski, J. M., and Aune,
R. M. (1998). Agricultural management decision aids driven by real-time satellite data.
Bulletin of American Meteorological Society, 79, 1345–1355.
Dyer, J. L. and Garza, R. (2004). A comparison of precipitation estimation techniques over
Lake Okeechobee, Florida. Weather Forecast, 19, 1029–1043.
Eberhart, R. C. and Shi, Y. (1998). Comparison between genetic algorithms and particle
swarm optimization, in Proceedings of the Seventh Annual Conference on Evolutionary
Programming: Evolutionary Programming VII, pp. 611–616. Springer, New York.
Fulton, R. A., Breidenbach, J. P., Seo, D., Miller, D. A., and O’Bannon, T. (1998). The WSR88D precipitation algorithm. Weather and Forecasting, 13, 377–395.
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University
Press, New York.
Precipitation Estimate Using NEXRAD Ground-Based Radar Images
shows that no one method can perform better than the others in terms of all evaluation coefficients for all days. For practical estimation of precipitation distribution,
implementation of multiple methods is recommended to predict spatial precipitation.
ACKNOWLEDGMENTS
The author would like to acknowledge Dr. Michael Van Liew of the Montana
Department of Environmental Quality and Dr. David Bosch of the Southeast
Watershed Research Laboratory, Agricultural Research Service, U.S. Department
of Agriculture (USDA), for providing part of the data used in this study. Dr. DongJun Seo of the National Weather Service provided valuable information on the multisensor precipitation estimator next-generation radar (MPE NEXRAD). Dr. Judi
Bradberry, Dr. David Kitzmiller, Dr. James Paul, and Dr. Ron Jones of the National
Weather Service provided the MPE NEXRAD data used in this study and valuable
discussion on the quality control and availability of NEXRAD. This work was partially supported by the U.S. Department of Energy (US DOE) Office of Biological
and Environmental Research under contract no. DE-A06-76RLO 1830.
REFERENCES
Ahmed, S. and de Marsily, G. (1987). Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity. Water Resources
Research, 23, 1727–1737.
Anagnostou, E. N. and Krajewski, W. F. (1998). Calibration of the WSR-88D precipitation
processing subsystem. Weather Forecast, 13, 396–406.
Bosch, D. D., Sheridan, J. M., and Marshall, L. K. (2007). Precipitation, soil moisture, and
climate database, Little River Experimental Watershed, Georgia, United States. Water
Resources Research, 43, W09472, doi:10.1029/2006WR005834.
Ciach, G. J. and Krajewski, W. F. (1999). Radar-rain gauge comparisons under observational
uncertainties. Journal of Applied Meteorology, 38, 1519–1525.
Ciach, G. J., Krajewski, W. F., and Villarini, G. (2007). Product-error-driven uncertainty model
for probabilistic quantitative precipitation estimation with NEXRAD data. Journal of
Hydrometeorology, 8, 1325–1347.
Cressie, N. (1985). Fitting variogram models by weighted least squares. Mathematical
Geology, 17, 563–586.
Crum, T. D., Saffle, R. E., and Wilson, J. W. (1998). An update on the NEXRAD program and
future WSR-88D support to operations. Weather Forecast, 13, 253–262.
Diak, G. R., Anderson, M. C., Bland, W. L., Norman, J. M., Mecikalski, J. M., and Aune,
R. M. (1998). Agricultural management decision aids driven by real-time satellite data.
Bulletin of American Meteorological Society, 79, 1345–1355.
Dyer, J. L. and Garza, R. (2004). A comparison of precipitation estimation techniques over
Lake Okeechobee, Florida. Weather Forecast, 19, 1029–1043.
Eberhart, R. C. and Shi, Y. (1998). Comparison between genetic algorithms and particle
swarm optimization, in Proceedings of the Seventh Annual Conference on Evolutionary
Programming: Evolutionary Programming VII, pp. 611–616. Springer, New York.
Fulton, R. A., Breidenbach, J. P., Seo, D., Miller, D. A., and O’Bannon, T. (1998). The WSR88D precipitation algorithm. Weather and Forecasting, 13, 377–395.
Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University
Press, New York.