scholarly journals A method for colocating satellite <i>X</i><sub>CO<sub>2</sub></sub> data to ground-based data and its application to ACOS-GOSAT and TCCON

2014 ◽  
Vol 7 (8) ◽  
pp. 2631-2644 ◽  
Author(s):  
H. Nguyen ◽  
G. Osterman ◽  
D. Wunch ◽  
C. O'Dell ◽  
L. Mandrake ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Weighted Average ◽  
Satellite Observations ◽  
Total Carbon ◽  
Satellite Measurements ◽  
Temporal Window ◽  
Squared Error ◽  
Scanning Imaging ◽  
Total Column ◽  
Absorption Spectrometer

Abstract. Satellite measurements are often compared with higher-precision ground-based measurements as part of validation efforts. The satellite soundings are rarely perfectly coincident in space and time with the ground-based measurements, so a colocation methodology is needed to aggregate "nearby" soundings into what the instrument would have seen at the location and time of interest. We are particularly interested in validation efforts for satellite-retrieved total column carbon dioxide (XCO2), where XCO2 data from Greenhouse Gas Observing Satellite (GOSAT) retrievals (ACOS, NIES, RemoteC, PPDF, etc.) or SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) are often colocated and compared to ground-based column XCO2 measurement from Total Carbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements of total column dry-air mole fractions of CO2 (XCO2) with ground-based measurements typically involve locating and averaging the satellite measurements within a latitudinal, longitudinal, and temporal window. We examine a geostatistical colocation methodology that takes a weighted average of satellite observations depending on the "distance" of each observation from a ground-based location of interest. The "distance" function that we use is a modified Euclidian distance with respect to latitude, longitude, time, and midtropospheric temperature at 700 hPa. We apply this methodology to XCO2 retrieved from GOSAT spectra by the ACOS team, cross-validate the results to TCCON XCO2 ground-based data, and present some comparisons between our methodology and standard existing colocation methods showing that, in general, geostatistical colocation produces smaller mean-squared error.

scholarly journals A method for colocating satellite <i>X</i><sub>CO<sub>2</sub></sub> data to ground-based data and its application to ACOS-GOSAT and TCCON

2014 ◽  
Vol 7 (2) ◽  
pp. 1495-1533
Author(s):  
H. Nguyen ◽  
G. Osterman ◽  
D. Wunch ◽  
C. O'Dell ◽  
L. Mandrake ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Total Carbon ◽  
Tropospheric Temperature ◽  
Satellite Measurements ◽  
Temporal Window ◽  
Squared Error ◽  
Scanning Imaging ◽  
Total Column

Abstract. Satellite measurements are often compared with higher-precision ground-based measurements as part of validation efforts. The satellite soundings are rarely perfectly coincident in space and time with the ground-based measurements, so a colocation methodology is needed to aggregate "nearby" soundings into what the instrument would have seen at the location and time of interest. We are particularly interested in validation efforts for satellite-retrieved total column carbon dioxide (XCO2), where XCO2 data from Greenhouse Gas Observing Satellite (GOSAT) retrievals (ACOS, NIES, RemoteC, PPDF, etc.) or SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCHIACHY) are often colocated and compared to ground-based column XCO2 measurement from Total Carbon Column Observing Network (TCCON). Current colocation methodologies for comparing satellite measurements of total column dry-air mole fractions of CO2 (XCO2) with ground-based measurements typically involve locating and averaging the satellite measurements within some latitudinal, longitudinal, and temporal window. We examine a geostatistical colocation methodology that takes a weighted average of satellite observations depending on the "distance" of each observation from a ground-based location of interest. The "distance" function that we use is a modified Euclidian distance with respect to latitude, longitude, time, and mid-tropospheric temperature at 700 hPa. We apply this methodology to XCO2 retrieved from Greenhouse Gas Observing Satellite (GOSAT) spectra by the ACOS team, cross-validate the results to TCCON XCO2 ground-based data, and present some comparison between our methodology and standard existing colocation methods showing that in general geostatistical colocation produces smaller mean-squared error.

scholarly journals Complete subset averaging with many instruments

2020 ◽  
Author(s):  
Seojeong Lee ◽  
Youngki Shin
Keyword(s):  
Demand Function ◽  
Simulation Experiment ◽  
Mean Squared Error ◽  
Asymptotic Optimality ◽  
Weighted Average ◽  
Subset Size ◽  
Extensive Simulation ◽  
Squared Error ◽  
Many Instruments ◽  
Class Of Estimators

Summary We propose a two-stage least squares (2SLS) estimator whose first stage is the equal-weighted average over a complete subset with k instruments among K available, which we call the complete subset averaging (CSA) 2SLS. The approximate mean squared error (MSE) is derived as a function of the subset size k by the Nagar (1959) expansion. The subset size is chosen by minimising the sample counterpart of the approximate MSE. We show that this method achieves asymptotic optimality among the class of estimators with different subset sizes. To deal with averaging over a growing set of irrelevant instruments, we generalise the approximate MSE to find that the optimal k is larger than otherwise. An extensive simulation experiment shows that the CSA-2SLS estimator outperforms the alternative estimators when instruments are correlated. As an empirical illustration, we estimate the logistic demand function in Berry et al. (1995) and find that the CSA-2SLS estimate is better supported by economic theory than are the alternative estimates.

scholarly journals The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

2016 ◽  
Vol 9 (7) ◽  
pp. 2753-2779 ◽  
Author(s):  
Steffen Beirle ◽  
Christoph Hörmann ◽  
Patrick Jöckel ◽  
Song Liu ◽  
Marloes Penning de Vries ◽  
...  
Keyword(s):  
Input Data ◽  
Polar Vortex ◽  
Chemical Transport ◽  
Estimation Algorithm ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Transport Models ◽  
Verification Algorithm ◽  
Chemical Transport Models ◽  
Total Column

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

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.

Linear Prediction of a True Score From a Direct Estimate and Several Derived Estimates

2007 ◽  
Vol 32 (1) ◽  
pp. 6-23 ◽  
Author(s):  
Shelby J. Haberman ◽  
Jiahe Qian
Keyword(s):  
Linear Prediction ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Statistical Prediction ◽  
True Score ◽  
Direct Estimate ◽  
Linear Predictor ◽  
Squared Error ◽  
Best Linear Predictor ◽  
Prediction Problems

Statistical prediction problems often involve both a direct estimate of a true score and covariates of this true score. Given the criterion of mean squared error, this study determines the best linear predictor of the true score given the direct estimate and the covariates. Results yield an extension of Kelley’s formula for estimation of the true score to cases in which covariates are present. The best linear predictor is a weighted average of the direct estimate and of the linear regression of the direct estimate onto the covariates. The weights depend on the reliability of the direct estimate and on the multiple correlation of the true score with the covariates. One application of the best linear predictor is to use essay features provided by computer analysis and an observed holistic score of an essay provided by a human rater to approximate the true score corresponding to the holistic score.

scholarly journals Retrieval of atmospheric CH<sub>4</sub> vertical information from TCCON FTIR spectra

2019 ◽  
Author(s):  
Minqiang Zhou ◽  
Bavo Langerock ◽  
Mahesh Kumar Sha ◽  
Nicolas Kumps ◽  
Christian Hermans ◽  
...  
Keyword(s):  
Mole Fraction ◽  
Distinct Species ◽  
Satellite Observations ◽  
Total Carbon ◽  
Systematic Uncertainties ◽  
Time Period ◽  
Accuracy And Precision ◽  
Retrieval Software ◽  
Total Column

Abstract. TCCON (Total Carbon Column Observing Network) 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 performing a profile scaling retrieval. In order to obtain several vertical information in addition to total column, in this study, the SFIT4 retrieval code is applied to retrieve CH4 mole fraction vertical profile using TCCON spectra (SFIT4TCCON) at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016−2017. The retrieval strategy of SFIT4TCCON is investigated. The degree of freedom for signal of the SFIT4TCCON retrieval is about 2.4, with two distinct species of information in the troposphere and in the stratosphere. The averaging kernel and error budget of the SFIT4TCCON retrieval are presented. The data accuracy and precision of the SFIT4TCCON 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, ACE-FTS satellite observations, TCCON proxy data and AirCore measurements. By comparison against TCCON standard retrievals, it is found that the retrieval uncertainty of SFIT4TCCON XCH4 is similar to that of TCCON standard retrievals with the systematic uncertainty within 0.35 % and the random uncertainty about 0.5 %. The tropospheric and stratospheric XCH4 from SFIT4TCCON retrievals are assessed by comparing with AirCore measurements at Sodankylä, and there is a 1.2 % overestimation in the SFIT4TCCON tropospheric XCH4 and a 4.0 % underestimation in the SFIT4TCCON stratospheric XCH4, which are within the systematic uncertainties of SFIT4TCCON retrieved partial columns in the troposphere and stratosphere, respectively.

scholarly journals Average Derivative Estimation from Biased Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Christophe Chesneau ◽  
Maher Kachour ◽  
Fabien Navarro
Keyword(s):  
Rate Of Convergence ◽  
Mean Squared Error ◽  
Weighted Average ◽  
Derivative Estimation ◽  
Squared Error ◽  
The Mean ◽  
Average Derivative Estimation ◽  
Average Derivative

We investigate the estimation of the density-weighted average derivative from biased data. An estimator integrating a plug-in approach and wavelet projections is constructed. We prove that it attains the parametric rate of convergence 1/n under the mean squared error.

scholarly journals The Total Carbon Column Observing Network

2011 ◽  
Vol 369 (1943) ◽  
pp. 2087-2112 ◽  
Author(s):  
Debra Wunch ◽  
Geoffrey C. Toon ◽  
Jean-François L. Blavier ◽  
Rebecca A. Washenfelder ◽  
Justus Notholt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Total Carbon ◽  
Global Network ◽  
Satellite Measurements ◽  
Accuracy And Precision ◽  
Science Investigations ◽  
Total Column ◽  
Better Than

A global network of ground-based Fourier transform spectrometers has been founded to remotely measure column abundances of CO 2 , CO, CH 4 , N 2 O and other molecules that absorb in the near-infrared. These measurements are directly comparable with the near-infrared total column measurements from space-based instruments. With stringent requirements on the instrumentation, acquisition procedures, data processing and calibration, the Total Carbon Column Observing Network (TCCON) achieves an accuracy and precision in total column measurements that is unprecedented for remote-sensing observations (better than 0.25% for CO 2 ). This has enabled carbon-cycle science investigations using the TCCON dataset, and allows the TCCON to provide a link between satellite measurements and the extensive ground-based in situ network.

scholarly journals Systematic detection of local CH<sub>4</sub> anomalies by combining satellite measurements with high-resolution forecasts

2021 ◽  
Vol 21 (6) ◽  
pp. 5117-5136
Author(s):  
Jérôme Barré ◽  
Ilse Aben ◽  
Anna Agustí-Panareda ◽  
Gianpaolo Balsamo ◽  
Nicolas Bousserez ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Observations ◽  
Anthropogenic Emissions ◽  
Synoptic Scale ◽  
Satellite Measurements ◽  
Satellite Retrieval ◽  
Quantitative Manner ◽  
Ch4 Concentration ◽  
Monitoring Service ◽  
Total Column

Abstract. In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.

scholarly journals Systematic detection of local CH<sub>4</sub> emissions anomalies combining satellite measurements and high-resolution forecasts

2020 ◽  
Author(s):  
Jérôme Barré ◽  
Ilse Aben ◽  
Anna Agustí-Panareda ◽  
Gianpaolo Balsamo ◽  
Nicolas Bousserez ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Satellite Observations ◽  
Satellite Measurements ◽  
Satellite Retrieval ◽  
Quantitative Manner ◽  
Ch4 Concentration ◽  
Monitoring Service ◽  
Total Column

Abstract. In this study we present a novel monitoring methodology to detect local CH4 concentration anomalies worldwide that are related to rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget. The method uses high resolution (7 km × 7 km) retrievals of total column CH4 from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 Precursor satellite. Observations are combined with high resolution CH4 forecasts (~ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) close to the native satellite resolution at appropriate time. Investigating the departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the large-scale biases on both forecasts and satellite observations. We then use a simple classification on the filtered departures to detect anomalies and plumes coming from CAMS emissions that are missing (e.g. pipeline or facility leaks), under-reported or over-reported (e.g. depleted drilling fields). Additionally, the classification helps to detect local satellite retrieval errors due to land surface albedo issues.

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