scholarly journals A method to correct sampling ghosts in historic near-infrared Fourier transform spectrometer (FTS) measurements

2013 ◽  
Vol 6 (8) ◽  
pp. 1981-1992 ◽  
Author(s):  
S. Dohe ◽  
V. Sherlock ◽  
F. Hase ◽  
M. Gisi ◽  
J. Robinson ◽  
...  
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Near Infrared ◽  
Sampling Error ◽  
Solar Spectrum ◽  
Total Carbon ◽  
Sampling Errors ◽  
Spectral Signatures ◽  
Residual Difference ◽  
Original Time

Abstract. The Total Carbon Column Observing Network (TCCON) has been established to provide ground-based remote sensing measurements of the column-averaged dry air mole fractions (DMF) of key greenhouse gases. To ensure network-wide consistency, biases between Fourier transform spectrometers at different sites have to be well controlled. Errors in interferogram sampling can introduce significant biases in retrievals. In this study we investigate a two-step scheme to correct these errors. In the first step the laser sampling error (LSE) is estimated by determining the sampling shift which minimises the magnitude of the signal intensity in selected, fully absorbed regions of the solar spectrum. The LSE is estimated for every day with measurements which meet certain selection criteria to derive the site-specific time series of the LSEs. In the second step, this sequence of LSEs is used to resample all the interferograms acquired at the site, and hence correct the sampling errors. Measurements acquired at the Izaña and Lauder TCCON sites are used to demonstrate the method. At both sites the sampling error histories show changes in LSE due to instrument interventions (e.g. realignment). Estimated LSEs are in good agreement with sampling errors inferred from the ratio of primary and ghost spectral signatures in optically bandpass-limited tungsten lamp spectra acquired at Lauder. The original time series of Xair and XCO2 (XY: column-averaged DMF of the target gas Y) at both sites show discrepancies of 0.2–0.5% due to changes in the LSE associated with instrument interventions or changes in the measurement sample rate. After resampling, discrepancies are reduced to 0.1% or less at Lauder and 0.2% at Izaña. In the latter case, coincident changes in interferometer alignment may also have contributed to the residual difference. In the future the proposed method will be used to correct historical spectra at all TCCON sites.

scholarly journals A method to correct sampling ghosts in historic near-infrared Fourier Transform Spectrometer (FTS) measurements

2013 ◽  
Vol 6 (2) ◽  
pp. 3545-3579 ◽  
Author(s):  
S. Dohe ◽  
V. Sherlock ◽  
F. Hase ◽  
M. Gisi ◽  
J. Robinson ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Sampling Error ◽  
Total Carbon ◽  
Sampling Errors ◽  
Step Procedure ◽  
Residual Difference ◽  
Correction Scheme ◽  
Parent Ratio ◽  
Good Agreement

Abstract. The Total Carbon Column Observing Network (TCCON) has been established to provide ground-based remote sensing measurements of the column-average dry air mole fractions of key greenhouse gases. To ensure the network wide consistency, biases between Fourier Transform spectrometers at different sites have to be well controlled. In this study we investigate a fundamental correction scheme for errors in the sampling of the interferogram. This is a two-step procedure in which the laser sampling error (LSE) is quantified using a subset of suitable interferograms and then used to resample all the interferograms in the timeseries. Timeseries of measurements acquired at the TCCON sites Izaña and Lauder are used to demonstrate the method. At both sites the sampling error histories show changes in LSE due to instrument interventions. Estimated LSE are in good agreement with sampling errors inferred from lamp measurements of the ghost to parent ratio (Lauder). The LSE introduce retrieval biases which are minimised when the interferograms are resampled. The original timeseries of Xair and XCO2 at both sites show discrepancies of 0.2–0.5% due to changes in the LSE associated with instrument interventions or changes in the measurement sample rate. After resampling discrepancies are reduced to 0.1% at Lauder and 0.2% at Izaña. In the latter case, coincident changes in interferometer alignment may also contribute to the residual difference.

scholarly journals Retrieval of atmospheric CH<sub>4</sub> vertical information from ground-based FTS near-infrared spectra

2019 ◽  
Vol 12 (11) ◽  
pp. 6125-6141 ◽  
Author(s):  
Minqiang Zhou ◽  
Bavo Langerock ◽  
Mahesh Kumar Sha ◽  
Nicolas Kumps ◽  
Christian Hermans ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Mole Fraction ◽  
Atmospheric Chemistry ◽  
Near Infrared ◽  
Satellite Observations ◽  
Total Carbon ◽  
Fourier Transform Spectrometer ◽  
Aircraft Measurements ◽  
Retrieval Software ◽  
Total Column

Abstract. The Total Carbon Column Observing Network (TCCON) column-averaged dry air mole fraction of CH4 (XCH4) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software used in performing a profile scaling retrieval. In order to obtain several vertical pieces of information in addition to the total column, in this study, the SFIT4 retrieval code is applied to retrieve the CH4 mole fraction vertical profile from the Fourier transform spectrometer (FTS) spectrum at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016–2017. The retrieval strategy of the CH4 profile retrieval from ground-based FTS near-infrared (NIR) spectra using the SFIT4 code (SFIT4NIR) is investigated. The degree of freedom for signal (DOFS) of the SFIT4NIR retrieval is about 2.4, with two distinct pieces of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4NIR retrieval are presented. The data accuracy and precision of the SFIT4NIR retrievals, including the total column and two partial columns (in the troposphere and stratosphere), are estimated by TCCON standard retrievals, ground-based in situ measurements, Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) satellite observations, TCCON proxy data and AirCore and aircraft measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4NIR XCH4 is similar to that of TCCON standard retrievals with systematic uncertainty within 0.35 % and random uncertainty of about 0.5 %. The tropospheric and stratospheric XCH4 from SFIT4NIR retrievals are assessed by comparison with AirCore and aircraft measurements, and there is a 1.0 ± 0.3 % overestimation in the SFIT4NIR tropospheric XCH4 and a 4.0 ± 2.0 % underestimation in the SFIT4NIR stratospheric XCH4, which are within the systematic uncertainties of SFIT4NIR-retrieved partial columns in the troposphere and stratosphere respectively.

scholarly journals First intercalibration of column-averaged methane from the Total Carbon Column Observing Network and the Network for the Detection of Atmospheric Composition Change

2012 ◽  
Vol 5 (1) ◽  
pp. 1355-1379
Author(s):  
F. Forster ◽  
R. Sussmann ◽  
M. Rettinger ◽  
N. M. Deutscher ◽  
D. W. T. Griffith ◽  
...  
Keyword(s):  
Time Series ◽  
Near Infrared ◽  
Calibration Factor ◽  
Time Dependent ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Composition Change ◽  
Data Set ◽  
The Difference ◽  
Difference Time

Abstract. We present the intercalibration of dry-air column-averaged mole fractions of methane (XCH4) retrieved from solar FTIR measurements of the Network for the Detection of Atmospheric Composition Change (NDACC) in the mid-infrared (MIR) versus near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON). The study uses multi-annual quasi-coincident MIR and NIR measurements from the stations Garmisch, Germany (47.48° N, 11.06° E, 743 m a.s.l.) and Wollongong, Australia (34.41° S, 150.88° E, 30 m a.s.l.). Direct comparison of the retrieved MIR and NIR time series shows a phase shift in XCH4 seasonality, i.e. a significant time-dependent bias leading to a standard deviation (stdv) of the difference time series (NIR-MIR) of 8.4 ppb. After eliminating differences in a prioris by using ACTM-simulated profiles as a common prior, the seasonalities of the (corrected) MIR and NIR time series agree within the noise (stdv = 5.2 ppb for the difference time series). The difference time series (NIR-MIR) do not show a significant trend. Therefore it is possible to use a simple scaling factor for the intercalibration without a time-dependent linear or seasonal component. Using the Garmisch and Wollongong data together, we obtain an overall calibration factor MIR/NIR = 0.9926(18). The individual calibration factors per station are 0.9940(14) for Garmisch and 0.9893(40) for Wollongong. They agree within their error bars with the overall calibration factor which can therefore be used for both stations. Our results suggest that after applying the proposed intercalibration concept to all stations performing both NIR and MIR measurements, it should be possible to obtain one refined overall intercalibration factor for the two networks. This would allow to set up a harmonized NDACC and TCCON XCH4 data set which can be exploited for joint trend studies, satellite validation, or the inverse modeling of sources and sinks.

scholarly journals Time-series Imputation Algorithm

2021 ◽  
Author(s):  
David Howe
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Time Series Data ◽  
Series Data ◽  
Allan Deviation ◽  
Original Time Series ◽  
Power Spectral ◽  
Power Spectral Densities ◽  
The Fourier Transform ◽  
Original Time

Statistical imputation is a field of study that attempts to fill missing data. It is commonly applied to population statistics whose data have no correlation with running time. For a time series, data is typically analyzed using the autocorrelation function (ACF), the Fourier transform to estimate power spectral densities (PSD), the Allan deviation (ADEV), trend extensions, and basically any analysis that depends on uniform time indexes. We explain the rationale for an imputation algorithm that fills gaps in a time series by applying a backward, inverted replica of adjacent live data. To illustrate, four intentional massive gaps that exceed 100% of the original time series are recovered. The L(f) PSD with imputation applied to the gaps is nearly indistinguishable from the original. Also, the confidence of ADEV with imputation falls within 90% of the original ADEV with mixtures of power-law noises. The algorithm in Python is included for those wishing to try it.

scholarly journals Fourier transform spectrometer measurements of column CO<sub>2</sub> at Sodankylä, Finland

2016 ◽  
Vol 5 (2) ◽  
pp. 271-279 ◽  
Author(s):  
Rigel Kivi ◽  
Pauli Heikkinen
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Data Processing ◽  
Greenhouse Gases ◽  
Growing Season ◽  
Total Carbon ◽  
Fourier Transform Spectrometer ◽  
Annual Variability ◽  
Time Period ◽  
First Time

Abstract. Fourier transform spectrometer (FTS) observations at Sodankylä, Finland (67.4° N, 26.6° E) have been performed since early 2009. The FTS instrument is participating in the Total Carbon Column Observing Network (TCCON) and has been optimized to measure abundances of the key greenhouse gases in the atmosphere. Sodankylä is the only TCCON station in the Fennoscandia region. Here we report the measured CO2 time series over a 7-year period (2009–2015) and provide a description of the FTS system and data processing at Sodankylä. We find the lowest monthly column CO2 values in August and the highest monthly values during the February–May season. Inter-annual variability is the highest in the June–September period, which correlates with the growing season. During the time period of FTS measurements from 2009 to 2015, we have observed a 2.2 ± 0.2 ppm increase per year in column CO2. The monthly mean column CO2 values have exceeded 400 ppm level for the first time in February 2014.

scholarly journals FTS measurements of column CO<sub>2</sub> at Sodankylä

2016 ◽  
Author(s):  
R. Kivi ◽  
P. Heikkinen
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Data Processing ◽  
Greenhouse Gases ◽  
Growing Season ◽  
Total Carbon ◽  
Fourier Transform Spectrometer ◽  
Time Period ◽  
First Time ◽  
Ppm Level

Abstract. Fourier Transform Spectrometer (FTS) observations at Sodankylä have been per formed since early 2009. The FTS instrument is participating in the Total Carbon Column Observing Network (TCCON) and has been optimized to measure abundances of the key greenhouse gases in the atmosphere. Here we report the measured CO2 time series over a six year period (2009–2014) and provide a description of the FTS system and data processing at Sodankylä. We find the lowest monthly column CO2 values in August and the highest monthly values during the February to May season. Inter-annual variability is the highest in June–September period, which correlates with the growing season. During the time period of FTS measurements from 2009 until 2014 we have observed a 2.4 &amp;pm; 0.3 ppm increase per year in column CO2. The monthly mean column CO2 values have exceeded 400 ppm level for the first time in February 2014.

scholarly journals First intercalibration of column-averaged methane from the Total Carbon Column Observing Network and the Network for the Detection of Atmospheric Composition Change

2013 ◽  
Vol 6 (2) ◽  
pp. 397-418 ◽  
Author(s):  
R. Sussmann ◽  
A. Ostler ◽  
F. Forster ◽  
M. Rettinger ◽  
N. M. Deutscher ◽  
...  
Keyword(s):  
Time Series ◽  
Near Infrared ◽  
A Priori ◽  
Time Dependent ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Composition Change ◽  
Data Set ◽  
The Difference ◽  
Difference Time

Abstract. We present the first intercalibration of dry-air column-averaged mole fractions of methane (XCH4) retrieved from solar Fourier transform infrared (FTIR) measurements of the Network for the Detection of Atmospheric Composition Change (NDACC) in the mid-infrared (MIR) versus near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON). The study uses multi-annual quasi-coincident MIR and NIR measurements from the stations Garmisch, Germany (47.48° N, 11.06° E, 743 m a.s.l.), and Wollongong, Australia (34.41° S, 150.88° E, 30 m a.s.l.). Direct comparison of the retrieved MIR and NIR XCH4 time series for Garmisch shows a quasi-periodic seasonal bias leading to a standard deviation (stdv) of the difference time series (NIR–MIR) of 7.2 ppb. After reducing time-dependent a priori impact by using realistic site- and time-dependent ACTM-simulated profiles as a common prior, the seasonal bias is reduced (stdv = 5.2 ppb). A linear fit to the MIR/NIR scatter plot of monthly means based on same-day coincidences does not show a y-intercept that is statistically different from zero, and the MIR/NIR intercalibration factor is found to be close to ideal within 2-σ uncertainty, i.e. 0.9996(8). The difference time series (NIR–MIR) do not show a significant trend. The same basic findings hold for Wollongong. In particular an overall MIR/NIR intercalibration factor close to the ideal 1 is found within 2-σ uncertainty. At Wollongong the seasonal cycle of methane is less pronounced and corresponding smoothing errors are not as significant, enabling standard MIR and NIR retrievals to be used directly, without correction to a common a priori. Our results suggest that it is possible to set up a harmonized NDACC and TCCON XCH4 data set which can be exploited for joint trend studies, satellite validation, or the inverse modeling of sources and sinks.

scholarly journals Solar 5-minute Oscillations at 2.23 μm

1994 ◽  
Vol 154 ◽  
pp. 271-276
Author(s):  
Torben Leifsen
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Near Infrared ◽  
Broad Band ◽  
Solar Observatory ◽  
Fourier Transform Spectrometer ◽  
Large Intensity ◽  
National Solar Observatory ◽  
Long Run ◽  
The Fourier Transform

Large amplitude solar 5-min intensity oscillations have recently been detected at 2.23 μm using broad band (650 Å FWHM) photometry (Leifsen and Maltby, 1990). Large intensity amplitudes in a broad range in the near infrared was unexpected, and several questions concerning the source of the high amplitudes were raised. In an attempt to study the nature of these oscillations, time series of spectra have been obtained with the Fourier Transform Spectrometer (FTS) of the McMath telescope at National Solar Observatory at Kitt Peak. We present preliminary results from a 10 day long run in May 1991 in support for the suggestion that the results may be useful in both helio- and asteroseismological investigations.

scholarly journals Validation of TANSO-FTS/GOSAT XCO<sub>2</sub> and XCH<sub>4</sub> glint mode retrievals using TCCON data from near-ocean sites

2016 ◽  
Vol 9 (3) ◽  
pp. 1415-1430 ◽  
Author(s):  
Minqiang Zhou ◽  
Bart Dils ◽  
Pucai Wang ◽  
Rob Detmers ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Near Infrared ◽  
Total Carbon ◽  
Fourier Transform Spectrometer ◽  
Infrared Sensor ◽  
Dry Air ◽  
Retrieval Algorithms ◽  
Operational Algorithm ◽  
Relative Scatter ◽  
Good Agreement

Abstract. The thermal And near infrared sensor for carbon observations Fourier transform spectrometer (TANSO-FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) applies the normal nadir mode above the land (“land data”) and sun glint mode over the ocean (“ocean data”) to provide global distributions of column-averaged dry-air mole fractions of CO2 and CH4, or XCO2 and XCH4. Several algorithms have been developed to obtain highly accurate greenhouse gas concentrations from TANSO-FTS/GOSAT spectra. So far, all the retrieval algorithms have been validated with the measurements from ground-based Fourier transform spectrometers from the Total Carbon Column Observing Network (TCCON), but limited to the land data. In this paper, the ocean data of the SRPR, SRFP (the proxy and full-physics versions 2.3.5 of SRON/KIT's RemoTeC algorithm), NIES (National Institute for Environmental Studies operational algorithm version 02.21) and ACOS (NASA's Atmospheric CO2 Observations from Space version 3.5) are compared with FTIR measurements from five TCCON sites and nearby GOSAT land data.For XCO2, both land and ocean data of NIES, SRFP and ACOS show good agreement with TCCON measurements. Averaged over all TCCON sites, the relative biases of ocean data and land data are −0.33 and −0.13 % for NIES, 0.03 and 0.04 % for SRFP, 0.06 and −0.03 % for ACOS, respectively. The relative scatter ranges between 0.31 and 0.49 %. For XCH4, the relative bias of ocean data is even less than that of the land data for the NIES (0.02 vs. −0.35 %), SRFP (0.04 vs. 0.20 %) and SRPR (−0.02 vs. 0.06 %) algorithms. Compared to the results for XCO2, the XCH4 retrievals show larger relative scatter (0.65–0.81 %).

Sign in / Sign up

Export Citation Format

Share Document