Retrieval Algorithm for Column CO2 Mixing Ratio Measurements from a Multi-wavelength IPDA Lidar

Xiaoli Sun,  NASA GSFC,  xiaoli.sun-1@nasa.gov (Presenter)
James B. Abshire,  NASA GSFC,  james.b.abshire@nasa.gov
Anand Ramanathan,  Audible, Inc.,  anandr.umd@gmail.com
Stephan Randolph Kawa,  NASA GSFC,  stephan.r.kawa@nasa.gov
Jianping Mao,  NASA GSFC/University of Maryland,  jianping.mao@nasa.gov

We report on the retrieval algorithm used for the CO2 sounder lidar, a multi-wavelength, integrated path differential absorption (IPDA) lidar developed at NASA Goddard Space Flight Center to measure atmospheric CO2. The lidar transmits laser pulses at multiple wavelengths across the 1572.33-nm CO2 absorption line. The receiver measures the received laser pulse energy at each laser wavelength, and, hence, a series of samples across the CO2 absorption line. The retrieval algorithm uses a least-squares fit between the CO2 line shape computed from a layered atmosphere model and that sampled by the lidar to solve for the column average CO2 dry air mole fraction (XCO2). To minimize bias in the retrieved XCO2, the algorithm also solves several other parameters which can influence the result. These include, the product of the surface reflectance and the two-way atmospheric transmission, the Doppler shift of the received laser signal, and the line depth of a secondary water vapor absorption feature on the wing of the CO2 absorption line, and a linear slope in the lidar receiver spectral response. The curve fit is linearized about the expected values of these parameters to allow the use of standard software tools for linear least squares minimization. The covariance matrix is derived, which can be used to calculate the standard deviation of the retrieved XCO2 based on the signal to noise ratio (SNR) of the lidar measurements. An averaging kernel is calculated similarly to that used in passive remote sensing for trace-gas column mixing ratio measurements.

We have also derived an analytical model to calculate the expected performance of the lidar from an airborne or space borne platform. The model prediction agrees well with the measurement data from our 2017 airborne campaigns. In this presentation, we give an overview of the XCO2 retrieval algorithm, the lidar performance model, and a few examples of our airborne measurements compared to the model predictions.

Poster: Poster_Sun__42_25.pdf 

Presentation Type: Poster

Session: 2.2a Retrieval algorithms and methods for inter-instrument and product Cal/Val

Session Date: Tuesday (6/15) 9:45 AM