Aerosol extinction coefficient (Raman method)

Slides:



Advertisements
Similar presentations
Cloud Radar in Space: CloudSat While TRMM has been a successful precipitation radar, its dBZ minimum detectable signal does not allow views of light.
Advertisements

ASLAF: DETECTOR OF THE DIRECT SOLAR LYMAN- ALPHA RADIATION. FUTURE ALTERNATIVES V.Guineva(1), G.Witt(2), J.Gumbel(3), M.Khaplanov(3), R.Werner(1), J.Hedin(3),
UPRM Lidar lab for atmospheric research 1- Cross validation of solar radiation using remote sensing equipment & GOES Lidar and Ceilometer validation.
Gamma-Ray Spectra _ + The photomultiplier records the (UV) light emitted during electronic recombination in the scintillator. Therefore, the spectrum collected.
7. Radar Meteorology References Battan (1973) Atlas (1989)
Studying the Physical Properties of the Atmosphere using LIDAR technique Dinh Van Trung and Nguyen Thanh Binh, Nguyen Dai Hung, Dao Duy Thang, Bui Van.
Calibration Scenarios for PICASSO-CENA J. A. REAGAN, X. WANG, H. FANG University of Arizona, ECE Dept., Bldg. 104, Tucson, AZ MARY T. OSBORN SAIC,
Uncertainty in Cloud Aerosol Transport System (CATS) Products and Measurements Presented by Patrick Selmer Goddard advisor: Dr. Matthew McGill Assisted.
Optical Wireless Communications
Distance observations
A novel concept for measuring seawater inherent optical properties in and out of the water Alina Gainusa Bogdan and Emmanuel Boss School of Marine Sciences,
Remote sensing in meteorology
Aerosol radiative effects from satellites Gareth Thomas Nicky Chalmers, Caroline Poulsen, Ellie Highwood, Don Grainger Gareth Thomas - NCEO/CEOI-ST Joint.
Error Propagation. Uncertainty Uncertainty reflects the knowledge that a measured value is related to the mean. Probable error is the range from the mean.
CPI International UV/Vis Limb Workshop Bremen, April Development of Generalized Limb Scattering Retrieval Algorithms Jerry Lumpe & Ed Cólon.
Work Package 2 – Overview of instrumentation, data gathering and calibration issues Lidar Calibration Ewan O’Connor, Anthony Illingworth and Robin Hogan.
NDACC Working Group on Water Vapor NDACC Working Group on Water Vapor Bern, July 5 -7, 2006 Raman Lidar activities at Rome - Tor Vergata F.Congeduti, F.Cardillo,
A 21 F A 21 F Parameterization of Aerosol and Cirrus Cloud Effects on Reflected Sunlight Spectra Measured From Space: Application of the.
LIDAR Development and its Applications at UPRM Getting to understand the planets radiation budget plays an important role in atmospheric studies, and consequently.
Fiber Optic Receiver A fiber optic receiver is an electro-optic device that accepts optical signals from an optical fiber and converts them into electrical.
Ben Kravitz November 5, 2009 LIDAR. What is LIDAR? Stands for LIght Detection And Ranging Micropulse LASERs Measurements of (usually) backscatter from.
Geneva, September 2010 EARLINET-ASOS Symposium Second GALION Workshop NA5 Optimization of data processing Aldo Amodeo, Ina Mattis, Christine Böckmann,
Lidar remote sensing for the characterization of the atmospheric aerosol on local and large spatial scale.
LIDAR: Introduction to selected topics
The ANTARES experiment is currently the largest underwater neutrino telescope and is taking high quality data since Sea water is used as the detection.
Geneva, September 2010 EARLINET-ASOS Symposium Second GALION Workshop Uncertainties evaluation for aerosol optical properties Aldo Amodeo CNR-IMAA.
Characterization of Silicon Photomultipliers for beam loss monitors Lee Liverpool University weekly meeting.
G O D D A R D S P A C E F L I G H T C E N T E R Goddard Lidar Observatory for Winds (GLOW) Wind Profiling from the Howard University Beltsville Research.
COST 723 Training School - Cargese October 2005 OBS 2 Radiative transfer for thermal radiation. Observations Bruno Carli.

Average Lifetime Atoms stay in an excited level only for a short time (about 10-8 [sec]), and then they return to a lower energy level by spontaneous emission.
Stochastic Monte Carlo methods for non-linear statistical inverse problems Benjamin R. Herman Department of Electrical Engineering City College of New.
Mike Newchurch 1, Shi Kuang 1, John Burris 2, Steve Johnson 3, Stephanie Long 1 1 University of Alabama in Huntsville, 2 NASA/Goddard Space Flight Center,
1 Atomic Absorption Spectroscopy Lecture Emission in Flames There can be significant amounts of emission produced in flames due to presence of flame.
Micro-Pulse Lidar (MPL)
Measurement Example III Figure 6 presents the ozone and aerosol variations under a light-aerosol sky condition. The intensity and structure of aerosol.
Radiometric Correction and Image Enhancement Modifying digital numbers.
Berechnung von Temperaturen aus Lidar-Daten Michael Gerding Leibniz-Institut für Atmosphärenphysik.
Adaphed from Rappaport’s Chapter 5
Validation of OMI NO 2 data using ground-based spectrometric NO 2 measurements at Zvenigorod, Russia A.N. Gruzdev and A.S. Elokhov A.M. Obukhov Institute.
CLN QA/QC efforts CCNY – (Barry Gross) UMBC- (Ray Hoff) Hampton U. (Pat McCormick) UPRM- (Hamed Parsiani)
October 02, st IHOP_2002 Water Vapor Intercomparison Workshop Status of intercomparisons and the next steps  Characterize moisture measuring techniques.
H 2 O retrieval from S5 NIR K. Weigel, M. Reuter, S. Noël, H. Bovensmann, and J. P. Burrows University of Bremen, Institute of Environmental Physics
A new method for first-principles calibration
Ceilometer absolute calibration to calculate aerosol extensive properties Giovanni Martucci Alexander Marc de Huu Martin Tschannen.
CO 2 an important driver for climate change. Currently only approximately half of the CO 2 produced by man can be accounted for in the atmosphere and oceans,
1 Chapter No. 17 Radiation Detection and Measurements, Glenn T. Knoll, Third edition (2000), John Willey. Measurement of Timing Properties.
UNIVERSITY OF BASILICATA CNR-IMAA (Consiglio Nazionale delle Ricerche Istituto di Metodologie per l’Analisi Ambientale) Tito Scalo (PZ) Analysis and interpretation.
Date of download: 6/1/2016 Copyright © 2016 SPIE. All rights reserved. (a) Vision of the Brillouin lidar operated from a helicopter. The center ray represents.
Chapter 24 Wave Optics. Young’s Double Slit Experiment Thomas Young first demonstrated interference in light waves from two sources in Light is.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
METR Advanced Atmospheric Radiation Dave Turner Lecture 11.
Development of Multi-Pixel Photon Counters (1)
HSAF Soil Moisture Training
LIDAR Ben Kravitz November 5, 2009.
Miscellaneous Measurements
G. Mevi1,2, G. Muscari1, P. P. Bertagnolio1, I. Fiorucci1
E. Ponce2-1, G. Garipov2, B. Khrenov2, P. Klimov2, H. Salazar1
Aeolus in heterogeneous atmospheric conditions
Neutrino astronomy Measuring the Sun’s Core
Atmospheric Aerosol Characterization using
Really Basic Optics Instrument Sample Sample Prep Instrument Out put
G. Mevi1,2, G. Muscari1, P. P. Bertagnolio1, I. Fiorucci1
Emma Hopkin University of Reading
Introduction to Atmospheric Science at Arecibo Observatory
Instrumentation for Colliding Beam Physics 2017
GAJENDRA KUMAR EC 3rd YR. ROLL NO
AN ALGORITHM FOR LOCALIZATION OF OPTICAL STRUCTURE DISTURBANCES IN BIOLOGICAL TISSUE USING TIME-RESOLVED DIFFUSE OPTICAL TOMOGRAPHY Potlov A.Yu, Frolov.
Effects and magnitudes of some specific errors
Remote sensing in meteorology
Presentation transcript:

Aerosol extinction coefficient (Raman method) Receiving Telescope: The portion of the laser radiation backscattered from the atmosphere at different altitude ranges is collected by a telescope. Two or more telescopes with different optical properties can be used to optimize lidar performances in different atmospheric regions (near range, far range). Detector: The Raman backscattered light is forwarded to the detector, consisting of a photomultiplier tube (PMT) or an avalanche photodiode (APD), where it is converted to an electical signal. Laser Transmitter λ L Receiving Telescope Spectral filtering/Raman backscattering λ R Detector Assembly Acquisition Assembly Laser Transmitter: Short light pulses at wavelength λL are vertically trasmitted into the atmoshere by a laser transmitter. Spectral filtering/Raman backscattering: The radiation collected by the telescope is forwarded to an optical system (consisting of lenses, mirrors, beam splitters and interference filters) where it is spectrally filtered, so as only the Raman backscattered light from the atmosphere, at wavelength λR, is transmitted to the following receiving system. Acquisition: A trigger circuit synchronizes the signal acquisition so that it measures the intensity of the backscattered light from the atmosphere at different distances from the transmitter. This is the Raman lidar signal. The acquisition of lidar signals can be performed in analog mode by an Analog to Didital Converter (ADC), or/and in photon counting mode by a counting system (a discriminator plus a counter). Lidar signals in analog mode have a high signal to noise ratio in the near range, but a low signal to noise ratio and possible distortions in the far range. On the other hand, lidar signals in photon counting mode have a very good signal to noise ratio in the far range, but they are problematic for very high count rates, that occurs in the near range. Raw Raman lidar signals Pre-processing Pre-processed Raman lidar signals Processing Aerosol extinction λ L Raw Raman lidar signals: Raman lidar signals are acquired with raw time and vertical resolutions depending on the lidar system and are provided with their absolute standard uncertainty. Pre-processed lidar signals: the pre-processed Raman lidar signals have time and vertical resolutions depending on temporal averaging and vertical smoothing performed in pre-processing module. They are provided with their absolute standard uncertainty . Aerosol extinction: the profile of aerosol extinction coefficient at wavelength λL has time resolution depending on temporal averaging performed in pre-processing module and effective vertical resolution depending on vertical smoothings performed in pre-processing and processing modules. It is provided with its absolute standard uncertainty . Standard uncertainty Uncertaintes due to pre-processing Standard uncertainty Calibration uncertaintes Uncertainty due to multiple scattering Multiple scattering: the residual error in extinction coefficient without correction for multiple scattering is neglectable in cloud- free atmosphere, 12% and 4% at the base and top of cirrus clouds, 10% and less than 3% at the base and inside cumulus clouds [Ansmann et al., 1992]. Standard uncertainty: Standard deviation of a Poisson distribution of photon counts for lidar signals in photon counting mode, or temporal and vertical moving averages for lidar signals in analog mode. Uncertaintes due to pre-processing: uncertaintes due to the temporal averaging of signals during varing atmospheric conditions and residual errors due to the overlap correction and the other corrections. The error due to the overlap correction can reach 50% for heights below the full overlap (Wandinger and Ansmann, 2002) Standard uncertainty: Standard deviation of a Poisson distribution of photon counts for signals in photon counting mode, or standard deviation of the temporal average of lidar signals in analog mode. Calibration uncertaintes: In the calibration procedure, the retrieval of a molecular density profile needs an estimate of temperature and pressure profiles that differ from the real profiles, in particular from the real temperature. Without temperature inversions, the residual error associated with this estimate is less than 5% and even lower using radiosondes [Ansmann et al., 1992]. On the other hand, the assumption of Angstrom exponent value causes residual errors in the order of 5%, by varing the assumed value of 0.5 [Ansmann et Muller, 2005]. Click to see the process Click to see more details Click to return to the main chain Main Process Data/ Product Instrument/ Physical items Uncertaintes Key M. Rosoldi and F. Madonna (CNR-IMAA)

Aerosol extinction coefficient (Raman method) First bin range/Trigger delay: In lidar systems the acquisition electronics receives a trigger signal synchronous to the emissions of laser pulses. In this way, lidar signals are acquired so that the first bin coincides with the instant of emission of each laser pulse, in the time domain, and with the zero altitude, in the spatial domain. All subsequent altitudes of lidar profiles are calculated starting from this zero altitude. Electronics can cause a discrepancy between the instant of emission of a laser pulse and the start of the acquisition related to that laser pulse. The start of the acquisition can be delayed (trigger delay) or in advance (first bin range) compared to the instant of emission of the laser pulse. For each acquisition channel the above discrepancy, trigger delay or first bin range, must be accurately measured, in order to correct the corresponding lidar signals. Overlap correction: Both summed and averaged signals are corrected with the overlap function, which describes the incomplete overlap between the emitted laser beam and the receiver field of view near the ground. The overlap function and the full overlap height can be determined theoretically, by raytracing simulations or methods of Kuze et al. [1998], Measures [1992], and Chourdakis et al. [2002], or experimentally, by measurements at different zenith angles under homogeneous and stationary atmospheric conditions, or methods of Wandinger and Ansmann [2002] and telecover method [Freudenthaler , 2007]. Dead time: Each acquisition system in photon counting mode is characterized by a dead time, a time interval during which the system is unable to count incident photons. As a result, the acquisition is characterized by a maximum count rate above which the observed count rate is no more proportional to the number of incident photons, but depends on the dead time duration. Therefore, the dead time must be accurately measured in order to correct the corresponding lidar signals so as to extend the linearity of the acquisition system up to high count rates. Background subtraction: In daytime conditions, it is necessary to subtract from raw lidar signals the contribution of solar background in order to consider only the backscattered radiation from the atmosphere. The contribution of solar background is usually obtained by averaging the raw signals on the far field range, where the backscattered radiation from the atmosphere is neglectable with respect to the solar background signal. Dark Subtraction: Before acquiring raw lidar signals, N dark signals are acquired and averaged for each channel. From raw lidar signals acquired with each channel the corresponding average dark signal is subtracted, in order to eliminate or reduce electronic distortions in lidar signals. Temporal averaging: Raw lidar signals are summed (photon counting) or averaged (analog) on one or more selected temporal intervals, in order to reduce their statistical uncertainty. Dark subtraction Background subtraction Dead time correction First bin range Trigger delay Temporal averaging Overlap correction First vertical smoothing Signal Gluing First vertical smoothing: Raw lidar signals can be vertically smoothed, by summing (photon counting) or averaging (analog) the acquired signals on different range gates, in order to reduce their statistical uncertainty . Raw Raman lidar signals Pre-processing Signal Gluing: If the acquisition of raw signals is performed both in analog and photon counting mode, it is possible to combine the averaged analog signal with that summed in photon counting mode, so as the main signal is that in photon counting mode and the analog signal becomes an extension in the near range of the signal in photon counting mode. This operation is called gluing between the analog and photon counting signals and allows to extend the dynamic range of lidar signals. Pre-processed Raman lidar signals Click to see the process Click to see more details Click to return to the main chain Main Process Data/ Product Instrument/ Physical items Uncertaintes Key M. Rosoldi and F. Madonna (CNR-IMAA)

Aerosol extinction coefficient (Raman method) Multiple scattering correction: the profile of aerosol extinction coefficient can be corrected for multiple scattering. This affects the extinction coefficient retrieval in an optically dense medium, as fog and clouds. When the laser beam goes through this medium, not only the singly backscattered photons, but also photons undergoing multiple scattering processes remain in the lidar receiver field of view and are forwarded to the receiving system. In these conditions, lidar equations and algorithms, valid only in single scattering approximation, lose their validity. The multiple scattering makes lidar signals higher and extinction coefficient lower than those measured in single scattering conditions. The correction is performed by introducing in lidar equations correction factors, estimated from multiple scattering models (e.g. Eloranta, 1998). These calculate multiple scattering intensities for lidar returns, considering the properties of the scattering medium and of the lidar system. Calibration: The processing requires the assumption of: 1) Molecular profiles of backscattering coefficient at Raman wavelength λR, and of extinction coefficients at laser and Raman wavelengths λL and λR. These profiles are calculated from models of molecular scattering cross section and a molecular number density profile, retrieved from Standard Atmosphere, radiosondes or mesoscale models; 2)Angstrom exponent, describing the wavelength dependence of aerosol extinction coefficients at wavelengths λL, and λR. Fixed values (0 for cirrus clouds and 1, 1.5 or user-defined values, variable according to actual meteorological conditions) are usually used. Alternatively, values measured with sun photometers or derived from multi-wavelength simultaneous measurements of extinction coefficient are used. Vertical smoothing: The processing requires the calculation of the derivative of the logarithm of the pre-processed Raman signals. There are several methods to calculate the derivative. The most common methods use linear fit or digital filters, such as Savitzky-Golay filter. The calculation of the derivative implies a vertical smoothing of aerosol extinction coefficient profile and a reduction of its vertical resolution and statistical uncertainty with respect to the pre-processed Raman signals. Standard uncertainty: The standard uncertainty of aerosol extinction coefficient profile can be estimated with the Monte Carlo method or analytically, by means of error propagation theory. Vertical smoothing Standard uncertainty estimation Calibration Multiple scattering correction Pre-processed Raman lidar signals Processing module Aerosol extinction λ L Click to see the process Click to see more details Click to return to the main chain Main Process Data/ Product Instrument/ Physical items Uncertaintes Key M. Rosoldi and F. Madonna (CNR-IMAA)