The Orbiting Carbon Observatory (OCO) mission was selected by NASA's Office of Earth Science as the fifth mission in its Earth System Science Pathfinder (ESSP) Program. OCO will make the first global, space-based measurements of atmospheric CO 2 with the precision, resolution, and coverage needed to characterize sources and sinks of this important green-house gas. These measurements will improve our ability to forecast CO 2 -induced climate change. OCO will fly in a 1:15 PM sun-synchronous orbit, sharing its ground track with the Earth Observing System (EOS) Aqua platform. It will carry high-resolution spectrometers to measure reflected sunlight in the molecular oxygen (O 2 ) A-band at 0.76 m and the CO 2 bands at 1.61 and 2.06 m to retrieve the column-averaged CO 2 dry air mole fraction, X CO 2 . A comprehensive validation and correlative measurement program has been incorporated into this mission to ensure that X CO 2 can be retrieved with precisions of 0.3% (1 ppm) on regional scales.

[1] The Earth Observing System (EOS) Microwave Limb Sounder (MLS) aboard the Aura satellite has provided essentially daily global measurements of ozone (O 3 ) profiles from the upper troposphere to the upper mesosphere since August of 2004. This paper focuses on validation of the MLS stratospheric standard ozone product and its uncertainties, as obtained from the 240 GHz radiometer measurements, with a few results concerning mesospheric ozone. We compare average differences and scatter from matched MLS version 2.2 profiles and coincident ozone profiles from other satellite instruments, as well as from aircraft lidar measurements taken during Aura Validation Experiment (AVE) campaigns. Ozone comparisons are also made between MLS and balloon-borne remote and in situ sensors. We provide a detailed characterization of random and systematic uncertainties for MLS ozone. We typically find better agreement in the comparisons using MLS version 2.2 ozone than the version 1.5 data. The agreement and the MLS uncertainty estimates in the stratosphere are often of the order of 5%, with values closer to 10% (and occasionally 20%) at the lowest stratospheric altitudes, where small positive MLS biases can be found. There is very good agreement in the latitudinal distributions obtained from MLS and from coincident profiles from other satellite instruments, as well as from aircraft lidar data along the MLS track.

[1] Precision requirements are determined for space-based column-averaged CO 2 dry air mole fraction (X CO 2 ) data. These requirements result from an assessment of spatial and temporal gradients in X CO 2 , the relationship between X CO 2 precision and surface CO 2 flux uncertainties inferred from inversions of the X CO 2 data, and the effects of X CO 2 biases on the fidelity of CO 2 flux inversions. Observational system simulation experiments and synthesis inversion modeling demonstrate that the Orbiting Carbon Observatory mission design and sampling strategy provide the means to achieve these X CO 2 data precision requirements.

[1] The objective, design, and implementation of the OCO inverse method are presented. The inverse method is the algorithm which finds the profile-weighted mean mixing ratio, X CO2 , which best fits the measured spectrum, given a ''forward model'' which calculates the spectrum for a given atmospheric state, surface, and instrument properties. Minimizing bias among comparative values of X CO2 is a critical objective. The algorithm uses an ''optimal,'' maximum a posteriori inverse method, with weak a priori constraint, and employs a state vector containing atmospheric and surface properties expected to vary significantly between soundings. An extensive operational characterization and error analysis will be employed, producing quantities designed to aid atmospheric modelers in use of the OCO data. In particular, comparison to inverse models of surface CO 2 flux will require use of the OCO column averaging kernel and a priori state vector. An off-line error analysis has also been developed for more detailed error studies, and its use is illustrated by prospective application to case studies of nadir observations in summer and winter at three sites. Uncertainties due to noise, geophysical variability, and spectroscopic parameters are considered in detail. At low and midlatitudes, the single-sounding errors due to these sources are expected to be $0.7-0.8 ppm for high-sun conditions and $1.5-2.5 ppm for low sun (winter). Errors from the same sources in semimonthly regional averages are predicted to be <1 ppm for all conditions.

[1] The Mars Climate Sounder (MCS) onboard the Mars Reconnaissance Orbiter is the latest of a series of investigations devoted to improving the understanding of current Martian climate. MCS is a nine-channel passive midinfrared and far-infrared filter radiometer designed to measure thermal emission in limb and on-planet geometries from which vertical profiles of atmospheric temperature, water vapor, dust, and condensates can be retrieved. Here we describe the algorithm that is used to retrieve atmospheric profiles from MCS limb measurements for delivery to the Planetary Data System. The algorithm is based on a modified Chahine method and uses a fast radiative transfer scheme based on the Curtis-Godson approximation. It retrieves pressure and vertical profiles of atmospheric temperature, dust opacity, and water ice opacity. Water vapor retrievals involve a different approach and will be reported separately. Pressure can be retrieved to a precision of 1-2% and is used to establish the vertical coordinate. Temperature profiles are retrieved over a range from 5-10 to 80-90 km altitude with a typical altitude resolution of 4-6 km and a precision between 0.5 and 2 K over most of this altitude range. Dust and water ice opacity profiles also achieve vertical resolutions of about 5 km and typically have precisions of 10 À4 -10 À5 km À1 at 463 cm À1 and 843 cm À1 , respectively. Examples of temperature profiles as well as dust and water ice opacity profiles from the first year of the MCS mission are presented, and atmospheric features observed during periods employing different MCS operational modes are described. An intercomparison with historical temperature measurements from the Mars Global Surveyor mission shows good agreement. Citation: Kleinböhl, A., et al. (2009), Mars Climate Sounder limb profile retrieval of atmospheric temperature, pressure, and dust and water ice opacity,

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[1] Space-based measurements of reflected sunlight in the near-infrared (NIR) region promise to yield accurate and precise observations of the global distribution of atmospheric CO 2 . The Orbiting Carbon Observatory (OCO) is a future NASA mission, which will use this technique to measure the column-averaged dry air mole fraction of CO 2 (X CO 2 ) with the precision and accuracy needed to quantify CO 2 sources and sinks on regional scales ($1000 Â 1000 km 2 ) and to characterize their variability on seasonal timescales. Here, we have used the OCO retrieval algorithm to retrieve X CO 2 and surface pressure from space-based Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) measurements and from coincident ground-based Fourier transform spectrometer (FTS) measurements of the O 2 A band at 0.76 mm and the 1.58 mm CO 2 band for Park Falls, Wisconsin. Even after accounting for a systematic error in our representation of the O 2 absorption cross sections, we still obtained a positive bias between SCIAMACHY and FTS X CO 2 retrievals of $3.5%. Additionally, the retrieved surface pressures from SCIAMACHY systematically underestimate measurements of a calibrated pressure sensor at the FTS site. These findings lead us to speculate about inadequacies in the forward model of our retrieval algorithm. By assuming a 1% intensity offset in the O 2 A band region for the SCIAMACHY X CO 2 retrieval, we significantly improved the spectral fit and achieved better consistency between SCIAMACHY and FTS X CO 2 retrievals. We compared the seasonal cycle of X CO 2 at Park Falls from SCIAMACHY and FTS retrievals with calculations of the Model of Atmospheric Transport and Chemistry/Carnegie-AmesStanford Approach (MATCH/CASA) and found a good qualitative agreement but with MATCH/CASA underestimating the measured seasonal amplitude. Furthermore, since SCIAMACHY observations are similar in viewing geometry and spectral range to those of OCO, this study represents an important test of the OCO retrieval algorithm and validation concept using NIR spectra measured from space. Finally, we argue that significant improvements in precision and accuracy could be obtained from a dedicated CO 2 instrument such as OCO, which has much higher spectral and spatial resolutions than SCIAMACHY. These measurements would then provide critical data for improving our understanding of the carbon cycle and carbon sources and sinks.

NOAA, through the Joint Polar Satellite System (JPSS) program, in partnership with the National Aeronautical and Space Administration, launched the Suomi National Polar-orbiting Partnership (S-NPP) satellite, a risk reduction and data continuity mission, on 28 October 2011. The JPSS program is executing the S-NPP Calibration and Validation program to ensure that the data products comply with the requirements of the sponsoring agencies. The Ozone Mapping and Profiler Suite (OMPS) consists of two telescopes feeding three detectors measuring solar radiance scattered by the Earth's atmosphere directly and solar irradiance by using diffusers. The measurements are used to generate estimates of total column ozone and vertical ozone profiles for use in near-real-time applications and extension of ozone climate data records. The calibration and validation efforts are progressing well, and both Level 1 (Sensor Data Records) and Level 2 (Ozone Environmental Data Records) have advanced to release at Provisional Maturity. This paper provides information on the product performance over the first 22 months of the mission. The products are evaluated through the use of internal consistency analysis techniques and comparisons to other satellite instrument and ground-based products. The initial performance finds total ozone showing negative bias of 2 to 4% with respect to correlative products and ozone profiles often within ±5% in the middle and upper stratosphere of current operational products. Potential improvements in the measurements and algorithms are identified. These will be implemented in coming months to reduce the differences further.