On February 13, 2013, the US Navy’s weather forecast system reached a milestone when the NAVy Global Environmental Model (NAVGEM) replaced the Navy Operational Global Atmospheric Prediction System (NOGAPS) for operational global weather prediction. The new operational system NAVGEM 1.1 combines a semi-Lagrangian/semi-implicit dynamical core together with advanced parameterizations of subgrid-scale moist processes, convection, ozone, and radiation. The NAVGEM dynamical core allows for much higher spatial resolutions without the need for the small time steps that would be necessary in NOGAPS. The increased computational efficiency is expected to enable significant increases in resolution in future NAVGEM releases. Model physics improvements in the NAVGEM 1.1 transition include representations of cloud liquid water, cloud ice water, and ozone as fully predicted constituents. Following successful testing of a new mass flux scheme, a second transition to NAVGEM 1.2 occurred on November 6, 2013. Addition of this mass flux parameterization to the eddy diffusion vertical mixing parameterization resulted in a reduction of the cold temperature bias of the lower troposphere over ocean and further increased the forecast skill of NAVGEM.
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Volume 27, No. 3
Pages 116 - 125
The Navy Global Environmental Model
By Timothy F. Hogan , Ming Liu , James A. Ridout , Melinda S. Peng , Timothy R. Whitcomb, Benjamin C. Ruston, Carolyn A. Reynolds, Stephen D. Eckermann , Jon R. Moskaitis, Nancy L. Baker , John P. McCormack , Kevin C. Viner , Justin G. McLay, Maria K. Flatau, Liang Xu , Chaing Chen , and Simon W. Chang
Hogan, T.F., M. Liu, J.A. Ridout, M.S. Peng, T.R. Whitcomb, B.C. Ruston, C.A. Reynolds, S.D. Eckermann, J.R. Moskaitis, N.L. Baker, J.P. McCormack, K.C. Viner, J.G. McLay, M.K. Flatau, L. Xu, C. Chen, and S.W. Chang. 2014. The Navy Global Environmental Model. Oceanography 27(3):116–125, https://doi.org/10.5670/oceanog.2014.73.
Chua, B., L. Xu, T. Rosmond, and E. Zaron. 2009. Preconditioning representer-based variational data assimilation systems: Application to NAVDAS-AR. Pp. 307–320 in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. Springer-Verlag, https://doi.org/10.1007/978-3-540-71056-1_16.
Clough, S.A., M.W. Shephard, E.J. Mlawer, J.S. Delamere, M.J. Iacono, K. Cady-Pereira, S. Boukabara, and P.D. Brown. 2005. Atmospheric radiative transfer modeling: A summary of the AER codes: Short communication. Journal of Quantitative Spectroscopy and Radiative Transfer 91:233–244.
Dee, D. 2004. Variational bias correction of radiance data in the ECMWF system. Pp. 97–112 in Proceedings of the ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP. June 28–July 1, 2004, European Centre for Medium-range Weather Forecasts (ECMWF), Reading, UK.
Eckermann, S.D. 2009. Hybrid σ-p coordinate choices for a global model. Monthly Weather Review 137:224–245, https://doi.org/10.1175/2008MWR2537.1.
Eckermann, S.D. 2011. Explicitly stochastic parameterization of nonorographic gravity-wave drag. Journal of the Atmospheric Sciences 68:1,749–1,765, https://doi.org/10.1175/2011JAS3684.1.
Eckermann, S.D., J.P. McCormack, J. Ma, T.F. Hogan, and K.A. Zawdie. 2014. Stratospheric analysis and forecast errors using hybrid and sigma coordinates. Monthly Weather Review 142:476–485, https://doi.org/10.1175/MWR-D-13-00203.1.
Han, J., and H.-L. Pan. 2011. Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Weather and Forecasting 26:520–533, https://doi.org/10.1175/WAF-D-10-05038.1.
Hogan, T.F. 2007. Land surface modeling in the Navy Operational Global Atmospheric Prediction System. Paper presented at AMS 22nd Conference on Weather Analysis and Forecasting/189th Conference on Numerical Weather Prediction, June 2007, Park City, Utah. Extended abstract and recorded presentation available online at: https://ams.confex.com/ams/22WAF18NWP/techprogram/paper_123403.htm (accessed May 12, 2014).
Hogan, T.F., T.E. Rosmond, and R. Gelaro. 1991. The NOGAPS Forecast Manual: A Technical Description. NOARL Technical Report 13, December 1991, 220 pp.
Hortal, M., and A.J. Simmons. 1991. Use of reduced Gaussian grids in spectral models. Monthly Weather Review 119:1,057–1,074, https://doi.org/10.1175/1520-0493(1991)119<1057:UORGGI>2.0.CO;2.
Iacono, M.J., E.J. Mlawer, S.A. Clough, and J.-J. Morcrette. 2000. Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate mode, CCM3. Journal of Geophysical Research 105:14,873–14,890, https://doi.org/10.1029/2000JD900091.
Louis, J.F., M. Tiedtke, and J.F. Geleyn. 1982. A short history of the operational PBL parameterization at ECMWF. Pp. 59–79 in ECMWF Workshop on Planetary Boundary Parameterizations. European Centre for Medium-range Weather Forecasts.
McCormack, J.P., S.D. Eckermann, D.E. Siskind, and T.E. McGee. 2006. CHEM2D-OPP: A new linearized gas-phase ozone photochemistry parameterization for high-altitude NWP and climate models. Atmospheric Chemistry and Physics 6:4,943–4,972, https://doi.org/10.5194/acp-6-4943-2006.
McCormack, J.P., K.W. Hoppel, and D.E. Siskind. 2008. Parameterization of middle atmospheric water vapor photochemistry for high-altitude NWP and data assimilation. Atmospheric Chemistry and Physics 8:7,519–7,532, https://doi.org/10.5194/acp-8-7519-2008.
Mlawer, E.J., S.J. Taubman, P.D. Brown, M.J. Iacono, and S.A. Clough. 1997. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research 102:16,663–16,682, https://doi.org/10.1029/97JD00237.
Morcrette, J.-J. 2001. Impact of the radiation-transfer scheme RRTM in the ECMWF forecasting system. ECMWF [European Centre for Medium-range Weather Forecasts] Newsletter No. 91.
Moorthi, S., H.-L. Pan, and P. Caplan. 2001. Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. NWS Technical Procedures Bulletin, Series 484. 14 pp. Available at http://www.nws.noaa.gov/om/tpb/484.htm.
Peng, M.S., J.A. Ridout, and T.F. Hogan. 2004. Recent modifications of the Emanuel Convective Scheme in the Navy Operational Global Atmospheric Prediction System. Monthly Weather Review 132:1,254–1,268, https://doi.org/10.1175/1520-0493(2004)132<1254:RMOTEC>2.0.CO;2.
Pincus, R., H.W. Barker, and J.-J. Morcrette. 2003. A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous clouds. Journal of Geophysical Research 108, 4376, https://doi.org/10.1029/2002JD003322.
Ritchie, H. 1987. Semi-Lagrangian advection on a Gaussian grid. Monthly Weather Review 115:608–619, https://doi.org/10.1175/1520-0493(1987)115<0608:SLAOAG>2.0.CO;2.
Ritchie, H. 1988. Application of the semi-Lagrangian method to a spectral model of the shallow water equations. Monthly Weather Review 116:1,587–1,598, https://doi.org/10.1175/1520-0493(1988)116<1587:AOTSLM>2.0.CO;2.
Ritchie, H. 1991. Application of the semi-Lagrangian method to a multilevel spectral primitive equation model. Quarterly Journal of the Royal Meteorological Society 117:91–106, https://doi.org/10.1002/qj.49711749705.
Ritchie, H., C. Temperaton, A. Simmons, M. Hortal, T. Davies, D. Dent, and M. Hamrud. 1995. Implementation of the semi-Lagrangian method in a high-resolution version of the ECMWF forecast model. Monthly Weather Review 123:489–514, https://doi.org/10.1175/1520-0493(1995)123<0489:IOTSLM>2.0.CO;2.
Robert, A.J. 1981. A stable numerical integration scheme for the primitive meteorological equations. Atmosphere-Ocean 19:35–46, https://doi.org/10.1080/07055900.1981.9649098.
Robert, A.J., H. Henderson, and C. Turbull. 1972. An implicit time integration scheme for baroclinic models of the atmosphere. Monthly Weather Review 100:329–335, https://doi.org/10.1175/1520-0493(1972)100<0329:AITISF>2.3.CO;2.
Rosmond, T.E., J. Teixeira, M. Peng, T.F. Hogan, and R. Pauley. 2002. Navy Operational Global Atmospheric Prediction System (NOGAPS): Forcing for ocean models. Oceanography 15(1):99–108, https://doi.org/10.5670/oceanog.2002.40.
Rosmond, T., and L. Xu. 2006. Development of NAVDAS-AR: Non-linear formulation and outer loop tests. Tellus 58A:45–58, https://doi.org/10.1111/j.1600-0870.2006.00148.x.
Schumacher, C., and R.A. Houze Jr. 2000. Comparison of radar data from the TRMM satellite and Kwajalein oceanic validation site. Journal of Applied Meteorology 39:2,151–2,164, https://doi.org/10.1175/1520-0450(2001)040<2151:CORDFT>2.0.CO;2.
Simpson, J., R.F. Adler, and G.R. North. 1988. A proposed Tropical Rainfall Measuring Mission (TRMM) satellite. Bulletin of the American Meteorological Society 69:278–295, https://doi.org/10.1175/1520-0477(1988)069<0278:APTRMM>2.0.CO;2.
Slingo, J. 1987. The development and verification of a cloud prediction scheme for the ECMWF model. Quarterly Journal of the Royal Meteorological Society 113:899–927, https://doi.org/10.1002/qj.49711347710.
Smith, S., J.A. Cummings, C. Rowley, P. Chu, J. Shriver, R. Helber, P. Spence, S. Carroll, and O.M. Smedstad. 2011. Validation Test Report for the Navy Coupled Ocean Data Assimilation 3D Variational Analysis (NCODA-VAR) System, Version 3.43. NRL Report NRL/MR/7320-11-9363. Naval Research Laboratory, Stennis Space Center, MS.
Sundqvist, H., E. Berge, and J.E. Kristjansson. 1989. Condensation and cloud paramaterization studies with mesoscale numerical weather prediction model. Monthly Weather Review 117:1,641–1,757, https://doi.org/10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2
Sušelj, K., J. Teixeira, and D. Chung. 2013. A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity/mass-flux parameterization. Journal of the Atmospheric Sciences 70:1,929–1,953, https://doi.org/10.1175/JAS-D-12-0106.1.
Sušelj, K., J. Teixeira, and G. Matheou. 2012. Eddy diffusivity/mass flux and shallow cumulus boundary layer: An updraft PDF multiple mass flux scheme. Journal of Atmospheric Sciences 69:1,513–1,533, https://doi.org/10.1175/JAS-D-11-090.1.
Teixeira, J., and T.F. Hogan. 2002. Boundary layer clouds in a global atmospheric model: Simple cloud cover parameterizations. Journal of Climate 15:1,261–1,276, https://doi.org/10.1175/1520-0442(2002)015<1261:BLCIAG>2.0.CO;2.
Webster, S., A.R. Brown, D.R. Cameron, and C.P. Jones. 2003. Improvements to the representation of orography in the Met Office Unified Model. Quarterly Journal of the Royal Meteorological Society 133:1,989–2,010, https://doi.org/10.1256/qj.02.133.
Winton, M. 2000. A reformulated three-layer sea ice model. Journal of Atmospheric and Oceanic Technology 17:525–531, https://doi.org/10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2.
Xu, L., T. Rosmond, and R. Daley. 2005. Development of NAVDAS-AR: Formulation and initial tests of the linear problem. Tellus 57A:546–559, https://doi.org/10.1111/j.1600-0870.2005.00123.x.
Zhao, Q., and F.H. Carr. 1997. A prognostic cloud scheme for operational NWP models. Monthly Weather Review 125:1,931–1,953, https://doi.org/10.1175/1520-0493(1997)125<1931:APCSFO>2.0.CO;2.
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