COSMIC and Land Data Assimilation Rafael Rosolem COSMOS 3 rd Workshop December 11, 2012 W. J. Shuttleworth 1, M. Zreda 1, A. Arellano 1, X. Zeng 1, T. Hoar 2, J. Anderson 2, T. Franz 1, S. A. K. Papuga 1, Z. M. S. Mejia 1, M. Barlage 2, J. S. Halasz University of Arizona 2 National Center for Atmospheric Research

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Why do we need a forward operator for COSMOS? 3  “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!!!

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 GOAL to update LSM soil moisture profiles by assimilating the cosmic-ray fast neutron count 4 Monte Carlo Neutron Particle model (MCNPx) does that but it is too slow for use in data assimilation Data Assimilation of Neutron Counts

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 neutronsfast neutrons NeNe z 5 COsmic-ray Soil Moisture Interaction Code (COSMIC)

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:  L 1, L 2 and L 4 are site-independent and are easily determined from MCNPX L 1 = g cm -2 L 2 = g cm -2 L 4 = 3.16 g cm -2  N,  and L 3 require multi-parameter optimization against site specific- specific runs of MCNPX for a range of hypothetical soil moisture profiles 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 6 COsmic-ray Soil Moisture Interaction Code (COSMIC)

7 Fort Peck Bondville Chestnut Ridge Santa Rita Coastal Sage Calibrating COSMIC Hypothetical soil water profiles

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Using COSMIC to estimate COSMOS counts from measured soil moisture profiles (TDT sensors) 31 COSMIC Performance at Santa Rita (AZ) Running time for a single soil moisture profile MCNPx ~ minutes COSMIC ~ 0.5 seconds

32 COSMIC COSMOS Data Assimilation Framework

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35 Soil Moisture Dynamics  40 ensembles with perturbed forcing data (Santa Rita, AZ): _00Z through _23Z (x-axis  hour timesteps)  No assimilation!!!  member runs are unconstrained!!!  “Damped” process  driven by (rainfall) pulses

36 Data Assimilation Results: Santa Rita (AZ) R 2 = 0.97, RMSE = 48 cph, BIAS = -26 cph R 2 = 0.84, RMSE = 840 cph, BIAS = -832 cph  40 ensembles: _00Z through _23Z  With and without assimilation of observed COSMOS neutron counts

37 NOAH Δ z 1 NOAH Δ z 2 NOAH Δ z 1 NOAH Δ z 2 Updated Soil Moisture Profiles No Assimilation Assimilated TDT (independent) measurements

38 Integrated (depth-weighted) Soil Moisture

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40 Low Spread and Negative Soil Moisture

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42 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