Stratospheric Aerosol and Gas Experiment (SAGE) III aerosol extinction data from the Arctic in December 2002 are presented and used to illustrate the instrument's capability for polar stratospheric cloud (PSC) observations. Numerous PSCs were observed during the month at potential temperatures from about 450–700 K, and Types 1a and 1b PSCs could be discriminated in plots of 1022‐nm extinction versus color ratio (the ratio of 1022‐nm and 449‐nm extinctions). Type 1a (1b) PSCs were observed primarily above (below) a threshold temperature of 4 K below the nitric acid trihydrate (NAT) equilibrium temperature. Mie calculations show that some of the Type 1a PSC observations are consistent with a mixture of background aerosol and very few large NAT particles. This suggests that SAGE III offers the potential for systematic observations of these clouds, which would greatly improve our understanding of how they form and lead to stratospheric denitrification.

Several multimodel ensemble methods are selected and further developed to improve the deterministic and probabilistic prediction skills of individual wake-vortex transport and decay models. The different multimodel ensemble methods are introduced, and their suitability for wake applications is demonstrated. The selected methods include direct ensemble averaging, Bayesian model averaging, and Monte Carlo simulation. The different methodologies are evaluated employing data from wake-vortex field measurement campaigns conducted in the United States and Germany.= forecast of ith model g i yjf i = probability density function of forecast L = likelihood PB = probability that B occurs PBjA = probability that B occurs, given that A occurs py = probability density function of the y forecast

This paper describes a methodology for the integration and evaluation of fast-time wake models with flight data. The National Aeronautics and Space Administration conducted detailed flight tests in 1995 and 1997 under the Aircraft Vortex Spacing System Program to characterize wake vortex decay and wake encounter dynamics. In this study, data collected during Flight 705 were used to evaluate NASA's fast-time wake transport and decay models. Deterministic and Monte-Carlo simulations were conducted to define wake hazard bounds behind the wake generator. The methodology described in this paper can be used for further validation of fast-time wake models using en-route flight data, and for determining wake turbulence constraints in the design of air traffic management concepts. Γ = vortex circulation (ft 2 /s) 0 Γ = initial vortex circulation (ft 2 /s) V 0 = initial vortex descent velocity (ft/s) b 0 = initial vortex pair separation (ft) y 0 = initial vortex pair lateral offset from a reference point (ft) z 0 = initial vortex pair vertical offset from a reference point (ft) N = dimensional Brunt-Väisälä frequency (s -1 ) N* = non-dimensional Brunt-Väisälä frequency = 1 0 0 − V Nb ε = dimensional eddy dissipation rate (ft 2 /s 3 ) ε * = non-dimensional eddy dissipation rate = ( ) 1 0 3 / 1 0 − V b ε θ = potential temperature/theta (K) 2 T = temperature (°F) T L = Sarpkaya time-to-link u = east-west velocity component (ft/s) v = north-south velocity component (ft/s) ρ = air density (sl/ft 3 ) ∆t e = vortex encounter time (s) ∆x = distance between generator and follower at time of encounter (nmi) g = acceleration due to gravity (ft/s 2 ) W G = weight of the generator (lb) b F = follower wing span (ft) b G = generator wing span (ft) F Y = y-coordinate of follower's center of gravity normalized by b G F Z = z-coordinate of follower's center of gravity normalized by b G s = half of vortex pair separation (b 0 ) normalized by b G c r = vortex core radius size normalized by b G I xx = roll moment of inertia (sl·ft 2 ) F λ = wing taper ratio of the follower V F = follower airspeed (ft/s) V G = generator airspeed (ft/s) q = free-stream dynamic pressure (lb/ft 2 ) v l C = vortex-induced rolling moment coefficient F L C α = three-dimensional lift curve slope of the follower c l C = pilot input roll control corresponding to maximum aileron deflection p l C = roll damping coefficient φ = bank angle (deg) ∞ φ = maximum bank angle of the aircraft without control input (deg) max φ = maximum vortex-induced bank angle (deg)

A pulsed, 2-µm coherent Differential Absorption Lidar (DIAL) / Integrated Path Differential Absorption (IPDA) transceiver, developed under the Laser Risk Reduction Program (LRRP) at NASA, is integrated into a fully functional lidar instrument. This instrument measures atmospheric CO 2 profiles (by DIAL) from a ground platform. It allows the investigators to pursue subsequent in science-driven deployments, and provides a unique tool for Active Sensing of CO 2 Emissions over Night, Days, and Seasons (ASCENDS) validation that was strongly advocated in the recent ASCENDS Workshop.

The accurate measurement of energy in the application of lidar system for CO 2 measurement is critical. Different techniques of energy estimation in the online and offline pulses are investigated for post processing of lidar returns. The cornerstone of the techniques is the accurate estimation of the spectrum of lidar signal and background noise. Since the background noise is not the ideal white Gaussian noise, simple average level estimation of noise level is not well fit in the energy estimation of lidar signal and noise. A brief review of the methods is presented in this paper.