1 / 23

Rashid Khosravi

HIRDLS Level II Algorithm. Rashid Khosravi. HIRDLS ScienceTeam Meeting 26 June, 2008 Oxford. Outline. HIRDLS L2 Algorithm Kapton Impact Forward Models Testing Errors and Sources Retrieval Characterization Sensitivity with Radiance Errors and a priori Errors Summary. L2 Heritage.

aliza
Download Presentation

Rashid Khosravi

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. HIRDLS Level II Algorithm Rashid Khosravi HIRDLS ScienceTeam Meeting 26 June, 2008 Oxford

  2. Outline HIRDLS L2 Algorithm Kapton Impact Forward Models Testing Errors and Sources Retrieval Characterization Sensitivity with Radiance Errors and a priori Errors Summary

  3. L2 Heritage Retrieval Physics/Code: Alyn Lambert/ISAMS Science Lead: Rashid Khosravi Contributions by Hyunah Lee Software Lead: Cheryl Craig with Charlie Krinsky FM: Gene Francis, Chris Halvorson

  4. Retrieval Algorithm, I Optimal Estimation Method

  5. Retrieval Algorithm, II Optimal Estimation Method Constrains the solution to prior knowledge of the atmospheric state weighted by the a priori errors Evaluates the difference between measured and simulated radiances weighted by the measurement errors Maximum likelihood solution: minimize with respect to X

  6. Solution Iteration Eq. Convergence Criteria a) set a tolerance limit; avoids waste of computational time b) set an optimal limit for number of iterations; avoids stopping before convergence, and never-converging retrievals

  7. Impact of kapton • Lack of scans in azimuth made it necessary to use GMAO T for LOS Temperature Gradient Correction New subsystem to collocate GMAO data and calculate LOS gradients Code changes to interface with the GMAO subsystem (Significant impact on SIPS) • Change in retrieval scheme Two stage retrievals: FM1/FM2 No feedback of retrieved products as contaminants in subsequent retrievals

  8. Forward Models Two Stage Retrievals, Two Forward Models FM1: Based on Curtis-Godson and Emissivity Growth approximations • Fast calculation of weighting functions • Band transmittances from tables as function of P, T, absorber amount • Accuracy: 1-5% depending on channel, tangent height FM2: Combines Curtis-Godson/Emissivity-Growth with a regression model “trained” on an ensemble of atmospheres • Accuracy: 0.5% in T channels, 1% in other channels

  9. Testing Suite of Tests Are Performed With Every New Build to Check Impact on Retrievals, if Any. Consistency Tests: Retrievals on Simulated (“truth”) Radiances Should be Very Close to “truth” Algorithm should retrieve the a priori state (CIRA for T, MOZART for gases) exactly

  10. Test Results, “truth” May 11, 2005

  11. T

  12. H2O

  13. O3

  14. Test Results, Retrieving the a priori State Retrieved a priori Diff T K K H2O ppmv %

  15. Errors Sources of Errors in Version 003 (Internally, v2.04.09) Detector Noise (Radiometric) Based on Calibration, Scaled by OAF Pointing Jitter Forward Model 0.3% FM Radiances A Priori: 20K for T, 300% for Constituents, 1000% for Aerosol Extinction

  16. Retrieval Characterization Averaging Kernels Response of Retrieval Due To Perturbation of the True State Vertical Resolution (FWHM) Fraction of Retrieval From a priori (Area of A)

  17. Sensitivity to Noise, Strongest Channels v003 Noise 2x Averaging Kernels Remain Sharply Peaked and Very Close to 1 As Noise is Increased 10x

  18. Noisiest Channel (#1) v003 Noise 2x Averaging Kernels Remain Sharply Peaked and Very Close to 1 As Noise Is Increased 10x HIRDLS Is a Very Low Noise Instrument

  19. Precision Remains Low as Noise Increases

  20. Precision Remains Low as Noise Increases

  21. Effect of a priori Errors v003 a priori Errors 1/8 1/16 Because of Low Noise, Retrievals Are Largely Independent of the a priori

  22. a priori Contribution Increases with Higher Noise 1/8 v003 a priori Errors v003 Noise 1/8 v003 a priori Errors 10x v003 Noise

  23. Summary • HIRDLS Has Very Low Radiometric Noise • Because of Low Noise, Retrievals Are Mostly Independent of a priori • Vertical Resolution and Precision Are Mostly Insensitive to Increasing Noise • Systematic Errors Due To the Blockage Will Be Analyzed and Incorporated When the kapton Correction Algorithm Matures More Fully

More Related