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Model-inspired predictors for model output statistics (MOS)

Piet Termonia (UGent) and Alex Deckmyn (UGent)
(2010) MONTHLY WEATHER REVIEW. 135(10). p.3496-3505
Author
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Abstract
This article addresses the problem of the choice of the predictors for the multiple linear regression in model output statistics. Rather than devising a selection procedure directly aimed at the minimization of the final scores, it is examined whether taking the model equations as a guidance may render the process more rational. To this end a notion of constant fractional errors is introduced. Experimental evidence is provided that they are approximately present in the model and that their impact is sufficiently linear to be corrected by a linear regression. Of particular interest are the forcing terms in the coupling of the physics parameterization to the dynamics of the model. Because such parameterizations are estimates of subgrid processes, they are expected to represent degrees of freedom that are independent of the resolved-scale model variables. To illustrate the value of this approach, it is shown that the temporal accumulation of sensible and latent heat fluxes and net solar and thermal radiation utilized as predictors add a statistically significant improvement to the 2-m temperature scores.
Keywords
MOS, PERFORMANCE, FORECASTS, VARIABLES, SYSTEM, TESTS

Citation

Please use this url to cite or link to this publication:

MLA
Termonia, Piet, and Alex Deckmyn. “Model-inspired Predictors for Model Output Statistics (MOS).” MONTHLY WEATHER REVIEW 135.10 (2010): 3496–3505. Print.
APA
Termonia, P., & Deckmyn, A. (2010). Model-inspired predictors for model output statistics (MOS). MONTHLY WEATHER REVIEW, 135(10), 3496–3505.
Chicago author-date
Termonia, Piet, and Alex Deckmyn. 2010. “Model-inspired Predictors for Model Output Statistics (MOS).” Monthly Weather Review 135 (10): 3496–3505.
Chicago author-date (all authors)
Termonia, Piet, and Alex Deckmyn. 2010. “Model-inspired Predictors for Model Output Statistics (MOS).” Monthly Weather Review 135 (10): 3496–3505.
Vancouver
1.
Termonia P, Deckmyn A. Model-inspired predictors for model output statistics (MOS). MONTHLY WEATHER REVIEW. 2010;135(10):3496–505.
IEEE
[1]
P. Termonia and A. Deckmyn, “Model-inspired predictors for model output statistics (MOS),” MONTHLY WEATHER REVIEW, vol. 135, no. 10, pp. 3496–3505, 2010.
@article{8560429,
  abstract     = {This article addresses the problem of the choice of the predictors for the multiple linear regression in model output statistics. Rather than devising a selection procedure directly aimed at the minimization of the final scores, it is examined whether taking the model equations as a guidance may render the process more rational. To this end a notion of constant fractional errors is introduced. Experimental evidence is provided that they are approximately present in the model and that their impact is sufficiently linear to be corrected by a linear regression. Of particular interest are the forcing terms in the coupling of the physics parameterization to the dynamics of the model. Because such parameterizations are estimates of subgrid processes, they are expected to represent degrees of freedom that are independent of the resolved-scale model variables. To illustrate the value of this approach, it is shown that the temporal accumulation of sensible and latent heat fluxes and net solar and thermal radiation utilized as predictors add a statistically significant improvement to the 2-m temperature scores.},
  author       = {Termonia, Piet and Deckmyn, Alex},
  issn         = {0027-0644},
  journal      = {MONTHLY WEATHER REVIEW},
  keywords     = {MOS,PERFORMANCE,FORECASTS,VARIABLES,SYSTEM,TESTS},
  language     = {eng},
  number       = {10},
  pages        = {3496--3505},
  title        = {Model-inspired predictors for model output statistics (MOS)},
  url          = {http://dx.doi.org/10.1175/mwr3469.1},
  volume       = {135},
  year         = {2010},
}

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