Start Submission Become a Reviewer

Reading: Linear and non-linear response to parameter variations in a mesoscale model

Download

A- A+
Alt. Display

Original Research Papers

Linear and non-linear response to parameter variations in a mesoscale model

Authors:

J. P. Hacker ,

Naval Postgraduate School, Department of Meteorology, 589 Dyer Rd., Monterey, CA, US
X close

C. Snyder,

National Center for Atmospheric Research, Boulder, CO, US
X close

S.-Y. Ha,

National Center for Atmospheric Research, Boulder, CO, US
X close

M. Pocernich

National Center for Atmospheric Research, Boulder, CO, US
X close

Abstract

Parameter uncertainty in atmospheric model forcing and closure schemes has motivated both parameter estimation with data assimilation and use of pre-specified distributions to simulate model uncertainty in short-range ensemble prediction. This work assesses the potential for parameter estimation and ensemble prediction by analysing 2 months of mesoscale ensemble predictions in which each member uses distinct, and fixed, settings for four model parameters. A space-filling parameter selection design leads to a unique parameter set for each ensemble member. An experiment to test linear scaling between parameter distribution width and ensemble spread shows the lack of a general linear response to parameters. Individual member near-surface spatial means, spatial variances and skill show that perturbed models are typically indistinguishable. Parameter—state rank correlation fields are not statistically significant, although the presence of other sources of noise may mask true correlations. Results suggest that ensemble prediction using perturbed parameters may be a simple complement to more complex model-error simulation methods, but that parameter estimation may prove difficult or costly for real mesoscale numerical weather prediction applications.

How to Cite: Hacker, J.P., Snyder, C., Ha, S.-Y. and Pocernich, M., 2011. Linear and non-linear response to parameter variations in a mesoscale model. Tellus A: Dynamic Meteorology and Oceanography, 63(3), pp.429–444. DOI: http://doi.org/10.1111/j.1600-0870.2010.00505.x
1
Views
1
Downloads
36
Citations
  Published on 01 Jan 2011
 Accepted on 15 Dec 2010            Submitted on 25 May 2010

References

  1. Aksoy , A. , Zhang , F. and Nielsen-Gammon , J. W . 2006a . Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model. Mon . Wea. Re v . 134 , 2951 – 2970 .  

  2. Aksoy , A. , Zhang , F. and Nielsen-Gammon , J. W . 2006b. Ensemble-based simultaneous state and parameter estimation with MM5. Geo-phys. Res. Lett . 33 , https://doi.org/10.1029/2006GL026186 .  

  3. Alapaty , K. , Raman , S. and Niyogi , D . 1997 . Uncertainty in the speci-fication of surface characteristics: a study on prediction errors in the boundary layer . Bound.-Layer Meteorol . 82 , 473 – 500 .  

  4. Annan , J. D. , Hargreaves , J. C. , Edwards , N. R. and Marsh , R . 2005a . Pa-rameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter . Ocean Modell . 8 , 135 – 154 .  

  5. Annan , J. D. , Lunt , D. J. and Valdes , P. J . 2005b . Parameter estimation in an atmospheric GCM using the ensemble Kalman filter . Nonlinear Proc. Geophys . 12 , 363 – 371 .  

  6. Ball , F. K . 1960 . Control of inversion height by surface heating . Q. J. R. Meteorol. Soc . 44 , 2823 – 2838 .  

  7. Benjamini , Y. and Hochberg , Y . 1995 . Controlling the false discovery rate: a practical and powerful approach to multiple testing . J. R. StaL Soc . 57B , 289 – 300 .  

  8. Bowler , N. E. , Arribas , A. , Mylne , K. R. , Robertson , K. B. and Beare , S. E . 2008 . The MOGREPS short-range ensemble prediction system . Q. J. R. Meteorol. Soc . 134 , 703 – 722 .  

  9. Clark , A. , Gallus Jr., W. A. and Chen , T.-C. 2008 . Contributions of mixed physics and perturbed lateral boundary conditions to the skill and spread of precipitation forecasts from a wrf ensemble. Mon. Wea. Rev . 136 , 2140 - 2156 .  

  10. Cohn , S. E . 1997 . An introduction to estimation theory . J. Meteorol. Soc. Jpn . 75 , 257 – 288 .  

  11. Dudhia , J . 1989 . Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sc i . 46 , 3077 – 3107 .  

  12. Eckel , F. A. and Mass , C. F . 2005 . Aspects of effective mesoscale, short-range, ensemble forecasting . Wea. Forecast . 20 , 328 – 350 .  

  13. Fritsch , J. M. and Chappel , C. F . 1980 . Numerical prediction of convec-tively driven mesoscale pressure systems. Part I: convective parame-terization. J. Atmos. Sc i . 37 , 1722 – 1733 .  

  14. Grimit , E. P. and Mass , C. F . 2002 . Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest . Wea. Forecast . 17 , 192 – 205 .  

  15. Hacker , J. P. , Ha , S.-Y. , Snyder , C. , Berner , J. , Eckel , F. A. , and co-authors . 2011 . The U.S. Air Force Weather Agency’s mesoscale en-semble: scientific description and performance results. Tellus 63A , this issue .  

  16. Hong , S.-Y. , Dudhia , J. and Chen , S.-H . 2004 . A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon . Wea. Re v . 132 , 103 – 120 .  

  17. Hong , S.-Y. and Pan , H.-L . 1996 . Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon . Wea. Re v . 124 , 2322 – 2339 .  

  18. Hou , D. , Kalnay , E. and Drogemeier , K . 2001 . Objective verification of the SAMEX ’98 ensemble experiments. Mon . Wea. Re v . 129 , 73 – 91 .  

  19. Jackson , C. , Sen , M. K. and Stoffa , P. L . 2004 . An efficient stochastic Bayesian approach to optimal parameter and uncertainty estimation for climate model prediction . J. Clim . 17 , 2828 – 2841 .  

  20. Kain , J. S . 2004 . The Kain-Fritsch convective parameterization: an up-date . J. Appl. Meteorol . 43 , 170 – 181 .  

  21. Kain , J. S. and Fritsch , J. M . 1990 . A one-dimensional entrain-ing/detraining plume model and its application in convective param-eterization. J. Atmos. Sc i . 47 , 2784 – 2802 .  

  22. Kleist , D. T. , Parrish , D. F. , Derber , J. C. , Treadon , R. , Wu , W.-S. and co-authors . 2009 . Introduction of the GSI into the NCEP Global Data Assimilation system . Wea. Forecast . 24 , 1691– 1705 .  

  23. Livezey , R. and Chen , W . 1983 . Statistical field significance and its determination by Monte Carlo techniques. Mon . Wea. Re v . 111 , 46 – 59 .  

  24. Lorenz , E. N . 1963 . Deterministic nonperiodic flow. J. Atmos. Sc i . 20 , 130 – 141 .  

  25. Marshall , J. S. and Palmer , W. M . 1948 . The distribution of raindrops with size . J. Meteorol . 5 , 165 – 166 .  

  26. Moeng , C. H. and Sullivan , P. P . 1994 . A comparison of shear and buoyancy-driven planetary boundary layer flows. J. Atmos. Sc i . 51 , 999 – 1022 .  

  27. Murphy , J. M. , Sexton , D. M. H. , Barnett , D. N. , Jones , G. S. , Webb , M. J. and co-authors . 2004 . Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430 , 768 - 772 .  

  28. Nielsen-Gammon , J. W. , Hu , X.-M ., Zhang, E and Pleim , J. E. 2010. Evaluation of planetary boundary layer scheme sensitivities for the purpose of parameter estimation. Mon. Wea. Rev . 138 , 3400-341 7 .  

  29. Noh , Y. , Cheon , W. G. , Hong , S. Y. and Raasch , S . 2003 . Improvement of the k-profile model for the planetary boundary layer based on large eddy simulation data . Bound.-Layer Meteorol . 107 , 401 – 427 .  

  30. Posselt , D. J. and Vukeéevié , T . 2010 . Robust characterization of model physics uncertainty for simulations of deep moist convection. Mon . Wea. Re v . 138 , 1513 – 1535 .  

  31. Raftery , A. E. , Gneiting , T ., Blabdaoui, E and Polakowski , M. 2005. Us-ing Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev . 133 , 1155-117 4 .  

  32. Rodwell , M. J. and Palmer , T. N . 2007 . Using numerical weather predic-tion to assess climate models . Q. J. R. Meteorol. Soc . 133 , 129 – 146 .  

  33. Sanderson , B. M. , Knutti , R. , Aina , T. , Chirstensen , C. , Fault , N. and co-authors . 2008. Constraints on model response to greenhouse gas forc-ing and the role of subgrid-scale processes. J. Clim . 21 , 2384-240 0 .  

  34. Santer , T. J. and Williams , B. J . 2003 . Design and Analysis of Computer Experiments . Springer , New York .  

  35. Sauvageot , H. and Lacaux , J.-P . 1995 . The shape of averaged drop size distributions. J. Atmos. Sc i . 52 , 1070 – 1083 .  

  36. Skamarock , W. C. , Klemp , J. B. , Dudhia , J. , Gill , D. O. , Barker , D. M. and co-authors. 2008 . A description of the advanced research WRF Version 3, Technical Report TN-475, National Center for Atmospheric Research.  

  37. Stainforth , D. A. , Aina , T. , Christensen , C. , Collins , M. , Faull , N. and co-authors . 2005 . Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433 , 403 - 406 .  

  38. Stensrud , D. , Bao , J.-W. and Warner , T. T . 2000 . Using initial condi-tion and model physics perturbations in short-range ensemble simu-lations of mesoscale convective systems. Mon . Wea. Re v . 128 , 2077 – 2107 .  

  39. Stensrud , D. J. and Yussouf , N . 2003 . Short-range ensemble predictions of 2-m temperature and dewpoint temperature over New England. Mon . Wea. Re v . 131 , 2510 – 2524 .  

  40. Tong , M. and Xue , M . 2008 . Simultaneous estimation of microphysi-cal parameters and atmospheric state with simulated radar data and ensemble square-root Kalman filter. Mon . Wea. Re v . 136 , 1630 – 1648 .  

  41. Torn , R. and Hakim , G . 2008 . Ensemble-based sensitivity analysis. Mon . Wea. Re v . 136 , 663 – 677 .  

  42. Troen , I. and Mahrt , L . 1986 . A simple model of the atmospheric bound-ary layer: sensitivity to surface evaporation . Bound-Layer MeteoroL 37 , 129 – 148 .  

  43. Ventura , V. , Paciorek , C. J. and Risbey , J. S . 2004 . Controlling the proportion of falsely rejected hypotheses when conducting multiple tests with climatological data . J. Climata 17 , 4343 – 4356 .  

  44. Waldvogel , A . 1974 . The No jump of raindrop spectra. J. Atmos. Sc i . 31 , 1067 – 1078 .  

  45. Wei , M. , Toth , Z. , Wobus , R. and Zhu , Y . 2008 . Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system . Tellus 60A , 62 – 79 .  

  46. Wilks , D. S . 2006 . On “field significance’ and the false discovery rate . J. AppL MeteoroL Climata 45 , 1181 – 1189 .  

  47. Ziehmann , C . 2000 . Comparison of a single-model EPS with a multi-model ensemble consisting of a few operational models . Tellus 52A , 280 – 299 .  

comments powered by Disqus