scholarly journals Retrieval algorithm for the column CO<sub>2</sub> mixing ratio from pulsed multi-wavelength lidar measurements

2021 ◽  
Vol 14 (5) ◽  
pp. 3909-3922
Author(s):  
Xiaoli Sun ◽  
James B. Abshire ◽  
Anand Ramanathan ◽  
Stephan R. Kawa ◽  
Jianping Mao
Keyword(s):  
Least Squares ◽  
Absorption Line ◽  
Linear Trend ◽  
Spectral Response ◽  
Retrieval Algorithm ◽  
Co2 Absorption ◽  
Mixing Ratio ◽  
Atmospheric Transmission ◽  
Least Squares Fit ◽  
Multi Wavelength

Abstract. The retrieval algorithm for CO2 column mixing ratio from measurements of a pulsed multi-wavelength integrated path differential absorption (IPDA) lidar is described. The lidar samples the shape of the 1572.33 nm CO2 absorption line at multiple wavelengths. The algorithm uses a least-squares fit between the CO2 line shape computed from a layered atmosphere model and that sampled by the lidar. In addition to the column-average CO2 dry-air mole fraction (XCO2), several other parameters are also solved simultaneously from the fit. These include the Doppler shift at the received laser signal wavelength, the product of the surface reflectivity and atmospheric transmission, and a linear trend in the lidar receiver's spectral response. The algorithm can also be used to solve for the average water vapor mixing ratio, which produces a secondary absorption in the wings of the CO2 absorption line under humid conditions. The least-squares fit is linearized about the expected XCO2 value, which allows the use of a standard linear least-squares fitting method and software tools. The standard deviation of the retrieved XCO2 is obtained from the covariance matrix of the fit. The averaging kernel is also provided similarly to that used for passive trace-gas column measurements. Examples are presented of using the algorithm to retrieve XCO2 from measurements of the NASA Goddard airborne CO2 Sounder lidar that were made at constant altitude and during spiral-down profile maneuvers.

scholarly journals Retrieval Algorithm for the Column CO<sub>2</sub> Mixing Ratio from Pulsed Multi-Wavelength Lidar Measurements

2020 ◽  
Author(s):  
Xiaoli Sun ◽  
James B. Abshire ◽  
Anand Ramanathan ◽  
S. Randy Kawa ◽  
Jianping Mao
Keyword(s):  
Least Squares ◽  
Absorption Line ◽  
Linear Trend ◽  
Spectral Response ◽  
Retrieval Algorithm ◽  
Co2 Absorption ◽  
Mixing Ratio ◽  
Atmospheric Transmission ◽  
Least Squares Fit ◽  
Multi Wavelength

Abstract. The retrieval algorithm for the column mixing ratio of CO2 from the measurements of a pulsed multi-wavelength integrated path differential absorption (IPDA) lidar is described. The lidar samples the shape of the 1572.33 nm CO2 absorption line at 15 or 30 wavelengths. The algorithm uses a least-squares fit between the CO2 line shape computed from a layered atmosphere model to that sampled by the lidar. In addition to the column average CO2 dry air mole fraction (XCO2), several other parameters are also solved simultaneously from the fit. These include the Doppler shift in the received laser signal wavelengths, the product of the surface reflectivity and atmospheric transmission and a linear trend in the lidar receiver's spectral response. The algorithm can also be used to solve for the average water vapor mixing ratio, which causes a secondary absorption in the wings of the CO2 absorption line under high humidity conditions. The least-squares fit is linearized about the expected XCO2 value which allows the use of a standard linear least-squares fitting method and software tools. The standard deviation of the retrieved XCO2 is obtained from covariance matrix of the fit. An averaging kernel is defined similarly to that used for passive trace-gas sounding. Examples are presented of using the algorithm to retrieve XCO2 from the measurements from NASA Goddard's airborne CO2 Sounder lidar made at a constant altitude and during spiral-down maneuvers.

scholarly journals Near infrared nadir sounding of vertical column densities: methodology and application to SCIAMACHY

2011 ◽  
Vol 4 (3) ◽  
pp. 3685-3737
Author(s):  
S. Gimeno García ◽  
F. Schreier ◽  
G. Lichtenberg ◽  
S. Slijkhuis
Keyword(s):  
Least Squares ◽  
Near Infrared ◽  
Nonlinear Least Squares ◽  
Retrieval Algorithm ◽  
Atmospheric Gases ◽  
Vertical Column ◽  
Least Squares Fit ◽  
Least Squares Optimization ◽  
Shortwave Infrared ◽  
And Inversion

Abstract. Nadir observations with the shortwave infrared channels of SCIAMACHY onboard the ENVISAT satellite can be used to derive information on atmospheric gases such as CO, CH4, N2O, CO2, and H2O. For the operational level 1b–2 processing of SCIAMACHY data a new retrieval code BIRRA (Beer InfraRed Retrieval Algorithm) has been developed: BIRRA performs a nonlinear least squares fit of the measured radiance, where molecular concentration vertical profiles are scaled to fit the observed data. Here we present the forward modeling (radiative transfer) and inversion (least squares optimization) fundamentals of the code along with the further processing steps required to generate higher level products such as global distributions and time series. Moreover, various aspects of level 1 (observed spectra) and auxiliary input data relevant for successful retrievals are discussed. BIRRA is currently used for operational analysis of carbon monoxide vertical column densities from SCIAMACHY channel 8 observations, and is being prepared for methane retrievals using channel 6 spectra. A set of representative CO retrievals and first CH4 results are presented to demonstrate BIRRA's capabilities.

Stable baseline correction of digital strong-motion data

1997 ◽  
Vol 87 (4) ◽  
pp. 932-944 ◽  
Author(s):  
Hung-Chie Chiu
Keyword(s):  
Least Squares ◽  
Linear Trend ◽  
Low Frequency ◽  
Strong Motion ◽  
Digital Data ◽  
Pass Filter ◽  
Strong Motion Data ◽  
Motion Data ◽  
Initial Values ◽  
Least Squares Fit

Abstract Most baseline errors of analog strong-motion data still exist in highresolution data. In this study, we identify the major baseline errors of digital strong-motion data and propose a three-step algorithm to correct these errors. The major baseline errors found in these digital data consist of constant drift in the acceleration, low-frequency instrument noise, low-frequency background noise, the small initial values for acceleration and velocity, and manipulation errors. This threestep algorithm includes fitting the baseline of acceleration by the least squares, applying a high-pass filter in acceleration, and subtracting the initial values in velocity. A least-squares fit of a straight line before filtering can effectively remove the baseline drift in acceleration. Then, the filtering removes the linear trend and other low-frequency errors that exist in the acceleration. Finally, the subtracting of the initial velocity removes the linear trend of displacement. Among these three steps, only the filtering in the second step may introduce a side effect. Compared to the Volume II routine developed by Trifunac and Lee (1973), this three-step processing significantly reduces computational efforts and side effects resulting from unnecessary manipulation of data. This algorithm has been successfully tested on several types of digital strong-motion data. Several independent validations show that the proposed algorithm is stable.

scholarly journals Spectral Calibration of the MethaneAIR Instrument

2021 ◽  
Author(s):  
Carly Staebell ◽  
Kang Sun ◽  
Jenna Samra ◽  
Jonathan Franklin ◽  
Christopher Chan Miller ◽  
...  
Keyword(s):  
Spectral Response ◽  
Stray Light ◽  
Lookup Table ◽  
Colorado Front Range ◽  
High Spectral Resolution ◽  
Retrieval Algorithm ◽  
Co2 Absorption ◽  
Central Wavelength ◽  
Spectral Calibration ◽  
Basin Scale

Abstract. MethaneAIR is the airborne simulator of MethaneSAT, an area-mapping satellite currently under development with the goal of locating and quantifying large anthropogenic point CH4 sources as well as diffuse basin-scale emissions. Built to closely replicate the forthcoming satellite, MethaneAIR consists of two imaging spectrometers. One detects CH4 and CO2 absorption around 1.65 and 1.61 μm, respectively, while the other constrains the optical path in the atmosphere by detecting O2 absorption near 1.27 μm. The high spectral resolution and stringent retrieval accuracy requirements of greenhouse gas remote sensing in this spectral range necessitate a reliable spectral calibration. To this end, on-ground laboratory measurements were used to derive the spectral calibration of MethaneAIR, serving as a pathfinder for the future calibration of MethaneSAT. Stray light was characterized and corrected through Fast Fourier Transform (FFT)-based Van Cittert deconvolution. Wavelength registration was examined and found to be best described by a linear relationship for both bands with a precision of ~0.02 spectral pixel. The instrument spectral spread function (ISSF), measured with fine wavelength steps of 0.005 nm near a series of central wavelengths across each band, was oversampled to construct the instrument spectral response function (ISRF) at each central wavelength and spatial pixel. The ISRFs were smoothed with a Savitzky-Golay filter for use in a lookup table in the retrieval algorithm. The MethaneAIR spectral calibration was evaluated through application to radiance spectra from an instrument flight over the Colorado Front Range.

scholarly journals Near infrared nadir retrieval of vertical column densities: methodology and application to SCIAMACHY

2011 ◽  
Vol 4 (12) ◽  
pp. 2633-2657 ◽  
Author(s):  
S. Gimeno García ◽  
F. Schreier ◽  
G. Lichtenberg ◽  
S. Slijkhuis
Keyword(s):  
Least Squares ◽  
Near Infrared ◽  
Retrieval Algorithm ◽  
Atmospheric Gases ◽  
Bound Constraints ◽  
Vertical Column ◽  
Least Squares Fit ◽  
Least Squares Optimization ◽  
Shortwave Infrared ◽  
And Inversion

Abstract. Nadir observations with the shortwave infrared channels of SCIAMACHY on-board the ENVISAT satellite can be used to derive information on atmospheric gases such as CO, CH4, N2O, CO2, and H2O. For the operational level 1b-2 processing of SCIAMACHY data, a new retrieval code BIRRA (Beer InfraRed Retrieval Algorithm) has been developed. BIRRA performs a nonlinear or separable least squares fit (with bound constraints optional) of the measured radiance, where molecular concentration vertical profiles are scaled to fit the observed data. Here we present the forward modeling (radiative transfer) and inversion (least squares optimization) fundamentals of the code along with the further processing steps required to generate higher level products such as global distributions and time series. Moreover, various aspects of level 1 (observed spectra) and auxiliary input data relevant for successful retrievals are discussed. BIRRA is currently used for operational analysis of carbon monoxide vertical column densities from SCIAMACHY channel 8 observations, and is being prepared for methane retrievals using channel 6 spectra. A set of representative CO retrievals and first CH4 results are presented to demonstrate BIRRA's capabilities.

scholarly journals Spectral calibration of the MethaneAIR instrument

2021 ◽  
Vol 14 (5) ◽  
pp. 3737-3753
Author(s):  
Carly Staebell ◽  
Kang Sun ◽  
Jenna Samra ◽  
Jonathan Franklin ◽  
Christopher Chan Miller ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Spectral Response ◽  
Point Sources ◽  
Stray Light ◽  
Lookup Table ◽  
High Spectral Resolution ◽  
Retrieval Algorithm ◽  
Co2 Absorption ◽  
Central Wavelength ◽  
Spectral Calibration

Abstract. MethaneAIR is the airborne simulator of MethaneSAT, an area-mapping satellite currently under development with the goal of locating and quantifying large anthropogenic CH4 point sources as well as diffuse emissions at the spatial scale of an oil and gas basin. Built to closely replicate the forthcoming satellite, MethaneAIR consists of two imaging spectrometers. One detects CH4 and CO2 absorption around 1.65 and 1.61 µm, respectively, while the other constrains the optical path in the atmosphere by detecting O2 absorption near 1.27 µm. The high spectral resolution and stringent retrieval accuracy requirements of greenhouse gas remote sensing in this spectral range necessitate a reliable spectral calibration. To this end, on-ground laboratory measurements were used to derive the spectral calibration of MethaneAIR, serving as a pathfinder for the future calibration of MethaneSAT. Stray light was characterized and corrected for through fast-Fourier-transform-based Van Cittert deconvolution. Wavelength registration was examined and found to be best described by a linear relationship for both bands with a precision of ∼ 0.02 spectral pixel. The instrument spectral spread function (ISSF), measured with fine wavelength steps of 0.005 nm near a series of central wavelengths across each band, was oversampled to construct the instrument spectral response function (ISRF) at each central wavelength and spatial pixel. The ISRFs were smoothed with a Savitzky–Golay filter for use in a lookup table in the retrieval algorithm. The MethaneAIR spectral calibration was evaluated through application to radiance spectra from an instrument flight over the Colorado Front Range.

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