Adaptive Optics Frame Rate Title (Tahoma, 88pt) Primary Briefer (Tahoma, 66pt) (list other team members in parenthesis) CTC/Sub-CTC Internal/External Funds Ex: 6.1-6.2/MDA TRL 1 Goal: XXX (Tahoma, 72pt) Title (Arial, 60pt) Title (Arial, 60pt) Title (Arial, 60pt) First year: We are concentrating on the daytime surface layer Field Campaign—Chestnut Site, Kirtland AFB Surface Layer -- Bottom of the boundary layer; fluxes vary by less than 10% -- Models based on assumption of constant flux Boundary Layer -- Part of troposphere directly influenced by surface -- Responds to surface forcings on time scales of an hour or less 100 to 3000 m 10 to 300 m Mixing Layer -- Boundary layer above Surface Layer: Includes coriolis effects Sub-Title (Arial, 36pt) Bullet (Arial, 24pt) XXXX Baseline Model Sorts Beacon, Sonic, Weather Station Use this Template (feel free to customize your color scheme, all images & graph axis clear) SORTS optical path Title (Arial, 36pt) Telescope Diameter Camera Camera speed Camera noise Adaptive Optics Frame Rate Reconstructor Speed/algorithm Number of Actuators/Sub-apertures BIL Return More effective optical design More effective system deployment Part 1—Partitioning Heat Energy Top of boundary layer Top of surface layer Ground Top of atmosphere Reflection Vegetation leads to surface roughness Water evaporation Conduction Plant growth IR re-radiation Air turbulence Turbulent kinematic heat flux Wind Predictions from Model vs Data From SOR Turbulence Sensor Need recognized by Air Force and DoD Part 2—Monin-Obukov Scaling Parameters Part 3—Optical Turbulence Parameters Anchoring model with field data Biggest Challenge: Calibration of QWERTY sensor Related Research Complements Our’s L = 149 km (93 miles) COMBAT Experiment Deep Turbulence MURI Low Atmosphere MRI JBCI for Horizontal Path Engagements Maui Hawaii CEBL After First Year Understand night time surface layer, boundary layer, terrain induced turbulence Develop models, design and carry out experiments to anchor models Areas that require further investigation: No memory in energy partition equation Empirical parameters in energy partition equation Optical turbulence equations derived assuming Kolmogorov turbulence SODAR Output LIDAR Output 4th term of daytime surface layer model is surprisingly important, highly non-linear, and has been neglected by previous researchers SODAR Balloon borne instrumentation AF and DoD needs define CEBL goals CEBL: Validating a way to predict turbulence caused by Earth’s boundary layer Unclassified: DIST C