1 / 42

COSMIC and Land Data Assimilation

COSMIC and Land Data Assimilation. Rafael Rosolem. 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 1. 1 University of Arizona

levi
Download Presentation

COSMIC and Land Data Assimilation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. COSMIC and Land Data Assimilation Rafael Rosolem 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 Arizona 2 National Center for Atmospheric Research COSMOS 3rd Workshop December 11, 2012

  2. 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!!!

  3. Data Assimilation of Neutron Counts GOAL to update LSM soil moisture profiles by assimilating the cosmic-ray fast neutron count Land Surface Model (LSM) Requires an accurate model to interpret modeled soil moisture profiles in terms of the above-ground fast neutron count Modeled Soil Moisture Profile Monte Carlo Neutron Particle model (MCNPx) does that but it is too slow for use in data assimilation

  4. COsmic-ray Soil Moisture Interaction Code (COSMIC) • 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 Ne z z Exponential reduction in the number of high energy neutrons with depth Isotropic creation of fast neutrons from high energy neutrons at level “z” Exponential reduction in the number of the fast neutrons created at level “z” before their surface measurement high energy neutrons fast neutrons

  5. COsmic-ray Soil Moisture Interaction Code (COSMIC) The resulting analytic function that describes the total number of fast neutrons reaching measurement point is: A few meters will do! Exponential reduction in the number of the fast neutrons created at level “z” before their surface measurement Exponential reduction in the number of high energy neutrons with depth Isotropic creation of fast neutrons from high energy neutrons at “z” • Two parameters measured in situ (soil bulk density and lattice water) and six to be defined: • L1 , L2 and L4are 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

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

  7. Output Data Input Data

  8. Output Data Input Data

  9. Output Data Input Data

  10. Output Data Input Data

  11. Output Data Input Data

  12. Output Data Input Data

  13. Output Data Input Data

  14. Output Data Input Data

  15. Output Data Input Data

  16. Output Data Input Data

  17. Output Data Input Data

  18. Output Data Input Data

  19. Output Data Input Data

  20. Output Data Input Data

  21. Output Data Input Data

  22. Output Data Input Data

  23. Output Data Input Data

  24. Output Data Input Data

  25. Output Data Input Data

  26. Output Data Input Data

  27. Output Data Input Data

  28. Output Data Input Data

  29. COSMIC Effective Depth

  30. COSMIC Performance at Santa Rita (AZ) Using COSMIC to estimate COSMOS counts from measured soil moisture profiles (TDT sensors) Running time for a single soil moisture profile MCNPx ~ 30-60 minutes COSMIC ~ 0.5 seconds

  31. Data Assimilation Framework COSMIC COSMOS http://www.ral.ucar.edu/research/land/technology/lsm.php http://www.image.ucar.edu/DAReS/DART/

  32. 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

  33. Data Assimilation Results: Santa Rita (AZ) • 40 ensembles: 2011-07-03_00Z through 2011-09-14_23Z • With and without assimilation of observed COSMOS neutron counts R2 = 0.97, RMSE = 48 cph, BIAS = -26 cph R2 = 0.84, RMSE = 840 cph, BIAS = -832 cph

  34. Updated Soil Moisture Profiles NOAH Δz1 NOAH Δz2 No Assimilation NOAH Δz1 NOAH Δz2 Assimilated TDT (independent) measurements

  35. Integrated (depth-weighted) Soil Moisture

  36. Low Spread and Negative Soil Moisture

  37. Surface Energy Fluxes

  38. 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

More Related