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Atmospheric Model for the Climate Forecast System Reanalysis and Retrospective Forecasts. Shrinivas Moorthi. Thanks to. Glenn White who prepared several slides in this presentation and YuTai Hou who prepared the slides related to radiation parameterization. GFS AM. - PowerPoint PPT Presentation
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Nov 7, 2007 Shrinivas Moorthi 1
Atmospheric Model for the Climate Forecast System Reanalysis and
Retrospective Forecasts
Shrinivas Moorthi
Nov 7, 2007 Shrinivas Moorthi 2
Thanks to
Glenn White who prepared several slides in this presentation
and
YuTai Hou who prepared the slides related to radiation parameterization
Nov 7, 2007 Shrinivas Moorthi 3
GFS AM
• Latest version of Global Forecast System (GFS) Atmospheric Model (AM) is being considered for CFSRR.
• GFS AM - developed by the staff of Global Climate and Weather Modeling Branch of EMC.
• The first reanalysis (NCEP/NCAR – R1) was based on the operational GFS AM of January 1995.
• GFS AM has undergone major revisions since the first reanalysis.
Nov 7, 2007 Shrinivas Moorthi 4
CDAS (R1) GFS AM (OPR)
Vertical coordinate Sigma Sigma/pressure
Spectral resolution T62 T382
Horizontal resolution ~210 km ~35 km
Vertical layers 28 64
Top level pressure ~3 hPa 0.266 hPa
Layers above 100 hPa ~7 ~24
Layers below 850 hPa ~6 ~13
Lowest layer thickness ~40 m ~20 m
Analysis scheme SSI GSI
Satellite data NESDIS temperature retrievals
Radiances
Nov 7, 2007 Shrinivas Moorthi 5
GFS AM improvements through reanalysis
• Some specific problems found in NCEP/NCAR reanalysis, addressed in later AM changes
• -- valley snow• -- wrong snow cover• -- wrong ocean albedo• -- SH paobs mislocated• -- ”pathological” problems in stratosphere
• New reanalysis will find problems in GFS that will be addressed and produce improved GFS, improved future reanalysis and improved future CFS
• We’ll keep doing it until we get it right(Glenn White)
Nov 7, 2007 Shrinivas Moorthi 6
Comparison between AMs in R1, CFS (opr) and GFS (opr)R1 (T62L28) OPR CFS AM (T126L64) OPR GFS AM (T382L64)
SAS Convection SAS Convection with momentum mixing
SAS Convection with momentum mixing
Tiedtke Shallow convection Tiedtke Shallow convection Tiedtke Shallow convection
Seasonal/zonal mean Ozone Prognostic Ozone Prognostic Ozone
OSU LSM (2 layers) OSU LSM (2 layers) Noah LSM (4 layers) and sea ice model
Diagnostic clouds Prognostic cloud condensate Prognostic cloud condensate
Boundary layer Nonlocal Boundary layer Non local boundary layer
Graviry wave drag Gravity wave drag GWD with Mountain Blocking
GFDL IR radiation
Random overlap
GFDL IR radiation
Random overlap
RRTM IR radiation
Max/random overlap
NCEP SW -93 radiation
(Chou ) Random overlap
NCEP SW -95 radiation (Chou) Random overlap
NCEP SW radiation (Chou)
Random overlap
Vertical diffusion Vertical diffusion Vertical diffusion with reduced background diffusion
2nd order horz diffusion 2nd order horz diffusion 6th order horz diffusion
Virtual Temperature Virtual Temperature Virtual Temperature
Nov 7, 2007 Shrinivas Moorthi 7
Operational CFS GFDL-LW Radiationvs. RRTM-LW Radiation
GFDL RRTM
Description: - 15 bands 16 bands- trans table look-up 140 cor-k terms- O3,H2O,CO2 O3,H2O,CO2,O2,CH4
CO, 4 CFCs
Advantages/ - comp efficient more comp efficientDisadvantages: - no aerosols effect aerosol effect capable
- fixed CO2 only varying CO2 capable- fixed sfc emis varying emis capable- random cld ovlp random or max-ran- larger errors, especially improved accuracy at upper stratosphere, at upper stratosphere- simple cloud optical prop advanced cloud optical
property property
Nov 7, 2007 Shrinivas Moorthi 8
Clear sky LW cooling comparison for tropical,mid-latitude and subarctic winter profiles
Nov 7, 2007 Shrinivas Moorthi 9
Cloudy sky LW cooling comparison for tropical,mid-latitude and subarctic winter profiles
Nov 7, 2007 Shrinivas Moorthi 10
The current operational GFS AM has Realistic moisture prediction with better depiction of no-rain areas Prognostic Ozone
Prognostic cloud condensate Cloud cover only where cloud condensate > 0 Momentum mixing in deep convection Fast and accurate AER RRTM for IR radiation Mountain blocking parameterization Noah land model Sea-ice model Improved treatment of snow, ice, orography Better hurricane track prediction ESMF based modern computer algorithms
Nov 7, 2007 Shrinivas Moorthi 11
Options in GFS AM being considered for next operational model
• Enthalpy (CpT) as a prognostic variable in place of Tv
• AER RRTM shortwave radiation with maximum-random cloud overlap
• IR and Solar radiation called every hour (Until now IR is called every 3 hours)
• Use of historical and spatially varying CO2 and volcanic aerosols
Nov 7, 2007 Shrinivas Moorthi 12
Why Enthalpy as a prognostic variable?
Collaboration between Space Environmental Center and
EMC to develop whole atmosphere model (0-600km) to be
coupled to global ionosphere plasmasphere model
More accurate thermodynamic equation is essential since top/sfc ~ 10-13
Variation of specific heats in space and time needs to be
accounted for
Nov 7, 2007 Shrinivas Moorthi 13
The thermodynamic equation used in the operational GFS AM has the form
Tv 1 Rv /Rd 1 q T
with ideal-gas law in the form
p RdTv
dTv
dt
Tv
p
dp
dtQ
where
Rd
CP
Rd
CPd CPv CPd q
d
1 CPv /CPd 1 q
Here Rd and Rv are gas constants for dry air and water vapor and Cpd, Cpv are specific heats at constant pressure for dry air and water vapor.
Nov 7, 2007 Shrinivas Moorthi 14
The ideal-gas law is
Qdt
dp
dt
TdC p
and defining enthalpy h as TCh p
the thermodynamic energy equation can be re-written as
dh
dt
h
p
dp
dtQ
RTp
The thermodynamic equation, derived from internal energy equation is (Akmaev, 2006 – Space Environmental Center)
which has the same form as operational one
Qdt
dp
p
T
dt
dT vv
Nov 7, 2007 Shrinivas Moorthi 15
However, here R and Cp are determined by their specific mixing ratios
Ntrac
iiid
Ntrac
iiii qRRqqRR
11
)1(
Ntrac
iiipdp
Ntrac
iiipp qCCqqCC
i11
)1(
Currently, GFS AM has three tracers – specific humidity, ozone and cloud water. Ignoring cloud water,We use : dry air sp. Hum ozone
Ri 287.05 461.50 173.2247Cpi 1004.6 1846.0 820.2391
Henry Juang of EMC implemented Enthalpy in the GFS AM
Nov 7, 2007 Shrinivas Moorthi 16
NCEP Operational SW Radiationvs. New RRTM SW Radiation
NCEP RRTM
Description: - 8 uv+vis, 1-nir 5 uv+vis, 9-nir bnds- 38 k-dis terms 112 cor-k terms- O3,H2O,CO2,O2 O3,H2O,CO2,O2,CH4
Advantages: - Comp. Efficient Accu. (use ARM’s data) clr-sky - 10-30 w/m2
reduction cld-sky - adv. scheme
Disadvantages: - large errors Comp. slow, 4 times clear-sky - und est slower than opr sw cloudy-sky - over est
YuTai Hou of EMC implemented RRTM in the GFS AM
Nov 7, 2007 Shrinivas Moorthi 17
Clear sky SW heating comparison for tropical,mid-latitude and subarctic winter profiles
Nov 7, 2007 Shrinivas Moorthi 18
Cloudy sky SW heating comparison for tropical,mid-latitude and subarctic winter profiles
Nov 7, 2007 Shrinivas Moorthi 19
Coupling of GFS to MOM3 (MOM4)
In the operational CFS, AM and OM are coupled daily with AM and OM running sequentially
In the new CFS, the coupling is MPI-level (developed by Dmitry Shenin) – AM, OM and the coupler run
simultaneously
Coupling frequency is flexible up to the OM time step
Same AM code can run in coupled or standalone mode
Coupler details for MOM4 will be presented later in this meeting
Nov 7, 2007 Shrinivas Moorthi 20
RRTM run shows reduced SST warm bias
SST predicted in 50 year coupled simulation (winter)
CTB sponsoredExperiment run byS. Saha and Y. Hou
Nov 7, 2007 Shrinivas Moorthi 21
SST predicted in 50 year coupled simulation (summer)
CTB sponsoredExperiment run byS. Saha and Y. Hou
Nov 7, 2007 Shrinivas Moorthi 22
Jack Woolen and others have spent years improving the data base of conventional observations--much more complete than before--errors better understood
Great deal of experience now with satellite bias corrections
Experienced with changes in observations in last 10 yearsKnowledge is being applied to new reanalysis
GFS produces much more skilled forecasts than CDAS--GFS has proven track record in forecasting hurricane tracks and in seasonal forecasts as CFS, indicating thatGFS produces much more realistic tropical atmosphere than CDAS in both analyses and forecasts
Nov 7, 2007 Shrinivas Moorthi 23
(unusually good month for GFS vs. ECMWF)
GFS has useful skill 1.5 days longer than CDAS
(Fang-Lin Yang)
September 2007
No. Hemisphere500 hPa heightAnomaly correlation
Nov 7, 2007 Shrinivas Moorthi 24
October 2007
GFS has useful skill 1 day longer than CDAS
Nov 7, 2007 Shrinivas Moorthi 25
September 2007
Southern Hemisphere
GFS has useful skill more than 1 day longer than CDAS
Nov 7, 2007 Shrinivas Moorthi 26
OPI CDAS1 CDAS2 GDAS
Global 2.62 2.97 3.42 3.23
Land 2.11 2.73 2.83 2.72
Ocean 2.84 3.07 3.67 3.45
Precipitation JJA 2007
Nov 7, 2007 Shrinivas Moorthi 27
Nov 7, 2007 Shrinivas Moorthi 28
GDAS has most similar pattern to independent estimate
CDAS 1 and 2 have too much rain over southeast US
Nov 7, 2007 Shrinivas Moorthi 29
Nov 7, 2007 Shrinivas Moorthi 30
CFS Reanalysis and Reforecast Scripts
9 (or 48) hr Coupled ModelForecast (first guess)
New GFS + MOM4 with Sea IceMPI-level Coupling
fcst.sh
Prep stepHurricane relocation
Data preparationprep.sh
GLDASGlobal Land Data Assimi-
lationlanl.sh
GDASGlobal Atmospheric Data Assimilation
GSI anal.sh
GODASGlobal Ocean Data
Assimilationoanl.sh
Run RetrospectiveForecast
fcst.sh
AM and OM Post
post.sh
Start hereCopy IC files
copy.sh
Time 00Z ?
Verifyvrfy.sh
Archivedata
arch.sh
RetrospectiveForecast?
CFSRRwebsite
Nov 7, 2007 Shrinivas Moorthi 31
Nov 7, 2007 Shrinivas Moorthi 32
CDAS1 CDAS2 GDAS K&T Range SRB
sh 24 16-27
lh 85 97 87 78 78-90
dsw 199 180 200 198 185
usw 42 24 25 30 24
nsw 157 156 175 168 142-174 161
dlw 346 350 345 324 348
ulw 405 406 408 390 396
nlw -59 -56 -62 66 40-72 48
netrad 98 100 112 102 99-119 113
nhf -4 -5 9
P 2.97 3.42 3.23 2.69 2.69-3.1
E
JJA07 Annual mean climatology
Nov 7, 2007 Shrinivas Moorthi 33
CDAS1 had wrong ocean albedo, reflected too much short wave
CDAS2 too low sensible heat flux
GDAS too much downward short wave, more net heat flux into ocean than CDAS1 or CDAS2
Nov 7, 2007 Shrinivas Moorthi 34
Nov 7, 2007 Shrinivas Moorthi 35
GDAS has pattern most like Air Force estimate, but has too little stratus clouds in eastern ocean too far displaced from coast
Nov 7, 2007 Shrinivas Moorthi 36
Nov 7, 2007 Shrinivas Moorthi 37
Nov 7, 2007 Shrinivas Moorthi 38
Nov 7, 2007 Shrinivas Moorthi 39
Nov 7, 2007 Shrinivas Moorthi 40
Nov 7, 2007 Shrinivas Moorthi 41
GDAS has less evporation, more sensible heat flux overContinents than CDAS1 or 2
COADS estimate based on little data in Southern HemisphereLatent heat estimate smaller than any of reanalyses—may reflectToo weak COADS (COADS fluxes tend to give net heat flux into Ocean) or too strong hyrdological cycle in reanalyses
Nov 7, 2007 Shrinivas Moorthi 42
Nov 7, 2007 Shrinivas Moorthi 43
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Nov 7, 2007 Shrinivas Moorthi 45
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Nov 7, 2007 Shrinivas Moorthi 48
GDAS has most reasonable pattern of surface short wave radiationBut has too much in tropics
CDAS1 has too high ocean surface albedo
Nov 7, 2007 Shrinivas Moorthi 49
Nov 7, 2007 Shrinivas Moorthi 50
Nov 7, 2007 Shrinivas Moorthi 51
Nov 7, 2007 Shrinivas Moorthi 52
GDAS has more net heat flux into ocean than otherReanalyses
COADS estimate substantially tuned to achieve balance
Nov 7, 2007 Shrinivas Moorthi 53
COADS climatological estimateReanalyses one season
Nov 7, 2007 Shrinivas Moorthi 54
CFS Reanalysis and Reforecast Scripts
9 (or 48) hr Coupled ModelForecast (first guess)
New GFS + MOM4 with Sea IceMPI-level Coupling
fcst.sh
Prep stepHurricane relocation
Data preparationprep.sh
GLDASGlobal Land Data Assimi-
lationlanl.sh
GDASGlobal Atmospheric Data Assimilation
GSI anal.sh
GODASGlobal Ocean Data
Assimilationoanl.sh
Run RetrospectiveForecast
fcst.sh
AM and OM Post
post.sh
Start hereCopy IC files
copy.sh
Time 00Z ?
Verifyvrfy.sh
Archivedata
arch.sh
RetrospectiveForecast?
CFSRRwebsite