global forecast system (gfs ) 8/27/20151shrinivas moorthi

Global Forecast System (GFS)

04/19/23 1Shrinivas Moorthi

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


04/19/23 Shrinivas Moorthi 3

From the beginning, I would like to make it clear that this presentation is an unpolished mix of slides prepared by various developers of different aspects of the GFS.

I have not had the opportunity of going over every slide and make sure that information content is completely correct.

Also, I may not be able to correctly interpret individual authors intent while making the slides.

There are lots of information in these slides, some are more detailed and some less. For a deeper understanding, I urge you to read the original references and the papers published by the developers, if any, and finally by looking at the code itself. (Also check NCEP website or simply google).

Developing these schemes and codes involves lots of engineering and not everything may have complete logical explanation.

No attempt is made here to be complete; I spent last one month making sure the system we are installing here works.

NCEP operational 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


• 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 possible04/19/23 5Shrinivas Moorthi

• 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.

Vertical Domain


Model Dynamics

• Prognostic equations– Primitive equations in hybrid sigma-pressure

vertical coordinates for vorticity, divergence, (or U,V), 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.– Please see NCEP Office notes #461, 462 for

details on equations and numerics04/19/23 7Shrinivas Moorthi

8

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

This resulted in computationally generated negative tracers

In the last implementation a positive-definite tracer transport scheme was implemented which minimized the generation of negative tracers. (Fanglin Yang)

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

Vertical Advection

04/19/23 Shrinivas Moorthi

9

Vertical Advection of Tracers: previous GFS Scheme

pq

p

q

p

q

t

q Flux form conserves mass

2

1

2

1

2

1

2

1

2

1

2

1

11kk

kk

kkkkk

k qp

qqp

A

2

1

2

1

kk

k ppp

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

The scheme is not positive definite and may produce negative tracers.

kkk

qqq

1

2

1 2

1

1

2

11

2

12

1kk

kkk

kk

k qqqqp

A

nk

nk

nk Atqq 211

kq

1kq

1kq

21kq

21kq21k

21k


10

Vertical Advection of Tracers: Flux-Limited Scheme

1211121

kHkkkk qqqq Thuburn (1993)0 if 21 k

121 2

1 kk

Hk qqq

1

11

11 k

kk

kr

rr

1

2

1

121

k

k

kk

kkk q

q

qq

qqr

Van Leer (1974) Limiter, anti-diffusive term

Lq

21Lq

21Lq021 L

21L

kq

1kq

1kq

21kq

21kq21k

21k

1q

2q

21q

211q2

11

021

0 since 0 1for 212121 qk

Special boundary conditions

1231123 qqqq H

1

11

11 r

rr

1

0

21

101 q

q

qq

qqr

0 if 2 ,0min

0 if 2 ,0max

121

1210

qqq

qqqq

2for k

0q04/19/23 Shrinivas Moorthi

11

Vertical Advection of Tracers: Flux-Limited Scheme

kHkkkk qqqq

2121

Thuburn (1993)0 if 21 k

121 2

1 kk

Hk qqq

k

kk

kr

rr

1

11

1

k

k

kk

kkk q

q

qq

qqr

Van Leer (1974) Limiter, anti-diffusive term

Lq

21Lq

21Lq021 L

21L

kq

1kq

1kq

21kq

21kq21k

21k

1q

2q

21q

211q2

11

021

Lfor kSpecial boundary condition

LHLLLL qqqq

2121

L

LL

Lr

rr

1 11

1

L

L

LL

LLL q

q

qq

qqr

0 if 2 ,0min

0 if 2 ,0max

1

11

LLL

LLLL qqq

qqqq

1Lq


12

Example: Removal of Negative Water Vapor

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

Sources of Negative Water Vapor

• DataVertical advection

• assimilation

• Spectral transform

• Borrowing by cloud water

• SAS ConvectionOps GFS

_

Positive-definite

Data Assimilation

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

Flux-Limited Vertically-Filtered Scheme, central in time

1*

2

1 nk

nk

nk AAA New

nk

nkhh AB

p

qqV

t

q

*11 2 nk

nk

nk

nk AtBtqq

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

Data Assimilation


13

Vertical Advection of Tracers: Idealized Case Study

wind

Upwind (diffusive)

Flux-Limited

GFS Central-in-Space

Initial condition


14

Summary: Negative Water Vapor in the GFS

Causes Importance Solutions

Vertical Advection 1. Semi-Lagrangian

2. 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 resolved04/19/23 Shrinivas Moorthi

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 Spectral ModelMike Young

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

• 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– 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

• 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 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 schemeJongil Han

• 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

z

URiflK hmhm

)(,

2,

2

z

U

zT

gRi v

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

0

111

lkzl

l0 = 150 m for unstable condition

30 m for stable condition

Local diffusion scheme (Louis, 1979)

z

uKwu surf

m

2

1

h

zzwK s

surfm

))((

)(2

vav

vacr hg

hURbh

Rbcr=0.25


hsurfh z

Kw

2

1

h

zzwK s

surfm

surfm

surfh KK 1Pr

hw

w

sh

0)(5.6

(Simplified after Lock et al., 2000)

MRF PBL Revised model

Heat flux

hsurfh

Sch

surfh K

zKKw

)/()(0

3pbbSc cRzh

gV

2/12

185.0

bb

b

bb

bSc

Sch zh

zz

zh

zzVK

phv c

Rcw

b

)(

,7.0 tep qLcif C=1.0

where c=0.2

(CTEI condition)04/19/23 23Shrinivas Moorthi

Model Physics

Sub-grid scale gravity wave drag and mountain blockingJordan Alpert


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


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.


Mass flux based shallow convection schemeJongil Han

• 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.


New shallow convection scheme

• Entrainment rate:

Siebesma et al.2003:

• Detrainment rate = Entrainment rate at cloud base

zce

1 ce =0.3 in this study


Siebesma & Cuijpers (1995, JAS)

Siebesma et al. (2003, JAS)

LES studies


New shallow convection scheme

Mass flux at cloud base:

Mb=0.03 w* (Grant, 2001)

3/1

00* ))(/( hwTgw v

(Convective boundary layer velocity scale)


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 schemeJongil Han

• 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. 04/19/23 Shrinivas Moorthi 34

SAS convection scheme

SL

DL

LFC

CTOP

h hs

Updraft mass flux

0.5

1.0

Downdraft mass flux

1.0

0.05

Entrainment

EntrainmentDetrainment

Environmental moist static energy

150mb


SAS convection scheme

Organized entrainment (Betchtold et al., 2008)

1100 )1()( FRHcFz

zz

1.0)(0

)()( 00 bzzz 4

1 100.1 c

)(0 bzz 3

1

2

0 ,

sb

s

sb

s

q

qF

q

qF

turb. org.

in sub-cloud layers

above cloud base


Updated SAS 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);

tg

pM b

max

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


Modification to deep convection(SAS) scheme

VVz

VMc

t

Vuu

1

)1(

• 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.


h*h

Modification in convection trigger

Operational pre Jul 2010:

P(ks)-P(k1)<150mb

k2-k1< 2

LFC

ks

k2

k1

Current operational:

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

P(k1)-P(k2) < 25mb h: moist static energy

h*: saturation moist static energy04/19/23 39Shrinivas Moorthi

ISCCP

Old opr. GFS New opr GFS


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

With original background diffusion


24 h accumulated precip ending 12 UTC 14 July 2009

Grid Point StormGrid Point Storm

Observed 48 h GFS Forecast


24 h accumulated precip ending 12 UTC 15 July 2009

Grid Point StormGrid Point Storm

Observed 72 h GFS Forecast


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.


Model Physics

Radiation

YuTai Hou


Unified Radiation Package in NCEP models

• 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.

Features:: Standardized component modules, General plug-in compatible, Simple to use, Easy to upgrade, Efficient, and Flexible in future expansion.


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

• Driver Module

initialization

main driver

Astronomy Module

initialization

solar params

mean coszen

Gases Module

initialization

ozone

co2

Cloud Module

initialization

prog cld1

prog cld2

diag cld

Aerosol Module

initialization

clim aerosols

Derived Type : aerosol_type

Surface Module

initialization

SW albedo

LW emissivity

Derived Type : sfcalb_type

SW Param Module

SW Data Table Module

SW Main Moduleinitialization

sw 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

LW Param Module

LW Data Table Module

LW Main Moduleinitialization

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)

rare gases

GOCART aerosols


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

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

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

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 forcing04/19/23 60Shrinivas Moorthi

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

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


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


• General expression of 1-D radiation flux calculation:where Fk are spectral corresponding fluxes, and thetotal number, Κ, depends on different RT schemes

Independent column approximation (ICA):where N is the number of total sub-columns ineach model grid

That leads to a double summation:

that is too expensive for most applications!

Monte-Carlo independent column approximation (McICA):

McICA sub-grid cloud approximation

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 beunbiased against ICA (Barker et al. 2002, Barker and Raisanen 2005)


McICA Distributions of Maximum-RandomOverlapped Multi-layer clouds

Instance 1

Instance 2


McICA Distribution of Maximum-RandomOverlapping Very Thick Cloud

Instance 1

Instance 2


Model Lower BoundaryOceanOcean

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


Model Lower Boundary

Land (surface) model (LSM)

Mike Ek and land team


Land modeling at NCEP

Shrinivas Moorthi, Michael Ekand the EMC Land-Hydrology Team

Environmental Modeling Center (EMC)National Centers for Environmental Prediction (NCEP)

5200 Auth Road, Room 207Suitland, Maryland 20732 USA

National Weather Service (NWS)National Oceanic and Atmospheric Administration (NOAA)

April 2011, Indian Institute of Tropical Meteorology, Pune, India04/19/23 69Shrinivas Moorthi

Uncoupled“NLDAS”(drought)

Air Quality

WRF NMM/ARWWorkstation WRF

WRF: ARW, NMMETA, RSM

Satellites99.9%

Regional NAMWRF NMM

(including NARR)

Hurricane GFDLHWRF

GlobalForecastSystem

Dispersion

ARL/HYSPLIT

Forecast

Severe Weather

Rapid Updatefor Aviation (ARW-based)

ClimateCFS

1.7B Obs/Day

Short-RangeEnsemble Forecast

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

MOM32-Way Coupled Oceans

HYCOM

WaveWatch III

NAM/CMAQ

Regional DataAssimilation

Global DataAssimilation

North American Ensemble Forecast System

GFS, Canadian Global Model

NOAH Land Surface Model

NCEP-NCAR

unified04/19/23 70Shrinivas Moorthi

• 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 land-surface model

• 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

Soil Type(1-deg, Zobler)

Vegetation Type(1-deg, UMD)

Green Vegetation Fraction (monthly, 1/8-deg,

NESDIS/AVHRR)

Max.-Snow Albedo(1-deg, Robinson)

Snow-Free Albedo(seasonal, 1-deg,

Matthews)

July JulyJan Jan


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 KTsoil 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.

Prognostic Equations


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

seasonal storage


Surface Energy Budget

Rnet = H + LE + G + SPC

Rnet = Net radiation = S - S + L - LS = incoming shortwave (provided by atmos. model)

S = reflected shortwave (snow-free albedo () providedby 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 fluxLE = 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.


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

Hydrological Cycle


Surface Water Budget

S = P – R – ES=change in land-surface water

P = precipitationR = runoffE = 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.04/19/23 77Shrinivas Moorthi

Potential Evaporation

open water surface

LEp = Rnet-G + cpChUe+

= slope of saturation vapor pressure curveRnet-G = net radiation= air density

cp = specific heatCh = surface-layer turbulent exchange coefficientU = wind speed

e= atmos. vapor pressure deficit (humidity)= psychrometric constant, fct(pressure)

(Penman)


Surface Latent Heat Flux

LE = LEc + LEt + LEd

LEc = function(canopy water %saturation) & LEp

LEt = function(Jarvis-Stewart “big-leaf” canopyconductance with vegetation parameters for S,atmos. temp., e & soil moisture avail.,) & LEp

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

soil

canopy

canopy water

Transpiration(LEt)

Canopy WaterEvap. (LEc)

Bare SoilEvaporation (LEd)

(Evapotranspiration)


Latent Heat Flux over Snow

LE (shallow snow) LE (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).

soil

snowpack

Shallow/Patchy SnowSnowcover<1

Deep snowSnowcover=1

LEsnow = LEp

LEsnow = LEp

LEns = 0

Sublimation (LEsnow)

LEns < LEp


Surface Sensible Heat Flux

soil

canopy snowpackbare soil

H = cpChU(Tsfc-Tair)

, cp= air density, specific heatCh = surface-layer turbulent exchange coeff.U = wind speed

Tsfc-Tair = surface-air temperature difference

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

(from canopy/soilsnowpack surface)


Ground (Subsurface Soil) Heat Flux

soil

canopy snowpackbare 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 thicknessTsfc-Tsoil= surface-upper soil layer temp. difference

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

G = (KT/z)(Tsfc-Tsoil)

(to canopy/soil/snowpack surface)


Model Lower Boundaryseaice



SEA ICE Model in GFSSEA ICE Model in GFS

Xingren WuEMC/NCEP and IMSG


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


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


Ice Model: Thermodynamics

Based on the principle of the conservation of energy, determine:

• Ice formation• Ice growth• Ice melting• Ice temperature structure


• 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.

Sea Icein the NCEP Global Forecast System


3-layer3-layerthermodynamicsthermodynamics

Ice modelIce model

SWHeat Flux

LWHeat Flux

TurbulentHeat Flux

OceanicHeat Flux

Salinity Fresh Water

Atmospheric modelAtmospheric model

Ocean modelOcean model

IceTemperature

SurfaceTemperature

Ice/SnowThickness

IceFraction

SnowRate

IceTemperature

surfaceTemperature

Ice/SnowThickness

IceFraction

Sea Ice in the NCEP GFS (cont.)

hH

U

d

model grid point

l(z)

Top View

l(z)

a

b

Fig 1. Representation of the low-level flow above andbelow the dividing streamline.


U h N z H z dzdh

H

d

2 2

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

Hijhxi

hxj


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):

D z C l z U Ud d / 2

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‘,

Where the geographical orientation of the orography minus the low level wind vector direction angle,


l z r m ax / ,2 1 0

2 h

h z

z hd

' 'm ax cos , sin

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‘/and a/b = 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.04/19/23 94Shrinivas Moorthi

5


global forecast system (gfs ) 8/27/20151shrinivas moorthi

Documents

shrinivas moorthi slide

global forecast system

ncep global spectral

gfs implementation

term gfs

shrinivas moorthi3

global analysis

vertical advection of