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Proposed Title 1 (Invited UC-Irvine Talk):
Prediction and Assimilation Challenges in the Hydrometeorology of the Atmosphere, Land and Ocean: the NCEP Perspective
Title Slide format & content TBD
(say by Laurie)
Proposed Title 2 (Invited UC-Irvine Talk):
Prediction and Assimilation Challenges in Hydrometeorology: The NCEP Perspective
Title Slide format & content TBD
(say by Laurie)
NCEP Prediction and Assimilation Challenges:Outline
• Provide seamless suite of weather/climate prediction models• Maintain upward trend in improving model prediction skill• Offer effective Research-to-Operations science infusion
– common model infrastructures: ESMF
• Commit to Weather-Climate connection in model development– Land surface model development: an example of the weather-climate
connection model development philosophy at NCEP
• Effective assimilation of exploding volume of satellite data– Direct assimilation of satellite radiances– The global surface water balance
• Precipitation spin-up and spin-down from data assimilation
• Participation in field programs and model intercomparisons – Recent examples from NAME, GLACE, CEOP, AMMA
• Periodic Global and Regional Reanalysis– Water and Energy Budget Examples from NCEP Regional Reanalysis– NCEP’s next Global Reanalysis and Climate Forecast System
Laurie/Louis: Is there a more recent or more preferred front-office version of following slide?
YearsYearsYearsYearsWeeksWeeksWeeksWeeksMinutesMinutesMinutesMinutes DaysDaysDaysDaysHoursHoursHoursHours SeasonsSeasonsSeasonsSeasonsMonthsMonthsMonthsMonths
Forecast Forecast UncertaintyUncertaintyForecast Forecast UncertaintyUncertainty
Socio-Economic Benefits ofSeamless Weather/Climate Forecast Suite
Typ
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Warnings & Alert Warnings & Alert CoordinationCoordination
WatchesWatches
ForecastsForecasts
Threat Assessments
GuidanceGuidance
OutlookOutlook
Lead Time
Protection of Protection of Life/PropertyLife/Property
Flood mitigationFlood mitigationNavigationNavigation
TransportationTransportationFire weatherFire weather
HydropowerHydropowerAgricultureAgriculture
EcosystemEcosystemHealthHealth
CommerceCommerceEnergyEnergy
Initi
al C
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Sens
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Boundary Condition
SensitivityReservoir controlReservoir control
RecreationRecreation
Forecast Forecast UncertaintyUncertaintyForecast Forecast UncertaintyUncertainty
MinutesMinutes
HoursHours
DaysDays
1 Week1 Week
2 Week2 Week
MonthsMonths
SeasonsSeasons
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NOAA Seamless Suite of ForecastProducts Spanning Climate and Weather
Weather Prediction Products
Climate PredictionProducts
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Laurie/Louis: Is there a more recent or more preferred front-office version of following slide?
Overview of mainmodeling suites developed by EMC
• CFS: Climate Forecast System– coupled global atmosphere/ocean/land model– uses MOM3 ocean model of GFDL– seasonal to annual climate prediction (1-10 months)
• GFS: Global Forecast System– coupled global atmosphere/land model– medium-range prediction (1-15 days)
• GEFS: Global Ensemble Forecast System– ensemble version of GFS executed at lower resolution (1-15 days)– breeding of ensemble of initial conditions
• NAM: North American Model (NAM)– mesoscale coupled atmosphere/land model (WRF/NMM)– short-range prediction (1-4 days)
• SREF: Short-range Ensemble Forecast System– Multi-model: WRF/NMM, WRF/ARW, Eta, RSM
Also marine models of EMC Marine Modeling Branch and Rapid Update Cycle (RUC) nowcasting suite developed by of NOAA/ESRL.
GFS
CFS
Hurricane WRF
SREF
NAM – WRFNMM
NOAH Land Surface Model
Dispersion
Air Quality
2007 NCEP Production Suite Atmospheric Model Dependencies
Forecast
GlobalData
Assimilation
WRF-NMMWRF-ARWETARSM
L D A S
Sev Wx
WRF-NMMWRF-ARW
NAEFS
NDAS
Rapid Update Cycle
EnsembleHurricane/GFDL
Global
Climate Regional
MOM3
HYCOMOcean
Climate/WeatherLinkage
Forecast Forecast UncertaintyUncertaintyForecast Forecast UncertaintyUncertainty
MinutesMinutes
HoursHours
DaysDays
1 Week1 Week
2 Week2 Week
MonthsMonths
SeasonsSeasons
YearsYears
NOAA Seamless Suite of ForecastProducts Spanning Climate and Weather
North American Ensemble Forecast System
Climate Forecast System
Tra
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Lea
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WatchesWatches
ForecastsForecasts
Threats Assessments
GuidanceGuidance
OutlookOutlook
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Life
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Short-Range Ensemble Forecast Ocean Model
Hurricane Models
Global Forecast System
North American ForecastRapid Update Cycle for Aviation
Dispersion Models for DHS-GFDL -WRF
The NCEP Vision forResearch to Operations (R2O)
and Operations to Research (O2R)
NCEP
R2O
1. Large “volume” of R&D, funded through AOs, Agency Labs…
2. Smaller set of R&D products suitable for operations. 3. Systematic transition steps Research-to-Operations (R2O).
Deliver skill-optimized forecast products founded on CTB-based innovation and& customer
feedback; Bring in customer requests
4. Systematic transition steps Operations-to-Applications (O2A).
5. Delivery of products to the
diverse community and customer
feedback
Applying the “Funnel” to the Transition ProcessApplying the “Funnel” to the Transition Process
2
NCEPis uniquelypositioned
to provide an operational
infra-structure for
the transition processes
User Community
R&D Community
OPERATIONS
1
3
EMCCFS
5
4
O2A
CTB
CPC
CTB role: facilitate transitions for the CPC specific product range (6-10 day, week 2, monthly, seasonal)
O2R
R2O
ESMFEarth System Modeling Framework
500mb 5 Day Global Forecasts
40
45
50
55
60
65
70
75
80
85
90
1980 1990 2000 2010
Year
An
om
aly
Co
rre
lati
on
NH GFS
SH GFS
NH Reanalysis
SH Reanalysis
Impact of Observations and Numerical Forecast System(NFS) Technology Growth on Global Forecasts
Obsonly
NFSTech
Growth+ Obs
NFS TechGrowth:
ComputingData Assim.
ModelsEnsembles
Why ESMF @ NCEP?
• Most of the improvement in weather forecast skill over the past 20 years is due to improving numerical forecast system technology.
• However, national investment in computing and model development in the geophysical sciences is both limited and dispersed.
• Therefore, it behooves us to leverage this investment with a common modeling framework and shared software development.
ESMF Component Framework
Application
ChangeResolution
SurfaceCycling
Atmosphere Postsome other
couplersome other component
ATMDynamics
ATMPhysics
Vertical PostProduct
GeneratorOutput
GRIB/BUFR
ESMF PartnersNSF NCAR
Tim Killeen, PIByron BovilleCecelia DeLucaRoberta JohnsonJohn MichalakesAl Kellie
MITJohn Marshall, PIChris Hill
NASA GMAOArlindo da Silva, PILeonid ZaslavskyWill Sawyer
Max Suarez Michele RieneckerChristian Keppenne
Christa Peters-Lidard
NOAA GFDLAnts LeetmaaV. BalajiRobert HallbergJeff Anderson
NOAA NCEPStephen LordMark IredellMike YoungJohn Derber
DOE Los Alamos National LabPhil Jones
DOE Argonne National LabJay LarsonBarry Smith
University of MichiganQuentin Stout
The Weather-Climate Connection:
The Major Over-arching Theme this year at Annual Meetings of
American Meteorological Society
NCEP is a leader in embracing the weather-climate connection
in the strategy for model development.
Over the past 8-10 years, the Environmental Modeling Center (EMC) of NCEP has embraced and benefited from the conviction that the advancement of the prediction skill of global and regional models is enhanced by the joint development of EMC models for both weather and seasonal climate prediction.
Embracing the weather-climate connection in EMC model development: Examples
• GFS and CFS global modeling suites share unified atmosphere/land model development– New atmosphere or land physics tested & assessed on four time scales:
• 1) short-range• 2) medium-range• 3) seasonal-to-annual• 4) multi-decadal
– Physics changes operationally implemented first in GFS medium-range suite– Upgrades to operational CFS less frequent but do embody recent operational version of GFS
• Growing impetus to extend above “Unified Physics” to EMC short-range mesoscale models– now testing explicit cloud microphysics of mesoscale model in global model– will soon test global model radiation physics in mesoscale model– expect testing soon of unified PBL and surface layer schemes for the mesoscale and global models– desire to unify deep and shallow convection schemes (likely the most difficult unification to achieve)– use same Land Surface Model (LSM) in mesoscale & global models– prospect for unified physics enhanced by similar operational vertical resolution in EMC global and
mesoscale models: of order 60-70 layers and hybrid sigma vertical coordinates in both• Ensemble forecasting
– Emerged first in global modeling for seasonal prediction experiments in 1980s– Later became mainstay for both NCEP operational GFS medium-range and CFS seasonal range– Subsequently also embraced in NCEP short-range prediction systems
• RCMs: Regional Climate Models– EMC has executed and assessed (but not yet operationally implemented) multi-year seasonal RCMs– Eta and RSM RCMs tested thus far, WRF RCM testing in near future
An example saga ofunifying physics across NCEP mesoscale and global models
from weather prediction to seasonal climate prediction:
Noah Land Surface Model
(Noah LSM)
The Noah land surface model is the first EMC physical parameterization package to achieve unification across EMC mesoscale and global models and spanning short-range to seasonal range. Achieving this milestone was a 10-12 year effort and greatly aided by the explicit weather-climate connection vision of the GCIP/GAPP/CPAA Program of the GEWEX (Global Energy and Water Cycle Experiment).
GFS
CFS
HurricaneWRF
SREF
NAM - WRF
NOAH Land Surface Model
Dispersion
Chem WRF*Air Quality
2007 NCEP Production Suite Atmospheric Model Dependencies
Forecast
Rapid Refresh WRF
GGSI
RGSI
WRF-NMMWRF-ARWETARSM
L D A S
Sev Wx
WRF-NMMWRF-ARW
*FY08
GENS
1) Develop/test/assess first on continental-scale basins in mesoscale models, then test in GCMs2) Promote multi-disciplinary approach between meteorologists, hydrologists & remote sensing
NCEP Embraced the R2O and Weather-Climate Connection in its Land Surface Model Development: Following the strategy of the GCIP/GAPP/CPPA Program of GEWEX over the past 10-12 years
WATERAND
ENERGYBUDGETS
IN SITUANDGIS
REMOTESENSING
SATELLITEAND
SURFACE
DATA SOURCES
GENERALCIRCULATION
MODELS
BASIN-SCALEHYDROLOGIC
MODELS
MESOSCALEATMOSPHERIC
MODELS
RETRIEVALS QUALITYASSURANCE
IMPROVEDCOUPLEDCLIMATEMODELS
REGIONALWATER
ASSESSMENT
SITE SPECIFIC NONSITE SPECIFIC TRANSPORTABILITY
FIELD AND ANALYTICAL STUDIES
GCIPDATABASE
MODELDEVELOPMENT
Step 1
Step 2Step 3
Step 4
GEWEX Land Modeling Collaborators of NCEP
Eric WoodJustin Sheffield
Princeton Univ.
Dan Tarpley NESDIS
Soroosh SorooshianJames Shuttleworth
Univ. Arizona
Dennis LettenmaierAndy Wood
Univ. Washington
AFWAJohn Eyelander
Ken MitchellMichael Ek
NCEP/EMC
Rachel PinkerHugo Berbery
Univ. Maryland
Ken CrawfordJeff Basara
Univ. Oklahoma
Alan RobockRutgers Univ.
John SchaakePedro Restrepo
NWS/OHD
Zong-Liang YangUniv. Texas
Christa Peters-LidardBrian Cosgrove
NASA/GSFC
Fei ChenNCAR
Paul DirmeyerCOLA
NOAA/FSLTanya Smirnova
Laurie/Louis: I will add more bullets and details to following slide of Noah LSM features
Features of the NCEP Noah LSM
• Flexible number of soil layers (presently 4)• Includes Seasonal cycle of vegetation cover• Treats sub-grid distribution of precipitation/infiltration• Surface energy and water balance• Comprehensive snowpack treatment• Soil freeze/thaw treatment• Provided as a community model for the research
community by NCEP, NCAR and NASA partnership– 1D column model test bed for external collaborators– 3D test bed for external collaborators
• Any domain from regional to national to global
Noah LSM Testing Hierarchy at NCEP
• 1D uncoupled column model – at individual surface-flux stations from field programs
• 3D uncoupled land model regionally and globally– Joint NCEP-NASA N. American and Global Land Data
Assimilation Systems (NLDAS, GLDAS)• 3D coupled mesoscale model
– Joint NCEP-NCAR Unified Noah LSM for the WRF mesoscale model
• 3D coupled medium-range global model: – NCEP atmosphere/land Global Forecast System (GFS)
• 3D coupled seasonal climate global model:– NCEP coupled atmosphere/ocean/land seasonal-range
global Climate Forecast System (CFS)
Improving soil heat fluxin uncoupled testingof the community 1-DNOAH LSM at the flux siteof Meyers and Hollinger(1998), 7-day time series ofobserved (circles) versusmodeled soil heat flux for:
Top Panel: old (dashed)versus new (solid) thermalconductivity formulation(both without new vegetationeffect), during April 1998,
Bottom Panel: new thermalconductivity formulation without (dashed) and with(solid) new vegetation effect,during August 1998. Fromcollaboration with E. Wood group of Princeton U.
Uncoupled1-D column model:testing Noah LSM
at single flux stations(Exp: Soil heat flux
Improvements)
so
il h
eat
flu
x (
W/m
*m)
AUGUST 1998 (high vegetation greenness)
NEW without veg effect
OBS
NEW with veg effect
Julian day
APRIL 1998 (low vegetation greenness, moist soil)s
oil
hea
t fl
ux
(W
/m*m
)
OLD
NEW OBS
Julian day
300
200
100
-100
0
100
50
-50
0
Laurie/Louis: Next slide requires 6 clicks to display fully. I will replace with a slide requiring only one click.
Bondville, IL (BV)alernating crops
Walker Branch, TN (WB)deciduous forest
Brookings, SD (SF)grassland
Black Hills, SD (BH)ponderosa pine
Ozarks, MO (MO)deciduous forest
July 2005 mean diurnal avg. sensible heat flux
Ft. Peck, MT (FP)grassland
OBS
NAMNAMX
3-D coupled mesoscale WRF/Noah Testing at multiple U.S. flux stations
Upper: Eta model layer 2 (10-40 cm) volumetric soil moisture is relatively moist (dry) inJuly 1999, left (July 2000, right). Lower: Verification of operational Eta model multi-station, monthly-mean2-m air temperature for interior Southwest: moister and cooler (warmer and drier) conditions inJuly 1999, left (July 2000, right) are well-captured.
Meso model monthly-mean 2-m (C) air
temperature vs obs:interior Southwest
interiorSouthwest
OPS COUPLED LAND-ATMOSPHERE NCEP MESOSCALE MODEL (Model captures interannual variability of daytime max temperature and model soil moisture)
Meso modelend-of-month
2nd layervolumetric
soil moisture Dry monsoon eventWet monsoon event
Eta forecast hour00 2412 36 48
obs
Eta
29 C
16
23
30 33 C
16
24
32
Eta forecast hour00 2412 36 48
obs
Eta
July 1999 July 2000
09-25 May 2005
17-day mean surfaceLatent heat flux
Operational GFS
Parallel GFS testusing improvedNoah LSM
Impact of Noah LSM implementation in GFS: example of warm season forecastsNoah LSM reduced longstanding high bias in GFS surface evaporation
over east half of CONUS
Noah LSM implementedin NCEP GFS in late May 05
Equitable Threat Score
Bias:
Ops GFS: solid(with older land model)
Test GFS: dashed(includes Noah LSM)
Precipitation Validation Scores:East half of CONUS
60-84 hour GFS fcst from 00Z12-31 May 2005
Impact of Noah LSM implementation in GFS: warm season forecastsNoah LSM implementation reduced longstanding high bias in GFS precipitation
over east half of CONUS
Ratio (y-axis) of forecast amount toobserved amount asfunction 24-hour precipitationamount (x-axis)
Target
Example of impact of Noah LSM upgrade on CFS southwest U.S. Monsoon Forecasts
Summer:1999 (wet U.S. monsoon)
vs.
2000 (dry U.S. monsoon) CFS/Noah/GLDAS
vs.CFS/OSU/GR2
10 CFS members each(initialized from late June)
Noah LSM will be implemented in next operational upgrade of CFS
Interannual Difference: 1999-minus-2000July Total Precipitation Anomalies (mm)
10-member Ensemble Mean initialized from late mid-to-late June
T126 CFS / Noah / GLDAS T126 CFS / OSU / GR2
CFS/Noah/GLDAS (upper left frame), which uses GLDAS/Noah initial land states, yields positive (negative) inter-annual difference over Southwest (Midwest) in July total precipitation, agreeing with the observations in lower right frame, while CFS/OSU/GR2 yields wrong sign of the inter-annualdifference in both Southwest and Midwest.
Observed Interannual Difference
Summary of Previous Slides
• NCEP/EMC has embraced the weather-climate connection via its strategy of simultaneous development and assessment of model physics improvements jointly across short-range, medium-range and seasonal scales
• The systematic development and assessment of the Noah LSM from short-range mesoscale to seasonal range global scale has resulted in its unified application across NCEP mesoscale and global models.
Data Assimilation is fundamental to the Environmental Forecast Process
Observations
Analysis
Model Forecast
Post-processed Model Data
Forecaster
User (public, industry…)
NumericalForecastSystem
Data Assimilation
Laurie/Louis: Insert here front-office slide from JCSDA material illustrating the exploding volume
of satellite data
Received = All observations received operationally from providersSelected = Observations selected as suitable for use (cloud free, …)*Assimilated = Observations actually assimilated into models
Five Order of Magnitude Increases in SatelliteData Over Ten Years (2000-2010)
Cou
nt (
Mill
ions
)
Daily Satellite & Radar Observation
Count
20001990 2010
100 M obs
125 M obs
Level 2 Radar
239 M obs
Satellite Data Ingest
Re
ceived
Da
ta
Daily Percentage of Data
Ingested into ModelsS
ele
cted
Data
100%
7%
Assim
ilate
d Da
ta
239.5M
17.3M5.2M2%
*2006 Data
3*Science, data resolution, computer issues,… need to be addressed
NCEP has been a leader in direct assimilation of satellite radiances
Currently Operational Data Sources in NCEP Data Assimilation:
• Mesoscale N. American Data Assimilation System (NDAS):– Satellite Data Sources: TBD by Ken– Convention Data Sources: TBD by Ken
• Global Data Assimilation (GDAS):– Satellite Data Sources: TBD by Ken– Convention Data Sources: TBD by Ken
NCEP Global Data Assimilation System (GDAS) assimilates satellite observed Tb in various spectral channels (infrared & microwave)
– Analysis increment is derived from the difference between forecast-simulated Tb and satellite observed Tb
JCSDA Community Radiative Transfer Model (CRTM)– Forecast-simulated Tb is product of GFS forecast of atmospheric states and earth surface
states (land, ice, sea) driving CRTM simulated Tb
(Tb)p = satellite brightness temperature for channel “p”Tatm = Atmospheric TemperatureTsurf = Land Surface Temperature (LST)α = atmospheric absorption of radiation in the given channelε = land surface emissivity of radiation in the given channel
For earth surface sensitive channels (so called “window channels”):atmospheric absorption (α) is weak, so land surface temperature (LST) and land surface emissivity become very important inputs to the simulation and assimilation of satellite radiances over land.
LST and land surface emissivity are strong functions of land surface states: snowpack, vegetation cover, soil water, soil ice
4DDA of Satellite Brightness Temperatures (Tb):Microwave channel
NOTE:
Earth Surface Term:Is dominant term for“window” channels.
Surface Emissivity Module in JCSDA Community Radiative Transfer Model: CRTM
Surface emissivity as function of satellite sensor channel, incidence angle and earth surface conditions
Surface Emissivity
Module
IR EM module over
snow-free land
IR EM module over
ice-free ocean
IR EM module over
land snow/ice
IR EM module over
sea ice
MW EM module over
snow-free land
MW EM module over
ice-free ocean
MW EM module over
land snowpack
MW EM module over
sea Ice
Impact of model simulated land states on atmospheric data assimilation is substantial.Such states include land temperature, snowpack, vegetation cover, soil moisture, soil ice.Rejection rate of satellite observed radiances over landmass is far greater than overthe ocean, pointing to strong need for better modeling and observation of land sfc
Satellite sources of Land Surface Temperature (LST):
• Satellite sources of LST– Geostationary (split window, sounder)– AVHRR– MODIS– SSMI, AMSU, other microwave platforms
• NCEP to date has utilized mostly GOES-based LST to validate its model LST (as in next 2 slides)– Operational in NESDIS: hourly temporal resolution– Test Bed at U.Md (Prof. R. Pinker)– Good definition of diurnal cycle
LST verification: May06 Monthly Mean Diurnal CycleBottom Panels: Blue=flux-station obs, Black=GOES retrieval, Other=various NCEP
model/assimilation suites, which have mid-day LST cold bias in the drier sparely vegetated west half of CONUS. GOES LST retrieval captures diurnal cycle well and agrees with flux-
station obs in monthly mean. Hence use GOES LST for spatial validation (next slide).
Monthly Mean 18Z LST [K] May2006
GOES LST Retrieval
NCEP Global Model NCEP Mesoscale Model
NCEP global and mesoscale modelshave warm-season mid-day LST cool bias, which is largest in southwest and largest in the global model. A primary cause (not shown) has been determined to be an overly large aerodynamic conductance (magnitude of surface turbulent exchange). Tests of improved formulation are in preparation.
NCEP was a key participant with many collaborators in the
North American Monsoon Experiment (NAME):
both the field observation program and NAME model intercomparison (NAMAP)
NORTH AMERICAN MONSOON EXPERIMENT (NAME)NORTH AMERICAN MONSOON EXPERIMENT (NAME)
YEAR (2000+) 00 01 02 03 04 05 06 07 08
Planning --------------|
Preparations ---------------|
Data Collection - - - - - - --------|
Principal Research ---------------------------------|
Data Management -----------------------------------------|
HYPOTHESIS:HYPOTHESIS:The NAMS provides a physical basis for determining the degreeof predictability of warm seasonprecipitation over the region.
OBJECTIVES:OBJECTIVES:Better understanding andsimulation of:
• warm season convective processes in complex terrain (TIER I);
• intraseasonal variability of the monsoon (TIER II);
• response to oceanic and continental boundary conditions (TIER III);
• monsoon evolution and variability (TIER I, II, III).
Low-level (925 mb) winds and observed precipitation
What was the NAME 2004 Field Campaign?
The NAME 2004 Field Campaign was an unprecedented opportunity to gather extensive atmospheric, oceanic, and land-surface observations in the core region of the North American Monsoon over NW Mexico, SW United States, and adjacent oceanic areas.
Gulf of California moisture surges can be triggered by various disturbances
Low-level moisture surges in the Gulf of California can occur in response to:
A) the passage of tropical cyclones to the south
B) in response to MCS outflows.
Adams and Comrie (1997)
Composite evolution of precipitation anomalies (mm) for Gulf of California surges keyed to Yuma, AZ
Higgins and Shi (2006)
• All surges: SENW progression of positive anomalies along the west coast of Mexico
• Surges that are related to TCs tend to be associated with much stronger and deeper low-level southerly flow, deeper plumes of tropical moisture, and wetter conditions over the core monsoon region than surges that are unrelated to TCs
TC : denotes Tropical Cyclones
Number (%) of Yuma surges by category(based on July-August 1977-2001)
Higgins and Shi (2006)
Surge Category Number of Events
All 132
TC-Related 65 (49%)
Not TC-Related 67 (51%)
Direct 38 (58% of TC-related)
Indirect 27 (42% of TC-related)
• Roughly half of all Gulf surges are TC related (1977-2001).
• The response to the surge is strongly influenced by the proximity of the TC to the Gulf of California region. Roughly 6 in 10 of the TC-related Gulf surges have a direct relationship in which the TC tracks in close proximity to the Gulf region.
NAME Model Assessment Project (NAMAP) (Gutzler et al 2005)
NAMAP was carried out before NAME 2004 to determine the state-of-the-art of warm season climate modeling and to provide benchmark simulations for testing the NAME hypothesis.
- 6 groups (4 regional models / 2 global models)- Simulated a common warm season (1990) with high rainfall.
NAMAP identified some key problems with current simulations:
NAMAP II is in progress, with heavy use of observations from NAME field program, which followed NAMAP I. NAMAP I helped guide formulation offield program strategy
The NAMAP analysis highlighted several key multi-scale issues:
• Weak coupling between the diurnal cycle, propagating convection and large-scale circulation / waves;
• Improper representation of coastal effects (e.g. sea/land breeze effects) on diurnal cycle of precipitation;
• Ineffective generation of precipitating systems over complex terrain (e.g. frequency; intensity) ;
• Absence / weakness of mesoscale systems (e.g. convective parameterizations are scale separated);
• Missing effects of transients (e.g. easterly waves; synoptic-scale waves) on organized convection;
• Difficulties with regime transitions (e.g. onset / demise of MJO and its influence on active/break periods and transients).
Multi-scale issues that need to be addressed in model simulations of the
North American Monsoon:
Coauthors:
Siegfried Schubert, Max Suarez (NASA/GSFC)Jae Schemm, Soo-Hyun Yoo (NOAA/NCEP/CPC)Hua-Lu Pan, Jongil Han (NOAA/NCEP/EMC)
Diurnal Cycle over the Great Plains in NCEP/GFS
Myong-In LeeNASA/GSFC Global Modeling and Assimilation Office
Climate Diagnostics and Prediction Workshop, 23-27 October 2006, Boulder, CO
SE
GP
Diurnal Cycle of Rainfall in 3 GCMs: Ensemble Mean and Spread
(NASA-NCEP Cooperative Study: M. Lee et al.)
Obs(HPD)GFDLNCEPNASA
(spread) (ensemble mean)
Key Points from Study in Previous Frame
Simulations of the warm season diurnal cycle of precipitation were compared in the three global climate models.
• While the models have basically similar convective schemes (buoyancy closures), they have rather different diurnal cycles (phase) in the land region, particularly over the Great Plains.
• NCEP/GFS captures the pronounced nocturnal precipitation signals over the Great Plains reasonably well, which feature is not properly simulated by other global models.
• Increased resolution has less of an impact on the simulated diurnal cycle of convection, suggesting the importance of model physics.
• The models commonly show reasonable diurnal variations of CAPE (local convective instability) and large-scale moisture influx (associated with GPLLJ).
• Source of the differences appears to be in the implementation details of the convection scheme, such as the convection trigger functions.
GLACE investigated the sensitivity of total July precipitation over land toSoil moisture anomaliesin at least a 15 versions of GCMs from various centers, including NCEP.
GLACE: Global Land Atmosphere Coupling Experiment
Higher values in figure at left showhigher sensitivity of model’s July precipitation over land to soil moisture anomalies.
Differences in convective trigger functions among the GCMs as illustrated in the previous slideis a leading candidate to explainthe widespread model differencesin the figure at left.
The NCEP Climate Forecast was made operational in August 2004
• Currently, two fully-coupled nine-month forecasts are made every day
• The present CFS operational system is frozen
• Development work is underway at EMC to improve the CFS
We anticipate a new CFS implementation will take in January 2010
For a new CFS implementation
Two main components:
Next NCEP Global Reanalysis (1979-2007) T254L64
CFS Retrospective Forecasts (1981-2007) T126L64
The Next NCEP Global Reanalysis:
For the next-generation NCEP Climate Forecast System
Analysis Systems:
A. Atmospheric 4dda: GDAS
B. New: addition of ocean 4dda (GODAS)
C. New: addition of land 4dda (GLDAS)
1. Atmospheric Model : GFS
2. Land Model: Noah LSM
3. Ocean Model : MOM4
(1/40 at the equator, 1/20 at globally beyond 100N and 100S)
4. New sea ice model
Frames still to be added (by Ken)
• From Glenn White: – GFS surface water balance example
• From Augustin Vintzileos:– AMMA field program & GFS skill over Sahel
Conclusions
• Still TBD
