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Cloud Layer Effects on Free Space Optical Communications Don Norquist AFRL/VSBYA Hanscom AFB, MA October 14, 2005 Acknowledgements: Radar, radiometer measurements – Paul Desrochers, AFRL/VSBYM Lidar measurements – Pat McNicholl, AFRL/VSBYM, Mitch Laird, Boston College Rawinsonde soundings – George Clement, Utah State University Forecasting, surface obs – John Roadcap, Boston College Satellite imagery analysis – Gary Gustafson, Bob d’Entremont, AER, Inc.
ORCLE: Optical and Radio Frequency Combined Link Experiment • Program to demonstrate world-wide high data rate communication network for the tactical user • Intent: connect airborne, terrestrial, maritime & space military operations for real-time communications • DARPA’s ORCLE focus on air-to-air, air-to-ground links • Goal: Demonstrate advantages of free space optical (FSO) communications operating with radio frequency (RF) communications • Technical Objective: Prototype, flight demonstrate hybrid FSO/RF air-to-air-to-ground links • combine the best attributes of both technologies • simulate hybrid network performance
VSBYA/VSBYM Fall 2004, Winter 2005 HAFB Campaign Purpose: Collect radiosonde observations (RAOBs), satellite imagery, surface observations [routine obs] & radar reflectivity, MW radiometer, lidar signal power [ground truth] Background: FSO designers need to have quantitative information on the effect on signal power resulting from laser propagation through various cloud conditions Study goal: evaluate “routine” obs as a proxy for “ground truth” obs in specifying impact on laser comm. Ten 3-hour collection periods, broken or overcast with ceilings > 1 km AGL, no precipitation reaching ground Current VSBY Initiative: Cloud Characterization
Air Force Cloud Profiling Radar (AFCPR) Ka-band (8.6 mm) Reflectivity, 21 Oct 2004 Satellite Imagery RAOB Launch
Portable Eyesafe Environmental Laser (PEELS, 1.574 µm) Signal Power 21 Oct 2004 Satellite imagery RAOB Launch
Geostationary Operational Environmental Satellite (GOES) -12 (11µm) Imagery, 21 Oct 2004, 1132 UTC KBED 211130Z 36005KT 10SM BKN120 04/02 A3020 RMK SLP241 T00390022
Cloud Characterization Analysis Processing Steps • AER, Inc. Cloud Detection and Property Retrieval (CDPR) • Inputs: GOES-12 five-channel imagery, MM5 short-term forecast for surrounding 3-h UTC times • Outputs: cloud top height (CTH), effective particle size (Deff) , ice water path (IWP), ice clouds only • Cloud-Met Data Fusion Algorithm (CMDF) • Inputs: RAOB profile of P, T, Z, RH; CDPR retrievals (SATOB) near RAOB; Sfc obs of clouds (SFCOB) • Process: CP = [a0 a1RH + a2 ] -1 for 3 temperature regimes, augmented by SATOB and SFCOB • Outputs: Cloud probability at each RAOB report level (use CP > 0.51 as indicator of cloudy level)
Alternative Cloud Vertical Structure (CVS) Techniques • Fuzzy Fusion • Use RAOB RH, AltDiff = | CDPR CTH – RAOB Z | as linguistic variables in fuzzy rule base: IF RH = High & AltDiff = Small THEN Cloud = IsCloud WITH 1.000000 IF RH = Low & AltDiff = Big THEN Cloud = CloudFree WITH 1.000000 • Membership functions for RH, AltDiff, Cloud from Mozer and Ayer (1998) • Wang and Rossow (1995, J. Appl. Meteor.) • Based strictly on RAOB RH, with set rules for determining where cloud layers exist • Uses fixed RH thresholds determined from soundings and cloud observations, primarily in the tropics
Comparison of Diagnosed CVS vs. Radar/Lidar Measurements, 21 Oct 2004 Trop CDPR CMDF Fuzzy Fusion Wang & Rossow Radar/Lidar
Laser Transmission and Clouds • Transmittance is fraction of initial laser power (W) remaining at a distance s from the source • Laser light directed vertically downward through N layers of equal thickness Δz has a transmittance of • Total extinction βe is sum of air molecule scattering, aerosol extinction, water vapor absorption, cloud particle scattering/absorption
βair , βaer and kvq in Δz = 20 m: FASCODE for the Environment (FASE90) applied to RAOB profile βcld for ice particles: use CDPR-retrieved IWP, Deff, Δzcld and CMDF-diagnosed CP > 0.51 Compute βcld using three scattering/absorption models Mie theory using Deff/2, IWC = IWP/ ΔZcld (bulk) Fu (1996) empirical equations using IWP, Deff (bulk) Ou et al. (2002) direct + forward scatter using Deff , βcld from Fu model to compute total power for each layer βcld for liquid droplets: reff, LWP from AFCPR reflectivity & radiometer following Frisch et al. (1995) Compute βcld from Mie theory (bulk) Laser Transmittance Calculations
Laser Transmittance Profile Exampleλ = 1.55 µm, P = 10 w, Dreceiver = 1 m Cloud optical depth: (Mie) 0.86, (Fu) 0.81, (UCLA) 0.57 CMDF Cloud Layer
Transmittances for Ice Cloud LayersOu et al., 2002 Model, Satellite Imagery * Suspect humidity sensor, biased low compared to nearby NWS RAOB
Future Work • Improve CMDF algorithm: decrease false alarms • Estimate Deff,IWC profiles from radar, lidar ice cloud measurements • Estimate reff, LWP from satellite, RAOB data • Identify, apply method to estimate βcld for mixed phase cloud layers • Use AFRL/VSBY thermosonde data sets to assess cloud layer laser impacts in other regions, seasons • Write, submit tech report to support ORCLE project
Cloud-Met Data Fusion Technique • Developed based on 14 Hanscom RAOBs, radar/lidar from August 2001, June 2002, August 2002 • Matched RAOB RH profiles with radar/lidar CTH, CBH to determine cloudy (Y), not cloudy (N) RAOB levels • Computed #Y / (#Y + #N) for each 1% RH category, from all RAOB levels in 14 cases using: • RAOB RH (RHL) for T > 0 C; RHI for T < -40 C; • RH = α RHL + (1- α) RHI ; α = (T/40 + 1) for 0 ≥ T ≥ -40 C • Applied five-point weighted ( #Y + #N) mean smoothing • Fit to nonlinear form: CP = [a0 a1RH + a2 ] -1 for 3 T regimes
CMDF Method Development for T > 0 C -40 < T < 0: RHc = 73%; T < -40: RHc = 67%
Truth Table SummaryBased on 17 Radar/Lidar Cloud Layers * < 50% of diagnosed layer coincided with observed layer
Cloud Layer Position, Thickness Summaryfor Diag/Obs and MisDiag/Obs Cases