1 / 18

STATISTICAL FORECASTING OF OPTICAL TURBULENCE

STATISTICAL FORECASTING OF OPTICAL TURBULENCE. Frank Ruggiero and Artie Jackson Air Force Research Laboratory Hanscom AFB, MA. Objective. To develop a real-time optical turbulence prediction approach for upper-troposphere – lower stratosphere Stably stratified flows

althea
Download Presentation

STATISTICAL FORECASTING OF OPTICAL TURBULENCE

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. STATISTICAL FORECASTING OF OPTICAL TURBULENCE Frank Ruggiero and Artie Jackson Air Force Research Laboratory Hanscom AFB, MA

  2. Objective • To develop a real-time optical turbulence prediction approach for upper-troposphere – lower stratosphere • Stably stratified flows • In support of developing directed-energy weapon and communication systems • To satisfy necessary domains, must rely on numerical weather prediction model fields as input

  3. Optical Turbulence • Phenomenon which makes stars “twinkle” • Turbulence occurring in layers with a length scale of a few cm to ~100m in upper troposphere and lower stratosphere • Caused by turbulent mechanical mixing which creates small-scale temperature and refractive index inhomogeneities • Acts like small lenses which tilt and defocus light as it is dwelling its target --------Atmosphere-------- Time Averaged Instantaneous Vacuum

  4. Optical Turbulence Structure Cn2 varies with atmospheric conditions by orders of magnitude Humidity Direction • This single layer of high turbulence dominates light propagation • This layer is associated with the tropopause and jet stream • Flow over topography and convection are also sources of optical turbulence • Tropopause is also a factor Stratosphere Tropopause Jet Stream Troposphere Temperature Speed Index of Refraction Structure Constant Index of Refraction Structure Constant

  5. Outline • Review of Existing AFRL Optical Turbulence Modeling Efforts • Dewan • Jackson • Description of Latest Approach

  6. Tatarski’s Formulation of Optical Turbulence Where: Reference: Tatarski, V. I., 1961: Wave Propagation in a Turbulent Medium, McGraw-Hill.

  7. Outer Length Scale - Dewan • Dewan et al. (1993) uses two relationships: • Troposphere: • Stratosphere: • where : Reference: Dewan, E. M., R. E. Good, B. Beland, and J. Brown, 1993: A Model for Cn2 (Optical Turbulence) Profiles using Radiosonde Data. Phillips Laboratory Technical Report, PL-TR-93-2043. ADA 279399.

  8. Outer Length Scale - Jackson • Jackson (2004) used thermosonde-derived values of Cn2 in Tatarski’s equation, solved for L04/3 and then correlated L04/3 with vertical wind shear and temperature gradients: • Troposphere: • Stratosphere: Reference: Jackson, A., 2004: Modified-Dewan Optical Turbulence Parameterizations. Air Force Research Laboratory Technical Report, AFRL-VS-HA-TR-2004-1116. ADA 432901.

  9. October 2003 21-28 Oct 12 thermosonde profiles over 8 days 0.5° Global data used for initial and lateral boundary conditions WRF run with 41 vertical levels & nests of x = 15 km & 5 km Forecasts were evaluated 12-18 hours into model integration Verification: Holloman AFB, NM

  10. Results: Rytov Variances Relationship of Observed Rytov Variance to Forecast

  11. Results: Categorical Forecasts – Dewan 45% Correct 14% Under-forecast 41% Over-forecast Low < 0.15 0.15 ≥ Moderate ≤ 0.25 High > 0.25

  12. Results: Categorical Forecasts – Jackson 50% Correct 41% Under-forecast 9% Over-forecast Low < 0.15 0.15 ≥ Moderate ≤ 0.25 High > 0.25

  13. Results: Probability of Detection &False Alarm Rate for Rytov Variance

  14. Current Situation in Optical Turbulence Prediction • Jackson parameterization significantly improved over Dewan • Dewan and Jackson parameterizations show opposite biases • Could be used in an ensemble approach to bracket optical turbulence • Accuracy and precision of forecasts still needs to be improved

  15. Development of Next Generation Optical Turbulence Prediction Algorithm • Expand number of dependent variables to examine • Single level variables • T, P, q, u, v, w, θ,….. • Powers of single level variables • T2, T4, …… • Vertical gradients • dt/dz, du/dz, RB, N, ….. • Squares and 2nd derivatives of vertical gradients • (dt/dz)2, d2t/dz2, …. • Products of variables • dV/dz*dT/dz, ….. • Other profile derived variables • Trop height, max wind, ….. • Horizontal gradients • Frontogenesis, horizontal deformation, …..

  16. Development of Next Generation Optical Turbulence Prediction Algorithm • Expand the amount of data used in dependent data • Multiple locations • Multiple seasons • Over 250 profiles in database • Significant amount of data available for independent verification (~100 profiles) • Use Cn2 as forecast variable (not L) • Investigate advanced statistical approaches • Probability density functions • Bayesian approaches

  17. Database Sample Analysis Combined Correlation of Shear and dT/dz with Cn2 Frequency of Shear and dT/dz Combinations Troposphere Stratosphere Tropopause

  18. Conclusions • Since 2000 AFRL has shown improvement in the ability to predict optical turbulence • Statistics-based approaches viable • Further improvement in accuracy and prediction is needed • AFRL is working on improved statistical approaches that will: • Improve accuracy • Provide measure of forecast confidence

More Related