1

Part 2 CFS: Where It’s Going

S. Lord, H-L Pan, S. Saha, D. Behringer, K. Mitchell

And the NCEP (EMC-CPC CFSRR Team)

2

Overview• Current (CFS-v1) description and status• CFS Reanalysis and Reforecast (CFSRR CFS-v2)

– Atmosphere– Ocean– Land surface – Sea ice

• Future development (CFS-v3)– Coupled A-O-L-S system– Long term Reanalysis strategy

• Possibilities for Multi-Model Ensembles (MMEs)• Weather-Climate forecasting

3

Seasonal to Interannual Prediction at NCEPOperational System since August 2004 (CFS-v1)

ClimateForecastSystem(CFS)

Ocean ModelMOMv3

quasi-global1ox1o (1/3o in tropics)

40 levels

Atmospheric ModelGFS (2003)

T62 (~200 km)64 levels

GODAS (2003)3DVAR

XBTTAOTritonPirataArgo

Salinity (syn.)TOPEX/Jason-1

Reanalysis-23DVART62L28

OIv2 SSTLevitus SSS clim.

Ocean reanalysis (1980-present) provides initial conditions for retrospective

CFS forecasts used for calibration and research

Stand-alone version with a 14-day lagupdated routinely

DailyCoupling

“Weather& Climate”

Model

Funded by NCPO/OCO

4

Number of Temperature Observations per Month as a Function of Depth

D. Behringer

5

1. High resolution data assimilation – Produces better initial conditions for operational hindcasts and

forecasts (e.g. MJO)– Enables new products for the monthly forecast system– Enables additional hindcast research

2. Coupled data assimilation– Reduces “coupling shock”– Improves spin up character of the forecasts

3. Consistent analysis-reanalysis and forecast-reforecast for – Improved calibration and skill estimates

4. Provide basis for a future coupled A-O-L-S forecast system running operationally at NCEP (1 day to 1 year)– (currently in parallel testing for “GFS” 1-14 day prediction)

CFS-v2 Highlights

Funded by NCPO/CDEP

6

CFSRR Components• Reanalysis

– 31-year period (1979-2009 and continued in NCEP ops) – Atmosphere– Ocean– Land– Seaice– Coupled system (A-O-L-S) provides background for analysis – Produces consistent initial conditions for climate and weather

forecasts

• Reforecast – 28-year period (1982-2009 and continued in NCEP ops )– Provides stable calibration and skill estimates for new operational

seasonal system

• Includes upgrades for A-O-L-S developed since CFS originally implemented in 2004– Upgrades developed and tested for both climate and weather

prediction– “Unified weather-climate” strategy (1 day to 1 year)

7

CFSRR Component UpgradesComponent Ops CFS 2010 CFSAtmosphere 1995 (R2) model

200 km/28 sigma levels

2008 model (upgrades to all physics)

38 km/64 sigma-pressure levels

Enthalpy-based thermodynamics

Variable CO2 (historical data, future scenarios)

R2 analysis

Satellite retrievals

GSI with simplified 4d-var (FOTO)

Radiances with bias-corrected spinup

Ocean MOM-3

60N – 65 S

1/3 x 1 deg.

MOM-4

Global domain

¼ x ½ deg.

Coupled sea ice forecast model

Ocean data assim.

750 m depth 2000 m

Land No separate land property analysis

Global Land Data Assim. Sys (GLDAS) driven by observed precipitation

1995 land model (2 levels) 2008 Noah model

Sea ice Daily analysis Daily hires analysis

Coupling None Fully coupled background forecast (same as free forecast)

8

00Z GDAS 06Z GDAS 18Z GSI

0Z GODAS

00Z GSI

One-day schematic of four 6-hourly cycles of CFSRR Global Reanalysis:

6Z GODAS 12Z GODAS 0Z GODAS

12Z GDAS

18Z GODAS

Atmospheric Analysis

Ocean Analysis

12Z GLDAS6Z GLDAS 18Z GLDAS 0Z GLDAS0Z GLDASLand Analysis

TimeS. Saha and S. Moorthi

9

Testing with CMIP Runs (variable CO2)

OBS is CPC Analysis (Fan and van den Dool, 2008) CTRL is CMIP run with 1988 CO2 settings (no variations in CO2, current operations) CO2 run is the ensemble mean of 3 NCEP CFS runs in CMIP mode

– realistic CO2 and aerosols in both troposphere and stratosphereProcessing: 25-month running mean applied to the time series of anomalies (deviations

from their own climatologies)

10

CFSRR at NCEP

GODAS3DVAR

Ocean ModelMOMv4

fully global1/2ox1/2o (1/4o in tropics)

40 levels

Atmospheric ModelGFS (2007)

T382 64 levels

Land Model Ice ModelLDAS

GDASGSI

6hr

24hr

6hr

Ice Ext6hr

Climate Forecast System V2

11

Future Development

• What’s going on and what’s needed– Land surface– Ocean & Sea ice– Atmosphere

12

Noah LSM replaces OSU LSM in new CFS

• Noah LSM– 4 soil layers (10, 30, 60, 100 cm)– Frozen soil physics included– Surface fluxes weighted by snow

cover fraction– Improved seasonal cycle of

vegetation cover– Spatially varying root depth– Runoff and infiltration account for

sub-grid variability in precipitation & soil moisture

– Improved soil & snow thermal conductivity

– Higher canopy resistance– More

• OSU LSM– 2 soil layers (10, 190 cm)– No frozen soil physics– Surface fluxes not weighted by

snow fraction– Vegetation fraction never less than

50 percent– Spatially constant root depth– Runoff & infiltration do not account

for subgrid variability of precipitation & soil moisture

– Poor soil and snow thermal conductivity, especially for thin snowpack and moist soils

Noah LSM replaced OSU LSM in operational NCEP medium-rangeGlobal Forecast System (GFS) in late May 2005

Some Noah LSM upgrades & assessments were result of collaborations with CPPA PIs

Funded by NCPO/CPPA K. Mitchell

13

CFSRR Reanalysis Land Component: Global Land Data Assimilation System (GLDAS)

• Applies same Noah LSM as in new CFS

• Uses same native grid (T382 Gaussian) as CFSRR atmospheric analysis

• Applies CFSRR atmospheric analysis forcing (except for precip)– hourly from previous 24-hours of atmospheric analysis– Precipitation forcing is from CPC analyses of observed precipitation

• Model precipitation is blended in only at very high latitudes

• GLDAS daily update of the CFSRR reanalysis soil moisture states– Reprocesses last 6-7 days to capture and apply most recent CPC precipitation

analyses

• Realtime GLDAS configuration will match reanalysis configuration– To sustain the relevance of the climatology of the retrospective reanalysis

• Applies LIS: uses the computational infrastructure of the NASA Land Information System (LIS), which is highly parallelized

14

LIS Capabilities• Flexible choice of 7 different land models

– Includes Noah LSM used operationally by NCEP and AFWA• Flexible domain and grid choice

– Global: such as NCEP global model Gaussian grid– Regional: including very high resolution (~.1-1 km)

• Data Assimilation– Based on Kalman Filter approaches

• High performance parallel computing– Scales efficiently across multiple CPUs

• Interoperable and portable– Executes on several computational platforms– NCEP and AFWA computers included

• Being coupled to NWP & CRTM radiative transfer models– Coupling to WRF model has been demonstrated– Coupling to NCEP global GFS model is under development– Coupling to JCSDA CRTM radiative transfer model is nearing completion

• Next-gen AFWA AGRMET model will utilize LIS with Noah• NCEP’s Global Land Data Assimilation utilizes LIS

K. Mitchell, C. Peters-Lidard

15

Impact of Noah vs. OSU Land Models and GLDAS Initial Land States in 25-years of CFS Summer & Winter Reforecasts:

Lessons Learned

• Land surface model (LSM) for CFS forecast must be same as for supporting land data assimilation system (LDAS)

• Impact of land surface upgrade on CFS seasonal precipitation forecast skill for is positive (but modest)– Significant only for summer season in neutral ENSO years (and

then only small positive impact)– Essentially neutral impact for winter season and non-neutral

ENSO summers

• Differences in CFS precipitation skill over CONUS between neutral and non-neutral ENSO years exceeds skill differences between two different land configurations for same sample of years– Indicates that impact of SST anomaly is substantially greater

than impact of land surface configuration

16

2009+ Land Surface Model Development

1 - Unify all NCEP model land components to use MODIS-based hi-res global land use with IGBP classes

2 - Improve global fields of land surface characteristics (vegetation cover, albedo,

emissivity) using satellite data (with Joint Center for Satellite Data Assimilation)

3 - Enhance land surface subgrid-variability with high-resolution sub-grid tiles

4 - Increase  number of soil layers (from 4 to about 10)

5 - Introduce dynamic seasonality of vegetation (to replace pre-specified seasonal cycle)

6 - Improve hydrology including addition of groundwater 7 - Add multi-layer treatment to snowpack physics 8 - Introduce carbon fluxes

Items 5-8 are being transitioned from the CPPA-funded work of PI Prof Z.-L. Yang and Dr. G.-Y. Niu of U.Texas/Austin

K. Mitchell

17

• Operational in 2010• MOMv4 (1/2o x 1/2o, 1/4o in the tropics, 40 levels) • Updated 3DVAR assimilation scheme

– Temperature profiles (XBT, Argo, TAO, TRITON, PIRATA)– Synthetic salinity profiles derived from seasonal T-S relationship – TOPEX/Jason-1 Altimetry– Data window is asymmetrical extending from 10-days before the

analysis date– Surface temperature relaxation to (or assimilation of) Reynolds

new daily, 1/4o OIv2 SST– Surface salinity relaxation Levitus climatological SSS– Coupled atmosphere-ocean background

• Current stand-alone operational GODAS will be upgraded in 2009 to the higher resolution MOMv4 and be available for comparison with the coupled version– Updated with new techniques and observations

GODAS in the CFSRR

D. Behringer

18

Assimilating Argo Salinity

ADCP GODAS GODAS-A/S

In the east, assimilating Argo salinity reduces the bias at the surface and sharpens the profile below the thermocline at 110oW.

In the west, assimilating Argo salinity corrects the bias at the surface and the depth of the undercurrent core and captures the complex structure at 165oE.

Comparison with independent ADCP currents.

D. Behringer

19

2009+ GODAS Activities• Complete CFSRR

– Evaluate ODA results• Add ARGO salinity• Improve climatological T-S relationships and synthetic salinity

formulation• ENVISAT data?• Improve use of surface observations

– Vertical correlations (mixed layer)• Situation-dependent error covariances (recursive filter formulation)• Investigate advanced ODA techniques

– Experimental Ensemble Data Assimilation system (with GFDL)– Reduced Kalman filtering (with JPL)– Improved observation representativeness errors (with Bob Miller, OSU-

JCSDA)• Impact of the GODAS mixed layer analysis on subseasonal

forecasting with the CFS. Augustin Vintzileos (EMC)

D. Behringer

20

Comparison of GODAS/KF and GODAS/3DVARwith TAO temperature and zonal velocity

anomaliesRe = [model explained variance] / [data variance]

20oC DynHtSST U

3DVAR - A

KF

3DVAR - B

KF

SST 20oC DynHt U

in collaboration with I Fukumori (JPL)

For points toward the top (GKF) and toward the right (G3DV) the models are closer to the data. For points above (below) the diagonal GKF (G3DV) is closer to the data.

21

Sea Ice Analysis from CFSRR

R. Grumbine

22

Atmospheric Model

• Improve CFS climatology and predictive skill with improved physical parameterizations

– Deep and/or shallow convection – Cloud/radiation/aerosol interaction and

feedback– Boundary layer processes – Orographic forcing – Gravity wave drag– Stochastic forcing– Cryosphere

23

Shallow Cloud Development

H.-L. Pan and J. Han

• Use a bulk mass-flux parameterization

• Based on the simplified Arakawa-Shubert (SAS) deep convection scheme, which is being operationally used in the NCEP GFS model

• Separation of deep and shallow convection is determined by cloud depth (currently 150 mb)

• Main difference between deep and shallow convection is specification of entrainment and detrainment rates

• Only precipitating updraft in shallow convection scheme is considered; downdraft is ignored

24

Siebesma & Cuijpers (1995, JAS)

Siebesma et al. (2003, JAS)

LES studies

Development based on LES studies

25

ISCCP

Control

Revised PBL & new shallow convection

Cloud cover improved

CombinedImpact ofRevised

PBL & New ShallowConvection

For CFS

J. Han

26

Revised PBL + New shallow (Winter 2007)

NH(20N-80N) SH(20S-80S)

500 hPa Height Anomaly Correlation

Skill scores are better (1)

27

12-36 hrs 36-60 hrs 60-84 hrs

CONUS Precipitation skill score Winter 2007

Skill scores are better (2)

28

Revised PBL + New shallow (Summer 2005)

NH(20N-80N) SH(20S-80S)

500 hPa Height Anomaly Correlation

Skill scores are better (3)

29

12-36 hrs 36-60 hrs 60-84 hrs

CONUS Precipitation skill score Summer 2005

Skill scores are possibly better (4)

30

ENSO SignalObserved SST Anomaly Nino 3.4 OIV2 Control SST Anomaly

50 year CMIP Run

ENSO too weak (early)Too strong later

RESULT: no implementation forWeather or climate

31

Observed DSWR from Visiting Scientist (Mechoso

– UCLA, CPPA sponsored through VOCALS)

Downward Shortwave Radiation at Ground2S-2N Annual Mean 50 Year Run

Observed

Year 1-20 Year 21-50Control Year 1-20 Experiment Year 21-50

Clouds TooThick in SE

Pacific(DSWR too small)

Can be Improved

With Shallow-DeepCloud Tuning

32

Phase (local time) of Maximum Precipitation (24-hour cycle)

Five-member ensembles driven by Climatological SST forcing (1983-2002 avg)

Myong-In Lee and Sieg Schubert (NASA/GMAO)

33

Impact of Diurnal SST (Xu Li)

Figure.2 Impact of NSST model on GFS predictive skill (OCN – CTL). January 2008 (31 x 4 samples)

850 hPa Wind Tropics

Forecast days

AC

x 1

00

Figure.1 The difference between two SST analysis (GSI – OPR)

GSI: Tr analysis + NSST model (7-day mean, Aug. 24~30, 2007)

OPR: Reynolds weekly analysis (Aug. 30, 2007)

34

T254T126T62

GDAS

CDAS-2

RMS Error Growth

Pattern Correlation

Resolution does not affect skill.

Forecasts initialized by GDAS are better (a gain of ~3-5 days).

Time evolution of mean energy at wave numbers 10-40 when CFS is initialized by R2 (red) or by GDAS (blue).

drift

Tropical Intraseasonal Forecasts (MJO)

A. Vintzileos

35

Ongoing Reanalysis Project

• CFS will be upgraded every ~7 years– New forecast system

• Upgrades from operations• New techniques• Higher resolution analysis• Aerosol and trace gas analysis• Carbon cycle• Hydrology, ground water, etc.

– New observations from data mining– Satellite data treatment (e.g. bias correction)

• Evolution to Integrated Earth System Analysis• Ongoing work to incorporate these improvements

– Preparation for Reanalysis production phase– All additions carefully tested

36

Proposed Concept of Operations

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Eff

ort

(%

)

1 2 3 4 5 6 7 8 9

year

Reanalysis Development and Production

Development

ProducionProductionPhase

2-3 years

DevelopmentPhase

3-4 years

37

Future Model Component UpgradesComponent 2010 CFS Possible Upgrades

Atmosphere - AER RRTM shortwave & longwave radiation

- Variable CO2 & aerosols

- Maximum random cloud overlap

Enthalpy-based thermodynamics

- Fractional cloudiness (impacts surface solar flux)

- Possible neural network emulation for radiation (trained on hindcasts)

- Sigma-pressure-theta hybrid

- Prognostic cloud water

- Non-local PBL

- Simplified Arakawa-Schubert conv.

- Ferrier microphysics (impacts radiation and precipitation type)

- Shallow convection (mass flux)

- Convective gravity wave

- Conservative, positive definite tracer advection

Land - Global Land Data Assim. Sys (GLDAS) driven by observed precipitation

- Dynamic vegetation (impacts drought)

- Groundwater (impacts soil wetness)

Ocean - MOM-4 -Ocean ensemble (HYCOM + MOM ?)-Salinity assimilation

- Situation-dependent background errors and other advanced techniques

Comprehensive Testing in Weather and Climate Modes• Daily data assimilation and 15 day forecasts• LDAS for balanced land states• CMIP runs (> 50 years)• Sample seasonal runs (May & October)

38

Multi-Model Ensemble Strategy

• International MME (IMME) with EUROSIP is under negotiation – Operational delivery– Consolidated products– Use for official duty only

• Full set of hindcasts required for bias correction and skill masking

• National MME– COLA is generating hindcasts for NCAR system– Issues are

• developing concept of operations (how partners will participate)

• identifying metrics for value added (e.g. consolidation)• building computing resources (particularly for reforecasts)

into computer acquisitions

39

IMME Status (1)

• Goal: produce operational ensemble products from CFS and EUROSIP seasonal climate products

• EUROSIP– ECMWF– Met Office– Meteo France

• Prospectus has been submitted to EUROSIP Counsel– Covers

• Licensing• Commercial interest and revenue sharing

– Consistent with EUROSIP general provisions

• Formal Memorandum of Understanding will be drafted

40

IMME Status (2)• Some tenets of a potential agreement

– E-partners and NCEP will be free to process individual forecasts into combined IMME products with their own procedures

– NCEP will distribute its combined IMME product to its internal users for official duty use in time to meet NCEP forecast schedules

– NCEP will distribute its combined products to the E-Partners as soon as possible each month, using ECMWF as the distributing agent

– NCEP and E-partners will coordinate distribution of IMME products to their users on a regular monthly schedule

– Product delivery will not compromise any organization’s operational delivery schedules and commitments

– NCEP wishes to join the EUROSIP Steering Group as a non-voting member and will participate in future meetings

41

Weather-Climate Forecasting

42

NCEP Production SuiteWeather, Ocean & Climate Forecast Systems

Version 3.0 April 9, 2004

0

20

40

60

80

100

0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00

6 Hour Cycle: Four Times/Day

Pe

rce

nt

Us

ed

RUCFIREWXWAVESHUR/HRWGFSfcstGFSanalGFSensETAfcstETAanalSREFAir QualityOCEANMonthlySeasonal

GD

AS

GF

S an

al

NA

M an

al

CFS

RTOFS

SR

EF NAM

AQ

GFSHUR

RD

AS

Current (2007)

GENS/NAEFS

Current - 2007

NCEP Production SuiteWeather, Ocean, Land & Climate Forecast Systems

43

Global Model SuiteDaily to S/I Forecasts

• All forecasts are Atmosphere-Land-Ocean coupled• All systems are ensemble-based except daily, high-resolution run• All forecasts initialized with LDAS, GODAS, GSI from GFS initial

conditions• Physics and dynamics packages may vary

– Anticipated that the weekly forecast will have most rapid implementations and code changes, seasonal configuration may be one (or at most two) versions behind weekly

ForecastProduct

Membershiprefresh period

Runs/day Number ofmembers perrefresh period

Horizontalresolution

(ratio,current value)

ForecastLength

Initializationtechnique

ComputingResource

ratio

Daily-hires 4x/day 4 1 1.0, T382 15 days GSI 1.0

Weekly daily 80 80 0.5, T170 15 days ET breeding 2.5

Monthly weekly 8 56 0.5, T170 60 days ?? 1.0

Seasonal monthly 2 60 0.33, T126 1 year Lagged analysis4x daily

0.44

44

CFS & MFS

NCEP Production SuiteWeather, Ocean & Climate Forecast Systems

Version 3.0 April 9, 2004

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cent

Use

d

RUCFIREWXWAVESHUR/HRWGFSfcstGFSanalGFSensETAfcstETAanalSREFAir QualityOCEANMonthlySeasonal

CFSMFS

Regional

Rap Refresh

Global

Hydro

Next Generation Prototype

AQ

WAVGFS

SREFReforecast

NAM

RTOFS

RT

OF

S

CFS & MFS & GODASAQ Hydro / NIDIS/FF

GENS/NAEFS

HU

R

HENS

NCEP Production SuiteWeather, Ocean, Land & Climate Forecast Systems

GDAS

RDAS

45

Summary• CFSRR CFS-v2

– High resolution reanalysis– CO2 trend– Upgrades models and data assimilation– Foundation for coupled “earth-system” reanalysis

• Beginning scientific development of CFS-v3– Fully coupled A-O-L-S system for IESA– Advanced data assimilation techniques

• Building a MME system with International and US contributions

• Focusing on Weather-Climate forecasting – 1 day to 3 years

46

ThanksQuestions?

47

Comparison of GODAS/M4 and GODAS/M3with TAO temperature and zonal velocity

In the thermocline both GM4 and GM3 are warm at 140w, while GM4 is warm and GM3 is cold at 110w.

The undercurrent is stronger than observed in GM4 and weaker in GM3. The vertical structure at 165e is better in GM4 than in GM3.

48

Land Information System (LIS)• NOAA-NASA-USAF collaboration

– K. Mitchell (NOAA)– C. Peters-Lidard (NASA)– J. Eylander (USAF)

• LIS hosts – Land surface models– Land surface data assimilationand provides– Regional or global land surface conditions for use

in • Coupled NWP models• Stand-alone land surface applications

K. Mitchell, C. Peters-Lidard

49

Science Plan for the CFS (II)

• Most effective way to improve the CFS (climate) GFS/CFS (weather) as one package

• We want to improve weather and climate forecasts by making physically based improvements to the atmospheric model parameterization packages.

• We have been successful when we apply rigorous tests to physically based parameterization improvements to both weather and climate models and want to continue along this way.

50

Science plan for the CFS (III)

• Deep and/or shallow convection These processes transport sub-grid scale heat and moisture vertically, which is especially important for climate prediction.

• Boundary layer processes As the CFS is a coupled model, the boundary layer is critical for communication of the ocean and land conditions with the atmosphere.

• Cloud/radiation/aerosol interaction and feedbackClouds and aerosols modulate the sources and sinks of the thermal energy in to the earth system. This interaction is crucial on climate time scales.

• Orographic forcing Orography determines many climate variables through form-drag, mountain blocking, and land/sea contrast.

51

Science Plan for the CFS (IV)

• Gravity wave dragGravity waves generated by the sub-grid scale orography and/or cumulus convection transport wave energy from the troposphere to the stratosphere and mesosphere and thus control the climate of those regions.

• Stochastic forcingStochastic forcing is not in the CFS at this time, but is important for parameterizing random, unresolved physical forcing.

• CryosphereThe cryosphere (glaciers, frozen land, sea ice) plays a crucial role in determining the earth's climate. Modeling of sea-ice and its interaction with the ocean and atmosphere, and modeling frozen land and its interaction with the atmosphere are all important to climate.

52

Science Plan for the CFS (V)

• Testing procedures are key to the road to making model implementations

• While transition to operation for MMEs requires only seasonal hindcasts to be evaluated, it is done because we expect the team maintaining the MME models to do their own rigorous tests.

• Tests in data assimilation modes and evaluated with forecasts are crucial for weather forecasts.

• Tests in multi-year coupled simulations and seasonal hindcasts are crucial for climate forecasts

• CTB computer resource is not sufficient and NCEP computer must be used when full-scale testing is needed

53

Gaps

• Insufficient EMC staff to collaborate with external investigators, train their staff (often post-docs) on use of the CFS, and develop new parameterization codes suitable for the CFS for the broad spectrum of possible areas listed above (O2R);

• Insufficient computing resources for experimentation and transition changes to the CFS;

• Insufficient EMC and NCEP Central Operations (NCO) staff to support the R2O (implementation) process;

• Insufficient knowledge within the research community about the tests needed to complete an implementation

54

We built a new shallow convection scheme a few years ago

• Use a bulk mass-flux parameterization

• Based on the simplified Arakawa-Shubert (SAS) deep convection scheme, which is being operationally used in the NCEP GFS model

• Separation of deep and shallow convection is determined by cloud depth (currently 150 mb)

• Main difference between deep and shallow convection is specification of entrainment and detrainment rates

• Only precipitating updraft in shallow convection scheme is considered; downdraft is ignored

55

Siebesma & Cuijpers (1995, JAS)

Siebesma et al. (2003, JAS)

LES studies

We build it based on LES studies

56

Cloud water cross-section looks better

57

PBL & Low clouds combined

(CFS run)

ISCCP

Control

Revised PBL & new shallow convection

Cloud cover looks better

58

Revised PBL + New shallow (Winter, 2007)

NH(20N-80N) SH(20S-80S)

500 hPa Height Anomaly Correlation

Skill scores were better

59

12-36 hrs 36-60 hrs 60-84 hrs

Precipitation skill score over US continent

Skill scores were better

60

Revised PBL + New shallow (Summer, 2005)

NH(20N-80N) SH(20S-80S)

500 hPa Height Anomaly Correlation

Skill scores were better

61

12-36 hrs 36-60 hrs 60-84 hrs

Precipitation skill score over US continent

Skill scores were slightly better

62

NINO3.4 OIV2

Observed ENSO signal

63

NINO3.4 set22

Multi-year simulation of the control looks ok

64

NINO3.4 set28b

The test version showed too weak ENSO in early years and too strong ENSO in later years. RESULTS : no implementation

65

With a VOCALS grant from CPPA, Mechoso worked with us to examine these runs. This is the downward short wave radiation reaching ground for the control

Srb2 is observation

66

There is too much radiation reaching ground for the new package over western Pacific but too little over central Pacific. More changes will have to be made.

67

Cloud water cross-section looks better

68

Climate Requirements for NCEP’s Next Operational System (2011)

Application Operational System Computing

X factor

Requirement Generator

Seasonal-Monthly Climate

CFS 32 Climate Prediction Center

Monthly fcst system 10+ “

GLDAS, NLDAS 2.5* NIDIS, CPC

Reforecast 9* “

• Ratio of ops:R2O computing– Currently 1:1.3– Requesting

• 2011: 1:2.0• 2013: 1:3.0• 2015: 1:4.0

+ Extension of Week2 system* New system

Climate (and other) computing requirementstotal a factor of 3X in additional funding

(above Moore’s Law – constant $$ capability)

69

NOAA Computing Resources andOperational Requirements for Climate Forecasting at NCEP

• Research– Including CFSRR

• Operations

70

Climate R&D Computing for Week2 to S/I

November 2007 - June 2008

15

52

33

CTB & CDEV

CFSRR

JCSDA&MTB

NOAA R&D Computer through November 2007

30

10

30

30

CTB

CDEV

JCSDA

MTB

June 2008+New Power6 system for CTB, CDEV, JCSDA, MTB (same % as previous)

CFSRR will use all Power5 system

Enables CFSRR to execute ¾ of required

production rate