Denis M. O'Brien

Precision requirements for space-based XCO 2 data

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.

Vicarious Calibration of the GOSAT Sensors Using the Railroad Valley Desert Playa

Japans Greenhouse Gases Observing Satellite (GOSAT) was successfully launched into a sun-synchronous orbit on January 23, 2009 to monitor global distributions of carbon dioxide ( CO2) and methane (CH4). GOSAT carries two instruments. The Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) measures reflected radiances in the 0.76 μm oxygen band and in the weak and strong CO2 bands at 1.6 and 2.0 μm. The TANSO Cloud and Aerosol Imager (TANSO-CAI) uses four spectral bands at 0.380, 0.674, 0.870, and 1.60 μm to identify clear soundings and to provide cloud and aerosol optical properties. Vicarious calibration was performed at Railroad Valley, Nevada, in the summer of 2009. The site was chosen for its flat surface and high spectral reflectance. In situ measurements of geophysical parameters, such as surface reflectance, aerosol optical thickness, and profiles of temperature, pressure, and humidity, were acquired at the overpass times. Because the instantaneous field of view of TANSO-FTS is large (10.5 km at nadir), the spatially limited reflectance measurements at the field sites were extrapolated to the entire footprint using independent satellite data. During the campaign, six days of measurements were acquired from two different orbit paths. Spectral radiances at the top of the atmosphere were calculated using vector radiative transfer models coupled with ground in situ data. The agreement of the modeled radiance spectra with those measured by the TANSO-FTS is within 7%. Significant degradations in responsivity since launch have been detected in the short-wavelength bands of both TANSO-FTS and TANSO-CAI.

Comparison of Cloud-Screening Methods Applied to GOSAT Near-Infrared Spectra

Several existing and proposed satellite remote sensing instruments are designed to derive concentrations of trace gases, such as carbon dioxide (CO2) and methane (CH4), from measured spectra of reflected sunlight in absorption bands of the gases. Generally, these analyses require that the scenes be free of cloud and aerosol, necessitating robust screening algorithms. In this work, two cloud-screening algorithms are compared. One applies threshold tests, similar to those used by the MODerate resolution Imaging Spectrometer (MODIS), to visible and infrared reflectances measured by the Cloud and Aerosol Imager aboard the Greenhouse gases Observing SATellite (GOSAT). The second is a fast retrieval algorithm that operates on high-resolution spectra in the oxygen A-band measured by the Fourier Transform Spectrometer on GOSAT. Near-simultaneous cloud observations from the MODIS Aqua satellite are used for comparison. Results are expressed in terms of agreement and disagreement in the identification of clear and cloudy scenes for land and non-sun glint viewing over water. The accuracy, defined to be the fraction of scenes that are classified the same, is approximately 80% for both algorithms over land when comparing with MODIS. The accuracy rises to approximately 90% over ocean. Persistent difficulties with identifying cirrus clouds are shown to yield a large fraction of the disagreement with MODIS.

Long-Term Vicarious Calibration of GOSAT Short-Wave Sensors: Techniques for Error Reduction and New Estimates of Radiometric Degradation Factors

This work describes the radiometric calibration of the short-wave infrared (SWIR) bands of two instruments aboard the Greenhouse gases Observing SATellite (GOSAT), the Thermal And Near infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (TANSO-CAI). Four vicarious calibration campaigns (VCCs) have been performed annually since June 2009 at Railroad Valley, NV, USA, to estimate changes in the radiometric response of both sensors. While the 2009 campaign ( VCC2009) indicated significant initial degradation in the sensors compared to the prelaunch values, the results presented here show that the stability of the sensors has improved with time. The largest changes were seen in the 0.76 μm oxygen A-band for TANSO-FTS and in the 0.380 and 0.674 μm bands for TANSO-CAI. This paper describes techniques used to optimize the vicarious calibration of the GOSAT SWIR sensors. We discuss error reductions, relative to previous work, achieved by using higher quality and more comprehensive in situ measurements and proper selection of reference remote sensing products from the Moderate Resolution Imaging Spectroradiometer used in radiative transfer calculations to model top-of-the-atmosphere radiances. In addition, we present new estimates of TANSO-FTS radiometric degradation factors derived by combining the new vicarious calibration results with the time-dependent model provided by Yoshida (2012), which is based on analysis of on-board solar diffuser data. We conclude that this combined model provides a robust correction for TANSO-FTS Level 1B spectra. A detailed error budget for TANSO-FTS vicarious calibration is also provided.

Preflight Radiometric Calibration of the Orbiting Carbon Observatory

This paper describes the radiometric calibration of the original Orbiting Carbon Observatory. The calibration process required characterizing both the dark current level and gain coefficients of each instrumental channel. The dark response was characterized with extensive testing and revealed some unexpected instrument behavior. The gain coefficients were characterized via illumination of the instrument spectrometers with a laboratory calibrated integrating sphere source. Comparison between the spectrometer output and a calibrated photodiode led to a set of calibration coefficients for each spectrometer channel. The calibration coefficients were validated by a novel approach involving observation of the solar spectrum through a transmission filter. Validation occurred by examination of both the ratio of the filtered to unfiltered spectra and retrievals of geophysical quantities such as surface pressure. The linearity of the calibration was established to approximately 0.2%. Finally, using the calibration data from the integrating sphere, a simple noise model was developed for each channel of the instrument. A summary of the signal to-noise performance is included.

Identification and Correction of Residual Image in the

The detector used for the O2 A-band (0.76 μm) of the National Aeronautics and Space Administrations Orbiting Carbon Observatory (OCO) employed a HyViSI Hawaii-1RG sensor, operating at 180 K in a rolling read-out mode. During the thermal vacuum testing of the flight instrument, it was discovered that the detector exhibited residual images that lasted for many seconds and were of sufficient magnitude to compromise the mission objectives. Independent testing of flight-spare detectors revealed that the problem was common to all and was not simply a fault of the flight detector. The residual image was found to depend upon even-order derivatives of the spectrum, and its decay was a function of the number of frames rather than time. An empirical model was developed, which represented the measured spectrum in terms of the true spectrum and a history of all previous changes in the spectra. On the basis of the model, an algorithm was devised to correct spectra for the effects of residual image, using a time-marching analysis of a history of previous spectra. The algorithm was tested with spectra acquired during the second thermal vacuum test of OCO and was found to reduce the effect of residual image to almost the noise level of the detector. Numerical simulations indicate that residual image has a negligible impact on retrieved concentrations of O2 and CO2 once the spectra have been corrected.

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The second NASA Earth Venture Mission, Geostationary Carbon Cycle Observatory (GeoCarb), will provide measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and solar-induced fluorescence (SIF) from Geostationary Orbit (GEO). The GeoCarb mission will deliver daily maps of column concentrations of CO2, CH4, and CO over the observed landmasses in the Americas at a spatial resolution of roughly 10 x 10 km. Persistent measurements of CO2, CH4, CO, and SIF will contribute significantly to resolving carbon emissions and illuminating biotic processes at urban to continental scales, which will allow the improvement of modeled biogeochemical processes in Earth System Models as well as monitor the response of the biosphere to disturbance. This is essential to improve understanding of the Carbon-Climate connection. In this paper, we introduce the instrument and the GeoCarb Mission, and we demonstrate the potential scientific contribution of the mission through a series of CO2 and CH4 simulation experiments. We find that GeoCarb will be able to constrain emissions at urban to continental spatial scales on weekly to annual time scales. The GeoCarb mission particularly builds upon the Orbiting Carbon Obserevatory-2 (OCO-2), which is flying in Low Earth Orbit.