North American Mesoscale (NAM) Analysis and Forecast System Characteristics
Forecast Model Dynamics / Physics
Component 

Comments/References 
Infrastructure 
NOAA Environental Modeling System (NEMS) 
http://www.emc.ncep.noaa.gov/index.php?branch=NEMS From 2015 NMMB Tutorial: http://www.emc.ncep.noaa.gov/mmb/mmbpll/misc/2015_NMMB_Tutorial/2015_NMMB_Tutorial_NEMS_overview.ppt 
Dynamics 
NonHydrostatic Multiscale Model on Bgrid (NMMB)

From 2015 NMMB Tutorial: http://www.emc.ncep.noaa.gov/mmb/mmbpll/misc/2015_NMMB_Tutorial/2015_NMMB_Tutorial_Dynamics.pptx
Janjic, Z., and R.L. Gall, 2012: Scientific documentation of the NCEP nonhydrostatic multiscale model on the B grid (NMMB). Part 1 Dynamics. NCAR Technical Note NCAR/TN489+STR http://opensky.library.ucar.edu/collections/TECHNOTE000000000857
Janjic, Z. I., J. P. Gerrity, Jr. and S. Nickovic, 2001: An Alternative Approach to Nonhydrostatic Modeling. Monthly Weather Review, Vol. 129, 11641178. Janjic, Z. I., 2003: A Nonhydrostatic Model Based on a New Approach. Meteorology and Atmospheric Physics, 82, 271285. http://dx.doi.org/10.1007/s0070300105876 Janjic, Z. I.: A unified model approach from meso to global scales. Geophys. Res. Abstracts, 7, SRef{ID: 1607{7962/gra/EGU05{A{05 582, 2005. Janjic, Z. I. and Black, T: A unified model approach from meso to global scales. Geophys. Res. Abstracts, 7, SRef{ID: 1607{7962/gra/EGU2007{A{05 025, 2007. Janjic, Z., Huang, H., and Lu, S.: A unified atmospheric model suitable for studying transport of mineral aerosols from meso to global scales, IOP C. Ser. Earth Env., 7, 012011, http: 25 //iopscience.iop.org/17551315/7/1/012011/refs, doi:10.1088/17551307/7/1/012011, 2009. Janjic, Z., 2010: Recent Advances in Global Nonhydrostatic Modeling at NCEP. Invited lecture at the ECMWF Workshop on Nonhydrostatic Modeling. November 2010, 14pp. ECMWF, Shinfield Park, Reading, Berkshire RG2 9AX, U.K. Janjic, Z., Janjic, T., and Vasic, R.: A Class of conservative fourth order advection schemes and impact of enhanced formal accuracy on extended range forecasts, Mon. Weather Rev., 0, null, doi:10.1175/2010MWR3448.1, 2011.

Model Top Pressure and Vertical Characteristics 
Hybrid sigmapressure vertical coordinate 2 mb model top pressure 60 vertical layers Bottom layer ~ 20 m

Figs. 1 and 2. 
Horizontal Resolution 
12 km (parent N.American domain) 4 km CONUS nest 6 km Alaska nest 3 km Hawaii nest 3 km Puerto Rico nest 1.33 km nest in CONUS or 1.5 km nest in Alaska 
Domain areal coverage : http://www.emc.ncep.noaa.gov/mmb/namgrids/namnests_domains.jpg and Fig. 3. The 1.33/1.5 km nest can be placed anywhere inside the CONUS or Alaska nest; it is used for fire weather forecasting during the fire season and for severe weather events during other times of the year 
Boundary Conditions 
Parent domain : 6h old GFS forecast, updated every 3h Nest domains : Every model time step from parent domain 

Horizontal diffusion 
Computed on hybrid surfaces 
Nonlinear Smagorinskytype
Janjic, Z.I.,1990: The stepmountain eta coordinate: Physical package. Mon. Wea. Rev., 118, 14291443. 
Gravity wave drag (also includes mountain blocking) 
Yes 
Turned on for 12 km NAM parent domain and 6 km Alaska nest domain only
Alpert, J., 2004: Subgrid scale mountain blocking at NCEP. Proc. 20^{Th} Conference on Weather on Analysis and Forecasting/17th Conference on Numerical Weather Prediction, American Meteorological Society. Seattle, WA. P2.4. [Available online at https://ams.confex.com/ams/pdfpapers/71011.pdf].

Vertical diffusion 
MYJ level 2.5 closure in free atmosphere 
Janjic, Z. I., 2001: Nonsingular Implementation of the MellorYamada Level 2.5 Scheme in the NCEP Meso model. NOAA/NWS/NCEP Office Note #437, 61 pp.

Landsurface 
Noah LSM 20 MODISIGBP land use categories 
http://www.emc.ncep.noaa.gov/annualreviews/day%201/05EKlandsfc.pptx
Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108 (D22), 16, doi:10.1029/2002JD003296.

SST 
NCEP 0.125 deg RTG_SST_HR 
http://polar.ncep.noaa.gov/mmab/tpbs/operational.tpbs/RTG_SST_HR.pdf Updated once/day with new analysis at start of 06z NDAS 
Snow/Sea ice 
National Ice Center IMS snow cover/sea ice 
http://www.emc.ncep.noaa.gov/jcsda/ggayno/snow/snow.txt Updated once/day at the start of the 06z NDAS Snow depth is cycled in the NAM Data Assimilation System (NDAS), 
Surface layer 
NMM 

Boundary layer scheme 
MYJ Level 2.5 
Janjic, Z. I.: The Stepmountain Eta Coordinate Model: Further developments of the convection, viscous sublayer and turbulence closure schemes, Mon. Weather Rev., 122, 927– 945, 1994.
Janjic, Z. I., 2001: Nonsingular implementation of the MellorYamada level 2.5 scheme in the NCEP meso model. NCEP Office Note, 91 pp. [437].

Shallow Convection 
BMJ 
Janjic, Z. I.: The Stepmountain Eta Coordinate Model: Further developments of the convection, viscous sublayer and turbulence closure schemes, Mon. Weather Rev., 122, 927– 945, 1994.
Parameterized convection only run in 12 km NAM parent domain and 6 km Alaska nest domain 
Deep Convection 
BMJ 
Janjic, Z. I.: The Stepmountain Eta Coordinate Model: Further developments of the convection, viscous sublayer and turbulence closure schemes, Mon. Weather Rev., 122, 927– 945, 1994.
Parameterized convection only run in 12 km NAM parent domain and 6 km Alaska nest domain 
Gridscale clouds, precip microphysics 
FerrierAligo 
Aligo, E., and coauthors : HighResolution NMMB Simulations of the 29 June 2012 Derecho, AMS 27^{th} WAF/23rd NWP Conference, Atlanta, GA,, viewable at https://ams.confex.com/ams/94Annual/videogateway.cgi/id/26141?recordingid=26141
Aligo, E. A., W. A. Gallus, G. Thompson, and B. S. Ferrier, 2011: Comparison of hydrometeor fall speed distributions in bin and bulk microphysical schemes. 24^{th} Conf. on Weather Analysis and Forecasting/20^{th} Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 13B.4.
Wolff, J. K., B. S. Ferrier, and C. F. Mass, 2012. Establishing Closer Collaboration to Improve Model Physics for Short Range Forecasts. Bull. Amer. Meteor. Soc., 93, pp.ES51ES53.
Ferrier, B. S., W. Wang, and E. Colon, 2011: Evaluating cloud microphysics schemes in nested NMMB forecasts. 24th Conf. on Weather Analysis and Forecasting/20th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc.
Ferrier, B. S., Y. Jin, Y. Lin, T. Black, E. Rogers, and G. DiMego, 2002: Implementation of a new gridscale cloud and precipitation scheme in the NCEP Eta model. Preprints, 15th Conf. on Numerical Weather Prediction, San Antonio, TX, Amer. Meteor. Soc., 280–283.

Shortwave Radiation 
RRTM tuned for NMMB 
Rogers E., and coauthors, 2014: The NCEP North American Mesoscale (NAM) Analysis and Forecast System : Nearterm plans and future evolution into a highresolution ensemble, AMS 27^{th} WAF/23rd NWP Conference, Atlanta, GA, viewable at https://ams.confex.com/ams/94Annual/videogateway.cgi/id/26188?recordingid=26188
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 correlatedk model for the longwave. J. Geophys. Res., 102, 16 663–16 682.

Longwave Radiation 
RRTM tuned for NMMB 
Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by longlived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.

Data Assimilation / Objective Analysis (Fig. 4)


Objective AnalysisData Assimilation Method 
Hybrid ensemble3DVar variational ensemble version of NCEP Gridpoint Statistical Interpolation Analysis 
http://www.dtcenter.org/comGSI/users/index.php
As of August 2014 NDAS/NAM GSI uses NCEP Global EnKF ensemble in background error covariance calculation
Wu, W.S., R. J. Purser, and D. F. Parrish, 2002: Threedimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 2905–2916.
Wang, X., 2010: Incorporating ensemble covariance in the gridpoint statistical interpolation variational minimization: A mathematical framework. Mon. Wea. Rev., 138, 2990–2995.

Data Assimilation / Spinup 
Partial cycling NAM Data Assimilation System (NDAS): 12h spinup from Global Data Assimilation System (GDAS) atmospheric states and NDAS soil states 
See Fig. 4 and schematic comparing NDAS vs GDAS at http://www.emc.ncep.noaa.gov/mmb/mmbpll/misc/NDAS_vs_GDAS.pdf
Partial cycling: Rogers, E., and Coauthors, 2009: The NCEP North American Mesoscale modeling system: Recent changes and future plans. Preprints, 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A.4. [Available online at http://ams.confex.com/ams/pdfpapers/154114.pdf].
12h spinup consists of 4 GSI analyses and 3h NMMN forecasts. 3h NDAS forecast valid at 00z/06z/12z/18z used as first guess for NAM analyses (12 km parent and nest domains)
NDAS 3h forecasts are initialized with a diabatic digital filter 
Data cutoff time 
NAM analysis : T+70 min NDAS analyses : See NDAS vs GDAS schematic linked above 

Conventional observations assimilated 
 Rawinsondes  Aircraft winds/temperature/ moisture  Surface pressure/wind/temperature/ moisture (including selected mesonets)  VAD wind profiles  Wind profilers  WSR88D Doppler radial velocities 

Satellite observations assimilated 
 Clouddrift winds from GOES and polar orbiting satellites  Radiances assimilated (using the Community Rapid Radiatieve Transfer Model) from GOES and polar orbiting satellites  GPS bending angle 

Precipitation assimilated 
 Merged Hourly Stage II/IV precipitation; drives NOAH landsurface physics during NDAS 3h forecast 
http://www.emc.ncep.noaa.gov/mmb/ylin/docs/precip_assim.pdf 
Fig. 1 NAM vertical levels from 0 m to 3 km AGL.
Fig. 2 NAM vertical levels from 3 km AGL to 30 km AGL.
Fig. 3. NAM Parent 12 km (black), 6 km Alaska nest (green), 4 km CONUS nest (red), 3 km Hawaii nest (blue), 3 km Puerto Rico nest (Purple), and ondemand Fire WX nest (pink, 1.33 km CONUS and 1.5 km Alaska) computational domains.
Fig. 4. NAM/NDAS data assimilation cycling diagram. Each forecast cycle begins with a 12 hour analysisforecast window during which analyses are conducted at three hour intervals (TM12, TM09, etc.). TM00 refers to the forecast initialization time (e.g. 00, 06, 12, or 18 UTC). At TM12 the first guess for the atmosphere is a 6 hour forecast from the GDAS. The land states are, however, still cycled from the previous NAM/NDAS cycle.