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AD-MODTRAN: An Enhanced MODTRAN Version for Sensitivity and Uncertainty Analysis

AD-MODTRAN: An Enhanced MODTRAN Version for Sensitivity and Uncertainty Analysis G. Scriven, N. Gat, J. Kriesel (OKSI) J. Barhen, D. Reister, Oak Ridge National Laboratory M. Fagan, Rice University. 26 th ANNUAL REVIEW CONFERENCE ON ATMOSPHERIC TRANSMISSION AND RADIANCE MODELS

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AD-MODTRAN: An Enhanced MODTRAN Version for Sensitivity and Uncertainty Analysis

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  1. AD-MODTRAN: An Enhanced MODTRAN Version for Sensitivity and Uncertainty Analysis G. Scriven, N. Gat, J. Kriesel (OKSI) J. Barhen, D. Reister, Oak Ridge National Laboratory M. Fagan, Rice University 26 th ANNUAL REVIEW CONFERENCE ON ATMOSPHERIC TRANSMISSION AND RADIANCE MODELS 23 and 24 September 2003 The Museum of Our National Heritage Lexington, Massachusetts

  2. Acknowledgements Missile Defense Agency (MDA): Lt. Col. Gary Barmore Col. Kevin Greaney Dr. Harry Heckathorn James Kiessling AFRL/PRSA: Dr. Robert Lyons Tom Smith

  3. Outline • What is Automatic Differentiation (AD)? • Creation of user friendly interface (GUI) • Demonstration of AD-MODTRAN • Application of AD-enhanced codes • Status of AD-MODTRAN • Recommendations

  4. What is Automatic Differentiation (AD)? Basic Enhancement Process Original code Adifor processor Derivative code User specified variables • How it works • AD computes analytic derivatives via symbolic differentiation • Applies the chain rule to compute derivatives of outputs w.r.t. inputs • Follows loops, conditional statements, subroutines, common blocks, etc. • Can create the entire sensitivity matrix (Jacobian) in a single run of the code

  5. Automatic Differentiation (AD) vs. Finite Differences (FD) Exact derivative y Finite Differences x FD Derivatives are approximate Depends on step size Multiple runs (one variable at a time) FD is 15-30 times slower than AD AD Derivatives are analytic (exact) Independent of step size Complete Jacobian with single execution AD is computationally more efficient • Historically, AD-enhanced codes have been difficult to create

  6. OKSI’s AD Implementation Process Original code any conflicts? Derivative code Create new user interface AD tools User specified variables no Setup files yes resolve yes Final product Any invalid results? User-friendly, validated AD-enhanced code Validate AD results Compile and link no • Only the differentiation is automatic, other steps require significant developer efforts (yellow) • OKSI created supplemental tools to further automate the process • These tools include GUI’s to make the operation of the AD-enhanced code more intuitive

  7. OKSI User Tools: Universal GUI Approach Applications Uncertainty Analysis Output GUI Wrapper Real-time Simulations Input GUI Inverse Problems AD-enhanced code Etc. Uncertainty GUI • The 3 GUI programs are designed to work with all AD-enhanced codes • Input GUI: handles case setup and independent variable (IV) selection • Output GUI: handles output data selection for visualization/application • Uncertainty GUI: handles bookkeeping of IV uncertainties • GUIs have been tested on AD-MODTRAN and AD-SPURC

  8. Demonstration of AD-MODTRAN

  9. Sample AD Output Sensitivity of target intensity (w/sr/mm) to atmospheric water vapor profile (g/m3) Surface Plots X-Y Plots sensitivity • 4 plot types available from Output GUI: • pie/bar charts 3) surface plots (2D) • X-Y plots (1D) 4) image cubes (3D)

  10. Applications of AD-enhanced Output Error Propagation Sensitivity/Uncertainty Analysis Real-time, Physics-based Simulations (ex: turbulent fluctuations) Inverse Problem Solutions (ex: atmospheric retrieval) 20% Movie 10% 5%

  11. Status of AD-MODTRAN • Handles about 70% of ALL inputs and 90% of all outputs • AD-MODTRAN should compile on any machine • GUIs run only on Windows based platforms • Minimal validation testing has been done • Currently available as an alpha release • Request form may obtained at: www.oksi.com; choose “projects”; then “AD-enhanced MODTRAN”

  12. Recommendations • Get user input! • Address ALL inputs and outputs in AD-MODTRAN • Create automated validation tools (using finite differences) • Apply AD to latest version of MODTRAN • Implement AD-MODTRAN in existing projects (atm. comp., simulations, …) • Apply AD to SAMM2

  13. Backup Slides

  14. Parameters Currently Handled by AD-MODTRAN Code List of DVs List of IVs # of possible sensitivities = 1.66 x 1011 in a single AD-MODTRAN execution! This list accounts for about 60% of the IVs and 80% of the DVs

  15. 4. Physics-based Simulations Example: fluctuating plume temperatures due to turbulent mixing/chemistry A) Assume temperatures fluctuate randomly with a Gaussian distribution 2s Tmean B) Compute resulting pixel radiances using AD derivatives Li,j Steady-state (SPURC) Sensitivity (AD-SPURC)

  16. AD vs. FD: computational accuracy Example case: IV – aspect angle (130°) DV – Total Intensity (178 kw/sr) Error = (AD-FD)/AD x 100% Increasing Truncation & round off error Increasing Nonlinearity error • Ideal FD step size is not known apriori • Multiple FD runs (per IV) required to determine appropriate step size • Optimal step may still have residual error

  17. AD vs. FD: computational efficiency • AD is about 5 times faster than FD (when ideal step size is known apriori) • In reality AD will be about 15 to 30 times faster (for unknown ideal step size)

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