Testing of new satellite-derived land surface products in NCEP

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Testing of new satellite-derived land surface
products in NCEPs Global and Regional Data
Assimilation Systems

• Ken Mitchell and Dan
  Tarpley
• NCEP/EMC (NOAA) NESDIS/ORA
  (NOAA)
• JCSDA Science Workshop
• May 31 June 1, 2006

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Acknowledgements(Coordination via bi-monthly
telecons hosted by NCEP/EMC)

• Joint Center for Satellite Data Assimilation
• External JCSDA-funded PIs
• U.Arizona X. Zeng, Boston.U M. Friedl,
  Princeton.U E. Wood, GMU P. Houser
• Internal
• NCEP/EMC Land Team
• K. Mitchell, G. Gayno, V. Wong, C. Marshall
• NESDIS/ORA Land Team
• D. Tarpley, L. Jiang, F. Kogan, I. Laszlo. P
  Romanov
• NASA GSFC/HSB
• C. Peters-Lidard, M. Rodell, B. Cosgrove, LIS
  Team
• NASA GSFC/GMAO
• R. Koster, R. Reichle
• Air Force Weather Agency (AFWA)
• J. Eylander

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Goals of Land-arena in JCSDAImproved Weather
and Climate Forecast Skill Through Use and
Assimilation of Satellite Land Data

• New and improved satellite products for
  prescribed land surface characteristics (focus of
  this presentation)
• Boston U. (M. Friedl), U.Arizona (X. Zeng),
  NESDIS/ORA (D. Tarpley)
• Improved land surface forward radiation models
• Princeton U. (E. Wood), NESDIS/ORA (F. Weng)
• Improved Noah land surface model physics as
  required to use satellite data (e.g. add
  groundwater component for GRACE 4dda)
• U.Arizona (X. Zeng), NCEP/EMC (K.Mitchell),
  NASA/HSB (C. Peters-Lidard)
• Model sensitivity studies on newly available
  satellite products
• U.Arizona (X. Zeng), NCEP/EMC, NASA/HSB,
  NASA/GMAO (R. Koster)
• Land data assimilation methodologies for improved
  initial conditions of land surface prognostic
  states (via Kalman filters or Adjoint models)
• George Mason U. (P. Houser), NASA/HSB, NASA/GMAO
• Transition to operations
• NCEP/EMC, NASA/HSB, NASA/GMAO, NESDIS/ORA

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Outline

• Vegetation type (aka landuse class)
• Vegetation phenology (annual cycle)
• Surface albedo
• Land surface temperature (LST)
• Snow cover and snowpack
• Soil moisture
• Surface emissivity
• Research to Operations transition strategy

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Noah Land Surface ModelThe land component in
NCEP global and regionalmodels and their data
assimilation systems(Noah LSM implemented in
NCEP ops GFS/GDAS in May 2005)
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Uses of land-surface satellite products in NCEP
modeling initiatives (operations or test beds)

• Define land surface characteristics
• Vegetation phenology annual cycle of green
  vegetation fraction (GVF)
• Operations monthly 0.14-deg monthly global
  climatology of GVF from AVHRR
• Test Bed
• External/U.Arizona MODIS-based global 2-km
  bi-monthly climatology of GVF
• Internal/NESDIS AVHRR-based realtime weekly
  global 0.144-deg (16-km) GVF
• Vegetation type (land use class)
• Operations AVHRR-based 12-class 1-deg (global
  model) or 24-class 1-km (regional model)
• Test Bed
• External/Boston.U MODIS-based 15-class 1-km
  global
• Surface albedo snow-free and maximum for deep
  snow
• Operations based on Briegleb (1992, 1986) and
  Matthews (1983,1984) seasonal, 1-deg global
• Test Bed
• External/Boston.U MODIS-based monthly 5-km
  global (snow-free)
• External/U.Arizona MODIS-based global 0.05-deg
  maximum albedo over deep snow
• Determine initial values of land prognostic
  states via data assimilation
• Snow cover and snowpack
• Operations Daily multi-sensor 4-km global (Geo,
  AVHRR, DMSP, AMSU, MODIS)
• Test Bed

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Global Vegetation Type Datasets(aka Landuse
Datasets)

• NCEP Operations AVHRR-based
• Global Model (1-deg global, SiB 12-classes,
  NASA/ISLSCP)
• Regional Model (1-km global, USGS 24-classes,
  EDC)
• CRTM (its own landuse classification, different
  from above two)
• GOAL unify landuse map in above 3 suites
• NCEP Testing MODIS-based
• Global 1-km
• U.MD 15 classes, same as IGBP less 2 sparse
  classes)
• M. Friedl et al. (Boston U., JCSDA)
• Fewer counts of mixed vegetation class
• Less mixed forest,
• less mixed cropland/grassland
• Less tropical rain forest, more savanna

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0 WATER BODIES 1 EVERGREEN NEEDLELEAF FOREST 2
EVERGREEN BROADLEAF FOREST 3 DECIDUOUS NEEDLELEAF
FOREST 4 DECIDUOUS BROADLEAF FOREST 5 MIXED
FORESTS 6 CLOSED SHRUBLANDS 7 OPEN SHRUBLANDS 8
WOODY SAVANNAS 9 SAVANNAS 10 GRASSLANDS 12
CROPLANDS 13 URBAN AND BUILT-UP 15 SNOW AND ICE
16 BARREN OR SPARSELY VEGETATED
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With tundra class added by NCEP/EMC Tundra class
believed to be important to CRTM surface
emissivity modeling. (closed and open shrubland
classes divided into high latitude and lower
latitude regimes)
1 EVERGREEN NEEDLELEAF FOREST 2 EVERGREEN
BROADLEAF FOREST 3 DECIDUOUS NEEDLELEAF FOREST 4
DECIDUOUS BROADLEAF FOREST 5 MIXED FORESTS 6
CLOSED SHRUBLANDS 7 OPEN SHRUBLANDS 8 WOODY
SAVANNAS 9 SAVANNAS 10 GRASSLANDS 12 CROPLANDS 13
URBAN AND BUILT-UP 15 SNOW AND ICE 16 BARREN OR
SPARSELY VEGETATED 17 WATER BODIES 18 WOODED
TUNDRA 19 MIXED TUNDRA 20 BARE GROUND TUNDRA
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MODIS-based (Red/Top) versus AVHRR-based
(Blue/Bottom) Evergreen needleleaf forest class
(left) and mixed forest class (right) Example
of fewer mixed-vegetation pixels in MODIS
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Annual Cycle of Vegetation Phenology
(Green Vegetation Fraction GVF) Original
(NESDIS) AVHRR-based, 16-km, Gutman
Ignatov (1998) New (U.Arizona)
MODIS-based, 2-km, Zeng et al. (2000)
IGBP land Pixel NDVIveg GVF
1 Evergreen needleleaf forest 5.03 0.63 0.90
2 Evergreen broadleaf forest 9.39 0.69 0.93
3 Deciduous needleleaf forest 1.52 0.63 0.92
4 Deciduous broadleaf forest 2.50 0.70 0.90
5 Mixed forest 4.86 0.68 0.88
6 Closed shrubland 2.01 0.60 0.72
7 Open shrubland 13.96 0.60 0.39
8 Woody savanna 7.87 0.62 0.86
9 Savanna 7.21 0.58 0.81
10 Grassland 8.53 0.49 0.71
11 Permanent wetland 1.02 0.56 0.85
12 Cropland 10.89 0.61 0.86
14 Natural vegetation 10.80 0.65 0.85
16 Barren 14.22 0.60 0.11

• To find NDVIveg and NDVIsoil, we introduce 2km
  IGBP land type classifications

Histogram of evergreen broadleaf
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GVF annual cycleOriginal (Gutman/AVHRR) versus
New (Zeng et al/MODIS)

• New GVF for the 7 most prevalent land cover types
  in CONUS.
• More realistic annual cycle in new GVF for
  evergreen needleleaf forest (Original GVF appears
  to get too low in winter.)
• New GVF is systematically higher in all land
  cover categories (possibly too high for deciduous
  broadleaf forest and cropland/grassland).

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Global Albedo Datasets

• Snow-free albedo
• NCEP Operations Pre-MODIS (1-deg global,
  quarterly)
• Briegleb et al. (1992,1986), E. Matthews (1984),
• NCEP Testing Post-MODIS (.05-deg global,
  monthly)
• M. Friedl et al. (Boston U., JCSDA)
• Maximum albedo for deep snow
• Serves as upper bound on surface albedo for deep
  snow in NCEP Noah LSM
• NCEP Operations Pre-MODIS (1-deg)
• Robinson and Kukla (1985, JAM, DMSP-based)
• NCEP Testing Post-MODIS (0.05-deg)
• Barlage and Zeng (2005, GRL, MODIS-based, JCSDA)

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Global VIS Albedo difference example for 25 May
06 Boston U. MODIS-based minus that in ops
GFS(rendered to ops GFS T382 computational grid
for 60-deg zenith angle)
-- MODIS-based albedo is generally less than
(darker) than ops GFS albedo, with notable
exceptions such as semi-arid regions. -- Tests
of the MODIS-based albedo are now underway in the
NCEP global model.
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NCEP Operational 1-deg Max Snow Albedo from
Robinson and Kukla (1985, JAM) DMSP visibile
imagery based
NCEP Test Bed 0.05-deg Max Snow Albedo from
Barlage and Zeng (2005, GRL) MODID-based
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Application of MODIS Maximum Snow AlbedoTesting
in WRF-NoahLSM coupled mesoscale model(Barlage
and Zeng, U. Arizona and JCSDA)

• Up to 0.5 C decreases in 2-m temperature in
  regions of high snow cover and significant albedo
  change
• Greater than 0.1 C increase in 2-m temperature
  even when snow depth is less than 1cm

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LST Land Surface Temperature Assimilation

• External initiatives
• GMU / Paul Houser separate talk this session
• Internal initiatives
• GMAO / Rolf Reichle separate talk this session
• EMC / Ken Mitchell
• LST assimilation via Noah LSM adjoint/tangent-line
  ar model (presented at last years workshop)
• LST for verifying land surface simulations next
  slides

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LST Land Surface Skin Temperature

• Satellite sources of LST
• Geostationary (split window, sounder)
• AVHRR
• MODIS
• SSMI, AMSU, other microwave platforms
• NCEP to date has utilized mostly GOES-based LST
• Operational in NESDIS hourly, 1/2-deg resolution
• Test Bed at U.Md (R. Pinker) hourly, 1/8th-deg
  resolution
• Split window technique through GOES (I-M)
• Sounder technique most recently
• Good definition of diurnal cycle

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July 1999
(18 UTC valid time)
Validation of LST from three land surface
models (one model per row) over the Oklahoma
region using LST observations from ARM
flux-stations (left column) and from GOES
(right column). Model validation via GOES
LST yields similar picture of model performance
and bias as obtained from ARM LST.
Model
vs ARM Model vs GOES
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LST Difference Noah LSM simulated minus
GOES-derived (18 UTC, 18 July 1999) (GOES LST is
obtained only where GOES retrieval infers
cloud-free conditions)
For Test Run of improved Noah LSM (e.g. revised
humidity stress function In canopy resistance
formulation) Noah LSM warm LST bias
is substantially reduced.
For Control Run of Noah LSM Noah LSM LST has
notable warm bias
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Snow Cover and Snowpack Assimilation

• External initiatives
• U.Arizona / Mark Barlage separate talk this
  session
• Princeton.U / Raf Wojcik separate talk this
  session
• Internal initiatives
• HSB MODIS snow cover assimilation
• NESDIS/ORA and NCEP/EMC next slides

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NESDIS Northern Hemisphere IMSInteractive
Multi-sensor Snow-cover Analysis

• NESDIS has produced satellite-derive N.H. snow
  cover analyses since 1965 (nearly 40 years)
• 1965-1996 weekly, 190-km resolution
• Jan 1997- Jan 2004 daily, 24-km resolution
• Feb 2004 present daily, new 4-km resolution
• IMS production is based on mix of
• A) automated satellite-based snow cover retrieval
• B) human analysis of satellite imagery via
  interactive workstation
• time loops of geostationary and polar visible
  imagery
• IMS sources of automated satellite retrievals of
  snow
• GOES and Meteosat visible, SSM/I, MODIS

Satellite-derived snow cover was the FIRST
satellite product of any kind ingested into NCEP
operational NWP models
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Example NESDIS IMS Snow Cover Analysis of 26 Feb
2004 (4-km) (includes sea-ice cover)
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IMS Snow Cover ExamplePacific Northwest U.S.
15 May 2006
Former 24 km resolution
New 4 km resolution
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Soil moisture assimilation

• External initiative
• GMU / Paul Houser separate presentation this
  session
• Internal initiative
• GMAO / Rolf Reichle separate presentation this
  session

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Land Surface Emissivity

• External initiative (not funded by JCSDA)
• Tom Schmugge U. New Mexico, MODIS-based
• Internal initiative
• John LeMarshall AIRS based (hyper-spectral)
• Fuzhong Weng IR sfc emission derived from MW

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Research to Operations Strategyfor Land Data
Assimilation Key thrusts for coming year

• Couple NASA/HSB Land Information System (LIS) to
  the NCEP GFS/GDAS via the Earth System Modeling
  Framework (ESMF)
• NASA funded
• Substantially expand CRTM testing and focus in
  LIS
• NCEP/EMC funded
• MODIS, AIRS and MW-based surface emissivity
• Substantially expand Ensemble Kalman Filter
  component and testing in LIS
• Joint NASA and NCEP and USAF
• Continue the expanding collaboration between
  NCEP/EMC, NASA/HSB, and NASA/GMAO in land data
  assimilation algorithms, methods, approaches