Modeling the performance of a diode laser-based (DLB) micro-pulse differential absorption lidar (MPD) for temperature profiling in the lower troposphere

Ground-based, network-deployable remote sensing instruments for thermodynamic profiling in the lower troposphere are needed by the atmospheric science research community. The recent development of a low-cost diode-laser-based (DLB) micro-pulse differential absorption lidar (DIAL) has begun to address the need for ground-based remote sensing instruments for water vapor profiling in the lower troposphere. Now, taking advantage of the broad spectral coverage of the DLB architecture, an enhancement to the water vapor micro-pulse DIAL (MPD) instrument is proposed to enable atmospheric temperature profiling. The new instrument is based on measuring a temperature-dependent oxygen (O-2) absorption coefficient and using this to retrieve the range-resolved temperature profile. In this paper, a retrieval method is proposed based on the recently developed perturbative solution to the DIAL equation that takes into account the Doppler broadening of the molecularly backscattered signal. This perturbative solution relies on an ancillary high spectral resolution lidar (HSRL) measurement of the backscatter ratio. Data from an operational water vapor MPD combined with a DLB-HSRL were used to create an atmosphere model, from which return signals for the O-2-MPD were generated. The perturbative retrieval was then applied to these data and a comparison of the retrieved temperature and the model temperature profile allowed the efficacy of retrieval to be evaluated. The results indicate that the temperature profile may be retrieved from a theoretical O-2-MPD instrument with a +/- 1 K accuracy up to 2.5 km and +/- 3 K accuracy up to 4.5 km with a 150 m range resolution and 30-minute averaging time. Using data from a recently developed O-2-MPD in combination with a WV-MPD, and a DLB-HSRL, an initial temperature retrieval is demonstrated. The results of this initial demonstration are consistent with the performance modeling.