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The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm has been delivering operational column-averaged carbon dioxide dry-air mole fraction (XCO2​​​​​​​) data for the Orbiting Carbon Observatory (OCO) missions since 2014. The ACOS Level 2 Full Physics (L2FP) algorithm retrieves a number of parameters, including aerosol and surface properties, in addition to atmospheric CO2. Past analysis has shown that while the ACOS retrieval meets mission precision requirements of 0.1 %–0.5 % in XCO2, residual biases and some sources of error remain unaccounted for (Wunch et al., 2017; Worden et al., 2017; Torres et al., 2019). Forward model and other errors can lead to systematic biases in the retrieved XCO2, which are often correlated with these additional retrieved parameters. The characterization of such biases is particularly essential to urban- and local-scale emissions studies, where it is critical to accurately distinguish source signals relative to background concentrations (Nassar et al., 2017; Kiel et al., 2021). In this study we explore algorithm-induced biases through the use of simulated OCO-3 snapshot area mapping (SAM) mode observations, which offer a unique window into these biases with their wide range of viewing geometries over a given scene. We focus on a small percentage of SAMs in the OCO-3 vEarly product which contains artificially strong across-swath XCO2 biases spanning several parts per million, related to observation geometry. We investigate the causes of swath bias by using the timing and geometry of real OCO-3 SAMs to retrieve XCO2 from custom simulated Level 1b radiance spectra. By building relatively simple scenes and testing a variety of parameters, we find that aerosol is the primary driver of swath bias, with a complex combination of viewing geometry and aerosol optical properties contributing to the strength and pattern of the bias. Finally, we seek to understand successful mitigation of swath bias in the new OCO-3 version 10 data product. Results of this study may be useful in uncovering other remaining sources of XCO2 bias and may help minimize similar retrieval biases for both present missions (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) and future missions (e.g., MicroCarb, GeoCarb, GOSAT-GW, CO2M).

Satellite measurements of surface wind speed over the ocean inform a wide variety of scientific pursuits. While both active and passive microwave sensors are traditionally used to detect surface wind speed over water surfaces, measurements of reflected sunlight in the near-infrared made by the Orbiting Carbon Observatory-2 (OCO-2) are also sensitive to the wind speed. In this work, retrieved wind speeds from OCO-2 glint measurements are validated against the Advanced Microwave Scanning Radiometer-2 (AMSR2). Both sensors are in the international Afternoon Constellation (A-Train), allowing for a large number of co-located observations. Several different OCO-2 retrieval algorithm modifications are tested, with the most successful being a single-band Cox–Munk-only model. Using this, we find excellent agreement between the two sensors, with OCO-2 having a small mean bias against AMSR2 of −0.22 m s−1, an RMSD of 0.75 m s−1, and a correlation coefficient of 0.94. Although OCO-2 is restricted to clear-sky measurements, potential benefits of its higher spatial resolution relative to microwave instruments include the study of coastal wind processes, which may be able to inform certain economic sectors.

Carbon dioxide (CO2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at the regional to city scale. Very few studies have focused on combustion efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for interpreting and deriving spatially resolved ERs from asynchronous satellite measurements, including (1) variations in meteorological conditions given the mismatch in satellite overpass times, (2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), (3) interferences from the biosphere and biomass burning, and (4) the mismatch in the daytime variations of CO and CO2 emissions. In this study, we extended an established emission estimate approach to arrive at spatially resolved ERs based on retrieved column-averaged CO2 (XCO2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influences of the confounding factors listed above and further attribute intra-urban variations in ERs to certain sectors, we leveraged a Lagrangian atmospheric transport model with an urban land cover classification dataset and reported ERCO values from the sounding level to the overpass and city level. We found that the differences in overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially resolved ERCO. Specifically, a time difference of >3 h typically led to dramatic changes in wind directions and urban plume shapes, thereby making the calculation of accurate sounding-specific ERCO difficult. After removing such cases from consideration and applying a simple plume shift method when necessary to account for changes in wind direction and speed, we discovered significant contrasts in combustion efficiencies between (1) two megacities versus two industry-oriented cities and (2) different regions within a city, based on six nearly coincident overpasses per city. Results suggest that the ERCO impacted by heavy industry in Los Angeles is slightly lower than the overall city-wide value ( ppb-CO/ppm-CO2). In contrast, the ERCO related to heavy industry in Shanghai is much higher than Shanghai's city mean and more aligned with the city means of two selected industry-oriented cities in China (approaching 20 ppb-CO/ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our unique approach (i.e., without using sector-specific information from emission inventories) provides new insights into assessing combustion efficiency within a city from future satellite missions, such as those that will map column CO2 and CO concentrations simultaneously with high spatiotemporal resolutions.

Isoprene is the atmosphere’s most important non-methane organic compound, with key impacts on atmospheric oxidation, ozone, and organic aerosols. In-situ isoprene measurements are sparse, and satellite-based constraints have employed an indirect approach using its oxidation product formaldehyde, which is affected by non-isoprene sources plus uncertainty and spatial smearing in the isoprene-formaldehyde relationship. Direct global isoprene measurements are therefore needed to better understand its sources, sinks, and atmospheric impacts. Here we show that the isoprene spectral signatures are detectable from space using the satellite-borne Cross-track Infrared Sounder (CrIS), develop a full-physics retrieval methodology for quantifying isoprene abundances from these spectral features, and apply the algorithm to CrIS measurements over Amazonia. The results are consistent with model output and in-situ data, and establish the feasibility of direct global space-based isoprene measurements. Finally, we demonstrate the potential for combining space-based measurements of isoprene and formaldehyde to constrain atmospheric oxidation over isoprene source regions., Isoprene is a key component of the atmosphere, with impacts on oxidation, ozone and organic aerosols, but in-situ measurements are limited. Here, the authors present a full-physics measurement framework based on satellite data that enables the direct observation of atmospheric isoprene from space.

All measurements of X CO 2 from space have systematic errors. To reduce a large fraction of these errors, a bias correction is applied to X CO 2 retrieved from GOSAT and OCO-2 spectra using the ACOS retrieval algorithm. The bias correction uses, among other parameters, the surface pressure difference between the retrieval and the meteorological reanalysis. Relative errors in the surface pressure estimates, however, propagate nearly 1:1 into relative errors in bias-corrected X CO 2 . For OCO-2, small errors in the knowledge of the pointing of the observatory (up to ∼130 arcsec ) introduce a bias in X CO 2 in regions with rough topography. Erroneous surface pressure estimates are also caused by a coding error in ACOS version 8, sampling meteorological analyses at wrong times (up to 3 h after the overpass time). Here, we derive new geolocations for OCO-2's eight footprints and show how using improved knowledge of surface pressure estimates in the bias correction reduces errors in OCO-2's v9 X CO 2 data.

The Orbiting Carbon Observatory-3 (OCO-3) is NASA's next instrument dedicated to extending the record of the dry-air mole fraction of column carbon dioxide (XCO2) and solar-induced fluorescence (SIF) measurements from space. The current schedule calls for a launch from the Kennedy Space Center no earlier than April 2019 via a Space-X Falcon 9 and Dragon capsule. The instrument will be installed as an external payload on the Japanese Experimental Module Exposed Facility (JEM-EF) of the International Space Station (ISS) with a nominal mission lifetime of 3 years. The precessing orbit of the ISS will allow for viewing of the Earth at all latitudes less than approximately 52∘, with a ground repeat cycle that is much more complicated than the polar-orbiting satellites that so far have carried all of the instruments capable of measuring carbon dioxide from space. The grating spectrometer at the core of OCO-3 is a direct copy of the OCO-2 spectrometer, which was launched into a polar orbit in July 2014. As such, OCO-3 is expected to have similar instrument sensitivity and performance characteristics to OCO-2, which provides measurements of XCO2 with precision better than 1 ppm at 3 Hz, with each viewing frame containing eight footprints approximately 1.6 km by 2.2 km in size. However, the physical configuration of the instrument aboard the ISS, as well as the use of a new pointing mirror assembly (PMA), will alter some of the characteristics of the OCO-3 data compared to OCO-2. Specifically, there will be significant differences from day to day in the sampling locations and time of day. In addition, the flexible PMA system allows for a much more dynamic observation-mode schedule. This paper outlines the science objectives of the OCO-3 mission and, using a simulation of 1 year of global observations, characterizes the spatial sampling, time-of-day coverage, and anticipated data quality of the simulated L1b. After application of cloud and aerosol prescreening, the L1b radiances are run through the operational L2 full physics retrieval algorithm, as well as post-retrieval filtering and bias correction, to examine the expected coverage and quality of the retrieved XCO2 and to show how the measurement objectives are met. In addition, results of the SIF from the IMAP–DOAS algorithm are analyzed. This paper focuses only on the nominal nadir–land and glint–water observation modes, although on-orbit measurements will also be made in transition and target modes, similar to OCO-2, as well as the new snapshot area mapping (SAM) mode.

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.

Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory-2 (OCO-2) has enabled fine scale (1.3-by-2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO-2 SIF with tower GPP over the course of a growing season at a well-characterized natural grassland site. Combining OCO-2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO-2 SIF to constrain the estimates of V_(cmax), one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall the linear relationship is more robust at ecosystem scale than the theory based on leaf-level processes might suggest. Our study also shows that the ability of SIF to constrain V_(cmax) is weak at the selected site.

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.

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.

Here, we report preliminary estimates of the column averaged carbon dioxide (CO2) dry air mole fraction, XCO2, retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global XCO2 field, including its variation with latitude and season. However, low yields of retrieved XCO2 over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT XCO2 retrievals with XCO2 estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) XCO2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global XCO2 bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT XCO2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O2 and CO2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in XCO2 on global scales as well as a ~30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO2 (XCO2) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in XCO2 south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the observed correlation between free-tropospheric potential temperature and XCO2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

One of the important science requirements of the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission is to be able to measure ozone with two degrees of freedom in the troposphere and sensitivity in the lowest 2 km (lowermost troposphere, LMT), in order to characterize air quality and boundary layer transport of pollution. Currently available remote sensing techniques utilize backscattered solar ultraviolet (UV) radiances or thermal infrared (TIR) emissions to perform ozone retrievals. However, in the TIR, measurement sensitivity to the LMT requires high thermal contrast between the Earth’s surface and the near-surface (tens to hundreds of meters above surface) atmosphere, while in the UV, the measurement sensitivity to the LMT is low because of Rayleigh scattering. In this paper, we explore the feasibility of using multi-spectral intensity measurements in the UV, visible (VIS), mid infrared (MIR) and TIR, and polarization measurements in the UV/VIS, to improve tropospheric and lowermost tropospheric ozone retrievals. Simulations for 16 cloud and aerosol free atmospheric profiles spanning a range of ozone mixing ratios indicate that adding VIS measurements to UV measurements significantly enhances the sensitivity to lowermost tropospheric ozone, but only makes a slight improvement to the total degrees of freedom for signal (DFS). On the other hand, the combination of UV and TIR significantly improves the total DFS as well as the lowermost tropospheric DFS. The analysis presented here is a necessary and important first step for defining spectral regions that can meet the GEO-CAPE measurement requirements, and subsequently, the requirements for instrumentation. In this work, the principle of multi-spectral retrievals has been extended from previously published literature and we show that the UV + VIS, UV + TIR and UV + VIS + TIR combinations have the potential to meet the GEO-CAPE measurement requirements for tropospheric ozone. Our analysis includes errors from water and surface properties; further analysis is needed to include temperature, additional gas interferents, clouds, aerosols and more realistic surface properties. These simulations must be run on a much larger dataset, followed by OSSEs (Observing System Simulation Experiments), where simulated retrievals are assimilated into chemical-transport models, to quantitatively assess the impact of the proposed measurements for constraining the spatiotemporal distribution of ozone in the LMT for basic science studies and applications such as air quality forecasts.

We conduct an Observing System Simulation Experiment (OSSE) to test the ability of geostationary satellite measurements of ozone in different spectral regions to constrain surface ozone concentrations through data assimilation. Our purpose is to define instrument requirements for the NASA GEO-CAPE geostationary air quality mission over North America. We consider instruments using different spectral combinations of UV (290–340 nm), Vis (560–620 nm), and thermal IR (TIR, 9.6 μm). Hourly ozone data from the MOZART global 3-D chemical transport model (CTM) are taken as the “true” atmosphere to be sampled by the instruments for July 2001. The resulting synthetic data are assimilated in the GEOS-Chem CTM using a Kalman filter. The MOZART and GEOS-Chem CTMs have independent heritages and use different assimilated meteorological data sets for the same period, making for an objective OSSE. We show that hourly observations of ozone from geostationary orbit improve the assimilation considerably relative to daily observation from low earth orbit, and that broad observation over the ocean is unnecessary if the objective is to constrain surface ozone distribution over land. We also show that there is little propagation of ozone information from the free troposphere to the surface, so that instrument sensitivity in the boundary layer is essential. UV + Vis and UV + TIR spectral combinations improve greatly the information on surface ozone relative to UV alone. UV + TIR is preferable under high-sensitivity conditions with strong thermal contrast at the surface, but UV + Vis is preferable under low-sensitivity conditions. Assimilation of data from a UV + Vis + TIR instrument reduces the GEOS-Chem error for surface ozone by a factor of two. Observation in the TIR is critical to obtain ozone information in the upper troposphere relevant to climate forcing.

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%).

The combination of multiple satellite instruments on a pixel-by-pixel basis is a difficult task, even for instruments collocated in space and time, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) on board the Earth Observing System (EOS) Aqua. Toward the goal of an improved collocation methodology, the channel- and scan angle–dependent spatial response functions of AIRS that were obtained from prelaunch measurements and calculated impacts from scan geometry are shown within the context of radiance comparisons. The AIRS spatial response functions are used to improve the averaging of MODIS radiances to the AIRS footprint, and the variability of brightness temperature differences (ΔTb) between MODIS and AIRS are quantified on a channel-by-channel basis. To test possible connections between ΔTb and the derived level 2 (L2) datasets, cloud characteristics derived from MODIS are used to highlight correlations between these quantities and ΔTb, especially for ice clouds in H2O and CO2 bands. Furthermore, correlations are quantified for temperature lapse rate (dT/dp) and the magnitude of water vapor mixing ratio (q) obtained from AIRS L2 retrievals. Larger values of dT/dp and q correlate well to larger values of ΔTb in the H2O and CO2 bands. These correlations were largely eliminated or reduced after the MODIS spectral response functions were shifted by recommended values. While this investigation shows that the AIRS spatial response functions are necessary to reduce the variability and skewness of ΔTb within heterogeneous scenes, improved knowledge about MODIS spectral response functions is necessary to reduce biases in ΔTb.

We analyze the theoretical basis of three different methods to validate and intercompare satellite measurements of atmospheric composition, and apply them to tropospheric ozone retrievals from the Tropospheric Emission Spectrometer (TES) and the Ozone Monitoring Instrument (OMI). The first method (in situ method) uses in situ vertical profiles for absolute instrument validation; it is limited by the sparseness of in situ data. The second method (CTM method) uses a chemical transport model (CTM) as an intercomparison platform; it provides a globally complete intercomparison with relatively small noise from model error. The third method (averaging kernel smoothing method) involves smoothing the retrieved profile from one instrument with the averaging kernel matrix of the other; it also provides a global intercomparison but dampens the actual difference between instruments and adds noise from the a priori. We apply the three methods to a full year (2006) of TES and OMI data. Comparison with in situ data from ozonesondes shows mean positive biases of 5.3 parts per billion volume (ppbv) (10%) for TES and 2.8 ppbv (5%) for OMI at 500 hPa. We show that the CTM method (using the GEOS-Chem CTM) closely approximates results from the in situ method while providing global coverage. It reveals that differences between TES and OMI are generally less than 10 ppbv (18%), except at northern mid-latitudes in summer and over tropical continents. The CTM method further allows for CTM evaluation using both satellite observations. We thus find that GEOS-Chem underestimates tropospheric ozone in the tropics due to possible underestimates of biomass burning, soil, and lightning emissions. It overestimates ozone in the northern subtropics and southern mid-latitudes, likely because of excessive stratospheric influx of ozone.

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.

Concurrent tropospheric O3 and CO vertical profiles from the Tropospheric Emission Spectrometer (TES) during the MILAGRO/INTEX-B aircraft campaigns over the Mexico City Metropolitan Area (MCMA) and its surrounding regions were used to examine Mexico City pollution outflow on a regional scale. The pollution outflow from the MCMA occurred predominantly at 600–800 hPa as evident in O3, CO, and NOx enhancements in the in situ aircraft observations. TES O3 and CO are sensitive to the MCMA pollution outflow due to their relatively high sensitivities at 600–800 hPa. We examined O3, CO, and their correlation at 600–800 hPa from TES retrievals, aircraft measurements, and GEOS-Chem model results. TES captures much of the spatial and day-to-day variability of O3 seen in the in situ data. TES CO, however, shows much less spatial and day-to-day variability compared with the in situ observations. The DO3/DCO slope is significantly higher in the TES data (0.43) than the in situ data (0.28) due partly to the lack of variability in TES CO. Extraordinarily high DO3/DCO slope (0.81) from TES observations at 618 hPa over the Eastern U.S. was previously reported by Zhang et al. [Zhang, L., Jacob, D.J., Bowman, K.W., et al., 2006. Ozone–CO correlations determined by the TES satellite instrument in continental outflow regions. Geophys. Res. Lett. 33, L18804. 10.1029/2006GL026399.]. Thus the application of TES CO–O3 correlation to map continental pollution outflow needs further examination. Published by Elsevier Ltd.

Satellite data from the Atmospheric Infrared Sounder (AIRS) is analyzed to examine regions of the upper troposphere that are supersaturated: where the relative humidity (RH) is greater than 100%. AIRS data compare well to other in situ and satellite observations of RH and provide daily global coverage up to 200 hPa, though satellite observations of supersaturation are highly uncertain. The climatology of supersaturation is analyzed statistically to understand where supersaturation occurs and how frequently. Supersaturation occurs in humid regions of the upper tropical tropopause near convection 10%–20% of the time at 200 hPa. Supersaturation is very frequent in the extratropical upper troposphere, occurring 20%–40% of the time, and over 50% of the time in storm track regions below the tropopause. The annual cycle of supersaturation is consistent for the ∼2.5 yr of data analyzed. More supersaturation is seen in the Southern Hemisphere midlatitudes, which may be attributed to higher temperature variance.

Recently available satellite observations from the Atmospheric Infrared Sounder (AIRS) are used to calculate relative humidity in the troposphere. The observations illustrate many scales of variability in the atmosphere from the seasonal overturning Hadley–Walker circulation to high-frequency transient variability associated with baroclinic storms with high vertical resolution. The Asian monsoon circulation has a strong impact on upper-tropospheric humidity, with large humidity gradients to the west of the monsoon. The vertical structure of humidity is generally bimodal, with high humidity in the upper and lower troposphere, and a dry middle troposphere. The highest variances in humidity are seen around the midlatitude tropopause. AIRS data are compared to a simulation from a state-of-the-art climate model. The model does a good job of reproducing the mean humidity distribution but is slightly moister than the observations in the middle and upper troposphere. The model has difficultly reproducing many scales of observed variability, particularly in the Tropics. Differences in humidity imply global differences in the top of atmosphere fluxes of ∼1 W m−2.

Ozone is a tropospheric pollutant and plays a key role in determining the air quality that affects human wellbeing. In this study, we compare the capability of two hypothetical grating spectrometers onboard a geostationary (GEO) satellite to sense ozone in the lowermost troposphere (surface and the 0–1 km column). We consider one week during the Northern Hemisphere summer simulated by a chemical transport model, and use the two GEO instrument configurations to measure ozone concentration (1) in the thermal infrared (GEO TIR) and (2) in the thermal infrared and the visible (GEO TIR+VIS). These configurations are compared against each other, and also against an ozone reference state and a priori ozone information. In a first approximation, we assume clear sky conditions neglecting the influence of aerosols and clouds. A number of statistical tests are used to assess the performance of the two GEO configurations. We consider land and sea pixels and whether differences between the two in the performance are significant. Results show that the GEO TIR+VIS configuration provides a better representation of the ozone field both for surface ozone and the 0–1 km ozone column during the daytime especially over land.

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 negative bias (1-10%) in the IASI profiles in the lower to mid-troposphere and a positive bias (up to 14%) in the upper troposphere/lower stratosphere (UTLS) region. 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), (0-11), 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 versus 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%., SCOPUS: ar.j, info:eu-repo/semantics/published

Aerosol-size distribution of fine aerosols were measured in Pocatello, Idaho along with hourly average PM 10 mass concentrations. Aerosol size distribution measurements are made with a laser optical particle counter while short-term mass concentrations are measured with a Tapered Element Oscillating Microbalance. The short-term mass concentration measurements show wide variations in mass concentration over 24 h periods. The accumulation mode of the aerosol-size distribution is bimodal in a few cases, primarily when elevated mass concentrations exist.

Size-resolved measurements of fine particle chemical composition and physical measurements of fine particle size distributions obtained during the Southern California Air Quality Study (SCAQS) are compared. Number distributions of the ambient aerosols were measured using optical particle counters and electrical aerosol analyzers. Optical counter data are reduced using an ambient-based calibration. Mass size distributions are inferred from the sum of size-resolved chemical composition as measured by impactors. Optical counter data reduced with an ambient-based calibration compare well to impactor measurements. Both sets of data show that the accumulation mode of the total mass size distribution may be bimodal. Condensation and droplet modes previously identified in chemical species size distributions are frequently apparent in the total mass size distribution.

Practical implementations of chemical OSSEs (Observing System Simulation Experiments) usually rely on approximations of the pseudo-observations by means of a prior parametrization of the averaging kernels, which describe the sensitivity of the observing systems to the target atmospheric species. This is intended to avoid the need for 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, and thus possibly lead to inaccurate results of OSSEs. 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 spatiotemporal interval is characterized by a peculiar 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. Then, we compared the full radiative transfer and the approximated pseudo-observations during a pollution event. We found that the approximations substantially overestimate the capability of the MAGEAQ to follow the spatiotemporal variations of the lower tropospheric ozone in selected areas. 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.

[1] Infrared spectra measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) on the SCISAT satellite were used to analyze the Sarychev volcanic aerosol after the eruption in June 2009. Evidence of the Sarychev eruptions was first detected in July 2009 from enhanced SO2 concentrations and atmospheric extinction. By February 2010, the atmosphere had returned to pre-Sarychev conditions. In July 2009, the volcanic plume was found between 8.5 km and 17.5 km in altitude at mid- and high latitudes (55°N–70°N). The first SO2 and sulfate aerosol retrievals carried out using the infrared solar occultation spectra recorded with the ACE-FTS are presented here. The size distribution parameters, the aerosol volume slant column and the composition of the sulfate aerosol were obtained by using a least squares algorithm. The maximum volume slant column of the aerosols was found to be 850 μm3 cm−3 km, which results in an approximate aerosol loading of 3 μm3 cm−3. One month after the eruption, the composition of the aerosols providing the best-fit is a 75% sulfuric acid-water solution with an effective radius (Reff) of 0.1–0.3 μm.

Aerosol size distribution data collected by the Southern California Air Quality Study (SCAQS) air monitoring network have been combined with values of the aerosol refractive index estimated from filter-based determinations of aerosol chemical composition. The resulting data have been used to calculate the aerosol light scattering coefficient values in time series at four air monitoring sites via Mie theory. It was found that the time series of the measured light scattering coefficient values could be reproduced well by the visibility model in those cases where redundant nephelometer measurements were available to assure that the measured light scattering coefficient values were not in doubt. Based on this study, it appears practical to maintain a network of monitoring sites that can be used to acquire size-distributed aerosol data simultaneously over a large geographic region. Such data will be needed in the near future for evaluation and testing of grid-based air quality models for the formation and transport of size-distributed particulate matter. Such data can be used at present to connect pollutant properties to visual range via modeling calculations based on Mie theory.

[1] We utilize a merged AIRS and MLS temperature and water vapor record (August 2004 to March 2010) to study the atmospheric variability of these quantities. The seasonal and spatial distribution of temperature and humidity shows the tropical western Pacific (TWP, 8°S–8°N, 120°E–170°E) to be a location with persistent deep convection and the tropical central Pacific (TCP, 8°S–8°N, 120°W–170°W) to be a region of subsidence, consistent with previous work. Furthermore, our estimates of 3.9 ± 0.3 ppmv and 4.9 ± 0.9 ppmv for the tropical mean stratospheric entry water vapor concentration and saturation mixing ratio, respectively, are well within previous estimates from a wide variety of observations. We also find that the interannual variability modes of the El Nino–Southern Oscillation (ENSO) and the quasi-biennial oscillation (QBO) both impact the tropopause region. The TWP (TCP) experiences enhancements (cancellation) of temperature anomalies when the ENSO and QBO are in phase. When these interannual modes fall out of phase the additive behavior of the anomalies migrate to the TCP with the TWP experiencing weaker anomalies. In both cases a change in the water vapor distribution is also observed. Our results are consistent with previous results, showing that this migration of anomaly enhancement and cancellation is a result of the ENSO impact on the Walker Circulation and the period when this occurs relative to the phase of the QBO. Our findings suggest that when the ENSO and QBO are out of phase, the TCP water vapor distribution may have a substantial impact on the tropical zonal water vapor distribution.

We examine the co-variations of tropospheric water vapor, its isotopic composition and cloud types and relate these distributions to tropospheric mixing and distillation models using satellite observations from the Aura Tropospheric Emission Spectrometer (TES) over the summertime tropical ocean. Interpretation of these process distributions must take into account the sensitivity of the TES isotope and water vapor measurements to variations in cloud, water, and temperature amount. Consequently, comparisons are made between cloud-types based on the International Satellite Cloud Climatology Project (ISSCP) classification; these are clear sky, non-precipitating (e.g., cumulus), boundary layer (e.g., stratocumulus), and precipitating clouds (e.g. regions of deep convection). In general, we find that the free tropospheric vapor over tropical oceans does not strictly follow a Rayleigh model in which air parcels become more dry and isotopically depleted through condensation. Instead, mixing processes related to convection as well as subsidence, and re-evaporation of rainfall associated with organized deep convection all play significant roles in controlling the water vapor distribution. The relative role of these moisture processes are examined for different tropical oceanic regions.

We combine CO column measurements from the MOPITT, AIRS, SCIAMACHY, and TES satellite instruments in a full-year (May 2004–April 2005) global inversion of CO sources at 4°×5° spatial resolution and monthly temporal resolution. The inversion uses the GEOS-Chem chemical transport model (CTM) and its adjoint applied to MOPITT, AIRS, and SCIAMACHY. Observations from TES, surface sites (NOAA/GMD), and aircraft (MOZAIC) are used for evaluation of the a posteriori solution. Global intercomparison of the different satellite datasets using GEOS-Chem as a common intercomparison platform shows consistency between the satellite datasets and with the in situ data. The majority of the differences between the datasets can be explained by different averaging kernels and a priori information. The global CO emission from combustion as constrained in the inversion is 1350 Tg a−1, with an additional 217 Tg a−1 from oxidation of co-emitted VOCs. This is much higher than current bottom-up emission inventories. Consistent with both the satellite and in situ data, a large fraction of the correction results from a seasonal underestimate of CO sources at northern mid-latitudes and suggests a larger-than-expected CO source from vehicle cold starts and residential heating. A posteriori emissions also indicate a general underestimation of biomass burning relative to the GFED2 inventory. However, the tropical biomass burning constraints are not consistent across the different datasets. Although the datasets reveal regional inconsistencies over tropical biomass burning regions, we find the global emission estimates to be a balance of information from all three instruments.

[1] Cloudy and clear-sky distributions of relative humidity with respect to ice (RHI) are derived from the Atmospheric Infrared Sounder and cloud profiles from CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation satellites. A peak frequency in RHI of 10–20% exists for clear sky, increasing to 75–95% within clouds detected by CloudSat. The range of values depends on the season, altitude, latitude, cloud type, and ice water content. Global distributions of RHI reveal persistent supersaturation in the clear and cloudy tropical upper troposphere and lower tropospheric polar regions, large but variable RHI in the cloudy midlatitude storm tracks, and small yet variable RHI in the clear-sky subtropics. Previous studies using satellite and in situ observations have shown a greater frequency of midlatitude supersaturation in the Southern Hemisphere (SH). Seasonal and interannual variations in RHI are also demonstrated with larger frequencies of supersaturation generally present in the winter hemisphere. An analytical method quantifies the impacts of mean temperature (T) and temperature variance (sT) on the distribution characteristics of RHI. The seasonal variation of RHI in the Northern Hemisphere is primarily modulated by seasonal changes in sT, whereas in the SH, both T and sT modulate variations in RHI. However, variations in T and sT do not explain the presence of slightly greater supersaturation frequency in the SH, suggesting a link between anthropogenic aerosol and ice cloud processes.

[1] We examine the photochemical processes governing the production of ozone in smoke from large Siberian fires that formed in July 2006 using colocated O 3 and CO profiles as measured by the Tropospheric Emission Spectrometer as well as NO 2 and aerosol optical depths as measured by the Ozone Monitoring Instrument. The Real-Time Air Quality Model (RAQMS) is used to explain the observed variations of O 3 . Enhanced levels of ozone up to 90 parts per billion (ppbv) are observed near and away from the Siberian fires (60°N and 100°E) when sunlight and NO x are available. We also observe significantly low O 3 amounts (less then 30 ppbv) in the smoke plume from Siberian fires in conjunction with optically thick aerosols. Despite this wide variance in observed ozone values, the mean ozone value for all observations of the smoke plume is close to background levels of approximately 55 ppbv in the free troposphere. Using RAQMS we show that optically thick aerosols in the smoke plume can substantially reduce the photochemical production of ozone and this can explain why the observed mean ozone amount for all plume observations is not much larger than background values of 55 ppbv. However, the anonymously low ozone amounts of 30 ppbv or less point toward other unresolved processes that reduce ozone below background levels in the plume.

[1] We compare matched retrievals of upper tropospheric water vapor (UTWV) mixing ratios from the Microwave Limb Sounder (MLS) instrument on the Aura satellite, and the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite. Because each instrument's sampling is affected by tropical conditions, about half of mutually observed scenes in the tropics yield simultaneous successful retrievals from both systems. The fraction of mutually retrieved scenes drops to 30% at higher latitudes where clouds significantly inhibit AIRS sounding. Essentially all scenes observed by MLS in extratropical and polar regions yield successful retrievals. At 250 hPa in the tropics, measurements from the two instruments are highly correlated, the differences of their means (q) are smaller than 10%, and the standard deviations of their differences (σq) are 30% or less. At 300 hPa, MLS means are drier by 10–15%, and σq is 40–60%, indicating that responses of MLS and AIRS to UTWV perturbations are not one-to-one. Root mean square agreement is also poorer over the poles at 300 hPa and at 200 and 150 hPa at lower latitudes. In these regions, ∣q∣ = 10% or more, and σq = 40–70%. Correlations between the two data sets are 0.7–0.9 at 300 and 250 hPa globally and at 200 hPa in the tropics. This high correlation indicates that σq of 50% or greater comes mainly from systematic differences in sensitivity of the two instruments, especially for small and large UTWV amounts; larger values of σq are generally not due to large random errors from either instrument. An AIRS low-end sensitivity threshold of 15–20 ppmv leads to poorer agreement under the driest conditions. Disagreement at 300 hPa likely comes from overestimation by MLS for the wettest conditions of >400 ppmv. While MLS is biased slightly dry overall at 300 hPa, it is biased wet in the wettest regions, particularly those associated with deep convection. These sensitivity differences explain nonunity slopes of linear fits to the two data sets. MLS everywhere has a greater dynamic range than AIRS, with larger maxima and smaller minima. Good agreement at 250 hPa suggests AIRS uncertainties of 25% up to the reported 250–200 hPa layer in the tropics and extratropics, consistent with previous comparisons with balloon- and aircraft-borne instruments. The agreement at 250 hPa also indicates that MLS is reliable from its reported 215-hPa level upward in altitude.

[1] We characterize and validate the cloud products from the Tropospheric Emission Spectrometer (TES) by comparing TES estimates of effective cloud optical depth and cloud top height to those from the Moderate Resolution Imaging Spectroradiometer (on EOS) (MODIS), the Atmospheric Infrared Sounder (AIRS), and to simulated data. TES measures in the infrared spectral region (650–2260 cm−1), where clouds have a ubiquitous impact on measured radiances and therefore on trace gas profile retrievals. The radiance contribution of clouds is parameterized in TES retrievals in terms of a set of frequency-dependent nonscattering effective optical depths and a cloud height. This unique approach jointly retrieves cloud parameters with surface temperature, emissivity, atmospheric temperature, and trace gases such as ozone from TES spectral radiances. We calculate the relationship between the true optical depth and the TES effective optical depth for a range of single-scatter albedo and phase functions to show how this varies with cloud type. We estimate the errors on retrieved cloud parameters using a simulated data set covering a wide range of cloud cases. For simulations with no noise on the radiances, cloud height errors are less than 30 hPa, and effective optical depth follows expected behavior for input optical depths of less than 3. When random noise is included on the radiances, and atmospheric variables are included in the retrieval, cloud height errors are approximately 200 hPa, and the estimated effective optical depth has sensitivity between optical depths of 0.3 and 10. The estimated errors from simulation are consistent with differences between TES and cloud top heights and optical depth from MODIS and AIRS.

[1] Comparisons of Tropospheric Emission Spectrometer (TES) water vapor retrievals with in situ measurements are presented. Global comparisons of TES water vapor retrievals with nighttime National Centers for Environmental Prediction RS90/RS92 radiosondes show a small (

[1] The Tropospheric Emission Spectrometer (TES) is an infrared, high-resolution Fourier transform spectrometer which was launched onboard NASA's Aura satellite in 2004 and is providing global, vertically resolved measurements of ozone in the troposphere. TES version 2 (V002) data profiles have been validated in the troposphere and lower stratosphere by way of comparison to ozonesondes and aircraft measurements. TES measurements also have sensitivity throughout the stratosphere, and therefore TES ozone profiles can be integrated to determine the total and stratospheric column in addition to the tropospheric column ozone values. In this work we compare the ozone in the stratosphere measured by TES to observations from the Microwave Limb Sounder (MLS) instrument in order to show the quality of the TES measurements in the stratosphere. We also compare the determination of a total column value for ozone based on the TES profiles to the column measured by the Ozone Monitoring Instrument (OMI). The TES tropospheric ozone column value is also calculated from the TES profiles and compared with column values determined from ozonesonde data. Column measurements are useful because the errors are markedly reduced from errors at the profile levels and can be used to assess both biases and quality of the TES ozone retrievals. TES observations of total or partial column ozone compare well with the other instruments but tend toward higher values than the other measurements. Specifically, TES is higher than OMI by ∼10 Dobson units (DU) for the total ozone column. TES measures higher values in the stratosphere (above 100 hPa) by ∼3 DU and measures higher ozone column values (∼4 DU) in the troposphere than ozonesondes. While the strength of the TES nadir ozone product is the vertical resolution it provides in the troposphere, a tropospheric column value derived from TES have utility in analyses using or validating tropospheric ozone residual products.

Global observations of cloud and humidity distributions in the upper troposphere within all geophysical conditions are critically important in order to monitor the present climate and to provide necessary data for validation of climate models to project future climate change. Towards this end, tropical oceanic distributions of thin cirrus optical depth (τ), effective diameter (De), and relative humidity with respect to ice (RHi) within cirrus (RHic) are simultaneously derived from the Atmospheric Infrared Sounder (AIRS). Corresponding increases in De and cloud temperature are shown for cirrus with τ>0.25 that demonstrate quantitative consistency to other surface-based, in situ and satellite retrievals. However, inferred cirrus properties are shown to be less certain for increasingly tenuous cirrus. In-cloud supersaturation is observed for 8–12% of thin cirrus and is several factors higher than all-sky conditions; even higher frequencies are shown for the coldest and thinnest cirrus. Spatial and temporal variations in RHic correspond to cloud frequency while regional variability in RHic is observed to be most prominent over the N. Indian Ocean basin. The largest cloud/clear sky RHi anomalies tend to occur in dry regions associated with vertical descent in the sub-tropics, while the smallest occur in moist ascending regions in the tropics. The characteristics of RHic frequency distributions depend on τ and a peak frequency is located between 60–80% that illustrates RHic is on average biased dry. The geometrical thickness of cirrus is typically less than the vertical resolution of AIRS temperature and specific humidity profiles and thus leads to the observed dry bias, shown with coincident cloud vertical structure obtained from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The joint distributions of thin cirrus microphysics and humidity derived from AIRS provide unique and important regional and global-scale insights on upper tropospheric processes not available from surface, in situ, and other contemporary satellite observing platforms.

[1] Validation of Tropospheric Emission Spectrometer (TES) tropospheric CO profiles with in situ CO measurements from the Differential Absorption CO Measurement (DACOM) instrument during the Intercontinental Chemical Transport Experiment (INTEX)-B campaigns in March to May 2006 are presented. For each identified DACOM CO profile, one to three TES CO profiles are selected closest in location to the small area that the DACOM profile covers. The time differences between the comparison profiles are within 2 hours. The DACOM CO vertical profiles are adjusted by applying nearest coincident TES averaging kernels and the a priori profiles. This step accounts for the effect of the vertical resolution of the TES CO retrievals and removes the influence of the a priori assumptions in the comparisons. Comparison statistics for data taken near Houston in March 2006 show good agreement between TES and the adjusted DACOM CO profiles in the lower and middle troposphere with a correlation coefficient of 0.87. On average, the TES CO volume mixing ratio profile is 0–10% lower than the adjusted DACOM CO profile from the lower to middle troposphere. This is within the 10–20% standard deviations of the TES or DACOM CO profiles taken in the Houston area. The comparisons of TES and DACOM CO profiles near Hawaii and Anchorage in April to May 2006 are not as good. In these regions the aircraft DACOM CO profiles are characterized by plumes or enhanced CO layers, consistent with known features in the tracer fields due to transpacific transport of polluted air parcels originating from East Asia. Although TES observations over the Pacific region also show localized regions of enhanced CO, the coincidence criteria for obtaining good comparisons with aircraft measurements are challenging. The meaning of validation comparisons in profile portions where TES retrievals have little sensitivity is addressed. Examinations of characteristic parameters in TES retrievals are important in data applications.

[1] Comparisons of tropospheric carbon monoxide (CO) volume mixing ratio profiles and total columns are presented from nadir-viewing measurements made by the Tropospheric Emission Spectrometer (TES) on the NASA Aura satellite and by the Measurements of Pollution in the Troposphere (MOPITT) instrument on the NASA Terra satellite. In this paper, we first explore the factors that relate the retrieved and the true species profiles. We demonstrate that at a given location and time the retrieved species profiles reported by different satellite instrument teams can be very different from each other. We demonstrate the influence of the a priori data and instrument characteristics on the CO products from TES and MOPITT and on their comparisons. Direct comparison of TES and MOPITT retrieved CO profiles and columns show significant differences in the lower and upper troposphere. To perform a more proper and rigorous comparison between the two instrument observations we allow for different a priori profiles and averaging kernels. We compare (1) TES retrieved CO profiles adjusted to the MOPITT a priori with the MOPITT retrievals and (2) the above adjusted TES CO profiles with the MOPITT profiles vertically smoothed by the TES averaging kernels. These two steps greatly improve the agreement between the CO profiles and the columns from the two instruments. No systematic differences are found as a function of latitude in the final comparisons. These results show that knowledge of the a priori profiles, the averaging kernels, and the error covariance matrices in the standard data products provided by the instrument teams and understanding their roles in the retrieval products are essential in quantitatively interpreting both retrieved profiles and the derived total or partial columns for scientific applications.

The consistency of cloud top temperature (Tc) and effective cloud fraction (f) retrieved by the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) observation suite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the EOS-Aqua platform are investigated. Collocated AIRS and MODIS TC and f are compared via an 'effective scene brightness temperature' (Tb,e). Tb,e is calculated with partial field of view (FOV) contributions from TC and surface temperature (TS), weighted by f and 1-f, respectively. AIRS reports up to two cloud layers while MODIS reports up to one. However, MODIS reports TC, TS, and f at a higher spatial resolution than AIRS. As a result, pixel-scale comparisons of TC and f are difficult to interpret, demonstrating the need for alternatives such as Tb,e. AIRS-MODIS Tb,e differences ((Delta)Tb,e) for identical observing scenes are useful as a diagnostic for cloud quantity comparisons. The smallest values of DTb,e are for high and opaque clouds, with increasing scatter in (Delta)Tb,e for clouds of smaller opacity and lower altitude. A persistent positive bias in DTb,e is observed in warmer and low-latitude scenes, characterized by a mixture of MODIS CO2 slicing and 11-mm window retrievals. These scenes contain heterogeneous cloud cover, including mixtures of multilayered cloudiness and misplaced MODIS cloud top pressure. The spatial patterns of (Delta)Tb,e are systematic and do not correlate well with collocated AIRS-MODIS radiance differences, which are more random in nature and smaller in magnitude than (Delta)Tb,e. This suggests that the observed inconsistencies in AIRS and MODIS cloud fields are dominated by retrieval algorithm differences, instead of differences in the observed radiances. The results presented here have implications for the validation of cloudy satellite retrieval algorithms, and use of cloud products in quantitative analyses.

[1] We estimate the accuracy of cloud top altitude (Z) retrievals from the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) observing suite (ZA) on board the Earth Observing System Aqua platform. We compare ZA with coincident measurements of Z derived from the micropulse lidar and millimeter wave cloud radar at the Atmospheric Radiation Measurement (ARM) program sites of Nauru and Manus islands (ZARM) and the inferred Z from vertically resolved Microwave Limb Sounder (MLS) ice water content (IWC) retrievals. The mean difference in ZA minus ZARM plus or minus one standard deviation ranges from −2.2 to 1.6 km ± 1.0 to 4.2 km for all cases of AIRS effective cloud fraction (fA) > 0.15 at Manus Island using the cloud radar only. The range of mean values results from using different approaches to determine ZARM, day/night differences, and the magnitude of fA; the variation about the mean decreases for increasing values of fA. Analysis of ZARM from the micropulse lidar at Nauru Island for cases restricted to 0.05 ≤ fA ≤ 0.15 indicates a statistically significant improvement in ZA − ZARM over the cloud radar-derived values at Manus Island. In these cases the ZA − ZARM difference is −1.1 to 2.1 km ± 3.0 to 4.5 km. These results imply that the operational ZA is quantitatively useful for constraining cirrus altitude despite the nominal 45 km horizontal resolution. Mean differences of cloud top pressure (PCLD) inferred from coincident AIRS and MLS ice water content (IWC) retrievals depend upon the method of defining AIRS PCLD (as with the ARM comparisons) over the MLS spatial scale, the peak altitude and maximum value of MLS IWC, and fA. AIRS and MLS yield similar vertical frequency distributions when comparisons are limited to fA > 0.1 and IWC > 1.0 mg m−3. Therefore the agreement depends upon the opacity of the cloud, with decreased agreement for optically tenuous clouds. Further, the mean difference and standard deviation of AIRS and MLS PCLD are highly dependent on the MLS tangent altitude. For MLS tangent altitudes greater than 146 hPa, the strength of the limb technique, the disagreement becomes statistically significant. This implies that AIRS and MLS “agree” in a statistical sense at lower tangent altitudes and “disagree” at higher tangent altitudes. These results provide important insights on upper tropospheric cloudiness as observed by nadir-viewing AIRS and limb-viewing MLS.

[1] The Tropospheric Emission Spectrometer (TES) on the Earth Observing System (EOS)-Aura spacecraft measures global profiles of atmospheric ozone with vertical resolution of 6–7 km in the troposphere for the nadir view. For a first validation of TES ozone measurements we have compared TES-retrieved ozone profiles to ozonesondes from fall, 2004. In some cases the ozonesonde data are from dedicated launches timed to match the Aura overpass, while other comparisons are performed with routine data available from the Southern Hemisphere Additional Ozonesonde (SHADOZ) archive and World Ozone and Ultraviolet Data Center (WOUDC) data archives. We account for TES measurement sensitivity and vertical resolution by applying the TES-averaging kernel and constraint to the ozonesonde data before differencing the profiles. Overall, for V001 data, TES ozone profiles are systematically higher than sondes in the upper troposphere but compare well in the lower troposphere, with respect to estimated errors. These comparisons show that TES is able to detect relative variations in the coarse vertical structure of tropospheric ozone.

We develop an approach to estimate and characterize trace gas retrievals in the presence of clouds in high spectral measurements of upwelling radiance in the infrared spectral region (650-2260/cm). The radiance contribution of clouds is parameterized in terms of a set of frequency-dependent nonscattering optical depths and a cloud height. These cloud parameters are retrieved jointly with surface temperature, emissivity, atmospheric temperature, and trace gases such as ozone from spectral data. We demonstrate the application of this approach using data from the Tropospheric Emission Spectrometer (TES) and test data simulated with a scattering radiative transfer model. We show the value of this approach in that it results in accurate estimates of errors for trace gas retrievals, and the retrieved values improve over the initial guess for a wide range of cloud conditions. Comparisons are made between TES retrievals of ozone, temperature, and water to model fields from the Global Modeling and Assimilation Office (GMAO), temperature retrievals from the Atmospheric Infrared Sounder (AIRS), tropospheric ozone columns from the Goddard Earth Observing System (GEOS) GEOS-Chem, and ozone retrievals from the Total Ozone Mapping Spectrometer (TOMS). In each of these cases, this cloud retrieval approach does not introduce observable biases into TES retrievals.

[1] The updraft and downdraft mass flux profiles generated by convective parameterizations differ significantly from each other. Most convective parameterizations are tested against temperature and relative humidity profiles from radiosondes. Chemical tracers provide important additional constraints on the vertical redistribution of mass by convective parameterizations. We compile tropical climatologies of water vapor (H2O), ozone (O3), carbon monoxide (CO), and nitric acid (HNO3) from a variety of satellite, aircraft, and balloon-based measurement platforms. These climatologies are compared with the profiles predicted by a variant of the Emanuel convective parameterization, a two-column model of the tropical atmosphere, and by the implementations of the Relaxed Arakawa Schubert (RAS) and Zhang and McFarlane (ZM) parameterizations in a three-dimensional global forecast model. In general, the models with more pronounced convective outflow in the upper troposphere compare more favorably with observations. These models are associated with increased evaporative moistening in the middle and lower troposphere.

[1] We present global, vertical profile estimates of the HDO/H2O ratio from the Tropospheric Emission Spectrometer (TES) on the Earth Observing System (EOS) Aura satellite. We emphasize in this paper the estimation approach and error characterization, which are critical to determining the very small absolute concentration of HDO relative to H2O and its uncertainty. These estimates were made from TES nadir-viewing (downlooking) thermal infrared spectral radiances observed on 20 September 2004. Profiles of HDO and H2O are simultaneously estimated from the observed radiances and a profile of the ratio is then calculated. This simultaneous, or “joint,” estimate is regularized with an a priori covariance matrix that includes expected correlations between HDO and H2O. This approach minimizes errors in the profile of the HDO/H2O ratio that are due to overlapping HDO and H2O spectroscopic lines. Under clear-sky conditions in the tropics, TES estimates of the HDO/H2O ratio are sensitive to the distribution of the actual ratio between the surface and about 300 hPa with peak sensitivity at 700 hPa. The sensitivity decreases with latitude through its dependence on temperature and water amount. We estimate a precision of approximately 1% to 2% for the ratio of the HDO/H2O tropospheric densities; however, there is possibly a bias of approximately 5% in the ratio due to the HDO spectroscopic line strengths. These global observations clearly show increased isotopic depletion of water vapor at higher latitudes as well as increased depletion in the upper troposphere versus the lower troposphere.

We examine differences in total precipitable water vapor (PWV) from the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Scanning Radiometer (AMSR-E) experiments sharing the Aqua spacecraft platform. Both systems provide estimates of PWV over water surfaces. We compare AIRS and AMSR-E PWV to constrain AIRS retrieval uncertainties as functions of AIRS retrieved infrared cloud fraction. PWV differences between the two instruments vary only weakly with infrared cloud fraction up to about 70%. Maps of AIRS-AMSR-E PWV differences vary with location and season. Observational biases, when both instruments observe identical scenes, are generally less than 5%. Exceptions are in cold air outbreaks where AIRS is biased moist by 10-20% or 10-60% (depending on retrieval processing) and at high latitudes in winter where AIRS is dry by 5-10%. Sampling biases, from different sampling characteristics of AIRS and AMSR-E, vary in sign and magnitude. AIRS sampling is dry by up to 30% in most high-latitude regions but moist by 5-15% in subtropical stratus cloud belts. Over the northwest Pacific, AIRS samples conditions more moist than AMSR-E by a much as 60%. We hypothesize that both wet and dry sampling biases are due to the effects of clouds on the AIRS retrieval methodology. The sign and magnitude of these biases depend upon the types of cloud present and on the relationship between clouds and PWV. These results for PWV imply that climatologies of height-resolved water vapor from AIRS must take into consideration local meteorological processes affecting AIRS sampling.

A method of cirrus detection at nighttime is presented that utilizes 3.8 and 10.4 (micro)m infrared (IR) window brightness temperature differences (dBT) and total column precipitable water (PW) measurements. This technique is applied to the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit A (AMSU-A) instrument suite on board EOS-Aqua, where dBT is determined from sets of carefully selected AIRS window channels, while PW is derived from the synergistic AIRS and AMSU-A water vapor retrievals. Simulated and observed dBT for a particular value of PW are not constant; several physical factors impact dBT, including the variability in temperature and relative humidity profiles, surface emissivity, instrument noise, and skin/ near-surface air temperature differences. We simulate clear-sky dBT over a realistic range of PWs using 8350 radiosondes that have varying temperature and relative humidity profiles. Thresholds between cloudy and uncertain sky conditions are derived once the scatter in the clear-sky dBT is determined. Simulations of optically thin cirrus indicate that this technique is most sensitive to cirrus optical depth in the 10 (micro)m window of 0.1-0.15 or greater over the tropical and subtropical oceans, where surface emissivity and skin/near-surface air temperature impacts on the IR radiances are minimal. The method at present is generally valid over oceanic regions only, specifically, the tropics and subtropics. The detection of thin cirrus, and other cloud types, is validated using observations at the Atmospheric Radiation Measurement (ARM) program site located at Manus Island in the tropical western Pacific for 89 coincident EOS-Aqua overpasses. Even though the emphasis of this work is on the detection of thin cirrus at nighttime, this technique is sensitive to a broad cloud morphology. The cloud detection technique agrees with ARM-detected clouds 82-84% of the time, which include thin cirrus, as well as other cloud types. Most of the disagreements are well explained by AIRS footprint-scale heterogeneity compared to ARM point measurements, cirrus overlying lower-layer water clouds, possible mixed phase microphysics in midlevel clouds, and significant IR channel noise for cold BT scenes over deep convective towers.

The high-resolution infrared absorption spectra of the Atmospheric Trace Molecule Spectroscopy (ATMOS) experiment are utilized to derive vertical profiles of sulfate aerosol volume density and extinction coefficient. Following the eruption of Mt. Pinatubo in June 1991, the ATMOS spectra obtained on three Space Shuttle missions (1992, 1993, and 1994) provide a unique opportunity to study the global stratospheric sulfate aerosol layer shortly after a major volcanic eruption and periodically during the decay phase. Synthetic sulfate aerosol spectra are fit to the observed spectra, and a global fitting inversion routine is used to derive vertical profiles of sulfate aerosol volume density. Vertical profiles of sulfate aerosol volume density for the three missions over portions of the globe are presented, with the peak in aerosol volume density occurring from as low as 10 km (polar latitudes) to as high as 20 km (subtropical latitudes). Derived aerosol volume density is as high as 2-3.5 (mu)m(exp 3) per cubic centimeter +/-10% in 1992, decreasing to 0.2-0.5 (mu)m(exp 3) per cubic centimeter +/-20% in 1994, in agreement with other experiments. Vertical extinction profiles derived from ATMOS are compared with profiles from Improved Stratospheric And Mesospheric Sounder (ISAMS) and Cryogenic Limb Array Etalon Spectrometer (CLAES) that coincide in space and time and show good general agreement. The uncertainty of the ATMOS vertical profiles is similar to CLAES and consistently smaller than ISAMS at similar altitudes.