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Flash-Type Discrimination

Flash-Type Discrimination. GOES-AWG/R3 GLM Science Meeting September 29-30, 2009 Huntsville, AL Dr. William Koshak; NASA-MSFC. Very Brief Summary of Results From ….

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Flash-Type Discrimination

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  1. Flash-Type Discrimination GOES-AWG/R3 GLM Science Meeting September 29-30, 2009 Huntsville, AL Dr. William Koshak; NASA-MSFC

  2. Very Brief Summary of Results From … Koshak, W. J., Optical Characteristics of OTD Flashes and the Implications for Flash-Type Discrimination, to be submitted to JTECH, 2009. Koshak, W. J., R. J. Solakiewicz, Retrieving the Fraction of Ground Flashes from Satellite Lightning Imager Data Using CONUS-Based Optical Statistics, to be submitted to JTECH, 2009. Koshak, W. J., A Mixed Exponential Distribution Model for Retrieving Ground Flash Fraction from Satellite Lightning Imager Data, to be submitted to JTECH, 2009.

  3. Paper #1 Highlights

  4. IDL FlashMovie.pro Analysis Typical Ground Flash: Typical Cloud Flash: “Return Stroke Detector” … large group areas seem to indicate presence of a return stroke

  5. Distributions of MGA Cloud Flashes: Ground Flashes:

  6. Use of Mean Data Example of CLT: • Mean optical data could be used to discriminate • flash type since Central Limit Theorem removes • distributional overlap. • Using mean data implies examining • several (say N )flashes. • Examining N flashes implies you are looking • for the fraction of the N flashes that are • ground flashes.

  7. Paper #2 Highlights

  8. Paper #2 Highlights Mean of kth optical charactertistic: “Poor Man” Retrievals (Ground Flash Fraction) Multiple optical characteristics: One optical characteristic:

  9. Paper #3 Highlights

  10. Mixed Exponential Distribution Model Ground MGAs Cloud MGAs Results from Paper #1 Distribution of MGA modeled as a Mixed Exponential Distribution: Population Means of MGA: Require that:

  11. 1st International Interactive Workshop on Inverse Methods 1976

  12. Bayesian Inversion Bayes’ Law: Find parameters that maximize the probability on LHS. Invoking ignorance prior, means you just maximize the following : Formally : Practically :

  13. Initialization for Numerical Search Population mean and variance of the mixture: Using 1st equation to solve for gives an equation quadratic in , hence: where initialization as follows results in an initialization of :

  14. Retrieval Errors: Effect of Finite Sampling • Simulated tests • 4000 known “mixtures” of CGs/ICs. • Retrieval errors of ground flash fraction shown • for different values of N (# flashes analyzed)

  15. Retrieval Errors: Sensitivity to Alpha N = 2000 N = 5000 • Simulated tests • 100 known “mixtures” of CGs/ICs for each ground flash fraction (alpha) bin • Mean ground flash fraction retrieval errors shown for 2 values of N • Errors largest for alpha near 0 or 1 due to well-known “label switching” ambiguity

  16. “Label Switching” Problem p(y) is invariant under the following transformation: Specifically:

  17. Global Retrieval of Ground Flash Fraction (Preliminary) • 5 yrs OTD data • 4,365,395 flashes • 4o resolution bins • 563 bins • >= 2000 flashes/bin • Mean Alpha = 0.158 • Min Alpha = 0.009 • Max Alpha = 0.95 Ground flash fraction (alpha)

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