cosmic and land data assimilation

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COSMIC and Land Data Assimilation

Rafael Rosolem

COSMOS 3rd WorkshopDecember 11, 2012

W. J. Shuttleworth1, M. Zreda1, A. Arellano1, X. Zeng1, T.

Hoar2, J. Anderson2, T. Franz1, S. A. K. Papuga1, Z. M. S.

Mejia1, M. Barlage2, J. S. Halasz1

1 University of Arizona2 National Center for Atmospheric Research

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Why do we need a forward operator for COSMOS? “Effective” measurement depth depends on soil moisture

Can reach several individual layers of a typical land surface model

Therefore, direct assimilation of neutron intensity is more desirable!!!

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Land Surface Model (LSM)

Modeled Soil

Moisture Profile

Requires an accurate model to interpret modeled soil moisture profiles in terms of the above-

ground fast neutron count

GOALto update LSM soil moisture profiles by assimilating the

cosmic-ray fast neutron count

Monte Carlo Neutron Particle model (MCNPx)

does that but it is too slow for use in data assimilation

Data Assimilation of Neutron Counts

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COSMIC is a simple analytic model which: captures the essential below-ground physics that MCNPX represents can be calibrated by optimization against MCNPX so that the nuclear

collision physics is re-captured in parametric form

Exponential reduction in the number of high energy

neutrons with depth

Isotropic creation of fast neutrons from high energy

neutrons at level “z”

z

Exponential reduction in the number of the fast neutrons created at level “z” before

their surface measurement

high energy neutrons fast neutrons

Ne

z

COsmic-ray Soil Moisture Interaction Code (COSMIC)

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The resulting analytic function that describes the total number of fast neutrons reaching measurement point is:

Two parameters measured in situ (soil bulk density and lattice water) and six to be defined:

L1 , L2 and L4 are site-independent and are easily determined from MCNPX

L1 = 162.0 g cm-2

L2 = 129.1 g cm-2

L4 = 3.16 g cm-2

N , and L3 require multi-parameter optimization against site specific-specific runs of MCNPX for a range of hypothetical soil moisture profiles

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1 2 3 40 0

( ) ( )( ) ( )2 1exp . . exp . .

coss sw w

COSMOS s w

m z m zm z m zN N z z d dz

L L L L

A few meters will do!

Exponential reduction in the number of high energy

neutrons with depth

Isotropic creation of fast neutrons from high

energy neutrons at “z”

Exponential reduction in the number of the fast neutrons created at level

“z” before their surface measurement

COsmic-ray Soil Moisture Interaction Code (COSMIC)

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Fort Peck

Bondville

Chestnut Ridge

Santa Rita

Coastal Sage

Calibrating COSMICHypothetical soil water profiles

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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Output DataInput Data

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COSMIC Effective Depth

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Using COSMIC to estimate COSMOS counts from measured soil moisture profiles (TDT sensors)

COSMIC Performance at Santa Rita (AZ)

Running time for a single soil moisture profile

MCNPx ~ 30-60 minutes

COSMIC ~ 0.5 seconds

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COSMIC

http://www.ral.ucar.edu/research/land/technology/lsm.php http://www.image.ucar.edu/DAReS/DART/

COSMOS

Data Assimilation Framework

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Soil Moisture Dynamics 40 ensembles with perturbed forcing data (Santa Rita, AZ): 2010-07-

28_00Z through 2010-08-23_23Z (x-axis hour timesteps)

No assimilation!!! member runs are unconstrained!!! “Damped” process driven by (rainfall) pulses

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Data Assimilation Results: Santa Rita (AZ)

R2 = 0.97, RMSE = 48 cph, BIAS = -26 cph

R2 = 0.84, RMSE = 840 cph, BIAS = -832 cph

40 ensembles: 2011-07-03_00Z through 2011-09-14_23Z With and without assimilation of observed COSMOS neutron

counts

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NOAH Δz1

NOAH Δz2

NOAH Δz1

NOAH Δz2

Updated Soil Moisture Profiles

No Assimilation

Assimilated

TDT (independent) measurements

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Integrated (depth-weighted) Soil Moisture

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Low Spread and Negative Soil Moisture

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Surface Energy Fluxes

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Problems to be solved

Reduced ensemble spread at lower soil moisture test other filter types test different inflation parameters log-transform: initial tests = simulation crashes assimilation of multiple observations (e.g., SMOS) adopt a minimum soil moisture threshold in DART but add

small noise when updating variables (to ensure individual ensemble members won’t converge to minimum allowed)

Calibrate key soil moisture/surface flux parameters in Noah (currently working on that)

DART