Some Approaches and Issues related to ISCCP-based Land Fluxes

Eric F WoodPrinceton University

1. Overview of two models that we’re using for continental-scale ET retrievals, the “Surface Energy Budget System” (SEBS) based on Su (2002) and a Penman Montheith-based approach.

2. Quick-views of some surface radiation products over the U.S. (MODIS, CERES, ISCCP)

3. Some initial results4. Some critical issues for Land Flux

success.5. Inferred ET (and surface budgets) from

LSM, reanalysis and atmospheric satellite data

Outine

Use the Surface Energy Balance Model (SEBS) to determine instantaneous daily ET predictions (limited

by surface temperature).

SEBS Model Description

Components of the radiation balance are used to determine the net radiation (Rn) – SW , α, ε, Ts, LW

Rn – G = H + LE

Rn = (1- α) SW + ε LW - εσ 4sT

The ground heat flux (G) is parameterized as a function of fractional cover – LAI/NDVI relationships,

which needs to be improved

SEBS Vertical Extent (ASL-PBL)

Viscous sublayerTransition layerInertial sublayer

Atmospheric Surface Layer (ASL)

Planetary (Convective) Boundary Layer (PBL)

Roughness sublayer

~ 10 1~2 m

~ 10 -1~1 m

~ 10 -3 m

~ 10 2-3 m

Free Atmosphere

Wind profile

Blending height

PBL height

Interfacial sublayer

Princeton University

L

z

L

dz

z

dz

k

uu m

mmm

00

0

0* ln

1

00

0

00* ln

L

z

L

dz

z

dzCkuH h

hhh

ap

kgH

uCL vp 3

*

Energy Balance Method - Turbulent Heat Fluxes

Princeton University

Use Similarity Theory for the Atmospheric Surface Layer

Wind, air temperature, humidity(aerodynamic roughness,

thermal dynamic roughness)

H

G0

LERn

?,, 000 hm zdz ?,, quTa

SEBS Model Description

CEOP observations used to assess ET predictionsForcing data from validation tower sites supplemented with MODIS data to produce estimates of surface fluxes.

Previous Tower Investigations – SMACEX 02

Examining the spatial equivalence for corn and soybean5 tower sites 3 tower sites

High resolution/quality data produces good quality estimates – examine model accuracy

Previous Investigations – SMACEX 02

~ 1020 m

Ê = 380.0 W/m2

σ = 35.7 W/m2

Ê = 392.3 W/m2

σ = 105.3 W/m2

~ 90 m

Ê = 367.5 W/m2

σ = 97.2 W/m2

~ 60 m

Penman-Monteith (P-M) Equation

a

s

a

aspan

rr

r

eeCGR

E

1

)().(

Rn – Net Radiation (W/m2)

G – Soil Heat Flux (W/m2)

a – Density of air (Kg/m3)

Cp – Specific Heat of Air (J/Kg/oC)

es – Saturated vapor pressure (Pa)

ea – Vapor pressure of air (Pa)

ea – Vapor pressure of air (Pa)

ra – Aerodynamic Resistance (s/m)

rs – Surface Resistance (s/m)

– Slope of saturated vapor pressure (Pa/oC)

– Psychrometric constant (Pa/oC)

Datasets

Data Type Variable Unit Source Platform Resolution

Surface Meteorological

Data

Air temp.

Pressure

Wind

Vapor Pressure

CKPa

m/s

KPa

AIRS / ISCCP

AIRS / ISCCP NLDAS

AIRS

Aqua / ISCCP

Aqua / ISCCP

NLDAS

Aqua

45 km

45 km

12.5 km

45 km

Radiative Energy Flux

Incident SW Rad.

Incident LW Rad.W/m2

W/m2

CERES

ISCCP

Aqua

ISCCP

0.2 deg

2.5 deg

Surface Temperature

Composite Radiometric Temp. (Soil +

Veg.)

K MODIS

ISCCPAqua 1 – 5 km

2.5 deg

Vegetation Parameters

Emissivity

Albedo

LAI

Veg. Type

-

-

-

-

MODIS / ISCCP

MODIS / ISCCP

MODIS

MODIS

Aqua/Terra

Aqua/Terra

Aqua/Terra

Terra (UMD)

1 - 5 km / 0.2 deg

1 km / 0.2 deg

1 - 5 km

1 Km

Incoming Shortwave Radiation – CERES

Incoming Shortwave Radiation – ISCCP

Incoming Shortwave Radiation (Oklahoma)

0 200 400 600 800 10000

200

400

600

800

1000

R = 0.4

CERES [W/m2]

ISC

CP

[W/m

2 ]

0 200 400 600 800 10000

200

400

600

800

1000

R = 0.47

CERES [W/m2]

ISC

CP

[W/m

2 ]

0 200 400 600 800 10000

200

400

600

800

1000

R = 0.83

CERES [W/m2]

ISC

CP

[W/m

2 ]

0 200 400 600 800 10000

200

400

600

800

1000

R = 0.54

CERES [W/m2]

ISC

CP

[W/m

2 ]

0 200 400 600 800 10000

200

400

600

800

1000

R = 0.49

CERES [W/m2]

ISC

CP

[W/m

2 ]

0 200 400 600 800 10000

200

400

600

800

1000

R = 0.92

CERES [W/m2]

ISC

CP

[W/m

2 ]

Incoming Shortwave Radiation (Instantaneous)

ISSCP (2.5deg) vs. CERES (upscaled to 2.5deg)

May 1–Aug. 31, 2003, instantaneous (NASA/Aqua)

ISSCP (2.5deg) vs. CERES (upscaled to 2.5deg)

May 2003 – August 2003,

Aggregated to monthly from NASA/Aqua overpass times

300 400 500 600 700 800 900300

400

500

600

700

800

900

R = 0.94

CERES [W/m2]

ISC

CP

[W/m

2 ]

CERES vs.ISSCPMississippi River Basin

May 2003 Monthly Means

300 400 500 600 700 800 900300

400

500

600

700

800

900

R = 0.95

CERES [W/m2]

ISC

CP

[W/m

2 ]

CERES vs.ISSCPMississippi River Basin

June 2003 Monthly Means

300 400 500 600 700 800 900300

400

500

600

700

800

900

R = 0.92

CERES [W/m2]

ISC

CP

[W/m

2 ]

CERES vs.ISSCPMississippi River Basin

July 2003 Monthly Means

300 400 500 600 700 800 900300

400

500

600

700

800

900

R = 0.87

CERES [W/m2]

ISC

CP

[W/m

2 ]

CERES vs.ISSCPMississippi River Basin

August 2003 Monthly Means

May 2003

July 2003

June 2003

Aug. 2003

Incoming Shortwave Radiation (Monthly)

Latent Heat Fluxes (Monthly Average) – SEBS

Latent Heat Fluxes (Monthly Average) – P-M

Latent Heat Fluxes (SEBS w. MODIS and ISCCP)

(Monthly Instantaneous Average)

APRIL 2003

Latent Heat Fluxes – Penman-Monteith

(Monthly Instantaneous Average)

APRIL 2003

APRIL 2003

Latent Heat Fluxes (SEBS w. MODIS and ISCCP)

(Monthly Instantaneous Average)

APRIL 2003

Latent Heat Fluxes – Penman-Monteith

(Monthly Instantaneous Average)

Critical Issues for LandFlux success

1. Scale – impact of coarse scale radiation, surface temperature, meteorology and properties.

2. Validation. Unconvinced that towers will do much for LandFlux.

3. Algorithm development/s, (multi-model merging of different retrievals?) Role of data assimilation?

4. Can we infer ET from other sources/models.

PGF501948-2000, 3hr, daily, 1.0degP, T, Lw, Sw, q, p, w

CRU1901-2000, Monthly, 0.5degP, T, Tmin, Tmax, Cld

GPCP1997-, Daily, 1.0degP

UW1979-2000, Daily, 2.0degP

TRMM2002-, 3hr, 0.25degP

SRB1985-2000, 3hr, 1.0degLw, Sw

NCEP/NCAR Reanalysis1948-, 3hr, 6hr, daily, T62P, T, Lw, Sw, q, p, w

ReanalysisHigh temporal/low spatial resolution

ObservationsGenerally low temporal/high

spatial resolution

Bias-CorrectedHigh temporal/high spatial resolution:

Princeton Global Forcing 50-year data set (PGF50)

Global Forcing Dataset(Sheffield et al. J Climate, 2006)

Global Mean Annual Runoff Ratio

Seasonal (JJA) Surface Soil MoistureVIC

Hydrology Model

Monthly time series (1979-2005) of Atmospheric-Land Water Budget over the Mississippi

Airs sounding data

USGS Gauge data

Conv (mm)

dw/dt (mm)

Precip. (mm)

Evap. (mm)

Runoff (mm)

ds/dt (mm)

Atmospheric-Land Water Budget over the Mississippi, 1998

Inferred P = ENARR – dw/dtNARR + convNARR

Inferred E = PNARR – dw/dtNARR + convNARR

Inferred ds/dt = convNARR - dw/dtNARR - QOBS

NARR NLDAS Inferred Observed

Mean Seasonal Cycle of P-E

Mean Seasonal Cycle of ET

Mean Seasonal Cycle of Land Storage Anomaly

Mean Seasonal Cycle of Runoff

Mean Distribution of Atmospheric-Land Budgets 1979-1999:Evapotranspiration

NARR ModeledVIC (NLDAS)

Inferred from NARR Atmospheric BudgetHigher NARR Modeled ET

Low Inferred ET

Mean Distribution of Atmospheric-Land Budgets 1979-1999:

NARR Convergence NARR dW/dt

NLDAS Precip

Low Inferred E a result of high Conv and high PNARR Precip

Mean Distribution of Atmospheric-Land Budgets 1979-1999:Observed Runoff and dS/dt from NLDAS (VIC) and Inferred from

NARR Atmospheric Budget and Obs. Runoff

Results in high ds/dt

Observed runoff

Inferred ds/dt from NARRAnd observed runoff

NLDAS ds/dt