1. The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis H. Bösch 1, B. Connor 2, B. Sen 1, G. C. Toon 1 1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, 2 National Institute of Water and Atmospheric Research, Lauder, New Zealand Contact: Hartmut Bösch, Phone: 818-393-5976, Email: hartmut.boesch@jpl.nasa.gov The OCO Mission OCO is a space-based mission solely dedicated to CO 2 measurements with precision, accuracy and resolution needed to quantify CO 2 sources and sinks OCO will target a regional, monthly averaged precision of 1-2 ppm without significant geographically coherent biases The payload consists of three bore-sighted, high resolution grating spectrometers (CO 2 bands at 1.61  m and 2.06  m and O 2 A-band at 0.76 m) OCO will switch from nadir observations (small footprint size of 3 km 2 ) to glint observations (high signal over oceans) every 16 days OCO will be launched in Sep. 2008 and will fly ahead of the A-Train constellation with a 1:15 PM equator crossing time and a 16 day repeat cycle Local Nadir Glint Spot Ground Track OCO observation strategyOCO ground track for nadir observations Simulated radiance spectra for the 3 OCO spectrometers O 2 A-Band 1.61 m CO 2 Band 2.06 m CO 2 Band Retrieval Algorithm X CO2 (dry air, column averaged, mole fraction of CO 2 ) will be retrieved from a simultaneous fit of O 2 and CO 2 bands using Optimal Estimation The forward model is based on Radiant, a multi-layer, spectral resolving, multiple scattering radiative transfer model [Mick Christi, CSU]. Polarization is corrected with a 2 orders of scattering approach. The algorithm retrieves profiles of CO 2, H 2 O, temperature and aerosol optical depth as well as surface pressure, surface albedo and spectral dispersion X CO2 is computed from the retrieved state after the iterative retrieval has converged Overview of the OCO retrieval algorithm Motivation OCO will measure CO 2 with very high precision and accuracy (0.3–0.5 %) which puts unprecedented demands on both instrument and analysis Here, we present an error analysis and retrieval characterization for OCO nadir observations which allows for verification and quantification of the precision and accuracy of our retrieval algorithm. Furthermore, a good understanding of the sensitivity and the errors of the space-based measurements is critical for inverse modeling of carbon sources and sinks. OCO Averaging Kernel The sensitivity of a space-based CO 2 measurement varies with height due to the physics of spectroscopy and radiative transfer and the instrument characteristics The averaging kernel describes this sensitivity as a function of height: ak(z) = X CO2 /x(z) The OCO averaging kernel depends on the solar zenith angle and surface albedo or type (and aerosol optical depth; not shown here) OCO averaging kernels for nadir observations as a function of solar zenith angle for 4 different surface types Error Analysis We show results from a linear error analysis for nadir observations at Park Falls, WI (46 N) in July (conifer) and Lauder, NZ (45 S) in July (frost), which represents well the expected range of our geophysical scenarios. The total error budget for X CO2 has several components which can be random and/or systematic: Measurement noise (random) Smoothing error (random and systematic): retrieved X CO2 depends on an a priori CO 2 profile and its covariance. Fine structures in ‘true’ CO 2 profiles are smoothed by the retrieval which causes a random error. An a priori CO 2 profile which is systematically too large/small results in a bias. Interference Error (random and systematic): Errors in X CO2 due to the interference of non-CO 2 retrieval parameters with CO 2 Model Parameter Errors (systematic): Errors due to uncertainties in forward model inputs (e.g. instrument parameters). We assume that these parameters will have systematically varying uncertainties. Spectroscopic errors are not included here, since they are predictable and can be largely reduced by validation. Forward Model Error (systematic): Errors due to inadequacies in the forward model (not included in the presented error budget). Aerosol optical properties, cirrus clouds or polarization can cause errors in the range of several ppm and our current forward model has to be improved to reduce these errors to less than 1 ppm. Error reduction for Park Falls due to the information in the measurement Cross talk for Park Falls. Shown is the averaging kernel scaled by the retrieved and the a priori uncertainty Single Sounding error budget for Park Falls and Lauder for an aerosol optical depth of 0.3. The total errors are 1.01 ppm (Park Falls) and 2.97 ppm (Lauder). For regional, monthly averages, random errors reduce by a factor ~ 100 and the total errors are dominated by systematic contributions. (H 2 O) (CO 2 ) (H 2 O) TES – T, P, H 2 O, O 3, CH 4, CO MLS – O 3, H 2 O, CO HIRDLS – T, O 3, H 2 O, CO 2, CH 4 OMI – O 3, aerosol climatology PARASOL – polarization data CloudSat – cloud climatology CALIPSO – vertical profiles of cloud & aerosol; cirrus particle size **OCO – O 2 A-Band Spectra** Coordinated Calibration/Validation Activities AIRS – T, P, H 2 O, CO 2, CH 4 MODIS – cloud/aerosols, albedo OCO – X CO2, P(surface), T, H 2 O, cloud, aerosol The Earth Observing System Afternoon Constellation, or A- Train