Impacts Of Atmospheric State On Differential Absorption Spectroscopy Retrievals Of Column XCO2 Mixing Ratios

The primary focus of several current aircraft and proposed space-based instruments is to assess the feasibility of monitoring global distribution of greenhouse gases including carbon dioxide (CO₂) and, more importantly, the associated time varying CO₂ fluxes using active technology. These concepts the NASA Decadal Survey Active Sensing of CO₂ Emissions over Nights, Days, and Seasons (ASCENDS), which will employ active technologies, such as laser differential absorption spectroscopy (LAS), to quantify global column CO₂ mixing ratio (XCO₂). While active sensing approaches have some significant benefits in their ability to make measurements under non-sunlit conditions and are potentially less susceptible to interference due to clouds and aerosols, they face technological challenges, as do their passive counterparts, which must be addressed in order to meet their stringent measurement goals and objectives. Not only are there a number of technological issues, e.g., hardware qualification, mass and power constraints that must be considered in any successful design, but also a number of atmospheric state and spectroscopy issues that impact end-to-end performance and overall measurement accuracy. Among the latter are the inter-play between uncertainties in the observed atmospheric state and its impact on the spectroscopic variability due to pressure, temperature, interference from other gaseous species, and the associated altitude-dependent measurement weighting functions. Desired product accuracies of < 1-2 ppm for XCO₂ result in derived requirements for both the sensor (i.e., instrument noise) and the data processing (overall processing algorithm accuracy). Understanding the impact of uncertainty in atmospheric state is critical to meet such requirements. For example, uncertainties in global surface pressure drive estimates of dry air column density. The vertical distributions of temperature and moisture also impact estimates of dry air column density as well as the associated spectroscopy and radiative transfer. Because of the stringent measurement requirements, this overall “atmospheric uncertainty” usually represents a large fraction of the overall sensor/system error budget. LAS line selection is a critical part of any such instrument design that must meet or exceed the stringent measurement requirements. This work assesses the impact of uncertainties in atmospheric state on LAS-based retrievals of XCO₂ using the integrated path differential absorption (IPDA) technique. LAS estimates of column XCO₂ are normally derived from a combination of observed CO₂ differential optical depths (Δτ) and measured, or estimated values of temperature, moisture and pressure along the viewing path. XCO₂ can be described as a function of the observed CO₂ differential Δτ, the residual observed optical depths due to other species, the CO₂ differential absorption cross section, the surface pressure, the local specific humidity and the observation on/off-line wavelengths. The accuracy of retrieved XCO₂ values depends on not only the error characteristics of the observed Δτ, but also the ability to accurately characterize P, T, and q along the observed path. In the case of global space-based monitoring systems it is often difficult, if not impossible, to provide collocated in situ measurements of the ancillary quantities for all observations. Therefore, retrievals often rely on collocated remotely sensed data or values derived from Numerical Weather Predictions (NWP) models to describe the atmospheric state. A radiative transfer (RT)-based simulation framework, combined with representative global upper-air observations and matched NWP profiles, was used to assess the impact of model differences in vertical temperature, vertical moisture, and pressure on estimates of column CO₂ and O₂ concentrations. These analyses focus on characterizing these errors for several CO₂ features in the 1.57 and 2.05µm region, and representative O2 features near 0.76 and 1.27µm. The results provide a set of signal-to-noise metrics that characterize the errors in retrieved values associated with uncertainties in knowledge of the atmospheric state, and provide a method for selecting optimal differential line pairs to minimize the impact of this noise term. These metrics provide information that have facilitated the design, development, and assessment of the NASA Langley Research Center's ASCENDS CarbonHawk Experiment Simulator (ACES), and may help define the instrument requirements needed to meet the objectives of the ASCENDS mission. While this work was conducted with LAS systems that utilize single on-line/off-line wavelength pairs, our analysis could be applied to systems that utilize multiple on/off-line wavelength pairs. Also, with only slight modifications this technique would also be applicable to understanding uncertainties in clouds, aerosols and surface topography. The meteorological uncertainties characterized in this work would factor into a comprehensive error budget for any IPDA instrument measurements.