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The vertical distribution of ozone plays an important role in atmospheric chemistry, climate change, air pollution, and human health. Over the twenty-first century, spaceborne remote sensing methods and instrumentation have evolved to better characterise this distribution. We quantify the ability of ozone retrievals to characterise this distribution through a combination of thermal infrared (TIR) and Ultra Violet (UV) spectral radiances, harnessing co-located TIR measurements from the Cross Track Infrared Sounder (CrIS), onboard the Suomi National Polar-orbiting Partnership (NPP), and UV measurements from the TROPospheric Monitoring Instrument (TROPOMI), which is on the Sentinel 5-Precursor (S5P) satellite. The combination of TIR and UV measurements improves the ability of satellites to characterise global ozone profiles, over the use of each band individually. The CrIS retrievals enhanced by TROPOMI radiances in the Huggins band (325–335 nm) show good agreement with independent datasets both in the troposphere and in the stratosphere in spite of calibration issues in the TROPOMI UV. Improved performance is characterised in the stratosphere from CrIS-TROPOMI. Comparable performance between CrIS-TROPOMI and CrIS-only is found in the troposphere with degrees of freedom for signal of about 2 globally, but higher in the tropics partitioned equally between the lower and upper troposphere. These results demonstrate that CrIS/TROPOMI retrievals have the potential to substantially improve our understanding of ozone. If spectral accuracy is improved in future TROPOMI calibration, the degrees of freedom of signal in the stratosphere could double when using bands 1 and 2 of TROPOMI (270–330 nm), while tropospheric degrees of freedom of signal could increase by 25 %.

Seasonal CO2 exchange in the boreal forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2 . Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize boreal forest seasonal cycles across longitudes with a spatially and temporally rich data set, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry air mole fractions of CO2 ( X CO 2 ) in boreal forest regions. In addition to the standard quality control (QC) filters recommended for the Atmospheric Carbon Observations from Space (ACOS) B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to boreal forest observations (boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of X CO 2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada, and Sodankyla, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN Fourier transform infrared spectrometers (FTSs) at Fairbanks, Alaska, USA. The EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50 ∘ N than the B8 QC, and the boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While boreal QC results in a substantial increase in passable retrievals, this is accompanied by increases in the standard deviations in biases at boreal forest sites from ∼1.4 parts per million (ppm) with B9 QC to ∼1.6 ppm with boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead, responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at Sodankyla and East Trout Lake than at Fairbanks. Long-term coincident observations from TCCON, EM27/SUN, and satellites from multiple locations would be necessary to determine whether the reduced seasonal variability in bias at Fairbanks is due to geography or instrumentation. Monthly average biases generally varied between −1 and +1 ppm at the three sites considered, with more negative biases in spring (March, April, and May – MAM) and autumn (September and October – SO) but more positive biases in the summer months (June, July, and August – JJA). Monthly standard deviations in biases ranged from approximately 1.0 to 2.0 ppm and did not exhibit strong seasonal dependence, apart from exceptionally high standard deviation observed with all three QC methods at Sodankyla in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of air mass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of X CO 2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these boreal forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

Genetically determined leukoencephalopathies comprise a group of rare inherited white matter disorders. The majority are progressive diseases resulting in early death. We performed a cross-sectional pilot study including 55 parents from 36 families to assess the level of stress experienced by parents of patients with genetically determined leukoencephalopathies, aged 1 month to 12 years. Thirty-four mothers and 21 fathers completed the Parenting Stress Index–4th Edition. One demographic questionnaire was completed per family. Detailed clinical data was gathered on all patients. Statistical analysis was performed with total stress percentile score as the primary outcome. Mothers and fathers had significantly higher stress levels compared with the normative sample; 20% of parents had high levels of stress whereas 11% had clinically significant levels of stress. Mothers and fathers had comparable total stress percentile scores. We identified pediatric behavioral difficulties and gross motor function to be factors influencing stress in mothers. Our study is the first to examine parental stress in this population and highlights the need for parental support early in the disease course. In this pilot study, we demonstrated that using the Parenting Stress Index–4th Edition to assess stress levels in parents of patients with genetically determined leukoencephalopathies is feasible, leads to valuable and actionable results, and should be used in larger, prospective studies.

Resume Objectif Decrire la demarche de mise a niveau de l’offre de soins pharmaceutiques en neurologie en ciblant l’epilepsie. Methodologie Etude descriptive composee d’une revue de la litterature traitant des roles et des impacts du pharmacien dans la prise en charge de l’epilepsie, d’une description de l’organisation des soins entourant les patients atteints d’epilepsie et de la mise a jour de l’offre de soins pharmaceutiques pour cette patientele. Resultats Quinze articles ont ete inclus dans la revue de la litterature. Neuf articles mesuraient l’impact du pharmacien, tandis que six etaient descriptifs. La majorite des articles presentaient un faible niveau de preuve compte tenu de leur methodologie. Les activites les plus mentionnees dans les articles portaient sur la proposition d’une therapie medicamenteuse optimale et sur la realisation de conseils aux patients. L’offre de soins pharmaceutiques a ete mise a jour selon 13 nouvelles opportunites qui vont s’integrer dans le cadre de l’offre de soins en pediatrie. Conclusion Il existe peu de litterature sur les roles et l’impact du pharmacien en epilepsie. Cette demarche, realisee en collaboration avec les neurologues, met en valeur les preuves disponibles et permet d’identifier les opportunites d’arrimage medecin-pharmacien.

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 (

Despite numerous advances in continental-scale hydrologic modeling and improvements in global Land Surface Models, an accurate representation of regional water table depth (WTD) remains a challenge. Data assimilation of observations from the Gravity Recovery and Climate Experiment (GRACE) mission leads to improvements in the accuracy of hydrologic models, ultimately resulting in more reliable estimates of lumped water storage. However, the usually shallow groundwater compartment of many models presents a problem with GRACE assimilation techniques, as these satellite observations also represent changes in deeper soils and aquifers. To improve the accuracy of modeled groundwater estimates and allow the representation of WTD at finer spatial scales, we implemented a simple, yet novel approach to integrate GRACE data, by augmenting the Variable Infiltration Capacity (VIC) hydrologic model. First, the subsurface model structural representation was modified by incorporating an additional (fourth) soil layer of varying depth (up to 1000 m) in VIC as the bottom ‘groundwater’ layer. This addition allows the model to reproduce water storage variability not only in shallow soils but also in deeper groundwater, in order to allow integration of the full GRACE-observed variability. Second, a Direct Insertion scheme was developed that integrates the high temporal (daily) and spatial (∼6.94 km) resolution model outputs to match the GRACE resolution, performs the integration, and then disaggregates the updated model state after the assimilation step. Simulations were performed with and without Direct Insertion over the three largest river basins in California and including the Central Valley, in order to test the augmented model's ability to capture seasonal and inter-annual trends in the water table. This is the first-ever fusion of GRACE total water storage change observations with hydrologic simulations aiming at the determination of water table depth dynamics, at spatial scales potentially useful for local water management.

While initial plans for measuring carbon dioxide from space hoped for 1-2 ppm levels of accuracy (bias) and precision in the CO2 column mean dry air mole fraction (XCO2), in the past few years it has become clear that accuracies better than 0.5 ppm are required for most current science applications. These include measuring continental (1000+ km) and regional scale (100s of km) surface fluxes of CO2 at monthly-average timescales. Considering the 400+ ppm background, this translates to an accuracy of roughly 0.1%, an incredibly challenging target to hit. Improvements in both instrument calibration and retrieval algorithms have led to significant improvements in satellite XCO2 accuracies over the past decade. The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm, including post-retrieval filtering and bias correction, has demonstrated unprecedented accuracy with our latest algorithm version as applied to the Orbiting Carbon Observatory-2 (OCO-2) satellite sensor. This presentation will discuss the performance of the v10 XCO2 product by comparisons to TCCON and models, and showcase its performance with some recent examples, from the potential to infer large-scale fluxes to its performance on individual power plants. The v10 product yields better agreement with TCCON over land and ocean, plus reduced biases over tropical oceans and desert areas as compared to a median of multiple global carbon inversion models, allowing better accuracy and faith in inferred regional-scale fluxes. More specifically, OCO-2 has single sounding precision of ~0.8 ppm over land and ~0.5 ppm over water, and RMS biases of 0.5-0.7 ppm over both land and water. Given the six-year and growing length of the OCO-2 data record, this also enables new studies on carbon interannual variability, while at the same time allowing identification of more subtle and temporally-dependent errors. Finally, we will discuss the prospects of future improvements in the next planned version (v11), and the long-term prospects of greenhouse gas retrievals in the coming years.

Seasonal CO2 exchange in the Boreal Forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2. Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize Boreal Forest seasonal cycles across longitudes with a spatially and temporally rich dataset, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry-air mole fractions of CO2 (XCO2) in Boreal Forest regions. In addition to the standard quality control filters recommended for ACOS B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to Boreal Forest observations (Boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of XCO2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada and Sodankylä, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN FTS at Fairbanks, Alaska, United States. EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50° N than the B8 QC, and the Boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While Boreal QC results in a substantial increase in passable retrievals this is accompanied by increases in the standard deviations in biases at Boreal Forest sites from ∼ 1.4 ppm with B9 QC to ∼ 1.6 ppm with Boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at the TCCON sites than when comparing to EM27/SUN observations in Fairbanks. Monthly average biases generally varied between −1 ppm and +1 ppm at the three sites considered, with more negative biases in spring (MAM) and autumn (SO), but more positive biases in summer months (JJA). Monthly standard deviations in biases ranged from approximately 1.0 ppm to 2.0 ppm and do not exhibit strong seasonal dependence apart from exceptionally high standard deviation observed with all three QC methods at Sodankylä in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of airmass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of XCO2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these Boreal Forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

Given the importance of tropospheric ozone as a greenhouse gas and a hazardous pollutant that impacts human health and ecosystems, it is critical to quantify and understand long-term changes in its abundance. Satellite records are beginning to approach the length needed to assess variability and trends in tropospheric ozone, yet an intercomparison of time series from different instruments shows substantial differences in the net change in ozone over the past decade. We discuss our efforts to produce Earth Science Data Records of tropospheric ozone and quantify uncertainties and biases in these records. We also discuss the role of changes in the magnitude and distribution of precursor emissions and in downward transport of ozone from the stratosphere in determining tropospheric ozone abundances over the past 15 years.

Background: Hormonal therapy is a standard treatment for infantile spasms. The high doses given and long treatment duration expose patients to the risk of adrenal insufficiency (AI). This study aims to quantify the incidence of AI among children with infantile spasms treated with corticosteroids and/or adrenocorticotropic hormone (ACTH). Methods: A retrospective chart review of patients treated for infantile spasms was performed between January 2009 to March 2020 in one pediatric specialized hospital. Variables collected included patient and treatment characteristics, risk factors of AI and adrenal function testing. Analysis included descriptive statistics. Results: Thirty-one patients met the inclusion criteria and received a total of 33 separated courses of treatment. Adrenal function following each course of treatment was evaluated in all patients. AI occurred in 25/33 (76% [95CI 58-89]) children. There was no predictive factor of AI. No drug regimen was deemed safe. The two patients (6%) with an acute adrenal crisis were the youngest of the cohort. Conclusions: Adrenal suppression is frequent and can lead to adrenal crisis after standard hormonal therapy for infantile spasms. A routine laboratory assessment of adrenal function should be done for all patients. Hydrocortisone replacement therapy should be given until testing results are obtained, particularly for younger infants.

This paper describes the preflight spectral calibration methods and results for the Orbiting Carbon Observatory 2 (OCO-2), following the approach developed for the first OCO. The instrument line shape (ILS) function and dispersion parameters were determined through laser-based spectroscopic measurements, and then further optimized by comparing solar spectra recorded simultaneously on the ground by the OCO-2 flight instrument and a collocated high-resolution Fourier transform spectrometer (FTS). The resulting ILS profiles and dispersion parameters, when applied to the FTS solar data, showed agreement between the spectra recorded by the spectrometers and FTS to approximately 0.2% RMS, satisfying the preflight spectral calibration accuracy requirement of

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

The Tropospheric Emission Spectrometer (TES) on the A-Train Aura satellite was designed to profile tropospheric ozone and its precursors, taking measurements from 2004 to 2018. Starting in 2008, TES global sampling of tropospheric ozone was gradually reduced in latitude, with global coverage stopping in 2011. To extend the record of TES, this work presents a multispectral approach that will provide O3 data products with vertical resolution and measurement error similar to TES by combining the single-footprint thermal infrared (TIR) hyperspectral radiances from the Aqua Atmospheric Infrared Sounder (AIRS) instrument and the ultraviolet (UV) channels from the Aura Ozone Monitoring Instrument (OMI). The joint AIRS+OMI O3 retrievals are processed through the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) retrieval algorithm. Comparisons of collocated joint AIRS+OMI and TES to ozonesonde measurements show that both systems have similar errors, with mean and standard deviation of the differences well within the estimated measurement error. AIRS+OMI and TES have slightly different biases (within 5 parts per billion) vs. the sondes. Both AIRS and OMI have wide swath widths (∼1650 km for AIRS; ∼2600 km for OMI) across satellite ground tracks. Consequently, the joint AIRS+OMI measurements have the potential to maintain TES vertical sensitivity while increasing coverage by 2 orders of magnitude, thus providing an unprecedented new data set with which to quantify the evolution of tropospheric ozone.

NASA's Orbiting Carbon Observatory-3 (OCO-3) was installed on the International Space Station (ISS) on 10 May 2019. OCO-3 combines the flight spare spectrometer from the Orbiting Carbon Observatory-2 (OCO-2) mission, which has been in operation since 2014, with a new Pointing Mirror Assembly (PMA) that facilitates observations of non-nadir targets from the nadir-oriented ISS platform. The PMA is a new feature of OCO-3, which is being used to collect data in all science modes, including nadir (ND), sun-glint (GL), target (TG), and the new snapshot area mapping (SAM) mode. This work provides an initial assessment of the OCO-3 instrument and algorithm performance, highlighting results from the first 8 months of operations spanning August 2019 through March 2020. During the In-Orbit Checkout (IOC) phase, critical systems such as power and cooling were verified, after which the OCO-3 spectrometer and PMA were subjected to a series of rigorous tests. First light of the OCO-3 spectrometer was on 26 June 2019, with full science operations beginning on 6 August 2019. The OCO-3 spectrometer on-orbit performance is consistent with that seen during preflight testing. Signal to noise ratios are in the expected range needed for high quality retrievals of the column-averaged carbon dioxide (CO2) dry-air mole fraction (XCO2) and solar-induced chlorophyll fluorescence (SIF), which will be used to help quantify and constrain the global carbon cycle. The first public release of OCO-3 Level 2 (L2) data products, called “vEarly”, is being distributed by NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC). The intent of the vEarly product is to evaluate early mission performance, facilitate comparisons with OCO-2 products, and identify key areas to improve for the next data release. The vEarly XCO2 exhibits a root-mean-squared-error (RMSE) of ≃ 1, 1, 2 ppm versus a truth proxy for nadir-land, TG&SAM, and glint-water observations, respectively. The vEarly SIF shows a correlation with OCO-2 measurements of >0.9 for highly coincident soundings. Overall, the Level 2 SIF and XCO2 products look very promising, with performance comparable to OCO-2. A follow-on version of the OCO-3 L2 product containing a number of refinements, e.g., instrument calibration, pointing accuracy, and retrieval algorithm tuning, is anticipated by early in 2021.

The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O2 A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 µm (weak CO2 band) and 2.06 µm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set.To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of ≃ 20–25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations.No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

Transport of pollution from remote sources into the state of Texas has been shown by modeling techniques, satellite, and in situ data. Attaining a better understanding of the impact (i.e., temporally) of remote pollution sources will provide a more robust/quantifiable basis for State Implementation Plans (SIPs) that govern air quality. Utilizing Tropospheric Emission Spectrometer (TES) and Ozone Monitoring Instrument (OMI) and in situ data for ozone (O3) and nitrogen dioxide (NO2) and Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT), we assess whether high-pollution events in Texas are primarily sourced locally (i.e., within Texas) or remotely. We focus on TES and OMI dates that exemplify high O3 and NO2, over Texas’s lower troposphere from August 5, 2006, to June 21, 2009. For all dates and altitudes, 4-day back trajectory analyses, exemplified by high TES O3, show that remotely sourced from the Gulf of Mexico, Southeast USA, Midwest USA, Northeast USA, the Atlantic Ocean, Pacific Ocean, Mexico to Texas. The only exception is air at 1 km on July 22, 2006, which shows that air at this altitude is sourced within Texas. Throughout half of the eastern portion of Texas, TES shows O3 enhancements in the boundary layer and OMI shows O3 and NO2 enhancements via tropospheric column profiles (O3 between 75 and 90 ppbv; NO2 ≥5.5 molecules cm−2). These enhancements complement the HYSPLIT 4-day trajectory analyses, which gives further indication that they are influenced by transport from remote sources. Dates with co-located satellite and in situ data (e.g., August 2, 2005) further exemplify the need to consider satellite and in situ data and modeling data/forecasts when creating SIPs for compliance with Environmental Protection Agency and the Texas Commission on Environmental Quality air quality standards. Despite the fact that quantifying local versus remote sources is in its early stages, Texas has become increasingly compliant with Environmental Protection Agency (EPA) regulations. Environmental Systems Research Institute’s ArcGIS exemplifies the noticeable decrease in the number of days that locales in Texas exceed EPA’s limit for O3. From 2005 to 2009, population standard deviation and standard error of the mean, and true sample deviation of the sample mean for O3 and NO2, at all 16 monitoring sites distributed throughout Texas, are temporally consistent and small—reinforcing the reliability of in situ data as they are consistent throughout. This investigation has global implications for regions within countries that enforce air quality mandates. Such governing bodies should consider utilizing data assimilation (of in situ data) for air quality prediction as a part of the governmental process that produces such laws. This could potentially keep regions more accountable for emissions both locally and far from high source points.

The Orbiting Carbon Observatory-2 (OCO-2), launched on 2 July 2014, is a NASA mission designed to measure the column-averaged CO2 dry air mole fraction, XCO2. Towards that goal, it will collect spectra of reflected sunlight in narrow spectral ranges centered at 0.76, 1.6 and 2.0 μm with a resolving power (λ/Δ λ) of 20 000. These spectra will be used in an optimal estimation framework to retrieve XCO2. About 100 000 cloud free soundings of XCO2 each day will allow estimates of net CO2 fluxes on regional to continental scales to be determined. Here, we evaluate the OCO-2 spectrometer performance using pre-launch data acquired during instrument thermal vacuum tests in April 2012. A heliostat and a diffuser plate were used to feed direct sunlight into the OCO-2 instrument and spectra were recorded. These spectra were compared to those collected concurrently from a nearby high-resolution Fourier Transform Spectrometer that was part of the Total Carbon Column Observing Network (TCCON). Using the launch-ready OCO-2 calibration and spectroscopic parameters, we performed total column scaling fits to all spectral bands and compared these to TCCON results. On 20 April, we detected a CO2 plume from the Los Angeles basin at the JPL site with strongly enhanced short-term variability on the order of 1% (3–4 ppm). We also found good (< 0.5 ppm) inter-footprint consistency in retrieved XCO2. The variations in spectral fitting residuals are consistent with signal-to-noise estimates from instrument calibration, while average residuals are systematic and mostly attributable to remaining errors in our knowledge of the CO2 and O2 spectroscopic parameters. A few remaining inconsistencies observed during the tests may be attributable to the specific instrument setup on the ground and will be re-evaluated with in-orbit data.

INTRODUCTION Earth’s carbon cycle involves large fluxes of carbon dioxide (CO 2 ) between the atmosphere, land biosphere, and oceans. Over the past several decades, net loss of CO 2 from the atmosphere to the land and oceans has varied considerably from year to year, equaling 20 to 80% of CO 2 emissions from fossil fuel combustion and land use change. On average, the uptake is about 50%. The imbalance between CO 2 emissions and removal is seen in increasing atmospheric CO 2 concentrations. In recent years, an increase of 2 to 3 parts per million (ppm) per year in the atmospheric mole fraction, which is currently about 400 ppm, has been observed. Almost a quarter of the CO 2 emitted by human activities is being absorbed by the ocean, and another quarter is absorbed by processes on land. The identity and location of the terrestrial sinks are poorly understood. This absorption has been attributed by some to tropical or Eurasian temperate forests, whereas others argue that these regions may be net sources of CO 2 . The efficiency of these land sinks appears to vary dramatically from year to year. Because the identity, location, and processes controlling these natural sinks are not well constrained, substantial additional uncertainty is added to projections of future CO 2 levels. RATIONALE The NASA satellite, the Orbiting Carbon Observatory-2 (OCO-2), which was launched on 2 July 2014, is designed to collect global measurements with sufficient precision, coverage, and resolution to aid in resolving sources and sinks of CO 2 on regional scales. Since 6 September 2014, the OCO-2 mission has been producing about 2 million estimates of the column-averaged CO 2 dry-air mole fraction ( X CO 2 ) each month after quality screening, with spatial resolution of 2 per sounding. Solar-induced chlorophyll fluorescence (SIF), a small amount of light emitted during photosynthesis, is detected in remote sensing measurements of radiance within solar Fraunhofer lines and is another data product from OCO-2. RESULTS The measurements from OCO-2 provide a global view of the seasonal cycles and spatial patterns of atmospheric CO 2 , with the anticipated year-over-year growth rate. The buildup of CO 2 in the Northern Hemisphere during winter and its rapid decrease in concentration as spring arrives (and the SIF increases) is seen in unprecedented detail. The enhanced CO 2 in urban areas relative to nearby background areas is observed with a single overpass of OCO-2. Increases in CO 2 due to the biomass burning in Africa are also clearly observed. The dense, global, X CO 2 and SIF data sets from OCO-2 are combined with other remote sensing data sets and used to disentangle the processes driving the carbon cycle on regional scales during the recent 2015–2016 El Niño event. This analysis shows more carbon release in 2015 relative to 2011 over Africa, South America, and Southeast Asia. Now, the fundamental driver for the change in carbon release can be assessed continent by continent, rather than treating the tropics as a single, integrated region. Small changes in X CO 2 were also observed early in the El Niño over the equatorial eastern Pacific, due to less upwelling of cold, carbon-rich water than is typical. CONCLUSION NASA’s OCO-2 mission is collecting a dense, global set of high-spectral resolution measurements that are used to estimate X CO 2 and SIF. The OCO-2 mission data set can now be used to assess regional-scale sources and sinks of CO 2 around the globe. The papers in this collection present early scientific findings from this new data set. El Niño impact on carbon flux in 2015 relative to 2011, detected from Greenhouse Gases Observing Satellite (GOSAT) and OCO-2 data. OCO-2 uses reflected sunlight to derive X CO 2 and SIF. This shows OCO-2 X CO 2 data over North America from 12 August 2015 to 26 August 2015.

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

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

INTRODUCTION Although the carbon budget is often presented in terms of global-scale fluxes, many of the contributing processes occur through localized point sources, which have been challenging to measure from space. Persistent anthropogenic carbon dioxide (CO 2 ) emissions have altered the natural balance of Earth’s carbon sources and sinks. These emissions are driven by a multitude of individual mobile and stationary point sources that combust fossil fuels, with urban areas accounting for more than 70% of anthropogenic emissions to the atmosphere. Natural point-source emissions are dominated by wildfires and persistent volcanic degassing. RATIONALE Comprehensive global measurements from space could help to more completely characterize anthropogenic and natural point-source emissions. In global carbon cycle models, anthropogenic point-source information comes from bottom-up emission inventories, whereas natural point-source information comes from a sparse in situ measurement network. Whereas clusters of urban CO 2 point-source plumes merge together, isolated point sources (e.g., remote power plants, cement production plants, and persistently degassing volcanoes) create localized plumes. Because turbulent mixing and diffusion cause rapid downwind dilution, they are challenging to detect and analyze. Point-source detection from space is complicated by signal dilution: The observed values of Δ X CO 2 (enhancement of the column-averaged dry-air CO 2 mole fraction) correspond to in situ CO 2 enhancements of 10-fold or higher. Space-based sensors that detect and quantify CO 2 in plumes from individual point sources would enable validation of reported inventory fluxes for power plants. These sensors would also advance the detectability of volcanic eruption precursors and improve volcanic CO 2 emission inventories. RESULTS Spaceborne measurements of atmospheric CO 2 using kilometer-scale data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) reveal distinct structures caused by known anthropogenic and natural point sources, including megacities and volcanoes. Continuous along-track sampling across Los Angeles (USA) by OCO-2 at its ~2.25-km spatial resolution exposes intra-urban spatial variability in the atmospheric X CO 2 distribution that corresponds to the structure of the urban dome, which is detectable under favorable wind conditions. Los Angeles X CO 2 peaks over the urban core and decreases through suburban areas to rural background values more than ~100 km away. Enhancements of X CO 2 in the Los Angeles urban CO 2 dome observed by OCO-2 vary seasonally from 4.4 to 6.1 parts per million (ppm). We also detected isolated CO 2 plumes from the persistently degassing Yasur, Ambrym, and Aoba volcanoes (Vanuatu), corroborated by near-simultaneous sulfur dioxide plume detections by NASA’s Ozone Mapping and Profiler Suite. An OCO-2 transect passing directly downwind of Yasur volcano yielded a narrow filament of enhanced X CO 2 ( Δ X CO 2 ≈ 3.4 ppm), consistent with plume modeling of a CO 2 point source emitting 41.6 ± 19.7 kilotons per day (15.2 ± 7.2 megatons per year). These highest continuous volcanic CO 2 emissions are collectively dwarfed by about 70 fossil fuel–burning power plants on Earth, which each emit more than 15 megatons per year of CO 2 . CONCLUSION OCO-2’s sampling strategy was designed to characterize CO 2 sources and sinks on regional to continental and ocean-basin scales, but the unprecedented kilometer-scale resolution and high sensitivity enables detection of CO 2 from natural and anthropogenic localized emission sources. OCO-2 captures seasonal, intra-urban, and isolated plume signals. Capitalizing on OCO-2’s sensitivity, a much higher temporal resolution would capture anthropogenic emission signal variations from diurnal, weekly, climatic, and economic effects, and, for volcanoes, precursory emission variability. Future sampling strategies will benefit from a continuous mapping approach with the sensitivity of OCO-2 to systematically and repeatedly capture these smaller, urban to individual plume scales of CO 2 point sources. OCO-2 detects urban CO 2 signals with unprecedented detail over Los Angeles. Individual “footprints” of OCO-2 X CO 2 data from early fall 2014 and summer 2015 over the city of Los Angeles strongly contrast with values over the distant, rural Antelope Valley. X CO 2 is the averaged dry-air molar CO 2 concentration between the spacecraft and Earth’s surface.

Many modern instruments generate more data than may be fully processed in a timely manner. For some atmospheric sounders, much of the raw data cannot be processed into meaningful observations due to suboptimal viewing conditions, such as the presence of clouds. Conventional solutions are quick, empirical-threshold filters hand-created by domain experts to weed out unlikely or unreasonable observations, coupled with randomized down sampling when the data volume is still too high. In this paper, we describe a method for the construction of a sub-sampling and ordering solution that maximizes the likelihood that a requested data subset will be usefully processed. The method can be used for any metadata-rich source and implicitly discerns informative vs. non-informative data features while still permitting user feedback into the final features selected for filter implementation. We demonstrate the method by creating a selector for the spectra of the Japanese GOSAT satellite designed to measure column averaged mixing ratios of greenhouse gases including carbon dioxide (CO2). This is done within the Atmospheric CO2 Measurements from Space (ACOS) NASA project with the intention of eventual use during the early Orbiting Carbon Observatory-2 (OCO-2) mission. OCO-2 will have a 1.5 orders of magnitude larger data volume than ACOS, requiring intelligent pre-filtration.

Thermal infrared radiances from the Tropospheric Emission Spectrometer (TES) between 10 and 15 μm contain significant carbon dioxide (CO2) information, however the CO2 signal must be separated from radiative interference from temperature, surface and cloud parameters, water, and other trace gases. Validation requires data sources spanning the range of TES CO2 sensitivity, which is approximately 2.5 to 12 km with peak sensitivity at about 5 km and the range of TES observations in latitude (40° S to 40° N) and time (2005–2011). We therefore characterize Tropospheric Emission Spectrometer (TES) CO2 version 5 biases and errors through comparisons to ocean and land-based aircraft profiles and to the CarbonTracker assimilation system. We compare to ocean profiles from the first three Hiaper Pole-to-Pole Observations (HIPPO) campaigns between 40° S and 40° N with measurements between the surface and 14 km and find that TES CO2 estimates capture the seasonal and latitudinal gradients observed by HIPPO CO2 measurements. Actual errors range from 0.8–1.8 ppm, depending on the campaign and pressure level, and are approximately 1.6–2 times larger than the predicted errors. The bias of TES versus HIPPO is within 1 ppm for all pressures and datasets; however, several of the sub-tropical TES CO2 estimates are lower than expected based on the calculated errors. Comparisons to land aircraft profiles from the United States Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) between 2005 and 2011 measured from the surface to 5 km to TES CO2 show good agreement with an overall bias of −0.3 ppm to 0.1 ppm and standard deviations of 0.8 to 1.0 ppm at different pressure levels. Extending the SGP aircraft profiles above 5 km using AIRS or CONTRAIL measurements improves comparisons with TES. Comparisons to CarbonTracker (version CT2011) show a persistent spatially dependent bias pattern and comparisons to SGP show a time-dependent bias of −0.2 ppm yr−1. We also find that the predicted sensitivity of the TES CO2 estimates is too high, which results from using a multi-step retrieval for CO2 and temperature. We find that the averaging kernel in the TES product corrected by a pressure-dependent factor accurately reflects the sensitivity of the TES CO2 product.

The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry-air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small (2 field. High measurement precision is therefore essential to resolve these small variations, and high accuracy is needed because small biases in the retrieved XCO2 distribution could be misinterpreted as evidence for CO2 fluxes. To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s−1 across a narrow (2. Each of these soundings has an unprecedented combination of spatial resolution (2/sounding), spectral resolving power (λ∕Δλ > 17 000), dynamic range (∼ 104), and sensitivity (continuum signal-to-noise ratio > 400). The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014, and joined the 705 km Afternoon Constellation on 3 August 2014. On monthly time scales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north-south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north-south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the northern hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high resolution, global data set.

The Tropospheric Emission Spectrometer (TES) is a high-resolution spaceborne sensor that is capable of observing tropospheric species. In order to exploit fully TES's potential for tropospheric constituent retrievals, an accurate and fast operational forward model was developed for TES. The forward model is an important component of the TES retrieval model, the Earth Limb and Nadir Operational Retrieval (ELANOR), as it governs the accuracy and speed of the calculations for the retrievals. In order to achieve the necessary accuracy and computational efficiency, TES adopted the strategy of utilizing precalculated absorption coefficients generated by the line-by-line calculations provided by line-by-line radiation transfer modeling. The decision to perform the radiative transfer with the highest monochromatic accuracy attainable, rather than with an accelerated scheme that has the potential to add algorithmic forward model error, has proven to be very successful for TES retrievals. A detailed description of the TES forward model and Jacobians is described. A preliminary TES observation is provided as an example to demonstrate that the TES forward model calculations represent TES observations. Also presented is a validation example, which is part of the extensive forward model validation effort. © 2006 IEEE.

We describe the approach for the estimation of the atmospheric state, e.g., temperature, water, ozone, from calibrated, spectral radiances measured from the Tropospheric Emission Spectrometer (TES) onboard the Aura spacecraft. The methodology is based on the maximum a posteriori estimate, which mathematically requires the minimization of the difference between observed spectral radiances and a nonlinear model of radiative transfer of the atmospheric state subject to the constraint that the estimated state must be consistent with an a priori probability distribution for that state. The minimization techniques employed here are based on the trust-region Levenberg-Marquardt algorithm. An analysis of the errors for this estimate include smoothing, random, spectroscopic, "cross-state," representation, and systematic errors. In addition, several metrics and diagnostics are introduced that assess the resolution, quality, and statistical significance of the retrievals. We illustrate this methodology for the retrieval of atmospheric and surface temperature, water vapor, and ozone over the Gulf of Mexico on November 3, 2004. © 2006 IEEE.

Accurate representation of ozone in the extratropical upper troposphere (UT) remains a challenge. However, the implementation of hyper-spectral remote sensing using satellite instruments such as the Tropospheric Emission Spectrometer (TES) provides an avenue for mapping ozone in this region, from 500 to 300 hPa. As a polar orbiting satellite TES observations are limited, but in this paper they are combined with geostationary satellite observations of water vapor. This paper describes a validation of the Multi-sensor UT Ozone Product (MUTOP). MUTOP, based on a statistical retrieval method, is an image product derived from the multiple regression of remotely sensed TES ozone, against geostationary (GOES) specific humidity (remotely sensed) and potential vorticity (a modeled dynamical tracer in the UT). These TES-derived UT ozone mixing ratios are compared to coincident ozonesonde measurements of layer-average UT ozone mixing ratios made during the NASA INTEX/B field campaign in the spring of 2006; the region for this study is effectively the GOES west domain covering the eastern North Pacific Ocean and the western United States. This intercomparison evaluates MUTOP skill at representing ozone magnitude and variability in this region of complex dynamics. In total, 11 ozonesonde launch sites were available for this study, providing 127 individual sondes for comparison; the overall mean ozone of the 500–300 hPa layer for these sondes was 78.0 ppbv. MUTOP reproduces in~situ measurements reasonably well, producing an UT mean of 82.3 ppbv, with a mean absolute error of 12.2 ppbv and a root mean square error of 16.4 ppbv relative to ozonesondes across all sites. An overall UT mean bias of 4.3 ppbv relative to sondes was determined for MUTOP. Considered in the context of past TES validation studies, these results illustrate that MUTOP is able to maintain accuracy similar to TES while expanding coverage to the entire GOES-West satellite domain. In addition MUTOP provides six-hour temporal resolution throughout the INTEX-B study period, making the visualization of UT ozone dynamics possible. This paper presents the overall statistical validation as well as a selection of ozonesonde case studies. The case studies illustrate that error may not always represent a lack of TES-derived product skill, but often results from discrepancies driven by observations made in the presence of strong meteorological gradients.

The utilization of Tropospheric Emission Spectrometer (TES) Level 2 (L2) retrieval products for the purpose of assessing long term changes in atmospheric trace gas composition requires knowledge of the overall radiometric stability of the Level 1B (L1B) radiances. The purpose of this study is to evaluate the stability of the radiometric calibration of the TES instrument by analyzing the difference between measured and calculated brightness temperatures in selected window regions of the spectrum. The Global Modeling and Assimilation Office (GMAO) profiles for temperature and water vapor and the Real-Time Global Sea Surface Temperature (RTGSST) are used as input to the Optimal Spectral Sampling (OSS) radiative transfer model to calculate the simulated spectra. The TES reference measurements selected cover a 4-year period of time from mid 2005 through mid 2009 with the selection criteria being; observation latitudes greater than −30° and less than 30°, over ocean, Global Survey mode (nadir view) and retrieved cloud optical depth of less than or equal to 0.01. The TES cloud optical depth retrievals are used only for screening purposes and no effects of clouds on the radiances are included in the forward model. This initial screening results in over 55 000 potential reference spectra spanning the four year period. Presented is a trend analysis of the time series of the residuals (observation minus calculations) in the TES 2B1, 1B2, 2A1, and 1A1 bands, with the standard deviation of the residuals being approximately equal to 0.6 K for bands 2B1, 1B2, 2A1, and 0.9 K for band 1A1. The analysis demonstrates that the trend in the residuals is not significantly different from zero over the 4-year period. This is one method used to demonstrate that the relative radiometric calibration is stable over time, which is very important for any longer term analysis of TES retrieved products (L2), particularly well-mixed species such as carbon dioxide and methane.

We compare Tropospheric Emission Spectrometer (TES) versions 3 and 4, V003 and V004, respectively, nadir-stare ozone profiles with ozonesonde profiles from the Arctic Intensive Ozonesonde Network Study (ARCIONS, http://croc.gsfc.nasa.gov/arcions/ during the Arctic Research on the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field mission. The ozonesonde data are from launches timed to match Aura's overpass, where 11 coincidences spanned 44° N to 71° N from April to July 2008. Using the TES "stare" observation mode, 32 observations are taken over each coincidental ozonesonde launch. By effectively sampling the same air mass 32 times, comparisons are made between the empirically-calculated random errors to the expected random errors from measurement noise, temperature and interfering species, such as water. This study represents the first validation of high latitude (>70°) TES ozone. We find that the calculated errors are consistent with the actual errors with a similar vertical distribution that varies between 5% and 20% for V003 and V004 TES data. In general, TES ozone profiles are positively biased (by less than 15%) from the surface to the upper-troposphere (~1000 to 100 hPa) and negatively biased (by less than 20%) from the upper-troposphere to the lower-stratosphere (100 to 30 hPa) when compared to the ozonesonde data. Lastly, for V003 and V004 TES data between 44° N and 71° N there is variability in the mean biases (from −14 to +15%), mean theoretical errors (from 6 to 13%), and mean random errors (from 9 to 19%).

We investigate the effects of anthropogenic and biogenic sources on tropospheric CO enhancements and outflow over North America and the Atlantic during July–August 2006, the 3rd warmest summer on record. The analysis is performed using the 3D Regional chEmical trAnsport Model (REAM), satellite data from TES on the Aura satellite, MOPITT on the Terra satellite and surface monitor data from the SEARCH network. The satellite measurements of CO provide insight into the location of regional CO enhancements along with the ability to resolve vertical features. Satellite and surface monitor data are used to compare with REAM, illustrating model's ability to reproduce observed CO concentrations. The REAM model used in this study features CO emissions reduced by 50% from the 1999 EPA NEI and biogenic VOC emissions scaled by EPA-observed isoprene concentrations (20% reduction). The REAM simulations show large variations in surface CO, lower tropospheric CO and column CO, which are also observed by the surface observations and satellite data. Over the US, during July–August 2006, the model estimates monthly CO production from anthropogenic sources (5.3 and 5.1 Tg CO) is generally larger than biogenic sources (4.3 and 3.5 Tg CO). However, the model shows that for very warm days, biogenic sources produce as much CO as anthropogenic sources, a result of increased biogenic production due to warmer temperatures. The satellite data show CO outflow occurs along the East Coast of the US and Canada in July and is more broadly distributed over the Atlantic in August. REAM results show the longitudinally exported CO enhancements from anthropogenic sources (3.3 and 3.9 Tg CO) are larger than biogenic sources (2.8 and 2.7 Tg CO) along the eastern boundary of REAM for July–August 2006. We show that when compared with the impacts of both sources on increasing tropospheric CO exports, the relative impacts in August are greater than in July because of preferable outflow transport.

SUMMARYA large outbreak of enterohaemorrhagicEscherichia coli(EHEC) infections occurred in southern Sweden during autumn 2002. A matched case-control study was performed and indicated an association between consumption of fermented sausage and EHEC infection (odds ratio 5·4,PE. coliO157:H7 in clinical faecal isolates, which was identical to a strain isolated from sausage samples obtained from households of infected individuals. A combination of microbiological and epidemiological results established a link between sausage consumption and the outbreak in 30 out of a total of 39 investigated cases. Contaminated beef was suspected to be the source of infection. Delayed start of fermentation, lack of heat-treatment and a short curing period in cold temperature were identified as the main factors enabling EHEC survival. EHEC can survive throughout the entire production process of fermented sausage if curing conditions are inadequate.

A key component in the regularization of vertical atmospheric trace gas retrievals is the construction of constraint matrices. We introduce a novel method for developing a constraint matrix based on altitude-varying combinations of zeroth-, first-, and second-order derivatives of the trace gas profile. This constraint matrix can be optimized to minimize the diagonal a posteriori error covariance and can also consider other factors such as degrees of freedom. This approach is applied to the calculation of constraint matrices for Tropospheric Emission Spectrometer nadir retrievals of atmospheric temperature, H/sub 2/O, O/sub 3/, CO, and CH/sub 4/. The retrieval error achieved with these constraints is comparable to the error achieved with the classical Bayesian constraint. Furthermore, these constraints are shown to be robust under uncertainty in the climatological conditions.

Most regional scale models that are used for air quality forecasts and ozone source attribution do not adequately capture the distribution of ozone in the mid- and upper troposphere, but it is unclear how this shortcoming relates to their ability to simulate surface ozone. We combine ozone profile data from the NASA Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) and a new joint product from TES and the Ozone Monitoring Instrument along with ozonesonde measurements and EPA AirNow ground station ozone data to examine air quality events during August 2006 in the Community Multi-Scale Air Quality (CMAQ) and National Air Quality Forecast Capability (NAQFC) models. We present both aggregated statistics and case-study analyses with the goal of assessing the relationship between the models’ ability to reproduce surface air quality events and their ability to capture the vertical distribution of ozone. We find that the models lack the mid-tropospheric ozone variability seen in TES and the ozonesonde data, and discuss future work to determine the conditions under which this variability appears to be important for surface air quality.

We characterize the spring and fall stratospheric distribution of CO at 49°N-55°S latitude from ATMOS profiles measured during 4 shuttle flights. Measured mixing ratios increase with potential temperature (θ) from 12 ppbv (10 -9 per unit volume) at 525 K, to 30-40 ppbv at 1750 K with only minor variations with latitude and season at a 0 level. Evidence for some confinement near 1150 K in the developing November 1994 vortex is indicated from comparison of CO and N 2 O horizontal gradients. Measured CO mixing ratios at the tropical tropopause are a factor of 10 higher than values calculated with a steady-state model using standard photochemistry constrained by correlative temperatures and pressures, and ATMOS measurements including CH 4 as inputs. Differences decrease with latitude at constant θ and are

Volume mixing ratio profiles of HCl, HOCl, ClNO3, CH3Cl, CFC-12, and CFC-11, CCl4, HCFC-22, and CFC-113 were measured simultaneously from 9 to 38 km by the Jet Propulsion Laboratory MkIV Fourier Transform Infrared solar absorption spectrometer during two balloon flights from Fairbanks, Alaska (64.8 N), on May 8 and July 8, 1997. The altitude variation of total organic chlorine (CCl(sub y)), total inorganic chlorine (Cl(sub y)), and the nearly constant value (3.7 +/- 0.2 ppbv) of their sum (Cl(sub TOT)) demonstrates that the stratospheric chlorine species available to react with O3 are supplied by the decomposition of organic chlorinated compounds whose abundances are well quantified. Measured profiles of HCl and ClNO3 agree well with profiles found by photochemical model (differences < 10% for altitudes below 35 km) constrained by various other constituents measured by MkIV. The production of HCl by ClO + OH plays a relatively small role in the partitioning of HCl and ClNO3 for the sampled air masses. However, better agreement with the measured profiles of HCl and ClNO3 is obtained when this source of HCl is included in the model. Both the measured and calculated [ClNO3]/[HCl] ratios exhibit the expected near linear variation with [O3](sup 2)/[CH4] over a broad range of altitudes. MkIV measurements of HCl, ClNO3, and CCl(sub y) agree well with ER-2 in situ observations of these quantities for directly comparable air masses. These results demonstrate good understanding of the budget of stratospheric chlorine and that the partitioning of inorganic chlorine is accurately described by photochemical models that employ JPL97 reaction rates and production of HCl from ClO + OH for the environmental conditions encountered: relatively warm temperatures, long periods of solar illumination, and relatively low aerosol surface areas.