LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Detlefsen, Nina K. (2006)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics
In agricultural production many operations depend on the weather. In this paper, a model is investigated that calculates the probability for the execution of a given operation which depends on several meteorological parameters. The model is based on a 48-hr numerical weather forecast with hourly resolution. The probability forecasts are compared to the numeric forecasts for the operation based on the numeric weather forecast. The model is a logistic regression model with generalized estimating equations. The Brier skill score, sharpness and reliability diagrams and relative operating characteristic curves are used to evaluate the model. The model setup described is dynamic in the sense that on a given date, parameters are estimated based on history and these parameter estimates are used for calculating the probability forecasts. This means that parameter estimates adapt automatically to seasonal changes in weather and to changes in numerical weather forecasts following developments in the forecast models. In this paper, we perform model output statistics, which tune the numeric weather forecast to an operation that depends on several meteorological parameters rather than only tuning a single weather parameter. Although some problems occurred, the model developed showed that the numerical forecast for such an operation could be improved.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Anadranistakis, M., Lagouvardos, K., Kotroni, V. and Skouras, K. 2002. Combination of Kalman filter and an empirical method for the correction of near-surface temperatur forecast: application over Greece. Geophys. Res. Lett. 29(16), art. no. 1776.
    • Anadranistakis, M., Lagouvardos, K., Kotroni, V. and Elefteriadis, H. 2004. Correcting temperature and humidity forecasts using kalman filtering: Potential for agricultural protection in northern greece. Atmos. Res. 71, 115-125.
    • Applequist, S., Gahrs, G. E. and Pfeffer, R. L. 2002. Comparison of methodologies for probabilistic quantitative precipitation forecasting. Wea. Forecast. 17, 783-799.
    • Bremnes, J. 2004. Probabilistic forecasts of precipitation in terms of quantiles using NWP model output. Mon. Wea. Rev 132(1), 338-347.
    • Changnon, S. A. 2004. Changing uses of climate predictions in agriculture: implications for prediction research, providers, and users. Wea. Forecast. 19(3), 606-613.
    • Dobson, A. J. 2002. An Introduction to Generalized Linear Models, Chapman & Hall/CRC, Florida.
    • Galanis, G. and Anadranistakis, M. 2002. A one-dimensional kalman filter for the correction of near surface temperature forecasts. Meteorol. Appl. 9, 437-441.
    • Jensen, A. L., Boll, P. S., Thysen, I. and Pathak, B. K. 2000. Pl@nteinfoa webbased system for personalised decision support in crop management. Comput. Electron. Agric. 25, 271-294.
    • Jolliffe, I. T. and Stephenson, D. B. (Eds.), 2003. Forecast Verification. A Practitioner's Guide in Atmospheric Science. Wiley, John Wiley & Sons Ltd., England.
    • Rasmussen, A., Sørensen, J. H., Nielsen, N. W. and Amstrup, B. 2000. Uncertainty of meteorological parameters from DMI-HIRLAM. Scientific Report 00-07, Danish Meteorological Institute.
    • Sass, B. H., Nielsen, N. W., Jørgensen, J. U., Amstrup, B., Kmit, M. and co-authors. 2002. The operational DMI-HIRLAM system. 2002- version. Technical Report 02-05, Danish Meteorological Institute.
    • Sohn, K. T., Lee, J. H., Lee, S. H. and Ryu, C. S. 2005. Statistical prediction of heavy rain in South Korea. Adv. Atmos. Sci. 22(5), 703- 710.
    • Sokol, Z. and Rezacova, D. 2000. Improvement of local categorical precipitation forecasts from an NWP model by various statistical postprocessing methods. Stud. Geophys. Geodaet. 44, 38-56.
    • Steffensen, M. 2002. Logistisk Kalman filter for kraftig nedbør. Technical Report 02-28, Danish Meteorological Institute.
    • Steffensen, M., Vejen, F. A. H., Overgaard, S., Scharling, M. and J u¨ngling, H. 2001. Evaluation of the AMIS gridded observations and radar derived 24-hour accumulated precipitation by comparison with climate grid - denmark gridded observations. Technical Report 01-13, Danish Meteorological Institute.
    • Suleiman, A. and Crago, R., 2004. Hourly and daytime evapotranspiration from grassland using radiometric surface temperatures. Agron. J. 96, 384-390.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from