Global Forecast System (GFS)

1
Global Forecast System (GFS)
2
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
3
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
• gt Operational version - T574 up to 192 hours and
  T190 to 384 hours
• gt Supported resolutions T574, T382, T254,
  T190, T170, T126 and T62

4

• Initialization
• 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

5
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.

6
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.

7
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.

8
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 definite and may produce negative
tracers.
9
Example Removal of Negative Water Vapor

• Sources of Negative Water Vapor
• DataVertical advection
• assimilation
• Spectral transform
• Borrowing by cloud water
• SAS Convection

_
Ops GFS
Data Assimilation
Flux-Limited Vertically-Filtered Scheme,
central in time
Data Assimilation
New
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
Positive-definite
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
10
Vertical Advection of Tracers Flux-Limited
Scheme
Thuburn (1993)
Van Leer (1974) Limiter, anti-diffusive term
Special boundary conditions
11
Vertical Advection of Tracers Flux-Limited
Scheme
Thuburn (1993)
Van Leer (1974) Limiter, anti-diffusive term
Special boundary condition
12
Vertical Advection of Tracers Idealized Case
Study
wind
Upwind (diffusive)
Flux-Limited
Initial condition
GFS Central-in-Space
13
Summary Negative Water Vapor in the GFS
Causes Importance Solutions
Vertical Advection Semi-Lagrangian Flux-Limited Positive-Definite Scheme for current Eulerian GFS
GSI Analysis Tuning factqmin and factqmax
Spectral Transform 1. Semi-Lagrangian GFS running tracers on grid, no spectral transform 2. Eulerian GFS no solution yet.
Cloud Water Borrowing Limiting the borrowing to available amount of water vapor
SAS Mass-Flux Remains to be resolved
14
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

15
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

16
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

17
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

18
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.

19
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

20
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)

21
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
Rbcr0.25
Use local diffusion scheme above PBL for both
MRF and new models
22

Heat flux
MRF PBL
Revised model


(Simplified after Lock et al., 2000)
where c0.2
C1.0
(CTEI condition)
23
Model Physics Sub-grid scale gravity wave drag
and mountain blocking
24
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-g
rid scale orography generates vertically
propagating gravity waves transferring momentum
aloft. Gravity wave Drag, implemented in 1987,
and 1997 Mountain Blocking, implemented 2004
25

• 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.

26

• 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.

27

• 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.

28
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
29
(No Transcript)
30
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.
31
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.
32
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, ?.
33
The function l(z) according to Lott and Miller
(1)
(2)
(3)
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.
34
(No Transcript)
35
5
36
(No Transcript)
37
(No Transcript)
38
(No Transcript)
39
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.

40
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.

41
Updated new shallow convection scheme

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

ce 0.3 in this study
42
Siebesma Cuijpers (1995, JAS) Siebesma et
al. (2003, JAS)
LES studies
43
Updated new shallow convection scheme
Mass flux at cloud base
Mb0.03 w (Grant, 2001)
(Convective boundary layer velocity scale)
44
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.

45
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.

46
SAS convection scheme
Updraft mass flux
CTOP
Entrainment
Downdraft mass flux
DL
1.0
1.0
hs
h
LFC
Entrainment
150mb
Detrainment
SL
0.5
Environmental moist static energy
0.05
47
Updated deep convection scheme
Organized entrainment (Betchtold et al., 2008)
org.
turb.
in sub-cloud layers
above cloud base
48
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)
49
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 c0.0 in operational SAS c0.55
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.
50
Modification in convection trigger
Operational pre Jul 2010 P(ks)-P(k1)lt150mb k2-k1lt
2
k2
LFC
k1
h
h
Current operational 120mbltP(ks)-P(k1)lt180mb
(proportional to w) P(k1)-P(k2) lt 25mb
ks
h moist static energy h saturation moist
static energy
51
ISCCP
Opr. GFS
New package
52
70 reduced backgroud diffusion in inversion
layers below 2.5km over ocean
With original background diffusion
53
(No Transcript)
54
(No Transcript)
55
Grid Point Storm
24 h accumulated precip ending 12 UTC 14 July 2009
Observed
48 h GFS Forecast
56
(No Transcript)
57
(No Transcript)
58
Grid Point Storm
24 h accumulated precip ending 12 UTC 15 July 2009
Observed
72 h GFS Forecast
59
(No Transcript)
60
(No Transcript)
61
(No Transcript)
62
(No Transcript)
63
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.

64
Model PhysicsRadiation
65
Unified Radiation Package in NCEP models
Features 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.

66
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)
67
Schematic Radiation Module Structure
Driver Module
Astronomy Module
Gases Module
Cloud Module

initialization
initialization
initialization
initialization
solar params
ozone
prog cld1
main driver
mean coszen
co2
prog cld2
rare gases
diag cld
SW Param Module
LW Param Module
Aerosol Module
SW Data Table Module
LW Data Table Module
initialization
clim aerosols
SW Main Module
LW Main Module
initialization
initialization
GOCART aerosols
Derived Type aerosol_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
initialization
SW albedo
LW emissivity
Derived Type sfcalb_type
68
Radiation_Astronomy Module

• Solar constant value (Cntl parm - ISOL)
• ISOL0 use prescribed solar constant (for NWP
  models)
• most recent cited value 1366 w/m2 (2002)
• ISOL1 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

69
Radiation_Gases Module

• CO2 Distribution (Cntrol parameter- ICO2)
• ICO20 use prescribed global annual mean
  value (currently set as 380ppmv)
• ICO21 use observed global annual mean value
• ICO22 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

70
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)
• ISUBC0 without sub-grid cloud approximation
• ISUBC1 with McICA sub-grid approximation
  (test mode with prescribed
• permutation seeds)
• ISUBC2 with McICA sub-grid approximation
  (random permutation seeds)
• (This option used in CFSV2 fore/hindcast model)

71
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 swlw tropospheric aerosols
  background stratospheric
• IAER 100 swlw stratospheric volcanic
  aerosols only
• IAER 101 sw tropospheric aerosol
  stratospheric volcanic forcing
• IAER 110 lw tropospheric aerosol
  stratospheric volcanic forcing
• IAER 111 swlw tropospheric aerosol
  stratospheric volcanic forcing

72
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

73
LW Radiation

• GFS CFS
• 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

74
SW Radiation

• GFS CFS
• 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

75
McICA sub-grid cloud approximation

• General expression of 1-D radiation flux
  calculation

where Fk are spectral corresponding fluxes, and
the total number, ?, depends on different RT
schemes
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
sub-columns
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)
76
McICA Distributions of Maximum-RandomOverlapped
Multi-layer clouds
Instance 1
Instance 2
77
McICA Distribution of Maximum-RandomOverlapping
Very Thick Cloud
Instance 1
Instance 2
78
Model Lower BoundaryOcean

• SST from the OI analysis at the initial condition
  time relaxed to climatology with e-folding time
  of 90 days

79
Model Lower BoundaryLand surface model (LSM)
80
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)

April 2011, Indian Institute of Tropical
Meteorology, Pune, India
81
Noah Land Model Connections in NOAAs NWS Model
Production Suite
Oceans HYCOM WaveWatch III
Climate CFS
2-Way Coupled
Hurricane GFDL HWRF
MOM3
1.7B Obs/Day
Satellites 99.9
Dispersion ARL/HYSPLIT
Regional NAM WRF NMM (including NARR)
Global Forecast System
Global Data Assimilation
Severe Weather
Regional Data Assimilation
WRF NMM/ARW Workstation WRF
Short-Range Ensemble Forecast
North American Ensemble Forecast System
WRF ARW, NMM ETA, RSM
Air Quality
GFS, Canadian Global Model
NAM/CMAQ
Rapid Update for Aviation (ARW-based)
82
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.
83
Land Data Sets
Max.-Snow Albedo (1-deg, Robinson)
Soil Type (1-deg, Zobler)
Vegetation Type (1-deg, UMD)
July
July
Jan
Jan
Green Vegetation Fraction (monthly, 1/8-deg,
NESDIS/AVHRR)
Snow-Free Albedo (seasonal, 1-deg, Matthews)
84
Prognostic Equations

• Soil Moisture (?)
• Richards 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.

85
Atmospheric Energy Budget

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

86
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., Tssurface 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.

87
Hydrological Cycle

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

88
Surface Water Budget
?S P R E
?S change in land-surface water P precipitat
ion R runoff E evapotranspiration P-R inf
iltration 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.
89
Potential Evaporation
(Penman)
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)
90
Surface Latent Heat Flux
LE LEc LEt LEd
(Evapotranspiration)
Canopy Water Evap. (LEc)
Transpiration (LEt)
Bare Soil Evaporation (LEd)
canopy water
canopy
soil
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
91
Latent Heat Flux over Snow
LE (shallow snow)
LE (deep snow)
lt
Sublimation (LEsnow)
LEsnow LEp
LEsnow LEp
snowpack
LEns 0
LEns lt LEp
soil
Shallow/Patchy SnowSnowcoverlt1
Deep snow Snowcover1
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).
92
Surface Sensible Heat Flux
(from canopy/soil snowpack surface)
canopy
snowpack
bare soil
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.
93
Ground (Subsurface Soil) Heat Flux
G (KT/?z)(Tsfc-Tsoil)
(to canopy/soil/snowpack surface)
canopy
snowpack
bare soil
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.
94
Model Lower Boundaryseaice
95
SEA ICE Model in GFS
Xingren Wu EMC/NCEP and IMSG
96
NSIDC
Arctic sea ice hits record low in 2007
9/16/2007
97
Outline

• 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

98
Sea Ice
Sea ice is a thin skin of frozen water covering
the polar oceans. It is a highly variable feature
of the earths surface.
Nilas Leads First-Year Ice
Pancake Ice
Multi-Year Ice
Greece Ice
Melt Pond
Snow-Ice
Rafting
99
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

100

• Issues related to sea ice forecast
• Data assimilation
• Initial conditions
• Sea ice models and coupling

101

• 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

102

• Sea ice model and coupling issues
• Ice thermodynamics
• Ice dynamics
• Ice model coupling to the atmosphere
• Ice model coupling to the ocean

103
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
104
Ice Model Thermodynamics

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

105
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.

106
Sea Ice in the NCEP GFS (cont.)
Atmospheric model
SW Heat Flux
LW Heat Flux
Turbulent Heat Flux
Ice Fraction
Snow Rate
Ice Fraction
Ice/Snow Thickness
Ice/Snow Thickness
3-layer thermodynamics Ice model
Ice Temperature
Ice Temperature
Surface Temperature
surface Temperature
Oceanic Heat Flux
Salinity
Fresh Water
Ocean model