124 results on '"David A. Pollard"'
Search Results
2. A Chinese Look at Literature: The Literary Values of Chou Tso-jen in Relation to the Tradition
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David E. Pollard and David E. Pollard
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- 2023
3. A Geostatistical Framework for Quantifying the Imprint of Mesoscale Atmospheric Transport on Satellite Trace Gas Retrievals
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Anthony D. Torres, Gretchen Keppel‐Aleks, Scott C. Doney, Michaela Fendrock, Kelly Luis, Martine De Mazière, Frank Hase, Christof Petri, David F. Pollard, Coleen M. Roehl, Ralf Sussmann, Voltaire A. Velazco, Thorsten Warneke, and Debra Wunch
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- 2019
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4. Validation of Carbon Trace Gas Profile Retrievals from the NOAA-Unique Combined Atmospheric Processing System for the Cross-Track Infrared Sounder
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Nicholas R. Nalli, Changyi Tan, Juying Warner, Murty Divakarla, Antonia Gambacorta, Michael Wilson, Tong Zhu, Tianyuan Wang, Zigang Wei, Ken Pryor, Satya Kalluri, Lihang Zhou, Colm Sweeney, Bianca C. Baier, Kathryn McKain, DebraWunch, Nicholas M. Deutscher, Frank Hase, Laura T. Iraci, Rigel Kivi, Isamu Morino, Justus Notholt, Hirofumi Ohyama, David F. Pollard, Yao Té, Voltaire A. Velazco, Thorsten Warneke, Ralf Sussmann, and Markus Rettinger
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Geosciences (General) - Abstract
This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH4) and carbon dioxide (CO2), from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), a NOAA enterprise algorithm that retrieves atmospheric profile environmental data records (EDRs) under global non-precipitating (clear to partly cloudy) conditions. Vertical information about atmospheric trace gases is obtained from the Cross-track Infrared Sounder (CrIS), an infrared Fourier transform spectrometer that measures high resolution Earth radiance spectra from NOAA operational low earth orbit (LEO) satellites, including the Suomi National Polar-orbiting Partnership (SNPP) and follow-on Joint Polar Satellite System (JPSS) series beginning with NOAA-20. The NUCAPS CO, CH4, and CO2 profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH4 and CO2 product accuracies are both found to be within ±1%, with precisions of ≈1.5% and ⪅0.5%, respectively, throughout the tropospheric column.
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- 2020
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5. Interannual variability in the Australian carbon cycle over 2015–2019, based on assimilation of Orbiting Carbon Observatory-2 (OCO-2) satellite data
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Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
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Atmospheric Science - Abstract
In this study, we employ a regional inverse modelling approach to estimate monthly carbon fluxes over the Australian continent for 2015–2019 using the assimilation of the total column-averaged mole fractions of carbon dioxide from the Orbiting Carbon Observatory-2 (OCO-2, version 9) satellite. Subsequently, we study the carbon cycle variations and relate their fluctuations to anomalies in vegetation productivity and climate drivers. Our 5-year regional carbon flux inversion suggests that Australia was a carbon sink averaging −0.46 ± 0.08 PgC yr−1 (excluding fossil fuel emissions), largely influenced by a strong carbon uptake (−1.04 PgC yr−1) recorded in 2016. Australia's semi-arid ecosystems, such as sparsely vegetated regions (in central Australia) and savanna (in northern Australia), were the main contributors to the carbon uptake in 2016. These regions showed relatively high vegetation productivity, high rainfall, and low temperature in 2016. In contrast to the large carbon sink found in 2016, the large carbon outgassing recorded in 2019 coincides with an unprecedented rainfall deficit and higher-than-average temperatures across Australia. Comparison of the posterior column-averaged CO2 concentration with Total Carbon Column Observing Network (TCCON) stations and in situ measurements offers limited insight into the fluxes assimilated with OCO-2. However, the lack of these monitoring stations across Australia, mainly over ecosystems such as savanna and areas with sparse vegetation, impedes us from providing strong conclusions. To a certain extent, we found that the flux anomalies across Australia are consistent with the ensemble means of the OCO-2 Model Intercomparison Project (OCO-2 MIP) and FLUXCOM (2015–2018), which estimate an anomalous carbon sink for Australia in 2016 of −1.09 and −0.42 PgC yr−1 respectively. More accurate estimates of OCO-2 retrievals, with the addition of ocean glint data into our system, and a better understanding of the error in the atmospheric transport modelling will yield further insights into the difference in the magnitude of our carbon flux estimates.
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- 2022
6. An 11-year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm
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Thomas E. Taylor, Christopher W. O'Dell, David Crisp, Akhiko Kuze, Hannakaisa Lindqvist, Paul O. Wennberg, Abhishek Chatterjee, Michael Gunson, Annmarie Eldering, Brendan Fisher, Matthäus Kiel, Robert R. Nelson, Aronne Merrelli, Greg Osterman, Frédéric Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Cheng Liu, Martine De Mazière, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Matthias Schneider, Coleen M. Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, and Debra Wunch
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General Earth and Planetary Sciences - Abstract
The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (
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- 2022
7. Bias Correction of the Ratio of Total Column CH4 to CO2 Retrieved from GOSAT Spectra.
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Haruki Oshio, Yukio Yoshida, Tsuneo Matsunaga, Nicholas M. Deutscher, Manvendra K. Dubey, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Coleen M. Roehl, Kei Shiomi, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Thorsten Warneke, and Debra Wunch
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- 2020
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8. Seasonal variability of stratospheric methane: implications for constraining tropospheric methane budgets using total column observations
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Katherine M. Saad, Debra Wunch, Nicholas M. Deutscher, David W. T. Griffith, Frank Hase, Martine De Mazière, Justus Notholt, David F. Pollard, Coleen M. Roehl, Matthias Schneider, Ralf Sussmann, Thorsten Warneke, and Paul O. Wennberg
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- 2016
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9. Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements
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Jeremy D. Silver, Zoe Loh, David F. Pollard, Jürgen Knauer, Nicholas M. Deutscher, David W. T. Griffith, Yohanna Villalobos, Steven Thomas, Vanessa Haverd, and Peter Rayner
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,Carbon sink ,chemistry.chemical_element ,Enhanced vegetation index ,Vegetation ,15. Life on land ,010501 environmental sciences ,Atmospheric sciences ,01 natural sciences ,Data assimilation ,Productivity (ecology) ,chemistry ,13. Climate action ,Environmental science ,Ecosystem ,Total Carbon Column Observing Network ,Carbon ,0105 earth and related environmental sciences - Abstract
In this study, we present the assimilation of data from the Orbiting Carbon Observatory-2 (OCO-2) (land nadir and glint data, version 9) to estimate the Australian carbon surface fluxes for the year 2015. To perform this estimation, we used both a regional-scale atmospheric transport–dispersion model and a four-dimensional variational assimilation scheme. Our results suggest that Australia was a carbon sink of −0.41 ± 0.08 PgC yr−1 compared to the prior estimate 0.09 ± 0.20 PgC yr−1 (excluding fossil fuel emissions). Most of the carbon uptake occurred in northern Australia over the savanna ecotype and in the western region over areas with sparse vegetation. Analysis of the enhanced vegetation index (EVI) suggests that the majority of the carbon uptake over the savanna ecosystem was due to an increase of vegetation productivity (positive EVI anomalies) amplified by an anomalous increase of rainfall in summer. Further from this, a slight increase of carbon uptake in Western Australia over areas with sparse vegetation (the largest ecosystem in Australia) was noted due to increased land productivity in the area caused by positive rainfall anomalies. The stronger carbon uptake estimate in this ecosystem was partially due to the land surface model (CABLE-BIOS3) underestimating the gross primary productivity of the ecosystem. To evaluate the accuracy of our carbon flux estimates from OCO-2 retrievals, we compare our posterior concentration fields against the column-averaged carbon retrievals from the Total Carbon Column Observing Network (TCCON) and ground-based in situ monitoring sites located around our domain. The validation analysis against TCCON shows that our system is able to reduce bias mainly in the summer season. Comparison with surface in situ observations was less successful, particularly over oceanic monitoring sites that are strongly affected by oceanic fluxes and subject to less freedom by the inversion. For stations located far from the coast, the comparison with in situ data was more variable, suggesting difficulties matching the column-integrated and surface data by the inversion, most likely linked to model vertical transport. Comparison of our fluxes against the OCO-2 model intercomparison (MIP) was encouraging. The annual carbon uptake estimated by our inversion falls within the ensemble of the OCO-2 MIP global inversions and presents a similar seasonal pattern.
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- 2021
10. Reply on RC2
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David F. Pollard
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- 2022
11. Retrievals of XCO2, XCH4 and XCO from portable, near-infrared Fourier transform spectrometer solar observations in Antarctica
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David Frank Pollard, Frank Hase, Mahesh Kumar Sha, Darko Dubravica, Carlos Alberti, and Dan Smale
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Earth sciences ,ddc:550 ,General Earth and Planetary Sciences - Abstract
The COllaborative Carbon Column Observing Network (COCCON) uses low-resolution, portable EM27/SUN Fourier transform spectrometers (FTSs) to make retrievals of column-averaged dry-air mole fractions (DMFs, represented as Xgas) of CO2, CH4, CO and H2O from near-infrared solar absorption spectra. The COCCON has developed rapidly over recent years and complements the Total Carbon Column Observing Network (TCCON). In this work, we provide details of the first seasonal time series of near-infrared XCO2, XCH4 and XCO retrievals from measurements made in Antarctica during the deployment of an EM27/SUN to the Arrival Heights laboratory on Ross Island over the austral summer of 2019–2020 under the auspices of the COCCON. The DMFs of all three species were lower in Antarctica than at mid-latitude, and for XCO2 and XCO, the retrieved values were less variable. For XCH4 however, the variability was significantly greater and it was found that this was strongly correlated to the proximity of the polar vortex. In order to ensure the stability of the instrument and the traceability of the retrievals, side-by-side comparisons to the TCCON station at Lauder, New Zealand and retrievals of the instrument line shape (ILS) were made before and after the measurements in Antarctica. These indicate that, over the course of the deployment, the instrument stability was such that the change in retrieved XCO2 was well below 0.1 %. The value of these data for satellite validation is demonstrated by making comparisons with the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 precursor (S5P) satellite. The dataset is available from the COCCON central facility hosted by the ESA Atmospheric Validation Data Centre (EVDC) https://doi.org/10.48477/coccon.pf10.arrivalheights.R02 (Pollard, 2021).
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- 2022
12. Supplementary material to 'National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the Global Stocktake'
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Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra Dubey, Sha Feng, Omaira García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O’Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
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- 2022
13. National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the Global Stocktake
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Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra Dubey, Sha Feng, Omaira García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O’Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, Ning Zeng, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation INVerse pour les mesures atmosphériques et SATellitaires (SATINV), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
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Earth sciences ,Temperature increase ,Carbon dioxide emission ,[SDU]Sciences of the Universe [physics] ,ddc:550 ,General Earth and Planetary Sciences ,Climate change - Abstract
Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.
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- 2022
14. iBem3D, a three-dimensional iterative boundary element method using angular dislocations for modeling geologic structures.
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Frantz Maerten, Laurent Maerten, and David D. Pollard
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- 2014
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15. Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals.
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Ella Kivimäki, Hannakaisa Lindqvist, Janne Hakkarainen, Marko Laine, Ralf Sussmann, Aki Tsuruta, Rob Detmers, Nicholas M. Deutscher, Edward J. Dlugokencky, Frank Hase, Otto Hasekamp, Rigel Kivi, Isamu Morino, Justus Notholt, David F. Pollard, Coleen M. Roehl, Matthias Schneider, Mahesh Kumar Sha, Voltaire A. Velazco, Thorsten Warneke, Debra Wunch, Yukio Yoshida, and Johanna Tamminen
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- 2019
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16. Evaluation of Bias Correction Methods for GOSAT SWIR XH2O Using TCCON data.
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Tran Thi Ngoc Trieu, Isamu Morino, Hirofumi Ohyama, Osamu Uchino, Ralf Sussmann, Thorsten Warneke, Christof Petri, Rigel Kivi, Frank Hase, David F. Pollard, Nicholas M. Deutscher, Voltaire A. Velazco, Laura T. Iraci, James R. Podolske, and Manvendra K. Dubey
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- 2019
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17. The Adaptable 4A Inversion (5AI): description and first XCO2 retrievals from Orbiting Carbon Observatory-2 (OCO-2) observations
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Coleen M. Roehl, Matthieu Dogniaux, Isamu Morino, Vincent Cassé, Thorsten Warneke, Rigel Kivi, Frank Hase, Dietrich G. Feist, Voltaire A. Velazco, Virginie Capelle, David F. Pollard, Martine De Mazière, Kimberly Strong, Yao Té, Justus Notholt, R. Armante, Omaira García, Cyril Crevoisier, Nicholas M. Deutscher, Thibault Delahaye, Kei Shiomi, Laura T. Iraci, and David W. T. Griffith
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,Optimal estimation ,Inversion (meteorology) ,7. Clean energy ,01 natural sciences ,Standard deviation ,Aerosol ,010309 optics ,13. Climate action ,Observatory ,Greenhouse gas ,0103 physical sciences ,Radiative transfer ,Environmental science ,Total Carbon Column Observing Network ,0105 earth and related environmental sciences ,Remote sensing - Abstract
A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Space-borne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on the Optimal Estimation algorithm, relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry air mole fraction of carbon dioxide (XCO2) from a sample of measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission. Those have been selected as a compromise between coverage and the lowest aerosol content possible, so that the impact of scattering particles can be neglected, for computational time purposes. For air masses below 3.0, 5AI XCO2 retrievals successfully capture the latitudinal variations of CO2 and its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a bias of 1.30±1.32 ppm (parts per million), which is comparable to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products over the same set of soundings. These nonscattering 5AI results, however, exhibit an average difference of about 3 ppm compared to ACOS results. We show that neglecting scattering particles for computational time purposes can explain most of this difference that can be fully corrected by adding to OCO-2 measurements an average calculated–observed spectral residual correction, which encompasses all the inverse setup and forward differences between 5AI and ACOS. These comparisons show the reliability of 5AI as an optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry air mole fractions of greenhouse gases.
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- 2021
18. Raman spectroscopy and multivariate regression analysis in biomedical research, medical diagnosis, and clinical analysis
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Noureen Siraj, David K. Bwambok, Sayo O. Fakayode, Amanda Jalihal, Brianda Elzey, Iris Denmark, Samantha Macchi, Gary A. Baker, Pamela Nicole Brady, David A. Pollard, Thuy Le, Megan Taylor, and Mujeebat Bashiru
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medicine.medical_specialty ,Multivariate statistics ,Pathogen detection ,Multivariate analysis ,ComputingMilieux_THECOMPUTINGPROFESSION ,Clinical pathology ,business.industry ,In vivo analysis ,Medical instrumentation ,Medicine ,Medical physics ,Medical diagnosis ,Operational costs ,business ,Instrumentation ,Spectroscopy - Abstract
High operational costs of modern medical devices and the required specialized, skilled personnel with certifications to operate most medical instrumentation remains a challenge and an impediment to...
- Published
- 2021
19. The Orbiting Carbon Observatory (OCO-2) Tracks 2-3 Peta-Gram Increase in Carbon Release to the Atmosphere During the 2014-2016 El Nino
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Prabir K Patra, David Crisp, Joohannes W Kaiser, Debra Wunch, Tazu Saeki, Kazuhito Ichii, Takashi Sekiya, Paul O Wennberg, Dietrich G Feist, David F Pollard, David W T Griffith, Voltaire A Velazco, M De Maziere, Mahesh K Sha, Coleen M Roehl, Abhishek Chatterjee, and Kentaro Ishijima
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Earth Resources And Remote Sensing - Abstract
The powerful El Niño event of 2015-2016 - the third most intense since the 1950s - has exerted a large impact on the Earth's natural climate system. The column-averaged CO2 dry-air mole fraction (XCO2) observations from satellites and ground based networks are analyzed together with in situ observations for the period of September 2014 to October 2016. From the differences between satellite (OCO-2) observations and simulations using an atmospheric chemistry-transport model, we estimate that, relative to the mean annual fluxes for 2014, the most recent El Niño has contributed to an excess CO2 emission from the Earth's surface (land+ocean) to the atmosphere in the range of 2.4+/-0.2 PgC (1 Pg = 10(exp 15) g) over the period of July 2015 to June 2016. The excess CO2 flux is resulted primarily from reduction in vegetation uptake due to drought, and to a lesser degree from increased biomass burning. It is about the half of the CO2 flux anomaly (range: 4.4-6.7 PgC) estimated for the 1997/1998 El Niño. The annual total sink is estimated to be 3.9+/-0.2 PgC for the assumed fossil fuel emission of 10.1 PgC. The major uncertainty in attribution arise from error in anthropogenic emission trends, satellite data and atmospheric transport.
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- 2017
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20. Intercomparison of Total Carbon Column Observing Network (TCCON) data from two Fourier transform spectrometers at Lauder, New Zealand
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Dan Smale, John Robinson, David F. Pollard, and Hisako Shiona
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,lcsh:TA715-787 ,lcsh:Earthwork. Foundations ,Fourier transform spectrometers ,Atmospheric sciences ,Mole fraction ,01 natural sciences ,lcsh:Environmental engineering ,010309 optics ,Greenhouse gas ,0103 physical sciences ,Environmental science ,lcsh:TA170-171 ,Total Carbon Column Observing Network ,0105 earth and related environmental sciences - Abstract
We describe the change in operational instrument for the routine measurement of column-averaged dry-air mole fraction of several greenhouse gases (denoted Xgas) at the Lauder Total Carbon Column Observing Network (TCCON) site and the steps taken to demonstrate comparability between the two observation systems following a systematic methodology. Further, we intercompare retrieved Xgas values during an intensive intercomparison period during October and November 2018, when both instruments were performing optimally, and on subsequent, less frequent occasions. The average difference between the two observing systems was found to be well below the expected level of uncertainty for TCCON retrievals for all compared species. In the case of XCO2 the average difference was 0.0264±0.0465 % (0.11±0.19 µmol mol−1).
- Published
- 2021
21. A decade of GOSAT Proxy satellite CH4 observations
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Kei Shiomi, Alex Webb, Antonio Di Noia, Paul O. Wennberg, Christof Petri, David F. Pollard, Kimberly Strong, Coleen M. Roehl, Nicholas M. Deutscher, Thorsten Warneke, Young-Suk Oh, Rigel Kivi, Justus Notholt, Paul I. Palmer, Frank Hase, Debra Wunch, Frédéric Chevallier, Nikoleta Kalaitzi, David W. T. Griffith, Isamu Morino, Liang Feng, Rocio Barrio Guillo, Ralf Sussmann, Peter Bergamaschi, Yao Té, Hartmut Boesch, Peter Somkuti, Jasdeep Anand, Mahesh Kumar Sha, Hirofumi Ohyama, Dietrich G. Feist, Robert J. Parker, and Voltaire A. Velazco
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Earth observation ,Future studies ,010504 meteorology & atmospheric sciences ,Correlation coefficient ,0211 other engineering and technologies ,Climate change ,02 engineering and technology ,01 natural sciences ,Proxy (climate) ,13. Climate action ,Greenhouse gas ,Climatology ,General Earth and Planetary Sciences ,Environmental science ,Total Carbon Column Observing Network ,Seasonal cycle ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences - Abstract
This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).
- Published
- 2020
22. Retrieval of greenhouse gases from GOSAT and greenhouse gases and carbon monoxide from GOSAT-2 using the FOCAL algorithm
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Stefan Noël, Maximilian Reuter, Michael Buchwitz, Jakob Borchardt, Michael Hilker, Oliver Schneising, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Robert J. Parker, Hiroshi Suto, Yukio Yoshida, Matthias Buschmann, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Markus Rettinger, Coleen M. Roehl, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, and Thorsten Warneke
- Abstract
Recently, the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm has been applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL has been originally developed for Orbiting Carbon Observatory-2 (OCO-2) retrievals with the focus on the derivation of carbon dioxide (XCO2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species. Here, we show new results from updated GOSAT and GOSAT-2 FOCAL retrievals. The main focus is placed on methane (XCH4; full physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2 it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. Compared to the previous product version (v1), the number of valid XCO2 data could be significantly increased in the updated version (v3.0) by 50 % for GOSAT and about a factor of two for GOSAT-2. All FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with TCCON (Total Carbon Column Observing Network) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes in the order of 15 ppb, which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb, which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are in the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data.
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- 2022
23. Supplementary material to 'Interannual variability in the Australian carbon cycle over 2015–2019, based on assimilation of OCO-2 satellite data'
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Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
- Published
- 2022
24. Integrating complementarity into the 2D displacement discontinuity boundary element method to model faults and fractures with frictional contact properties.
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Elizabeth Ritz, Ovunc Mutlu, and David D. Pollard
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- 2012
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25. Evaluation of the Eshelby solution for the ellipsoidal inclusion and heterogeneity.
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Chunfang Meng, Will Heltsley, and David D. Pollard
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- 2012
- Full Text
- View/download PDF
26. TCCON and NDACC XCO measurements: difference, discussion and application
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Rigel Kivi, Minqiang Zhou, Voltaire A. Velazco, Michel Ramonet, Dan Smale, Corinne Vigouroux, Omaira García, Christian Hermans, Mathias Palm, Matthias Schneider, Bavo Langerock, Huilin Chen, David F. Pollard, Martine De Mazière, Jean-Marc Metzger, Mahesh Kumar Sha, Pauli Heikkinen, Nicholas B. Jones, and Thorsten Warneke
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Atmospheric Science ,010504 meteorology & atmospheric sciences ,Mean value ,0211 other engineering and technologies ,Northern Hemisphere ,02 engineering and technology ,Atmospheric sciences ,01 natural sciences ,Atmospheric composition ,Data assimilation ,13. Climate action ,Satellite data ,Absolute bias ,Environmental science ,Total Carbon Column Observing Network ,Smoothing ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences - Abstract
Column-averaged dry-air mole fraction of CO (XCO) measurements are obtained from two ground-based Fourier transform infrared (FTIR) spectrometer networks: the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). In this study, the differences between the TCCON and NDACC XCO measurements are investigated and discussed based on six NDACC–TCCON sites using data over the period 2007–2017. A direct comparison shows that the NDACC XCO measurements are about 5.5 % larger than the TCCON data at Ny-Ålesund, Bremen, and Izaña (Northern Hemisphere), and the absolute bias between the NDACC and TCCON data is within 2 % at Saint-Denis, Wollongong and Lauder (Southern Hemisphere). The hemispheric dependence of the bias is mainly attributed to their smoothing errors. The systematic smoothing error of the TCCON XCO data varies in the range between 0.2 % (Bremen) and 7.9 % (Lauder), and the random smoothing error varies in the range between 2.0 % and 3.6 %. The systematic smoothing error of NDACC data is between 0.1 % and 0.8 %, and the random smoothing error of NDACC data is about 0.3 %. For TCCON data, the smoothing error is significant because it is higher than the reported uncertainty, particularly at Southern Hemisphere sites. To reduce the influence from the a priori profiles and different vertical sensitivities, the scaled NDACC a priori profiles are used as the common a priori profiles for comparing TCCON and NDACC retrievals. As a result, the biases between TCCON and NDACC XCO measurements become more consistent (5.6 %–8.5 %) with a mean value of 6.8 % at these sites. To determine the sources of the remaining bias, regular AirCore measurements at Orléans and Sodankylä are compared to co-located TCCON measurements. It is found that TCCON XCO measurements are 6.1 ± 1.6 % and 8.0 ± 3.2 % smaller than the AirCore measurements at Orléans and Sodankylä, respectively, indicating that the scaling factor of TCCON XCO data should be around 1.0000 instead of 1.0672. Further investigations should be carried out in the TCCON community to determine the correct scaling factor to be applied to the TCCON XCO data. This paper also demonstrates that the smoothing error must be taken into account when comparing FTIR XCO data, and especially TCCON XCO data, with model or satellite data.
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- 2019
27. Evaluation of MOPITT Version 7 joint TIR–NIR XCO retrievals with TCCON
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Martine De Mazière, Helen M. Worden, Coleen M. Roehl, Kimberly Strong, Sébastien Roche, Markus Rettinger, Isamu Morino, Justus Notholt, Yao Té, Dylan B. A. Jones, Ralf Sussmann, Tai-Long He, Jacob K. Hedelius, Osamu Uchino, Hirofumi Ohyama, Thorsten Warneke, David W. T. Griffith, Paul O. Wennberg, Kei Shiomi, Wei Wang, Laura T. Iraci, David F. Pollard, Pascal Jeseck, Young-Suk Oh, Voltaire A. Velazco, Bianca C. Baier, Colm Sweeney, Nicholas M. Deutscher, Matthäus Kiel, Rebecca R. Buchholz, Manvendra K. Dubey, Rigel Kivi, Frank Hase, Cheng Liu, Debra Wunch, Dietrich G. Feist, and Matthias Schneider
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Atmospheric Science ,Accuracy and precision ,010504 meteorology & atmospheric sciences ,Pixel ,0211 other engineering and technologies ,Scale (descriptive set theory) ,02 engineering and technology ,Snow ,01 natural sciences ,MOPITT ,Troposphere ,13. Climate action ,Environmental science ,Total Carbon Column Observing Network ,Scaling ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing - Abstract
Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.
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- 2019
28. An eleven year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm
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Coleen M. Roehl, Martine De Mazière, Matthaeus Kiel, Frédéric Chevallier, Paul I. Palmer, R. R. Nelson, Rigel Kivi, Paul O. Wennberg, Kimberly Strong, Mahesh Kumar Sha, Isamu Morino, Frank Hase, Kei Shiomi, Laura T. Iraci, Matthias Schneider, Annmarie Eldering, Aronne Merrelli, Mihalis Vrekoussis, David F. Pollard, David Crisp, Markus Rettinger, Young-Suk Oh, Yao Té, Thorsten Warneke, Hirofumi Ohyama, Hannakaisa Lindqvist, Akhiko Kuze, Ralf Sussmann, Debra Wunch, Dietrich G. Feist, Voltaire A. Velazco, Manvendra K. Dubey, Thomas E. Taylor, Gregory B. Osterman, Omaira Elena García Rodríguez, Cheng Liu, Brendan Fisher, Nicholas M. Deutscher, Abhishek Chatterjee, Justus Notholt, David W. T. Griffith, Michael R. Gunson, Liang Feng, and Christopher W. O'Dell
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Spectrometer ,Greenhouse gas ,Near-infrared spectroscopy ,Fourier transform spectrometers ,Satellite ,Bias correction ,Total Carbon Column Observing Network ,Retrieval algorithm ,Remote sensing - Abstract
The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (
- Published
- 2021
29. Molecular (Raman, NIR, and FTIR) spectroscopy and multivariate analysis in consumable products analysis1
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Gary A. Baker, A’ja V. Duncan, Rocío L. Pérez, Sayo O. Fakayode, David A. Pollard, Nakara Bhawawet, Brianda Elzey, Samantha Macchi, Isiah M. Warner, Noureen Siraj, and David K. Bwambok
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Multivariate analysis ,Materials science ,010401 analytical chemistry ,Near-infrared spectroscopy ,Analytical chemistry ,02 engineering and technology ,021001 nanoscience & nanotechnology ,01 natural sciences ,0104 chemical sciences ,symbols.namesake ,Goods distribution ,symbols ,Fourier transform infrared spectroscopy ,0210 nano-technology ,Raman spectroscopy ,Instrumentation ,Spectroscopy - Abstract
Ensuring the integrity of quality control and assurance of consumable products across consumer goods distribution and supply chains is pertinent to ensure consumer products of high quality to guara...
- Published
- 2019
30. Correction: Dupuy, E., et al. Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network. Remote Sens. 2016, 8, 414
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Eric Dupuy, Isamu Morino, Nicholas M. Deutscher, Yukio Yoshida, Osamu Uchino, Brian J. Connor, Martine De Mazière, David W. T. Griffith, Frank Hase, Pauli Heikkinen, Patrick W. Hillyard, Laura T. Iraci, Shuji Kawakami, Rigel Kivi, Tsuneo Matsunaga, Justus Notholt, Christof Petri, James R. Podolske, David F. Pollard, Markus Rettinger, Coleen M. Roehl, Vanessa Sherlock, Ralf Sussmann, Geoffrey C. Toon, Voltaire A. Velazco, Thorsten Warneke, Paul O. Wennberg, Debra Wunch, and Tatsuya Yokota
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n/a ,Science - Abstract
n/a
- Published
- 2016
- Full Text
- View/download PDF
31. Comment on amt-2021-31
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Benjamin Ertl, Cyril Crevoisier, Martin Steinbacher, Frank Meinhardt, David W. T. Griffith, Eliezer Sepúlveda, Huilin Chen, Christopher Diekmann, Thomas von Clarmann, Rigel Kivi, Amelie N. Röhling, F. Khosrawi, Frank Hase, Omaira García, V. Velazco, Matthias Schneider, Thomas Laemmel, Michel Ramonet, Alba Lorente, Tobias Borsdorff, David F. Pollard, Jérome Pernin, Jochen Landgraf, Darko Dubravica, and Nicholas M. Deutscher
- Published
- 2021
32. Constructive Disruptor
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David R. Pollard and Colin Keogh
- Published
- 2021
33. Problem, Solution, Validation Impact Canvas
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Roisin Lyons, David R. Pollard, and Colin Keogh
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Architectural engineering ,Computer science ,Social impact ,Business Model Canvas ,Problem solution ,Open innovation - Published
- 2021
34. Challenge, Solution, Validation Impact Canvas
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Roisin Lyons, David R. Pollard, and Colin Keogh
- Published
- 2021
35. Long-term column-averaged greenhouse gas observations using a COCCON spectrometer at the high surface albedo site Gobabeb, Namibia
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Matthias M. Frey, Frank Hase, Thomas Blumenstock, Darko Dubravica, Jochen Groß, Frank Göttsche, Martin Handjaba, Petrus Amadhila, Roland Mushi, Isamu Morino, Kei Shiomi, Mahesh Kumar Sha, Martine de Mazière, and David F. Pollard
- Abstract
In this study we present column-averaged dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO) from a recently established measurement site in Gobabeb, Namibia. Gobabeb is a hyperarid desert site at the sharp transition zone between the sand desert and the gravel plains, offering unique characteristics with respect to surface albedo properties. Measurements started January 2015 and are performed utilizing a ground-based Fourier transform infrared (FTIR) EM27/SUN spectrometer of the COllaborative Carbon Column Observing Network (COCCON). Gobabeb is the first measurement site observaing XCO2 and XCH4 on the African mainland and improves the global coverage of ground-based remote-sensing sites. In order to achieve the high level of precision and accuracy necessary for meaningful greenhouse gas observations, we performed calibration measurements for eight days between November 2015 and March 2016 with the COCCON reference EM27/SUN spectrometer operated at the Karlsruhe Institute of Technology. We derived scaling factors for XCO2, XCH4 and XCO with respect to the reference instrument that are close to 1.0. We compare the results obtained in Gobabeb to measurements at Reunion Island and Lauder from the Total Carbon Column Observing Network (TCCON). We choose these TCCON sites because, while 4000 km apart, the instruments at Gobabeb and Reunion Island operate at roughly the same latitude. The Lauder station is the southernmost TCCON station and functions as a background site without a pronounced XCO2 seasonal cycle. We find a good agreement for the absolute Xgas values and representative diurnal variability. Together with the absence of long term drifts this highlights the quality of the COCCON measurements. In Southern hemispheric summer we observe lower XCO2 values at Gobabeb compared to the TCCON stations, likely due to the influence of the African biosphere. We performed coincident measurements with the Greenhouse Gases Observing Satellite (GOSAT), where GOSAT observed three nearby specific observation points, over the sand desert south of the station, directly over Gobabeb and over the gravel plains to the north. GOSAT H-gain XCO2 and XCH4 agree with the EM27/SUN measurements within the 1 σ uncertainty limit. The number of coincidence soundings is limited, but we confirm a bias of 1.2–2.6 ppm between GOSAT M-gain and H-gain XCO2 soundings depending on the target point. This is in agreement with results reported by a previous study and the GOSAT validation team. We also report a bias of 5.9–9.8 ppb between GOSAT M-gain and H-gain XCH4 measurements which is within the range given by the GOSAT validation team. Finally we use the COCCON measurements to evaluate inversion-optimized CAMS model data. For XCO2 we find high biases of 0.9 ± 0.5 ppm for the OCO-2 assimilated product and 1.1 ± 0.6 ppm for the in situ-driven product with R2 > 0.9 in both cases. These biases are comparable to reported offsets between the model and TCCON data. The OCO-2 assimilated model product is able to reproduce the drawdown of XCO2 observed by the COCCON instrument beginning of 2017, opposed to the in situ-optimized product. Also for XCH4 the observed biases are in line with prior model comparisons with TCCON.
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- 2021
36. Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements
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Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
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010504 meteorology & atmospheric sciences ,13. Climate action ,15. Life on land ,01 natural sciences ,0105 earth and related environmental sciences - Abstract
In this study, we present the assimilation of data from the Orbiting Carbon Observatory-2 (OCO-2) to estimate the Australian CO2 surface fluxes for the year 2015. We used a regional-scale atmospheric transport-dispersion model and a four-dimensional variational assimilation scheme. Our results suggest that Australia was a carbon sink of −0.3 ± 0.09 PgC y−1 compared to the prior estimate 0.09 ± 0.17 PgC y−1 (excluding fossil fuel emissions). Most of the uptake occurred over northern savannas, the Mediterranean ecotype in southern Australia and the sparsely vegetated ecotype in central Australia. Our results suggest that the majority of the carbon uptake over Mediterranean was associated with positive EVI anomalies (relative to 2000–2014). However, the stronger posterior carbon uptake estimated over savanna and sparsely vegetated ecosystem was due primarily to underestimation of the gross primary productivity by the land surface model (CABLE-BIOS3 model). To evaluate the accuracy of our posterior flux estimates, we compare our posterior CO2 concentration simulations against the column- averaged carbon retrievals from the Total Carbon Column Observing Network (TCCON) and ground-based in-situ monitoring sites located around our Australia domain. In general, the performance of our posterior concentration compared well with TCCON observations, except when TCCON concentrations were dominated by ocean fluxes which were tightly constrained to their prior values. Comparisons with in-situ measurements also show encouraging results though with similar difficulties for coastal stations. For stations located far from the coast, the comparison with in situ data was more variable, suggesting difficulties to match the column-integrated and surface data by the inversion, most likely linked to model vertical transport.
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- 2021
37. Supplementary material to 'Was Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2 satellite measurements'
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Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
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- 2021
38. The True Story of Lu Xun
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David E. Pollard
- Published
- 2021
39. Validation of methane and carbon monoxide from Sentinel-5 Precursor using TCCON and NDACC-IRWG stations
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Mahesh Kumar Sha, Bavo Langerock, Jean-François L. Blavier, Thomas Blumenstock, Tobias Borsdorff, Matthias Buschmann, Angelika Dehn, Martine De Mazière, Nicholas M. Deutscher, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Michel Grutter, James W. Hannigan, Frank Hase, Pauli Heikkinen, Christian Hermans, Laura T. Iraci, Pascal Jeseck, Nicholas Jones, Rigel Kivi, Nicolas Kumps, Jochen Landgraf, Alba Lorente, Emmanuel Mahieu, Maria V. Makarova, Johan Mellqvist, Jean-Marc Metzger, Isamu Morino, Tomoo Nagahama, Justus Notholt, Hirofumi Ohyama, Ivan Ortega, Mathias Palm, Christof Petri, David F. Pollard, Markus Rettinger, John Robinson, Sébastien Roche, Coleen M. Roehl, Amelie N. Röhling, Constantina Rousogenous, Matthias Schneider, Kei Shiomi, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Osamu Uchino, Voltaire A. Velazco, Mihalis Vrekoussis, Pucai Wang, Thorsten Warneke, Tyler Wizenberg, Debra Wunch, Shoma Yamanouchi, Yang Yang, and Minqiang Zhou
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010504 meteorology & atmospheric sciences ,Sentinel-5 Precursor ,0211 other engineering and technologies ,02 engineering and technology ,TROPOMI ,Carbon monoxide ,01 natural sciences ,Methane ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences - Abstract
The Sentinel-5 Precursor (S5P) mission with the TROPOspheric Monitoring Instrument (TROPOMI) onboard has been measuring solar radiation backscattered by the Earth's atmosphere and its surface since its launch on 13 October 2017. Methane (CH4) and carbon monoxide (CO) data with a spatial resolution (initially 7 x 7 km2, upgraded to 5.5 x 7 km2 on 6th of August 2019) have been retrieved from shortwave infrared (SWIR) and near-infrared (NIR) measurements since the end of November 2017 and made available to the experts for early validation and quality checks before the official product release. In this paper, we present for the first time the S5P CH4 and CO validation results (covering a period from November 2017 to September 2020) using global Total Carbon Column Observing Network (TCCON) and Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) network data, accounting for a priori alignment and smoothing uncertainties in the validation, and testing the sensitivity of validation results towards the application of advanced co-location criteria.We found that the required bias (systematic error) of 1.5 % and random error of 1 % for the S5P standard and bias-corrected methane data are met for measurements over land surfaces with pixels having quality assurance (QA) value > 0.5. The systematic difference between the S5P standard XCH4 and TCCON data is on average −0.69 ± 0.73 %. The systematic difference changes to a value of −0.25 ± 0.57 % for the S5P bias-corrected XCH4 data. We found a correlation of above 0.6 for most stations, which is mostly dominated by the seasonal cycle. The contributions of smoothing uncertainty at the individual stations are estimated and found to be dependent on the location. The highest contribution of the smoothing uncertainty is observed for mid-latitude TCCON stations and high latitude stations for NDACC. A seasonal dependency of the relative bias is seen. We observe a high bias during the springtime measurements at high SZA and a decreasing bias with increasing SZA for the rest of the year.We found that the required bias (systematic error) of 15 % and random error of < 10 % for the S5P carbon monoxide data are met in general for measurements over all surfaces with pixels having quality assurance value of > 0.5. There are a few stations where this is not the case, mostly due to co-location mismatches and the limited availability of co-located data. We compared the S5P XCO data with respect to standard TCCON XCO and unscaled TCCON XCO (without application of the empirical scaling factor) data sets. The systematic difference between the S5P XCO and the TCCON data is on average 9.14 ± 3.33 % (standard TCCON XCO data) and 2.36 ± 3.22 % (unscaled TCCON XCO data). We found that the systematic difference between the S5P CO column and NDACC CO column data (excluding two stations that were obvious outliers) is on average 6.44 ± 3.79 %. We found a correlation of above 0.9 for most TCCON and NDACC stations indicating that the temporal variations in CO column captured by the ground-based instruments are reproduced very similarly by the S5P CO column. The contribution of smoothing uncertainty at the individual stations is estimated and found to be significant. They are found to be dependent on the location with large changes seen for stations located in the Southern Hemisphere as compared to the Northern Hemisphere and at highly polluted stations. A cone co-location criterion, which gives a better match between the ground-based instrument's line-of-sight and satellite pixels, seems to give better results for high latitude stations and stations located close to emission sources. The validation results for the clear-sky and cloud cases of S5P pixels are comparable to the validation results including all pixels with quality assurance value of > 0.5. We observe that the relative bias increases with increasing SZA. We estimated this increase is about 10 % over the complete range of measurement SZAs.The study shows the high quality of S5P CH4 and CO data by validating the products against reference global TCCON and NDACC stations covering a wide range of latitudinal bands, atmospheric conditions, and surface conditions.
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- 2021
40. Synergetic use of IASI and TROPOMI space borne sensors for generating a tropospheric methane profile product
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Rigel Kivi, Frank Hase, Jochen Landgraf, Huilin Chen, Alba Lorente, Nicholas M. Deutscher, Frank Meinhardt, Jérome Pernin, Benjamin Ertl, Martin Steinbacher, Tobias Borsdorff, David F. Pollard, Michel Ramonet, Voltaire A. Velazco, Christopher Diekmann, Darko Dubravica, Amelie N. Röhling, Thomas Laemmel, Matthias Schneider, Omaira García, David W. T. Griffith, Cyril Crevoisier, Eliezer Sepúlveda, and Farahnaz Khosrawi
- Subjects
Atmospheric methane ,IASI ,Reference data (financial markets) ,TROPOMI ,Infrared atmospheric sounding interferometer ,Trace gas ,Troposphere ,Total column data ,Thermal ,Nadir ,Environmental science ,Atmospheric trace gas ,Total Carbon Column Observing Network ,Remote sensing - Abstract
The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully used for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Spectra of scattered solar radiation observed in the near and/or short wave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument) offer higher sensitivity near ground and are used for the retrieval of total column averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total column data. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations (i.e. calculation after the individual retrievals). We show that this approach is largely equivalent to applying the spectra of the different sensors together in a single retrieval procedure, but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors. We demonstrate the method exemplarily for atmospheric methane (CH4). We perform a theoretical evaluation and show that the a posteriori combination method yields a total column averaged CH4 product (XCH4) that conserves the good sensitivity of the corresponding TROPOMI product while merging it with the upper tropospheric and lower stratospheric (UTLS) CH4 partial column information of the corresponding IASI product. As consequence, the combined product offers in addition sensitivity for the tropospheric CH4 partial column, which is not provided by the individual TROPOMI nor the individual IASI product. The theoretically predicted synergetic effects are verified by comparisons to CH4 reference data obtained from collocated XCH4 measurements at six globally distributed TCCON (Total Carbon Column Observing Network) stations, CH4 profile measurements made by 24 individual AirCore soundings, and lower tropospheric CH4 data derived from continuous ground-based in-situ observations made at two nearby Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect XCH4 signals and allows to distinguish between tropospheric and UTLS CH4 partial column averaged mixing ratios, which is not possible by the individual TROPOMI and IASI products. We find indications of a weak positive bias of about +1 % of the combined lower tropospheric data product with respect to the references. For the UTLS CH4 partial columns we find no significant bias.
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- 2021
41. The True Story of Lu Xun
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David E. Pollard and David E. Pollard
- Published
- 2002
42. Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network.
- Author
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Eric Dupuy, Isamu Morino, Nicholas M. Deutscher, Yukio Yoshida, Osamu Uchino, Brian J. Connor, Martine De Mazière, David W. T. Griffith, Frank Hase, Pauli Heikkinen, Patrick W. Hillyard, Laura T. Iraci, Shuji Kawakami, Rigel Kivi, Tsuneo Matsunaga, Justus Notholt, Christof Petri, James R. Podolske, David F. Pollard, Markus Rettinger, Coleen M. Roehl, Vanessa Sherlock, Ralf Sussmann, Geoffrey C. Toon, Voltaire A. Velazco, Thorsten Warneke, Paul O. Wennberg, Debra Wunch, and Tatsuya Yokota
- Published
- 2016
- Full Text
- View/download PDF
43. XCO2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm
- Author
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Stefan Noël, Maximilian Reuter, Michael Buchwitz, Jakob Borchardt, Michael Hilker, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Hiroshi Suto, Yukio Yoshida, Matthias Buschmann, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Isamu Morino, Justus Notholt, Hirofumi Ohyama, Christof Petri, James R. Podolske, David F. Pollard, Mahesh Kumar Sha, Kei Shiomi, Ralf Sussmann, Yao Té, Voltaire A. Velazco, and Thorsten Warneke
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010504 meteorology & atmospheric sciences ,13. Climate action ,01 natural sciences ,0105 earth and related environmental sciences - Abstract
Since 2009, the Greenhouse gases Observing SATellite (GOSAT) performs radiance measurements in the shortwave-infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 are also available. We present first results from the application of the Fast atmOspheric traCe gAs retrieval (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL has initially been developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast it is currently considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission, to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This includes particularly modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT/GOSAT-2 is the independent use of both S and P polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT’s wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O) and solar induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semiheavy water vapour (HDO) and (in the case of GOSAT-2) also carbon monoxide (CO) total columns and possibly nitrous oxide (XN2O). Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal an agreement of long-term temporal variations within about 1 ppm over one decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a mean regional bias of the new GOSAT-FOCAL product to the ground based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results.
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- 2020
44. QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs †
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Rocío L. Pérez, Souvik Banerjee, Caitlan E. Ayala, Brianda Elzey, Charuksha Walgama, Isiah M. Warner, Jason D. Rodriguez, David K. Bwambok, Samantha Macchi, David A. Pollard, Sayo O. Fakayode, Noureen Siraj, Nathaniel E. Larm, and Gary A. Baker
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Food industry ,Computer science ,near infrared spectroscopy ,media_common.quotation_subject ,Supply chain ,Review ,Raw material ,lcsh:Chemical technology ,01 natural sciences ,Biochemistry ,multivariate analyses ,Analytical Chemistry ,0404 agricultural biotechnology ,Quality (business) ,lcsh:TP1-1185 ,Food-Processing Industry ,Electrical and Electronic Engineering ,Instrumentation ,media_common ,analysis quartz crystal microbalance ,Spectroscopy, Near-Infrared ,Foodborne pathogen ,business.industry ,Quality assessment ,food-quality-assessment ,010401 analytical chemistry ,04 agricultural and veterinary sciences ,040401 food science ,Atomic and Molecular Physics, and Optics ,0104 chemical sciences ,electroanalytical sensors ,Food products ,Calibration ,Multivariate Analysis ,Quartz Crystal Microbalance Techniques ,Biochemical engineering ,business ,Food contaminant - Abstract
Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.
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- 2020
45. The Adaptable 4A Inversion (5AI): Description and first XCO2 retrievals from OCO-2 observations
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C. D. Crevoisier, Matthieu Dogniaux, Kei Shiomi, Laura T. Iraci, R. Armante, Coleen M. Roehl, Dietrich G. Feist, Yao Té, Nicholas M. Deutscher, Kimberly Strong, Rigel Kivi, Frank Hase, Thorsten Warneke, Virginie Capelle, David F. Pollard, Justus Notholt, Martine De Mazière, David W. T. Griffith, Voltaire A. Velazco, Omaira García, Vincent Cassé, Thibault Delahaye, and Isamu Morino
- Subjects
Optimal estimation ,Greenhouse gas ,Bayesian probability ,Radiative transfer ,Climate change ,Environmental science ,Inversion (meteorology) ,Total Carbon Column Observing Network ,Standard deviation ,Remote sensing - Abstract
A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission, and uses an empirically corrected absorption continuum in the O2 A-band. For airmasses below 3.0, XCO2 retrievals successfully capture the latitudinal variations of CO2, as well as its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a difference of 1.33 ± 1.29 ppm, which is similar to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products. We show that the systematic differences between 5AI and ACOS results can be fully removed by adding an average calculated – observed spectral residual correction to OCO-2 measurements, thus underlying the critical sensitivity of retrieval results to forward modelling. These comparisons show the reliability of 5AI as a Bayesian optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry-air mole fractions of greenhouse gases.
- Published
- 2020
46. Solar tracker with optical feedback and continuous rotation
- Author
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Hisako Shiona, David F. Pollard, John Robinson, and Dan Smale
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Atmospheric Science ,Offset (computer science) ,010504 meteorology & atmospheric sciences ,Spectrometer ,business.industry ,BitTorrent tracker ,Computer science ,lcsh:TA715-787 ,Real-time computing ,lcsh:Earthwork. Foundations ,Modular design ,01 natural sciences ,Photodiode ,law.invention ,Solar tracker ,lcsh:Environmental engineering ,010309 optics ,Azimuth ,Software ,law ,0103 physical sciences ,lcsh:TA170-171 ,business ,0105 earth and related environmental sciences - Abstract
Solar trackers are often used by spectrometers to measure atmospheric trace gas concentrations using direct sun spectroscopy. The ideal solar tracker should be sufficiently accurate, highly reliable, and with a longevity that exceeds the lifetime of the spectrometer that it serves. It should also be affordable, easy to use, and not too complex should maintenance be required. In this paper we present a design that fulfils these requirements using some simple innovations. Our altitude–azimuth design features a custom coaxial power transformer, enabling continuous 360∘ azimuth rotation. This increases reliability and avoids the need to reverse the tracker each day. In polar regions, measurements can continue uninterrupted through the summer polar day. Tracking accuracy is enhanced using a simple optical feedback technique that adjusts error offset variables while monitoring the edges of a focused solar image with four photodiodes. Control electronics are modular, and our software is written in Python, running as a web server on a recycled laptop with a Linux operating system. Over a period of 11 years we have assembled four such trackers. These are in use at Lauder (45∘ S), New Zealand, and Arrival Heights (78∘ S), Antarctica, achieving a history of good reliability even in polar conditions. Tracker accuracy is analysed regularly and can routinely produce a pointing accuracy of 0.02∘.
- Published
- 2020
47. Intercomparison of TCCON data from two Fourier transform spectrometers at Lauder, New Zealand
- Author
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Hisako Shiona, David F. Pollard, Dan Smale, and John Robinson
- Subjects
010309 optics ,010504 meteorology & atmospheric sciences ,Greenhouse gas ,0103 physical sciences ,Fourier transform spectrometers ,Environmental science ,Total Carbon Column Observing Network ,Atmospheric sciences ,01 natural sciences ,0105 earth and related environmental sciences - Abstract
We describe the change of operational instrument for the routine measurement of column-averaged dry-air mole fraction of several greenhouse gases (denoted Xgas) at the Lauder Total Carbon Column Observing Network (TCCON) site and the steps taken to demonstrate comparability between the two observation systems following a systematic methodology. Further, we intercompare retrieved Xgas values during an intensive intercomparison period during October and November 2018, when both instruments were performing optimally, and on subsequent, less frequent occasions. The average difference between the two observing systems was found to be well below the expected level of uncertainty for TCCON retrievals for all compared species. In the case of XCO2 the average difference was 0.0264 ± 0.0465 % (0.11 ± 0.19 μmol mol−1).
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- 2020
48. Methane retrieved from TROPOMI: improvement of the data product and validation of the first two years of measurements
- Author
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Alba Lorente, Tobias Borsdorff, Andre Butz, Otto Hasekamp, Joost aan de Brugh, Andreas Schneider, Frank Hase, Rigel Kivi, Debra Wunch, David F. Pollard, Kei Shiomi, Nicholas M. Deutscher, Voltaire A. Velazco, Coleen M. Roehl, Paul O. Wennberg, Thorsten Warneke, and Jochen Landgraf
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010504 meteorology & atmospheric sciences ,13. Climate action ,010501 environmental sciences ,01 natural sciences ,0105 earth and related environmental sciences - Abstract
The TROPOspheric Monitoring Instrument (TROPOMI) aboard of the Sentinel 5 Precursor (S5-P) satellite provides methane (CH4) measurements with high accuracy and exceptional temporal and spatial resolution. TROPOMI CH4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH4 from TROPOMI using the RemoTeC full-physics algorithm. The updated TROPOMI CH4 product features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data, and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH4 data product is the implementation of a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate two years of TROPOMI CH4 data that shows the good agreement of the updated TROPOMI CH4 with TCCON (−3.4 ± 5.6 ppb) and GOSAT (−10.3 ± 16.8 pbb) (mean bias and standard deviation). Low and high albedo scenes as well as snow covered scenes are the most challenging for the CH4 retrieval algorithm, and although the posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause.
- Published
- 2020
49. Structural Geology: A Quantitative Introduction
- Author
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David D. Pollard and Stephen J. Martel
- Abstract
Tackling structural geology problems today requires a quantitative understanding of the underlying physical principles, and the ability to apply mathematical models to deformation processes within the Earth. Accessible yet rigorous, this unique textbook demonstrates how to approach structural geology quantitatively using calculus and mechanics, and prepares students to interface with professional geophysicists and engineers who appreciate and utilize the same tools and computational methods to solve multidisciplinary problems. Clearly explained methods are used throughout the book to quantify field data, set up mathematical models for the formation of structures, and compare model results to field observations. An extensive online package of coordinated laboratory exercises enables students to consolidate their learning and put it into practice by analyzing structural data and building insightful models. Designed for single-semester undergraduate courses, this pioneering text prepares students for graduates studies and careers as professional geoscientists.
- Published
- 2020
50. A Decade of GOSAT Proxy Satellite CH4 Observations
- Author
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Robert J. Parker, Alex Webb, Hartmut Boesch, Peter Somkuti, Rocio Barrio Guillo, Antonio Di Noia, Nikoleta Kalaitzi, Jasdeep Anand, Peter Bergamaschi, Frederic Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Coleen Roehl, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Te, Voltaire A. Velazco, Thorsten Warneke, Paul O. Wennberg, and Debra Wunch
- Subjects
010504 meteorology & atmospheric sciences ,13. Climate action ,010501 environmental sciences ,01 natural sciences ,0105 earth and related environmental sciences - Abstract
This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, this data has been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of this data in order to highlight how this latest version may be used in the future. We describe in detail how the data is generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun-glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement to TCCON, with an overall correlation coefficient of 0.92 for the 88,345 co-located measurements. The single measurement precision is found to be 13.72 ppb and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4/XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 ppb to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. This data is available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).
- Published
- 2020
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