Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Gérard Ancellet x
  • Refine by Access: All Content x
Clear All Modify Search
Gerard M. Ancellet, Robert T. Menzies, and William B. Grant

Abstract

A pulsed CO2 lidar with coherent detection has been used to measure the correlation time of the backscatter of an ensemble of atmospheric aerosol particles which are illuminated by the pulsed radiation. The correlation time of the backscatter of the return signal, which is directly related to the velocity spectral width, can be used to study the velocity structure constant of atmospheric turbulence and wind shear. Various techniques for correlation time measurement are discussed, and several measurement results are presented for the technique using the information contained in the statistical distribution of a set of lidar return signal intensities.

Full access
Jean-Luc Zarader, Gérard Ancellet, Alain Dabas, Nacer K. M'Sirdi, and Pierre H. Flamant

Abstract

An adaptive notch filter (ANF) is proposed for range-resolved frequency estimates of Doppler lidar atmospheric returns. The ANF is based on the spectral filtering of lidar return to remove the atmospheric contribution from noise. An adaptive algorithm is used to retrieve the filter parameters at a time k knowing both the input signal and filter output at times ki, where i = [1, k]. It is shown that ANF performs well at low SNR (−5 dB) compared to the poly-pulse-pair (PPP) estimator currently used for Doppler lidar signal processing. The standard deviation of frequency estimates is 0.01F s − 0.02F s (F s is the sampling frequency) at SNR = −5 dB, depending on the signal spectral width. It corresponds to a wind velocity uncertainty of 2–4 m s−1 for F s = 40 MHz and a laser wavelength λ = 10 µm. The ANF also proved to perform better than PPP in tracking a time-varying frequency, and in the presence of a colored noise.

Full access
Katharine S. Law, Andreas Stohl, Patricia K. Quinn, Charles A. Brock, John F. Burkhart, Jean-Daniel Paris, Gerard Ancellet, Hanwant B. Singh, Anke Roiger, Hans Schlager, Jack Dibb, Daniel J. Jacob, Steve R. Arnold, Jacques Pelon, and Jennie L. Thomas

Given the rapid nature of climate change occurring in the Arctic and the difficulty climate models have in quantitatively reproducing observed changes such as sea ice loss, it is important to improve understanding of the processes leading to climate change in this region, including the role of short-lived climate pollutants such as aerosols and ozone. It has long been known that pollution produced from emissions at midlatitudes can be transported to the Arctic, resulting in a winter/spring aerosol maximum known as Arctic haze. However, many uncertainties remain about the composition and origin of Arctic pollution throughout the troposphere; for example, many climate–chemistry models fail to reproduce the strong seasonality of aerosol abundance observed at Arctic surface sites, the origin and deposition mechanisms of black carbon (soot) particles that darken the snow and ice surface in the Arctic is poorly understood, and chemical processes controlling the abundance of tropospheric ozone are not well quantified. The International Polar Year (IPY) Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, Climate, Chemistry, Aerosols and Transport (POLARCAT) core project had the goal to improve understanding about the origins of pollutants transported to the Arctic; to detail the chemical composition, optical properties, and climate forcing potential of Arctic aerosols; to evaluate the processes governing tropospheric ozone; and to quantify the role of boreal forest fires. This article provides a review of the many results now available based on analysis of data collected during the POLARCAT aircraft-, ship-, and ground-based field campaigns in spring and summer 2008. Major findings are highlighted and areas requiring further investigation are discussed.

Full access