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Global Forecast System (GFS ). What is GFS?. Global Forecast System (GFS) is often mislabeled or misunderstood. Global Forecast System is the full global scale numerical weather prediction system – It includes both the global analysis and forecast components

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what is gfs
What is GFS?

Global Forecast System (GFS) is often mislabeled or misunderstood.

Global Forecast System is the full global scale numerical weather prediction system – It includes both the global analysis and forecast components

However, the term GFS has also been used to imply that it is the NCEP global spectral model.

Therefore, we may use the term GFS to imply both the atmospheric model as well as the whole forecast system

ncep global spectral model horizontal representation
NCEP Global Spectral model Horizontal Representation
  • Spectral (spherical harmonic basis functions) with transformation to a Gaussian grid for calculation of nonlinear quantities and physics
  • Horizontal resolution
  • > Operational version - T574 up to 192 hours and T190 to 384 hours
  • > Supported resolutions – T574, T382, T254, T190, T170, T126 and T62
    • Digital filter initialization with 3 hour window.

Time integration scheme:

    • Leapfrog for nonlinear advection terms
    • Semi-implicit for gravity waves and zonal advection of vorticity and specific humidity.
    • Asselin (1972) time filter to control computational mode
    • Time split physics adjustments with implicit treatment when possible
vertical domain
Vertical Domain
  • Sigma-Pressure hybrid coordinate system
  • Terrain following near the lower boundary
  • Constant pressure surfaces in the stratosphere and beyond
  • Operationally 64 hybrid layers (15 levels below ~ 800 hPa and 24 levels above 100hPa.
  • 28, 42 and 91 layer options available.
model dynamics
Model Dynamics
  • Prognostic equations
    • Primitive equations in hybrid sigma-pressure vertical coordinates for vorticity, divergence, ln(Ps), virtual temperature, and tracers.
    • Tracers can be specific humidity, ozone mixing ratio and cloud condensate mixing ratio or any other aerosol/dust etc.
    • Operationally only three tracers.
vertical advection
Vertical Advection

Until the last GFS implementation, the vertical advection of tracers were based on ca entered difference scheme

This resulted in to the computationally generated negative tracers

In the last implementation a positive-definite tracer transport scheme was implemented which minimised the generation of negative tracers.

This change was necessary for the newly implemented GSI which is sensitive to the negative water vapor.

vertical advection of tracers previous gfs scheme
Vertical Advection of Tracers: previous GFS Scheme

Flux form conserves mass

Current GFS uses central differencing in space and leap-frog in time.

The scheme is not positive definiteand may produce negative tracers.

example removal of negative water vapor
Sources of Negative Water Vapor

DataVertical advection


Spectral transform

Borrowing by cloud water

SAS Convection

Example: Removal of Negative Water Vapor



Data Assimilation

Flux-Limited Vertically-Filtered Scheme,central in time

Data Assimilation


B: horizontal advection, computed in spectral form with central differencing in space

A: vertical advection, computed in finite-difference form with flux-limited positive-definite scheme in space


Fanglin Yang et al., 2009: On the Negative Water Vapor in the NCEP GFS: Sources and Solution. 23rd Conference on Weather Analysis and Forecasting/19th Conference on Numerical Weather Prediction, 1-5 June 2009, Omaha, NE

vertical advection of tracers flux limited scheme
Vertical Advection of Tracers: Flux-Limited Scheme

Thuburn (1993)

Van Leer (1974) Limiter, anti-diffusive term

Special boundary conditions

vertical advection of tracers flux limited scheme1
Vertical Advection of Tracers: Flux-Limited Scheme

Thuburn (1993)

Van Leer (1974) Limiter, anti-diffusive term

Special boundary condition

vertical advection of tracers idealized case study
Vertical Advection of Tracers: Idealized Case Study


Upwind (diffusive)


Initial condition

GFS Central-in-Space

horizontal diffusion
Horizontal Diffusion
  • Scale selective 8th order diffusion of Divergence, vorticity, virtual, temperature, specific humidity, ozone and cloud condensate.
  • Temperature diffusion in done on quasi-pressure surfaces
algorithm of the gfs spectral model
Algorithm of the GFS Spectral Model

One time step loop is divided into :

  • Computation of the tendencies of divergence, log of surface pressure and virtual temperature and of the predicted values of the vorticity and moisture (grid)
  • Semi-implicit time integration
  • Time filter does not require the predicted variables
  • Time split physics (transform grid)
  • Damping to simulate subgrid dissipation
  • Completion of the time filter
gfs parallelism spectral
GFS Parallelism Spectral
  • Spectral fields separated into their real and imaginary parts to remove stride problems in the transforms
  • Hybrid 1-D MPI with OpenMP threading
    • Spectral space 1-D MPI distributed over zonal wave numbers (l\'s). Threading used on variables x levels
      • Cyclic distribution of l\'s used for load balancing the MPI tasks due to smaller numbers of meridional points per zonal wave number as the wave number increases. For example for 4 MPI tasks the l\'s would be distributed as 12344321
gfs parallelism grid
GFS Parallelism-Grid
  • Grid space 1-D MPI distributed over latitudes. Threading used on longitude points.
    • Cyclic distribution of latitudes used for load balancing the MPI tasks due to smaller number of longitude points per latitude as latitude increases (approaches the poles). For example for 4 MPI tasks the latitudes would be distributed as 12344321
    • NGPTC (namelist variable) defines number (block) of longitude points per group (vector length per processor) that each thread will work on
gfs scalability
GFS Scalability
  • 1-D MPI scales well to 2/3 of the spectral truncation. For T574 about 400 MPI tasks.
  • OpenMP threading performs well to 8 threads and still shows decent scalability to 16 threads.
  • T574 scales to 400 x 16 = 6400 processors.
model physics planetary boundary layer and vertical diffusion pbl
Model PhysicsPlanetary Boundary Layer and vertical diffusion (PBL)
  • Nonlocal PBL scheme originally proposed by Troen and Mahrt (1986) and implemented by Hong and Pan (1996)
  • First order vertical diffusion scheme
  • PBL height estimated iteratively from ground up using bulk Richardson number
  • Diffusivity calculated as a cubic function of height and determined by matching with surface fluxes
  • Counter-gradient flux parameterization based on the surface fluxes and convective velocity scale.
  • Recent update – stratocumulus top driven vertical diffusion scheme to enhance diffusion in cloudy regions when CTEI exists
  • For the nighttime stable PBL, local diffusivity scheme used.
  • Exponentially decreasing diffusivity for heat and moisture
  • Constant background diffusivity of 3 m2/s for momentum

New PBL scheme

  • Include stratocumulus-top driven turbulence mixing.
  • Enhance stratocumulus top driven diffusion when the condition for cloud top entrainment instability is met.
  • Use local diffusion for the nighttime stable PBL.
  • Background diffusion in inversion layers below 2.5km over ocean is reduced by 70% to decrease the erosion of stratocumulus along the costal area. (Moorthi)

Diffusion in stable boundary layer

MRF PBL Revised model

Local diffusion scheme (Louis, 1979)

l0 = 150 m for unstable condition

30 m for stable condition


* Use local diffusion scheme above PBL for both MRF and new models


Heat flux

MRF PBL Revised model

(Simplified after Lock et al., 2000)

where c=0.2


(CTEI condition)


Model Physics

Sub-grid scale gravity wave drag and mountain blocking


Correction of model bias from sub-grid scale parameterization is an

on-going process.

Atmospheric flow is significantly influenced by orography, creating lift and

frictional forces

The unresolved sub-grid scale orography has significant impact on the

evolution of the model atmosphere and must be parameterized.

Sub-grid scale orography generates vertically propagating gravity waves

transferring momentum aloft.

Gravity wave Drag, implemented in 1987, and 1997

Mountain Blocking, implemented 2004

Mountain blocking of wind flow around sub-gridscale orography is a process that retards motion at various model vertical levels near or in the boundary layer.
  • Flow around the mountain encounters larger frictional forces by being in contact with the mountain surfaces for longer time as well as the interaction of the atmospheric environment and vortex shedding which is shown to occur in numerous observations and tank simulations.
  • Snyder, et al., 1985, observed the behavior of flow around or over obstacles and used a dividing streamline to analyze the level where flow goes around a barrier or over it.
Lott and Miller (1997) incorporated the dividing streamline into the ECMWF global model, as a function of the stable stratification, where above the dividing streamline, gravity waves are potentially generated and propagate vertically, and below, the flow is expected to go around the barrier with increased friction in low layers.
An augmentation to the gravity wave drag scheme in the NCEP global forecast system (GFS), following the work of Alpert et al., (1988, 1996) and Kim and Arakawa (1995), is incorporated from the Lott and Miller (1997) scheme with minor changes and including the dividing streamline.

•The idea of a dividing streamline at some level, hd, as in Snyder et al. (1985) and Etling, (1989), dividing air parcels that go over the mountain from those forced around an obstacle is used to parameterize mountain blocking effects.

Lott and Miller (1997) incorporated the dividing streamline into the ECMWF global model, as a function of the stable stratification.

Above the dividing streamline, gravity waves are potentially generated and propagate vertically.

Below, the flow is expected to go around the barrier with increased friction in lower layers


The dividing streamline height, of a sub-grid scale obstacle, can be found from comparing the potential and kinetic energies of up stream large scale wind and sub-grid scale air parcel movements. These can be defined by the wind and stability as measured by N, the Brunt Vaisala frequency. The dividing streamline height, hd, can be found by solving an integral equation for hd:

where H is the maximum elevation within the sub-grid scale grid box of the actual orography, h, from the GTOPO30 dataset of the U.S. Geological Survey.


In the formulation, the actual orography is replaced by an equivalent elliptic mountain with parameters derived from the topographic gradient correlation tensor, Hij:

and standard deviation, h\'. The model sub-grid scale orography is represented by four parameters, after Baines and Palmer (1990), h\', the standard deviation,

and g, s, Q, the anisotropy, slope and geographical orientation of the orography form the principal components of Hij, respectively. These parameters will change with changing model resolution.


In each model layer below the dividing streamline a drag from the blocked flow is exerted by the obstacle on the large scale flow and is calculated as in Lott and Miller (1997):

where l(z) is the length scale of the effective contact length of the obstacle on the sub grid scale at the height z and constant Cd ~ 1.

l(z) = F(z, hd, h‘, g, s, Q, )

Where  = Q -, the geographical orientation of the

orography minus the low level wind vector direction angle, .


The function l(z) according to Lott and Miller:




Term (1) relates the the eccentricity parameters, a,b, to the sub-grid scale orography parameters, a ~ h‘/s and a/b =g and allows the drag coefficient, Cd to vary with the aspect ratio of the obstacle as seen by the incident flow since it is twice as large for flow normal to an elongated obstacle compared to flow around an isotropic obstacle. Term (2) accounts for the width and summing up a number of contributions of elliptic obstacles, and Term (3) takes into account the flow direction in one grid region.

model physics shallow convection parameterization
Model PhysicsShallow convection parameterization
  • Until July 2010, the shallow convection parameterization was based on Tiedtke (1983) formulation in the form of enhanced vertical diffusion within the cloudy layers.
  • In july 2010, a new massflux based shallow convection scheme based on Han and pan (2010) was implemented operationally.
  • Model code still contains the old shallow convection scheme as an option (if you set old_monin=.true.) with an option to limit the cloud top to below low level inverstion when CTEI does not exist.

Updated new mass flux shallow convection scheme

  • Detrain cloud water from every updraft layer
  • Convection starting level is defined as the level of maximum moist static energy within PBL
  • Cloud top is limited to 700 hPa.
  • Entrainment rate is given to be inversely proportional to height and detrainment rate is set to be a constant as entrainment rate at the cloud base.
  • Mass flux at cloud base is given to be a function of convective boundary layer velocity scale.

Updated new shallow convection scheme

  • Entrainment rate:
    • Siebesma et al.2003:
  • Detrainment rate = Entrainment rate at cloud base

ce =0.3 in this study


Siebesma & Cuijpers

(1995, JAS)

Siebesma et al.

(2003, JAS)

LES studies


Updated new shallow convection scheme

Mass flux at cloud base:

Mb=0.03 w* (Grant, 2001)

(Convective boundary layer velocity scale)

model physics deep convection parameterization
Model PhysicsDeep convection parameterization
  • Simplified Arakawa Schubert (SAS) scheme is used operationally in GFS (Pan and Wu, 1994, based on Arakawa-Schubert (1974) as simplified by Grell (1993))
  • Includes saturated downdraft and evaporation of precipitation
  • One cloud-type per every time step
  • Until July 2010, random clouds were invoked.
  • Significant changes to SAS were made during July 2010 implementation which helped reduce excessive grid-scale precipitation occurrences.

Updated deep convection scheme

  • No random cloud top – single deep cloud assumed
  • Cloud water is detrained from every cloud layer.
  • Specified finite entrainment and detrainment rates for heat, moisture, and momentum
  • Similar to shallow convection scheme, in the sub-cloud layers, the entrainment rate is inversely proportional to height and the detrainment rate is set to be a constant equal to the cloud base entrainment rate.
  • Above cloud base, an organized entrainment is added, which is a function of environmental relative humidity.

SAS convection scheme

Updraft mass flux



Downdraft mass flux












Environmental moist static energy



Updated deep convection scheme

Organized entrainment (Betchtold et al., 2008)



in sub-cloud layers

above cloud base


Updated deep convection scheme

Maximum mass flux [currently 0.1 kg/(m2s)] is defined for the local Courant-Friedrichs-Lewy (CFL) criterion to be satisfied (Jacob and Siebesman, 2003);

Then, maximum mass flux is as large as 0.5 kg/(m2s)

modification to deep convection sas scheme
Modification to deep convection(SAS) scheme
  • Include the effect of convection-induced pressure gradient force in momentum transport (Han and Pan, 2006)

c: effect of convection-induced pressure gradient force

c=0.0 in operational SAS

c=0.55 in modified SAS following Zhang and Wu (2003)

* Note that this effect also changes updraft and downdraft properties inside the SAS scheme and thus, one should not simply reduce momentum change by convection outside the scheme.


Modification in convection trigger

Operational pre Jul 2010:


k2-k1< 2





Current operational:

120mb<P(ks)-P(k1)<180mb (proportional to w)

P(k1)-P(k2) < 25mb



h: moist static energy

h*: saturation moist static energy



Opr. GFS

New package


70% reduced backgroud diffusion in inversion layers below 2.5km over ocean

With original background diffusion


Grid Point Storm

24 h accumulated precip ending 12 UTC 14 July 2009


48 h GFS Forecast


Grid Point Storm

24 h accumulated precip ending 12 UTC 15 July 2009


72 h GFS Forecast

model physics large scale condensation and precipitation
Model PhysicsLarge-scale condensation and precipitation
  • The large-scale condensation and precipitation is parameterized following Zhao and Carr (1997) and Sundqvist et al (1989)
  • This was implemented in GFS along with prognostic cloud condensate in 2001 (Moorthi et al, 2001)
  • Partitioning between cloud water and ice is made based on the temperature.
  • Convective cloud detrainment is a source of cloud condensate which can either be precipitated or evaporated through large scale cloud microphysics.
unified radiation package in ncep models
Unified Radiation Package in NCEP models


Standardized component modules,General plug-in compatible, Simple to

use, Easy to upgrade, Efficient, and Flexible in future expansion.

  • References:
  • Hou et al. (2011): NCEP Office Note (in preparation)
  • Hou et al. (2002): NCEP Office Note 441 (ref for clouds, aerosols, and surface albedo processes)
  • Mlawer and Clough (1998): Shortwave and longwave enhancements in the rapid radiative transfer model, in Proceedings of the 7th Atmospheric Radiation Measurement (ARM) Science Team Meeting.
  • Mlawer and Clough (1997): On the extension of rapid radiative transfer model to the shortwave region, in Proceedings of the 6th Atmospheric Radiation Measurement (ARM) Science Team Meeting.
  • Mlawer et al. (1997): RRTM, a validated correlated-k model for the longwave, JGR.

Overview Module Structures:

Driver Module - prepares atmospheric profiles incl. aerosols,

gases, clouds, and surface conditions, etc.

Astronomy Module - obtains solar constant, solar zenith angles

Aerosol Module - establishes aerosol profiles and optical properties

Gas Module - sets up absorbing gases’ profiles (O3, CO2, rare gases, etc.)

Cloud module - prepares cloud profiles incl. fraction, ice/water

paths, and effective size parameters, etc.

Surface module - sets up surface albedo and emissivity

SW radiation module - computes SW fluxes and heating rates (contains three parts: parameters, data tables, and main program)

LW radiation module - computes LW fluxes and heating rates (contains three parts: parameters, data tables, and main program)

schematic radiation module structure
Schematic Radiation Module Structure

Driver Module

Astronomy Module

Gases Module

Cloud Module





solar params


prog cld1

main driver

mean coszen


prog cld2

rare gases

diag cld

SW Param Module

LW Param Module

Aerosol Module

SW Data Table Module

LW Data Table Module


clim aerosols

SW Main Module

LW Main Module



GOCART aerosols

Derived Type :


sw radiation

lw radiation

Outputs :

total sky heating rates

surface fluxes (up/down)

toa atms fluxes (up/down)

Optional outputs:

clear sky heating rates

spectral band heating rates

fluxes profiles (up/down)

surface flux components

Outputs :

total sky heating rates

surface fluxes (up/down)

toa atms fluxes (up/down)

Optional outputs:

clear sky heating rates

spectral band heating rates

fluxes profiles (up/down)

Surface Module


SW albedo

LW emissivity

Derived Type :


radiation astronomy module
Radiation_Astronomy Module

Solar constant value : (Cntl parm - ISOL)

  • ISOL=0: use prescribed solar constant (for NWP models)
  • most recent cited value = 1366 w/m2 (2002)
  • ISOL=1: use prescribed solar constant with 11-year cycle (for climate models)
  • variation range: 1365.7 – 1370 w/m2
  • obsv data range: 1944 -2006 **tabulated by H. Vandendool
radiation gases module
Radiation_Gases Module

CO2 Distribution : (Cntrol parameter- ICO2)

ICO2=0: use prescribed global annual mean value (currently set as 380ppmv)

ICO2=1: use observed global annual mean value

ICO2=2: use observed monthly 2-d data table in 15° horizontal resolution

O3 Distribution : interactive or climatology

Rare Gases : (currently use global mean climatology values)

CH4 - 1.50 x 10-6 N2O - 0.31 x 10-6 O2 - 0.209

CO - 1.50 x 10-8 CF11 - 3.52 x 10-10 CF12- 6.36 x 10-10

CF22 - 1.50 x 10-10 CF113- 0.82 x 10-10 CCL4- 1.40 x 10-1

** all units are in ppmv

radiation clouds module
Radiation_Clouds Module

Cloud prediction scheme:

Prognostic 1: based on Zhao/Moorthi microphysics

Prognostic 2: based on Ferrier/Moorthi microphysics

Diagnostic : legacy diagnostic scheme based on RH-table lookups

Cloud overlapping method: (Cntl parm - IOVR)

IOVR = 0: randomly overlapping vertical cloud layers

IOVR = 1: maximum-random overlapping vertical cloud layers

Sub-grid cloud approximation: (CFS Cntl parm - ISUBC)

ISUBC=0: without sub-grid cloud approximation

ISUBC=1: with McICA sub-grid approximation (test mode with prescribed

permutation seeds)

ISUBC=2: with McICA sub-grid approximation (random permutation seeds)

(This option used in CFSV2 fore/hindcast model)

radiation aerosols module
Radiation_aerosols Module

Aerosol distribution: (Cntl parm - IAER)

Troposphere: monthly global aerosol climatology in 15° horizontal resolution

(GOCART interactive aerosol scheme under development)

Stratosphere: historical recorded volcanic forcing in four zonal mean bands (1850-2000)

IAER – 3-digit integer flag for volcanic, lw, sw, respectively

IAER = 000: no aerosol effect in radiation calculations

IAER = 001: sw tropospheric aerosols + background stratospheric

IAER = 010: lw tropospheric aerosols + background stratospheric

IAER = 011: sw+lw tropospheric aerosols + background stratospheric

IAER = 100: sw+lw stratospheric volcanic aerosols only

IAER = 101: sw tropospheric aerosol + stratospheric volcanic forcing

IAER = 110: lw tropospheric aerosol + stratospheric volcanic forcing

IAER = 111: sw+lw tropospheric aerosol + stratospheric volcanic forcing

radiation surface module
Radiation_surface Module

SW surface albedo: (Cntl parm - IALB)

IALB = 0: vegetation type based climatology scheme (monthly data in 1° horizontal resolution)

IALB = 1: MODIS retrievals based monthly mean climatology

LW surface emissivity: (CFS Cntl parm - IEMS)

IEMS = 0: black-body emissivity (=1.0)

IEMS = 1: monthly climatology in 1° horizontal resolution

lw radiation
LW Radiation


  • NCEP version: RRTM1 RRTM3
  • crpnd AER version: RRTMG_LW_2.3 RRTMG_LW_4.82
  • No. of bands: 16 16
  • No. of g-points: 140 140
  • Absorbing gases: H2O, O3, CO2, CH4, N2O, O2, CO, CFCs
  • Aerosol effect: yes yes
  • Cloud overlap: max-rand max-rand
  • Sub-grid clouds: no McICA
sw radiation
SW Radiation


  • NCEP version: RRTM2 RRTM3
  • crpnd AER version: RRTMG_SW_2.3 RRTMG_SW_3.8
  • No. of bands: 14 14
  • No. of g-points: 112 112
  • Absorbing gases: --- H2O, O3, CO2, CH4, N2O, O2 ---
  • Aerosol effect: yes yes
  • Cloud overlap: max-rand max-rand
  • Sub-grid clouds: no McICA

McICA sub-grid cloud approximation

where Fk are spectral corresponding fluxes, and the

total number, Κ, depends on different RT schemes

  • General expression of 1-D radiation flux calculation:

Independent column approximation (ICA):

where N is the number of total sub-columns in

each model grid

That leads to a double summation:

that is too expensive for most applications!

Monte-Carlo independent column approximation (McICA):

In a correlated-k distribution (CKD) approach, if the number of quadrature points (g-points) are sufficient large and evenly treated, then one may apply the McICA to reduce computation time.

where k is the number of randomly generated


McICA is a complete separation of optical characteristics from RT solver and is proved to be

unbiased against ICA (Barker et al. 2002, Barker

and Raisanen 2005)

model lower boundary ocean
Model Lower BoundaryOcean
  • SST from the OI analysis at the initial condition time relaxed to climatology with e-folding time of 90 days
land modeling at ncep

Shrinivas Moorthi, Michael Ek

and the EMC Land-Hydrology Team

Environmental Modeling Center (EMC)

National Centers for Environmental Prediction (NCEP)

5200 Auth Road, Room 207

Suitland, Maryland 20732 USA

National Weather Service (NWS)

National Oceanic and Atmospheric Administration (NOAA)

Land modeling at NCEP

April 2011, Indian Institute of Tropical Meteorology, Pune, India

noah land model connections in noaa s nws model production suite

NCEP-NCAR unified





NOAH Land Surface Model

Noah Land Model Connections in NOAA’s NWS Model Production Suite



WaveWatch III



2-Way Coupled

Hurricane GFDL



1.7B Obs/Day





Regional NAM


(including NARR)




Global Data


Severe Weather

Regional Data



Workstation WRF


Ensemble Forecast

North American Ensemble Forecast System



Air Quality

GFS, Canadian Global Model


Rapid Update

for Aviation (ARW-based)


Noah land-surface model

• Surface energy (linearized) & water budgets; 4 soil layers.

• Forcing: downward radiation, precip., temp., humidity, pressure, wind.

• Land states: Tsfc, Tsoil*, soil water* and soil ice, canopy water*, snow depth and snow density. *prognostic

• Land data sets: veg. type, green vegetation fraction, soil type, snow-free albedo & maximum snow albedo.

• Noah model is coupled with the NCEP Global Forecast System (GFS, medium-range), and Climate Forecast System (CFS, seasonal), & other NCEP models.


Land Data Sets

Max.-Snow Albedo

(1-deg, Robinson)

Vegetation Type

(1-deg, UMD)

Soil Type

(1-deg, Zobler)





Green Vegetation Fraction

(monthly, 1/8-deg, NESDIS/AVHRR)

Snow-Free Albedo

(seasonal, 1-deg,



Prognostic Equations

Soil Moisture ():

• “Richard’s Equation”; D (soil water diffusivity) and K (hydraulic conductivity), are nonlinear functions of soil moisture and soil type (Cosby et al 1984); F is a source/sink term for precipitation/evapotranspiration.

Soil Temperature (T):

• CT (thermal heat capacity) and KTsoil thermal conductivity; Johansen 1975), are nonlinear functions of soil moisture and soil type.

Canopy water (Cw):

• P (precipitation) increases Cw, while Ec (canopy water evaporation) decreases Cw.


seasonal storage

Atmospheric Energy Budget

• Noah land model closes the surface energy budget, & provides surface boundary condition to GFS & CFS.


Surface Energy Budget

Rnet = H + LE + G + SPC

Rnet = Net radiation = S - S + L - L

S = incoming shortwave (provided by atmos. model)

S = reflected shortwave (snow-free albedo () provided

by atmos. model;  modified by Noah model over snow)

L = downward longwave (provided by atmos. model)

L = emitted longwave = Ts4(=surface emissivity,

=Stefan-Boltzmann const., Ts=surface skin temperature)

H = sensible heat flux

LE = latent heat flux (surface evapotranspiration)

G = ground heat flux (subsurface soil heat flux)

SPC = snow phase-change heat flux (melting snow)

• Noah model provides: , L, H, LE, G and SPC.


Hydrological Cycle

• Noah land model closes the surface water budget, & provides surface boundary condition to GFS & CFS.


Surface Water Budget

S = P – R – E

S = change in land-surface water

P = precipitation

R = runoff

E = evapotranspiration

P-R = infiltration of moisture into the soil

• S includes changes in soil moisture, snowpack (cold season), and canopy water (dewfall/frostfall and intercepted precipitation, which are small).

• Evapotranspiration is a function of surface, soil and vegetation characteristics: canopy water, snow cover/ depth, vegetation type/cover/density & rooting depth/ density, soil type, soil water & ice, surface roughness.

• Noah model provides: S, R and E.


LEp = Rnet-G + cpChUe


Potential Evaporation


open water surface

 = slope of saturation vapor pressure curve

Rnet-G = net radiation

 = air density

cp = specific heat

Ch = surface-layer turbulent exchange coefficient

U = wind speed

e = atmos. vapor pressure deficit (humidity)

 = psychrometric constant, fct(pressure)


Surface Latent Heat Flux

LE = LEc + LEt + LEd


Canopy Water

Evap. (LEc)



Bare Soil

Evaporation (LEd)

canopy water



LEc = function(canopy water %saturation) & LEp

LEt = function(Jarvis-Stewart “big-leaf” canopy

conductance with vegetation parameters for S,

atmos. temp., e & soil moisture avail.,) & LEp

LEd= fct(soil type, near-surface soil %sat.) & LEp


Latent Heat Flux over Snow

LE (shallow snow)


LE (deep snow)

Sublimation (LEsnow)

LEsnow = LEp

LEsnow = LEp


LEns < LEp

LEns = 0


Shallow/Patchy SnowSnowcover<1

Deep snow


• LEns = “non-snow” evaporation (evapotranspiration terms).

• 100% snowcover a function of vegetation type, i.e. shallower for grass & crops, deeper for forests.

• Soil ice = fct(soil type, soil temp., soil moisture).


H = cpChU(Tsfc-Tair)

Surface Sensible Heat Flux

(from canopy/soil

snowpack surface)


bare soil



, cp = air density, specific heat

Ch = surface-layer turbulent exchange coeff.

U = wind speed

Tsfc-Tair = surface-air temperature difference

• “effective” Tsfc for canopy, bare soil, snowpack.


Ground (Subsurface Soil) Heat Flux

G = (KT/z)(Tsfc-Tsoil)

(to canopy/soil/snowpack surface)


bare soil



KT = soil thermal conductivity (function of soil type: larger for moister soil, larger for clay soil; reduced through canopy, reduced through snowpack)

z = upper soil layer thickness

Tsfc-Tsoil = surface-upper soil layer temp. difference

• “effective” Tsfc for canopy, bare soil, snowpack.


SEA ICE Model in GFS

Xingren Wu


Shrinivas Moorthi



Arctic sea ice hits record low in 2007


Shrinivas Moorthi



  • Sea Ice
  • Sea Ice in the Weather and Climate System
  • Sea Ice in the NCEP Forecast System
  • - Analysis/Assimilation
  • - Forecast: GFS, CFS
  • Sea Ice in the CFS Reanalysis

Shrinivas Moorthi


Sea Ice

Sea ice is a thin skin of frozen water covering the polar oceans. It is a highly variable feature of the earth’s surface.

Pancake Ice

Nilas & Leads

First-Year Ice

Greece Ice

Multi-Year Ice

Melt Pond



Shrinivas Moorthi


Sea ice affects climate and weather related processes

  • Sea ice amplifies any change of climate due to its “positive feedback” (coupled climate model concern):

Sea ice is white and reflects solar radiation back to space. More sea ice cools the Earth, less of it warms the Earth. A cooler Earth means more sea ice and vice versa.

  • Sea ice restricts the exchange of heat/water between the air and ocean (NWP concern)
  • Sea ice modifies air/sea momentum transfer, ocean fresh water balance and ocean circulation:

The formation of sea ice injects salt into the ocean which makes the water heavier and causes it to flow downwards to the deep waters and drive a massive ocean circulation

Shrinivas Moorthi


Issues related to sea ice forecast

      • Data assimilation
      • Initial conditions
      • Sea ice models and coupling

Shrinivas Moorthi


Data assimilation issues

  • Sea ice concentration data are available but velocity data lack to real time
  • Lack of sea ice and snow thickness data
  • Initial condition issues
  • Sea ice concentration data are available but velocity data lack to real time
  • Sea ice and snow thickness data are based on model spin-up values or climatology

Shrinivas Moorthi


Sea ice model and coupling issues

    • Ice thermodynamics
    • Ice dynamics
    • Ice model coupling to the atmosphere
    • Ice model coupling to the ocean

Shrinivas Moorthi


NCEP Sea Ice Analysis Algorithm

  • 5 minutes latitude-longitude grid from the 85GHz SSMI information based on NASA Team Algorithm
  • Half degrees version of the product is used in GFS (as initial condition).

Courtesy: Robert Grumbine

Shrinivas Moorthi


Ice Model: Thermodynamics

  • Based on the principle of the conservation of energy, determine:
  • Ice formation
  • Ice growth
  • Ice melting
  • Ice temperature structure

Shrinivas Moorthi


Sea Ice

in the NCEP Global Forecast System

  • A three-layer thermodynamic sea ice model was embedded into GFS (May 2005).
  • It predicts sea ice/snow thickness, the surface temperature and ice temperature structure.
  • In each model grid box, the heat and moisture fluxes and albedo are treated separately for ice and open water.

Shrinivas Moorthi


Sea Ice in the NCEP GFS (cont.)

Atmospheric model




Heat Flux


Heat Flux


Heat Flux











Ice model











Heat Flux

Fresh Water

Ocean model

Shrinivas Moorthi