Title Tahoma 88 pt Primary Briefer Tahoma 66

Title (Tahoma, 88 pt) Primary Briefer (Tahoma, 66 pt) (list other team members in parenthesis) CTC/Sub-CTC Internal/External Funds Ex: 6. 1 -6. 2/MDA TRL 1 Goal: XXX (Tahoma, 72 pt) Title (Arial, 60 pt) Sub-Title (Arial, 36 pt) • Bullet (Arial, 24 pt) Mixing Layer -- Boundary layer above Surface Layer: Includes coriolis effects Boundary Layer -- Part of troposphere directly influenced by surface -- Responds to surface forcings on time scales of an hour or less 10 to 300 m Surface Layer -- Bottom of the boundary layer; fluxes vary by less than 10% -- Models based on assumption of constant flux • XXXX Title (Arial, 36 pt) Field Campaign—Chestnut Site, Kirtland AFB Sorts Beacon, Sonic, Weather Station More effective system deployment Top of atmosphere Number of Actuators/Sub-apertures Adaptive Optics Frame Rate Top of boundary layer IR re-radiation Water evaporation Camera Reconstructor Speed/algorithm SORTS optical path • Part 1—Partitioning Heat Energy More effective optical design Telescope Diameter First year: We are concentrating on the daytime surface layer Title (Arial, 60 pt) Baseline Model 100 to 3000 m BIL Return e t a l p e z i m e m T o s t l i s l h a u t c , e e o s t m U ree e s i h x c f s a l r h e o e p l f a o ( r c g r u & o s ) y age r a e l c m i Title (Arial, 60 pt) Camera speed Camera noise Reflection Top of surface layer Air turbulence Turbulent kinematic heat flux Wind • Predictions from Model vs Data From SOR Turbulence Sensor Vegetation leads to surface roughness Plant growth Ground Conduction Need recognized by Air Force and Do. D • 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 CEBL After First Year Maui L = 149 km (93 miles) COMBAT Experiment Hawaii Deep Turbulence MURI Areas that require further investigation: • No memory in energy partition equation • Empirical parameters in energy partition equation • Optical turbulence equations derived assuming Kolmogorov turbulence Low Atmosphere MRI JBCI for Horizontal Path Engagements 4 th AF and Do. D needs define CEBL goals term of daytime surface layer model is surprisingly important, highly non-linear, and has been neglected by previous researchers Unclassified: DIST C Understand night time surface layer, boundary layer, terrain induced turbulence • Develop models, design and carry out experiments to anchor models SODAR Output SODAR LIDAR Output Balloon borne instrumentation CEBL: Validating a way to predict turbulence caused by Earth’s boundary layer