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

Non-Hydrostatic Multiscale Model on B-grid (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/TN-489+STR

http://opensky.library.ucar.edu/collections/TECH-NOTE-000-000-000-857


Janjic, Z. I., J. P. Gerrity, Jr. and S. Nickovic, 2001: An Alternative Approach to Nonhydrostatic Modeling. Monthly Weather Review, Vol. 129, 1164-1178.

Janjic, Z. I., 2003: A Nonhydrostatic Model Based on a New Approach. Meteorology and Atmospheric Physics, 82, 271-285. http://dx.doi.org/10.1007/s00703-001-0587-6

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/1755-1315/7/1/012011/refs, doi:10.1088/1755-1307/7/1/012011, 2009.

Janjic, Z., 2010: Recent Advances in Global Nonhydrostatic Modeling at NCEP. Invited lecture at the ECMWF Workshop on Non-hydrostatic 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 sigma-pressure 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 : 6-h old GFS forecast, updated every 3-h

Nest domains : Every model time step from parent domain


Horizontal diffusion

Computed on hybrid surfaces

Nonlinear Smagorinsky-type


Janjic, Z.I.,1990: The step-mountain eta coordinate: Physical package. Mon. Wea. Rev., 118, 1429-1443.

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: Sub-grid scale mountain blocking at NCEP. Proc. 20Th 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 Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model. NOAA/NWS/NCEP Office Note #437, 61 pp.


Land-surface

Noah LSM

20 MODIS-IGBP land use categories

http://www.emc.ncep.noaa.gov/annualreviews/day%201/05-EK-landsfc.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 Step-mountain 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 Mellor-Yamada level 2.5 scheme in the NCEP meso model. NCEP Office Note, 91 pp. [437].



Shallow Convection

BMJ

Janjic, Z. I.: The Step-mountain 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 Step-mountain 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

Ferrier-Aligo

Aligo, E., and coauthors : High-Resolution NMMB Simulations of the 29 June 2012 Derecho, AMS 27th 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. 24th Conf. on Weather Analysis and Forecasting/20th 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.ES51-ES53.


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 grid-scale 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 : Near-term plans and future evolution into a high-resolution ensemble, AMS 27th 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 correlated-k 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 long-lived 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 ensemble-3DVar variational ensemble version of NCEP Gridpoint Statistical Interpolation Analysis

http://www.dtcenter.org/com-GSI/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: Three-dimensional 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, 29902995.




Data Assimilation / Spin-up

Partial cycling NAM Data Assimilation System (NDAS):

12-h spin-up 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].



12-h spin-up consists of 4 GSI analyses and 3-h NMMN forecasts. 3-h NDAS forecast valid at 00z/06z/12z/18z used as first guess for NAM analyses (12 km parent and nest domains)


NDAS 3-h 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

- WSR-88D Doppler radial velocities


Satellite observations assimilated

- Cloud-drift 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 land-surface physics during NDAS 3-h 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 on-demand 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 analysis-forecast 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.