D. P. Edwards

National Center for Atmospheric Research, Boulder, Colorado, United States

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Publications (140)254.4 Total impact

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    ABSTRACT: Distinguishing the relative contribution of transported and local sources of atmospheric pollution is fundamental to developing realistic air quality policies and providing accurate air quality forecasts. We use the different sensitivities of satellite and ground based remote sensing instruments to provide complementary information about sources of carbon monoxide (CO). Total column amounts of CO are compared between the satellite-borne Measurements of Pollution in the Troposphere (MOPITT) and ground-based solar FTIR instruments in the TCCON and NDACC measurement networks. Observations are compared at three Southern Hemisphere stations: Darwin and Wollongong in Australia and Lauder in New Zealand. MOPITT has maximum sensitivity in the free troposphere and measurements are used to interpret long-range transport from continental sources. However, satellite measurements provide limited fine-scale information due to sparse measurement timing and spatial averaging, often missing local pollution events. In contrast, ground-based solar-tracking FTIR instruments have enhanced sensitivity closer to the surface, and can help interpret fine-scale chemistry and dynamic influence. However, FTIR measurements are limited to one location and have trouble interpreting transported signals. Anomalies in the CO timeseries from each instrument are discussed in relation to pollution delivery pathways of local, regional and long-distance origin. While large-scale pollution events are captured by both instruments, only the satellite instrument can provide regional and global context. For example, the wider geographical impact of Australian fires, such as the severe bushfires around Canberra in 2003, can be traced in the satellite observations and resulting CO plumes tracked out across the Pacific Ocean. MOPITT can also be used to track long-range transport of pollution from biomass burning in South America and southern Africa, reflecting the hemispheric impact of these sources. In comparison, the FTIR can additionally capture local urban and biomass burning influences. Seasonal and interannual variability of CO is significantly different at each site. The roles of emissions and meteorology in CO variability is investigated using global atmospheric modeling with CAM-chem. These modeling studies help quantify the relative impact of local and remote pollution sources to total column CO at the three stations.
    Full-text · Conference Paper · Jan 2016
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    ABSTRACT: A range of measurement techniques are required to understand atmospheric composition. No single instrument can measure all you need to know about the atmosphere, due to differences in temporal and spatial scales. Satellites help interpret synoptic-scale contributions to composition, but provide little fine-scale information due to sparse measurement timing and spatial averaging. In contrast, ground-based solar-tracking FTIR instruments can capture fine-scale chemistry and dynamic influence, but being point measurements, have trouble identifying transported signals. Knowing the relative contribution of transported to local sources of atmospheric pollution is important for developing realistic air quality policies and providing accurate air quality forecasts. In this study, we exploit the complementary limitations and sensitivities of two instruments to gain information about carbon monoxide (CO) sources at three stations in Australasia: Darwin and Wollongong in Australia and Lauder in New Zealand. Total column amounts of CO are compared between the satellite-borne Measurements of Pollution in the Troposphere (MOPITT) and ground-based solar FTIR instruments in the TCCON and NDACC networks. Several CO timeseries anomalies are highlighted as representative of pollution delivery pathways in relation to local, regional and long-distance contributions. Large-scale pollution events are captured by both instruments, but only the satellite instrument can provide regional and global context. MOPITT identifies long-range transport of pollution from biomass burning in South America and southern Africa, while the FTIR can additionally capture local urban and biomass burning influences. Unusually low CO, sourced from southern latitudes, is also measured by both instruments. Interannual variability is significantly different at each site and is diagnosed with chemical transport modeling (CAM-chem) to quantify the role of emissions versus meteorology.
    Full-text · Conference Paper · Dec 2015
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    Full-text · Conference Paper · Nov 2015
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    ABSTRACT: The Tropospheric Emission Spectrometer (TES) on Aura and Infrared Atmospheric Sounding Interferometer (IASI) on MetOp-A together provide a time series of ten years of free-tropospheric ozone with an overlap of three years. We characterise the differences between TES and IASI ozone measurements and find that IASI's coarser vertical sensitivity leads to a small (< 5 ppb) low bias relative to TES for the free troposphere. The TES-IASI differences are not dependent on season or any other factor and hence the measurements from the two instruments can be merged, after correcting for the offset, in order to study decadal-scale changes in tropospheric ozone. We calculate time series of regional monthly mean ozone in the free troposphere over Eastern Asia, the Western United States (US), and Europe, carefully accounting for differences in spatial sampling between the instruments. We show that free-tropospheric ozone over Europe and the Western US has remained relatively constant over the past decade, but that, contrary to expectations, ozone over Asia in recent years does not continue the rapid rate of increase observed from 2004–2010.
    No preview · Article · Nov 2015 · Atmospheric Chemistry and Physics
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    ABSTRACT: In this paper, we assess how daily ozone (O3) measurements from the Infrared Atmospheric Sounding Interferometer (IASI) on MetOp-A platform can contribute to the analyses of the processes driving O3 variability in the troposphere and the stratosphere and, in the future, to the monitoring of long-term trends. The time development of O3 during the first 6 years of IASI (2008–2013) operation is investigated with multivariate regressions separately in four different layers (ground–300, 300–150, 150–25, 25–3 hPa), by adjusting to the daily time series averaged in 20° zonal bands, seasonal and linear trend terms along with important geophysical drivers of O3 variation (e.g. solar flux, quasi biennial oscillations). The regression model is shown to perform generally very well with a strong dominance of the annual harmonic terms and significant contributions from O3 drivers, in particular in the equatorial region where the QBO and the solar flux contribution dominate. More particularly, despite the short period of IASI dataset available to now, two noticeable statistically significant apparent trends are inferred from the daily IASI measurements: a positive trend in the upper stratosphere (e.g. 1.74 ± 0.77 DU yr−1 between 30–50° S) which is consistent with the turnaround for stratospheric O3 recovery, and a negative trend in the troposphere at the mid-and high northern latitudes (e.g. −0.26 ± 0.11 DU yr−1 between 30–50° N), especially during summer and probably linked to the impact of decreasing ozone precursor emissions. The impact of the high temporal sampling of IASI on the uncertainty in the determination of O3 trend has been further explored by performing multivariate regressions on IASI monthly averages and on ground-based FTIR measurements.
    No preview · Article · Oct 2015 · Atmospheric Chemistry and Physics
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    ABSTRACT: This paper introduces the Weather Research and Forecasting Model with chemistry/Data Assimilation Research Testbed (WRF-Chem/DART) chemical transport forecasting/data assimilation system together with the assimilation of "compact phase space retrievals" of satellite-derived atmospheric composition products. WRF-Chem is a state-of-the-art chemical transport model. DART is a flexible software environment for researching ensemble data assimilation with different assimilation and forecast model options. DART's primary assimilation tool is the ensemble adjustment Kalman filter. WRF-Chem/DART is applied to the assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) trace gas retrieval profiles. Those CO observations are first assimilated as quasi-optimal retrievals (QORs). Our results show that assimilation of the CO retrievals: (i) reduced WRF-Chem's CO bias in retrieval and state space, and (ii) improved the CO forecast skill by reducing the Root Mean Square Error (RMSE) and increasing the Coefficient of Determination (R2). Those CO forecast improvements were significant at the 95 % level. Trace gas retrieval data sets contain: (i) large amounts of data with limited information content per observation, (ii) error covariance cross-correlations, and (iii) contributions from the retrieval prior profile that should be removed before assimilation. Those characteristics present challenges to the assimilation of retrievals. This paper addresses those challenges by introducing the assimilation of "compact phase space retrievals" (CPSRs). CPSRs are obtained by preprocessing retrieval datasets with an algorithm that: (i) compresses the retrieval data, (ii) diagonalizes the error covariance, and (iii) removes the retrieval prior profile contribution. Most modern ensemble assimilation algorithms can efficiently assimilate CPSRs. Our results show that assimilation of MOPITT CO CPSRs reduced the number of observations (and assimilation computation costs) by ~ 35 % while providing CO forecast improvements comparable to or better than with the assimilation of MOPITT CO QORs.
    No preview · Article · Sep 2015 · Geoscientific Model Development Discussions
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    ABSTRACT: This review paper provides a framework for the application of the Observing System Simulation Experiment (OSSE) methodology to satellite observations of atmospheric constituents relevant for air quality. The OSSEs are experiments used to determine the potential benefit of future observing systems using an existing monitoring or forecasting system and by this can help to define optimal characteristics of future instruments. To this end observations from future instruments are simulated from a model representing the realistic state of the atmosphere and an instrument simulator. The added value of the new observations is evaluated through assimilation into another model or model version and comparison with the simulated true state and a control run.
    Full-text · Article · May 2015 · Atmospheric Environment
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    ABSTRACT: Carbon monoxide (CO) is a key atmospheric compound that can be remotely sensed by satellite on the global scale. Fifteen years of continuous observations are now available from the MOPITT/Terra mission (2000 to present). Another fifteen and more years of observations will be provided by the IASI/MetOp instrument series (2007–2023>). In order to study long term variability and trends, a homogeneous record is required, which is not straightforward as the retrieved products are instrument and processing dependent. The present study aims at evaluating the consistency between the CO products derived from the MOPITT and IASI missions, both for total columns and vertical profiles, during a six year overlap period (2008–2013). The analysis is performed by first comparing the available 2013 versions of the retrieval algorithms, and second using a dedicated reprocessing of MOPITT CO profiles and columns based on the IASI a priori constraints. MOPITT v5T total columns are generally slightly higher over land (bias ranging from 0 to 13%) than IASI v20100815 data. When IASI and MOPITT data are retrieved with the same a priori constraints, correlation coefficients are slightly improved. Large discrepancies (total column bias over 15%) observed in the Northern Hemisphere during the winter months are reduced by a factor of 2 to 2.5. The detailed analysis of retrieved vertical profiles compared with collocated aircraft data from the MOZAIC-IAGOS network, illustrates the advantages and disadvantages of a constant vs. a variable a priori. On one hand, MOPITT agrees better with the aircraft profiles for observations with persisting high levels of CO throughout the year due to pollution or seasonal fire activity (because the climatology-based a priori is supposed to be closer to the real atmospheric state). On the other hand, IASI performs better when unexpected events leading to high levels of CO occur, due to the less constrained variance-covariance matrix.
    No preview · Article · Apr 2015
  • M. N. Deeter · D. P. Edwards · J. C. Gille · H. M. Worden

    No preview · Article · Jan 2015 · Journal of Geophysical Research Atmospheres
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    ABSTRACT: We apply the Tropospheric Emission Spectrometer (TES) ozone retrieval algorithm to Infrared Atmospheric Sounding Instrument (IASI) radiances and characterise the uncertainties and information content of the retrieved ozone profiles. This study focuses on mid-latitudes for the year 2008. We validate our results by comparing the IASI ozone profiles to ozone sondes. In the sonde comparisons, we find a positive bias in the IASI ozone profiles in the UTLS region of up to 14% on average. For the described cases, the degrees of freedom for signal are on average 3.2, 0.3, 0.8, and 0.9 for the columns 0 km-top of atmosphere, (0-6) km, (0-11) km, and (8-16) km, respectively. We find that our biases with respect to sondes and our degrees of freedom for signal for ozone are comparable to previously published results from other IASI ozone algorithms. In addition to evaluating biases, we validate the retrieval errors by comparing predicted errors to the sample covariance matrix of the IASI observations themselves. For the predicted vs. empirical error comparison, we find that these errors are consistent and that the measurement noise and the interference of temperature and water vapour on the retrieval together mostly explain the empirically derived random errors. In general, the precision of the IASI ozone profiles is better than 20%.
    Preview · Article · Dec 2014 · Atmospheric Measurement Techniques
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    ABSTRACT: The Measurements of Pollution in the Troposphere (MOPITT) Version 6 (V6) product for carbon monoxide (CO) incorporates several enhancements which will benefit many users of MOPITT data. V6 algorithm improvements are described in detail, and V6 validation results are presented. First, a geolocation bias related to the orientation of the MOPITT instrument relative to the TERRA platform was characterized and eliminated. Second, the variable a priori for CO concentrations for V6 is based on simulations performed with the chemical transport model Community Atmosphere Model with Chemistry (CAM-chem) for the years 2000–2009 instead of the model-derived climatology for 1997–2004 used for V5. Third, meteorological fields required for V6 retrieval processing are extracted from the MERRA (Modern-Era Retrospective Analysis For Research And Applications) reanalysis. Finally, a significant latitude-dependent retrieval bias in the upper troposphere in Version 5 products has been substantially reduced.
    No preview · Article · Nov 2014 · Atmospheric Measurement Techniques

  • No preview · Article · Sep 2014

  • No preview · Article · Jan 2014
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    ABSTRACT: The Measurements of Pollution in the Troposphere (MOPITT) instrument on the NASA Terra platform has now acquired over thirteen years of global tropospheric carbon monoxide (CO) observations, forming the longest satellite record for an important pollutant. MOPITT products are routinely exploited for characterizing CO sources and for analyzing air quality. For retrieving CO concentrations in the lower troposphere, MOPITT is equipped with both thermal-infrared and near-infrared channels.
    No preview · Article · Jan 2014 · Annals of geophysics = Annali di geofisica
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    ABSTRACT: A current obstacle to the observation system simulation experiments (OSSEs) used to quantify the potential performance of future atmospheric composition remote sensing systems is a computationally efficient method to define the scene-dependent vertical sensitivity of measurements as expressed by the retrieval averaging kernels (AKs). We present a method for the efficient prediction of AKs for multispectral retrievals of carbon monoxide (CO) and ozone (O3) based on actual retrievals from MOPITT (Measurements Of Pollution In The Troposphere) on the Earth Observing System (EOS)-Terra satellite and TES (Tropospheric Emission Spectrometer) and OMI (Ozone Monitoring Instrument) on EOS-Aura, respectively. This employs a multiple regression approach for deriving scene-dependent AKs using predictors based on state parameters such as the thermal contrast between the surface and lower atmospheric layers, trace gas volume mixing ratios (VMRs), solar zenith angle, water vapor amount, etc. We first compute the singular value decomposition (SVD) for individual cloud-free AKs and retain the first three ranked singular vectors in order to fit the most significant orthogonal components of the AK in the subsequent multiple regression on a training set of retrieval cases. The resulting fit coefficients are applied to the predictors from a different test set of test retrievals cased to reconstruct predicted AKs, which can then be evaluated against the true retrieval AKs from the test set. By comparing the VMR profile adjustment resulting from the use of the predicted vs. true AKs, we quantify the CO and O3 VMR profile errors associated with the use of the predicted AKs compared to the true AKs that might be obtained from a computationally expensive full retrieval calculation as part of an OSSE. Similarly, we estimate the errors in CO and O3 VMRs from using a single regional average AK to represent all retrievals, which has been a common approximation in chemical OSSEs performed to date. For both CO and O3 in the lower troposphere, we find a significant reduction in error when using the predicted AKs as compared to a single average AK. This study examined data from the continental United States (CONUS) for 2006, but the approach could be applied to other regions and times.
    No preview · Article · Jul 2013 · Atmospheric Measurement Techniques
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    ABSTRACT: [1] Validation results are reported for the MOPITT (Measurements of Pollution in the Troposphere) “Version 5” (V5) product for tropospheric carbon monoxide (CO) and are compared to results for the “Version 4” product. The V5 retrieval algorithm introduces (1) a method for reducing retrieval bias drift associated with long-term instrumental degradation, (2) a more exact representation of the effects of random errors in the radiances and, for the first time, (3) the use of MOPITT's near-infrared (NIR) radiances to complement the thermal-infrared (TIR) radiances. Exploiting TIR and NIR radiances together facilitates retrievals of CO in the lowermost troposphere. V5 retrieval products based (1) solely on TIR measurements, (2) solely on NIR measurements and (3) on both TIR and NIR measurements are separately validated and analyzed. Actual retrieved CO profiles and total columns are compared with equivalent retrievals based on in situ measurements from (1) routine NOAA aircraft sampling mainly over North America and (2) the “HIAPER Pole to Pole Observations” (HIPPO) field campaign. Particular attention is focused on the long-term stability and geographical uniformity of the retrieval errors. Results for the retrieved total column clearly indicate reduced temporal bias drift in the V5 products compared to the V4 product, and do not exhibit a positive bias in the Southern Hemisphere, which is evident in the V4 product.
    Full-text · Article · Jun 2013
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    ABSTRACT: Practical implementations of chemical OSSEs (Observing System Simulation Experiments) usually rely on approximations of the pseudo-observations by means of a predefined parametrization of the averaging kernels, which describe the sensitivity of the observing system to the target atmospheric species. This is intended to avoid the use of a computationally expensive pseudo-observations simulator, that relies on full radiative transfer calculations. Here we present an investigation on how no, or limited, scene dependent averaging kernels parametrizations may misrepresent the sensitivity of an observing system. We carried out the full radiative transfer calculation for a three-days period over Europe, to produce reference pseudo-observations of lower tropospheric ozone, as they would be observed by a concept geostationary observing system called MAGEAQ (Monitoring the Atmosphere from Geostationary orbit for European Air Quality). The selected spatio-temporal interval is characterised by an ozone pollution event. We then compared our reference with approximated pseudo-observations, following existing simulation exercises made for both the MAGEAQ and GEOstationary Coastal and Air Pollution Events (GEO-CAPE) missions. We found that approximated averaging kernels may fail to replicate the variability of the full radiative transfer calculations. In addition, we found that the approximations substantially overestimate the capability of MAGEAQ to follow the spatio-temporal variations of the lower tropospheric ozone in selected areas, during the mentioned pollution event. We conclude that such approximations may lead to false conclusions if used in an OSSE. Thus, we recommend to use comprehensive scene-dependent approximations of the averaging kernels, in cases where the full radiative transfer is computationally too costly for the OSSE being investigated.
    Full-text · Article · Mar 2013 · Atmospheric Measurement Techniques
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    ABSTRACT: A current obstacle to the Observation System Simulation Experiments (OSSEs) used to quantify the potential performance of future atmospheric composition remote sensing systems is a computationally efficient method to define the scene-dependent vertical sensitivity of measurements as expressed by the retrieval averaging kernels (AKs). We present a method for the efficient prediction of AKs for multispectral retrievals of carbon monoxide (CO) and ozone (O3) based on actual retrievals from MOPITT on EOS-Terra and TES and OMI on EOS-Aura, respectively. This employs a multiple regression approach for deriving scene-dependent AKs using predictors based on state parameters such as the thermal contrast between the surface and lower atmospheric layers, trace gas volume mixing ratios (VMR), solar zenith angle, water vapor amount, etc. We first compute the singular vector decomposition (SVD) for individual cloud-free AKs and retain the 1st three ranked singular vectors in order to fit the most significant, orthogonal components of the AK in the subsequent multiple regression on a training set of retrieval cases. The resulting fit coefficients are applied to the predictors from a different test set of retrievals cased to reconstruct predicted AKs, which can then be evaluated against the true test set retrieval AKs. By comparing the VMR profile adjustment resulting from the use of the predicted vs. true AKs, we quantify the CO and O3 VMR profile errors associated with the use of the predicted AKs compared to the true AKs that might be obtained from a computationally expensive full retrieval calculation as part of an OSSE. Similarly, we estimate the errors in CO and O3 VMRs from using a single regional average AK to represent all retrievals, which has been a common approximation in chemical OSSEs performed to-date. For both CO and O3 in the lower troposphere, we find a significant reduction in error when using the predicted AKs as compared to a single average AK. This study examined data from the continental United States (CONUS) for 2006, but the approach could be applied to other regions and times.
    Full-text · Article · Mar 2013
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    ABSTRACT: Atmospheric carbon monoxide (CO) distributions are controlled by anthropogenic emissions, biomass burning, transport and oxidation by reaction with the hydroxyl radical (OH). Quantifying trends in CO is therefore important for understanding changes related to all of these contributions. Here we present a comprehensive record of satellite observations from 2000 through 2011 of total column CO using the available measurements from nadir-viewing thermal infrared instruments: MOPITT, AIRS, TES and IASI. We examine trends for CO in the Northern and Southern Hemispheres along with regional trends for Eastern China, Eastern USA, Europe and India. We find that all the satellite observations are consistent with a modest decreasing trend ∼-1% yr-1 in total column CO over the Northern Hemisphere for this time period and a less significant, but still decreasing trend in the Southern Hemisphere. Although decreasing trends in the United States and Europe have been observed from surface CO measurements, we also find a decrease in CO over E. China that, to our knowledge, has not been reported previously. Some of the interannual variability in the observations can be explained by global fire emissions, but the overall decrease needs further study to understand the implications for changes in anthropogenic emissions.
    Full-text · Article · Jan 2013 · Atmospheric Chemistry and Physics
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    ABSTRACT: The new Version 5 MOPITT (Measurements of Pollution in the Troposphere) product for carbon monoxide (CO) is the first satellite product to exploit simultaneous near-infrared and thermal-infrared observations to enhance retrieval sensitivity in the lower troposphere. This feature is important to air quality analyses and studies of CO sources. However, because of the influence of both thermal contrast and geophysical noise, the retrieval characteristics for this new multispectral product are highly variable. New V5 products for surface-level CO concentrations have been evaluated over the contiguous United States using both in situ vertical profiles and NOAA ground-based "Tall Tower" measurements. Validation results based on the in situ profiles indicate that retrieval biases are on the order of a few percent. However, direct comparisons with the Tall Tower measurements demonstrate that smoothing error, which depends on both the retrieval averaging kernels and CO variability in the lower troposphere, exhibits significant geographical and seasonal variability.
    No preview · Article · Jul 2012 · Journal of Geophysical Research Atmospheres

Publication Stats

3k Citations
254.40 Total Impact Points

Institutions

  • 1996-2015
    • National Center for Atmospheric Research
      • Division of Atmospheric Chemistry
      Boulder, Colorado, United States
  • 2002-2013
    • National Research Center (CO, USA)
      Boulder, Colorado, United States