Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


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.


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


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Reynolds, Carolyn A.; Ridout, James A.; McLay, Justin G. (2011)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article

Classified by OpenAIRE into

arxiv: Physics::Atmospheric and Oceanic Physics
The impact of parameter variations on the Navy Operational Global Atmospheric Prediction System ensemble performance is examined, and subsets of ensemble members are used to identify the relative impact of the individual parameters. Two sets of parameter variations are considered. The first set has variations in the parametrization of cumulus convection only. The second set has variations in both convection and boundary layer parametrizations. In the tropics, parameter variations significantly increase ensemble spread in wind and temperature fields, and significantly reduce Brier scores for low-level wind speed and temperature, primarily through improvements to the resolution (the impact in the extratropics is negligible). There are also small but significant improvements in the ensemble mean tropical cyclone track forecasts. For the metrics considered here, the second set of parameter variations outperforms the first set. Examination of the spread within ensemble subsets suggests that the parameter with the biggest overall impact is one that helps to control the convective updraft parcel temperature deficit at cloud base level. Variations in the von Kármán constant significantly increase ensemble spread in the low-level tropical winds near the date line, and in the low-level temperature field throughout the tropics and subtropics.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Alhamed, A., Lakshmivarahan, S. and Stensrud, D. J. 2002. Cluster analysis of multimodel ensemble data from SAMEX. Mon. Wea. Rev. 130, 226-256.
    • Andreas, E. L. 2009. A new value of the von Ka´rma´n constant: implications and implementation. J. Appl. Met. Clim. 48, 923-944.
    • Berner, J., Schutts, G. J., Leutbecher, M. and Palmer, T. N. 2009. A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci. 66, 603-626.
    • Berner, J., Ha, S.-Y., Hacker, J. P., Fournier, A. and Snyder, C. 2011. Model uncertainty in a mesoscale ensemble prediction system: stochastic versus multi-physics representations. Mon. Wea. Rev. 139, 1972-1995.
    • Betts, A. K. and Miller, M. J. 1986. A new convective adjustment scheme. Part II: single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc. 112, 693-709.
    • Bishop, C. H. and Toth, Z. 1999. Ensemble transformation and adaptive observations. J. Atmos. Sci. 56, 1748-1765.
    • Bowler, N. E., Arribas, A., Beare, S. E., Mylne, K. R. and Schutts, G. J. 2009. The local ETKF and SKEB: upgrades to the MOGREPS shortrange ensemble prediction system. Quart. J. Roy. Meteor. Soc. 135, 767-776.
    • Bowler, N. E., Arribas, A., Mylne, K. R., Robertson, K. B. and Beare, S. E. 2008. The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc. 134, 703-722.
    • Businger, J. A., Wyngaard, J. C., Izumi, Y. and Bradley, E. F. 1971. Flux profile relationships in the atmospheric surface layer. J. Atmos. Sci. 28, 181-189.
    • Bright, D. R. and Mullen, S. L. 2002. Short-range ensemble forecasts of precipitation during the southwest monsoon. Wea. Forecast. 17, 1080-1100.
    • Buizza, R. and Palmer, T. N. 1998. Impact of ensemble size on ensemble prediction. Mon. Wea. Rev. 126, 2503-2518.
    • Buizza, R., Miller, M. and Palmer, T. N., 1999. Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc. 125, 2887-2908.
    • Charron, M., Pellerin, G., Spacek, L., Houtekamer, P. L., Gangon, N. and co-authors. 2010. Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev. 138, 1877-1901.
    • Chen, S. S., Houze, Jr., R. A. and Mapes, B. E. 1996. Multiscale variability of deep convection in relation to large-scale circulation in TOGA COARE. J. Atmos. Sci. 53, 1380-1409.
    • Chen, S., Cummings, J., Doyle, J., Hodur, R., Holt, T. and coauthors. 2003. COAMPS version 3 model description-general theory and equations. Naval Research Laboratory Technical Report, NRL/ PU7500-04-448. 141 pp.
    • Daley, R. and Barker, E. 2001. NAVDAS: formulation and diagnostics. Mon. Wea. Rev. 129, 869-883.
    • Emanuel, K. A., 1991. A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci. 48, 2313-2335.
    • Emanuel, K.A. and Zivkovic-Rothman, M. 1999. Development and evaluation of a convection scheme for use in climate models. J. Atmos. Sci. 56, 1766-1782.
    • Foken, T. 2008. Interactive comment on “The von Ka´rma´n constant retrieved from CASE-97 dataset using a variational method” by Y., Zhang et al. Atmos. Chem. Phys. Discuss. 8, S67655-S6659, www.atmos-chem-phys-discuss.net/8/S6655/2008/.
    • Goerss, J. S. 2000. Tropical cyclone track forecasts using an ensemble of dynamical models. Mon. Wea. Rev. 128, 1187-1193.
    • Harshvardhan, Davies, R., Randall, D. A. and Corsetti, T. G. 1987. A fast radiation parameterization for atmospheric circulation models. J. Geophys. Res. 92, 1009-1016.
    • Hayashi, Y.-Y. and Sumi, A. 1986. The 30-40 day oscillations simulated in an “aqua-planet” model. J. Meteorol. Soc. Japan 64, 451-466.
    • Hacker, J. P., Ha, S.-Y., Snyder, C., Berner, J., Eckel, F. A. and coauthors. 2011a. The U.S. Air Force Weather Agency's mesoscale ensemble: scientific description and performance results. Tellus 63A, 625-641.
    • Hacker, J. P., Snyder, C., Ha, S.-Y. and Pocernich, M. 2011b. Linear and non-linear response to parameter variations in a mesoscale model. Tellus 63A, 429-444.
    • Hendon, H. H. 1988. A simple model of the 40-50 day oscillation. J. Atmos. Sci. 45, 569-584.
    • Hodur, R. M. 1997. The Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). Mon. Wea. Rev. 125, 1414-1430.
    • Hogan, T. F. and Pauley, R. L. 2007. The impact of convective momentum transport on tropical cyclone track forecasts using the Emanuel cumulus parameterization. Mon. Wea. Rev. 135, 1195-1207.
    • Houtekamer, P.L., Lefaivre, L., Derome, J., Ritchie, H. and Mitchell, H. L. 1996. A system simulation approach to ensemble prediction. Mon. Wea. Rev. 124, 1225-1242.
    • Kain, J. S. and Fritsch, J. M. 1992. The role of the convective “trigger function” in numerical forecasts of mesoscale convective systems. Meteorol. Atmos. Phys. 49, 93-106.
    • Li, X., Charron, M., Spacek, L. and Candille, G. 2008. A regional ensemble prediction system based on moist targeted singular vectors and stochastic parameter perturbations. Mon. Wea. Rev. 136, 443- 462.
    • Lin, J. W.-B. and Neelin, J. D. 2000. Influence of a stochastic moist convective parameterization on tropical climate variability. Geophys. Res. Lett. 27, 3691-3694.
    • Louis, J. F. 1979. A parametric model of vertical eddy fluxes in the atmosphere. Bound.-Layer Meteor. 17, 187-202.
    • Louis, J. F., Tiedtke, M. and Geleyn, J. F. 1982. A short history of the operational PBL parameterization at ECMWF. In Proceedings of the ECMWF Workshop on Planetary Boundary Parameterizations, Shinfield Park, Reading, UK, 59-79.
    • Madden, R. and Julian, P. 1971. Detection of a 40-50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci. 28, 702-708.
    • Madden, R. and Julian, P. 1972. Description of global scale circulation cells in the tropics with a 40-50 day period. J. Atmos. Sci. 29, 1109-1123.
    • Madden, R. and Julian, P. 1994. Observations of the 40-50 day tropical oscillation-a review. Mon. Wea. Rev. 122, 814-837.
    • McLay, J. G. and Reynolds, C. A. 2009. Two alternative implementations of the ensemble-transform (ET) analysis-perturbation scheme: the ET with extended cycling intervals, and the ET without cycling. Quart. J. Roy. Meteor. Soc. 135, 1200-1213.
    • McLay, J. G., Bishop, C. H. and Reynolds, C. A. 2008. Evaluation of the ensemble transform analysis perturbation scheme at NRL. Mon. Wea. Rev. 136, 1093-1108.
    • McLay, J. G., Bishop, C. H. and Reynolds, C. A. 2007. The ensemble transform scheme adapted for the generation of stochastic perturbations. Quart. J. Roy. Meteor. Soc. 133, 1257-1266.
    • Peng, M. S., Ridout J. A. and Hogan, T. F. 2004. Recent Modifications of the Emanuel Convective Scheme in the Navy Operational Global Atmospheric Prediction System. Mon. Wea. Rev. 132, 1254-1268.
    • Raymond, D. J. 1995. Regulation of moist convection over the west Pacific warm pool. J. Atmos. Sci. 52, 3945-3959.
    • Reynolds, C. A., McLay, J. G., Goerss, J. S., Serra, E. A., Hodyss, D. and co-authors. 2011. Impact of resolution and design on the U.S. Navy Global Ensemble Performance in the Tropics. Mon. Wea. Rev. 139, 2145-2155.
    • Reynolds, C. A., Teixeira, J. and McLay, J. G. 2008. Impact of stochastic convection on the ensemble transform. Mon. Wea. Rev. 136, 4517-4526.
    • Ridout, J. A., Jin, Y. and Liou, C.-S. 2005. A cloud-base quasi-balance constraint for parameterized convection: application to the KainFritsch cumulus scheme. Mon. Wea. Rev. 133, 3315-3334.
    • Sampson, C. R. and Schrader, A. J. 2000. The Automated Tropical Cyclone Forecasting System (Version 3.2). Bull. Am. Meteor. Soc. 81, 1231-1240.
    • Schumacher, C. and Houze Jr. R. A. 2000. Comparison of radar data from the TRMM satellite and Kwajalein oceanic validation site. J. Appl. Meteor. 39, 2151-2164.
    • Shutts, G. and Palmer, T. N. 2004. The use of high-resolution numerical simulations of tropical circulation to calibrate stochastic physics schemes. In ECMWF/CLIVAR Proc. Simulation and Prediction of Intra-seasonal Variability with Emphasis on the MJO. ECMWF, Reading, United Kingdom, 83-102.
    • Shutts, G. 2005. A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc. 131, 3079-3102.
    • Simpson, J., Adler, R. F. and North G. R. 1988. Proposed Tropical Rainfall Measuring Mission (TRMM) satellite. Bull. Am. Meteor. Soc. 69, 278-295.
    • Stensrud, D. J., Bao, J.-W. and Warner, T. T. 2000. Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems. Mon. Wea. Rev. 128, 2077-2107.
    • Teixeira, J. and Hogan, T.F. 2002. Boundary layer clouds in a global atmospheric model: simple cloud cover parameterizations. J. Climate, 15, 1261-1276.
    • Teixeira, J. and Reynolds, C. A. 2008. Stochastic nature of physical parameterizations in ensemble prediction: a stochastic convection approach. Mon. Wea. Rev. 136, 483-496.
    • Toth, Z. and Kalnay, E. 1993. Ensemble forecasting at NMC: the generation of perturbations. Bull. Am. Meteor. Soc. 74, 2317-2330.
    • Wang, X. and Bishop, C. H. 2003. A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci. 60, 1140-1158.
    • Wei, M., Toth, Z., Wobus, R. and Zhu, Y. 2008. Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operation forecast system. Tellus 60A, 62-79.
    • Wei, M., Toth, Z., Wobus, R., Zhu, Y., Bishop, C. H. and co-authors. 2006. Ensemble transform Kalman filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus, 58A, 28-44.
    • Wilks, D. S. 2006. Statistical Methods in the Atmospheric Sciences. 2nd Edition. Academic Press, Burlington, MA, USA, 627 pp.
    • Zhang, Y., Ma, J. and Cao, Z. 2008. The von Ka´rma´n constant retrieved from CASES-97 dataset using a variational method. Atmos. Chem. Phys. 8, 7045-7053.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from