The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis

H. Bösch1, B. Connor2, B. Sen1, G. C. Toon1

1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, 2National 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 CO2 measurements with precision, accuracy and resolution needed to quantify CO2 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 (CO2 bands at 1.61 m and 2.06 m and O2 A-band at 0.76 m)

• OCO will switch from nadir observations (small footprint size of 3 km2) 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 strategy OCO ground track for nadir observations

Simulated radiance spectra for the 3 OCO spectrometers

O2 A-Band 1.61 m CO2 Band

2.06 m CO2 Band

Retrieval Algorithm• XCO2 (dry air, column averaged, mole fraction of CO2) will be retrieved from a simultaneous fit of O2 and CO2 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 CO2, H2O, temperature and aerosol optical depth as well as surface pressure, surface albedo and spectral dispersion• XCO2 is computed from the retrieved state after the iterative retrieval has converged

Overview of the OCO retrieval algorithm

Motivation

• OCO will measure CO2 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 CO2 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) = XCO2/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 XCO2 has several components which can be random and/or systematic:

• Measurement noise (random)• Smoothing error (random and systematic): retrieved XCO2 depends on an a priori CO2 profile and its covariance. Fine structures in ‘true’ CO2 profiles are smoothed by the retrieval which causes a random error. An a priori CO2 profile which is systematically too large/small results in a bias. • Interference Error (random and systematic): Errors in XCO2 due to the interference of non-CO2 retrieval parameters with CO2

• 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.

(H2O)

(CO2) (CO2)

(H2O)

TES – T, P, H2O, O3, CH4, COMLS – O3, H2O, COHIRDLS – T, O3, H2O, CO2, CH4

OMI – O3, aerosol climatology

PARASOL – polarization data

CloudSat – cloud climatologyCALIPSO – vertical profiles of cloud & aerosol; cirrus particle size

**OCO – O2 A-Band Spectra**

Coordinated Calibration/Validation Activities

AIRS – T, P, H2O, CO2, CH4

MODIS – cloud/aerosols, albedo

OCO – XCO2, P(surface), T, H2O, cloud, aerosol The Earth Observing System

Afternoon Constellation, or A-Train

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