Global estimation of range resolved thermodynamic profiles from micropulse differential absorption lidar.

We demonstrate thermodynamic profile estimation with data obtained using the MicroPulse DIAL such that the retrieval is entirely self contained. The only external input is surface meteorological variables obtained from a weather station installed on the instrument. The estimator provides products of temperature, absolute humidity and backscatter ratio such that cross dependencies between the lidar data products and raw observations are accounted for and the final products are self consistent. The method described here is applied to a combined oxygen DIAL, potassium HSRL, water vapor DIAL system operating at two pairs of wavelengths (nominally centered at 770 and 828 nm). We perform regularized maximum likelihood estimation through the Poisson Total Variation technique to suppress noise and improve the range of the observations. A comparison to 119 radiosondes indicates that this new processing method produces improved temperature retrievals, reducing total errors to less than 2 K below 3 km altitude and extending the maximum altitude of temperature retrievals to 5 km with less than 3 K error. The results of this work definitively demonstrates the potential for measuring temperature through the oxygen DIAL technique and furthermore that this can be accomplished with low-power semiconductor-based lidar sensors.

2D signal estimation for sparse distributed target photon counting data.

In this study, we explore the utilization of penalized likelihood estimation for the analysis of sparse photon counting data obtained from distributed target lidar systems. Specifically, we adapt the Poisson Total Variation processing technique to cater to this application. By assuming a Poisson noise model for the photon count observations, our approach yields denoised estimates of backscatter photon flux and related parameters. This facilitates the processing of raw photon counting signals with exceptionally high temporal and range resolutions (demonstrated here to 50 Hz and 75 cm resolutions), including data acquired through time-correlated single photon counting, without significant sacrifice of resolution. Through examination involving both simulated and real-world 2D atmospheric data, our method consistently demonstrates superior accuracy in signal recovery compared to the conventional histogram-based approach commonly employed in distributed target lidar applications.

Optimization of linear signal processing in photon counting lidar using Poisson thinning.

Photon counting lidar signals generally require smoothing to suppress random noise. While the process of reducing the resolution of the profile reduces random errors, it can also create systematic errors due to the smearing of high gradient signals. The balance between random and systematic errors is generally scene dependent and difficult to find, because errors caused by blurring are generally not analytically quantified. In this work, we introduce the use of Poisson thinning, which allows optimal selection of filter parameters for a particular scene based on quantitative evaluation criteria. Implementation of the optimization step is relatively simple and computationally inexpensive for most photon counting lidar processing.

Demonstration of a combined differential absorption and high spectral resolution lidar for profiling atmospheric temperature.

This work presents the first demonstration of atmospheric temperature measurement using the differential absorption lidar (DIAL) technique. While DIAL is routinely used to measure atmospheric gases such as ozone and water vapor, almost no success has been found in using DIAL to measure atmospheric temperature. Attempts to measure temperature using a well-mixed gas like oxygen (O2) have largely failed based on a need for quantitative ancillary measurements of water vapor and atmospheric aerosols. Here, a lidar is described and demonstrated that simultaneously measures O2 absorption, water vapor number density, and aerosol backscatter ratio. This combination of measurements allows for the first measurements of atmospheric temperature with useful accuracy. DIAL temperature measurements are presented to an altitude of 4 km with 225 m and 30 min resolution with accuracy better than 3 K. DIAL temperature data is compared to a co-located Raman lidar system and radiosondes to evaluate the system's performance. Finally, an analysis of current performance characteristics is presented, which highlights pathways for future improvement of this proof-of-concept instrument.

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 (O2) 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 O2-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 O2-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 O2-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.

Fast computation of absorption spectra for lidar data processing using principal component analysis.

We describe a principal component-based technique for approximating absorption and scattering spectra commonly needed for lidar signal processing. Where previously these calculations had been bottlenecks in our lidar signal processing, the described approach has increased our spectrum calculation speed by over two orders of magnitude. The described approach also allows analytically calculated temperature and pressure derivatives, which is useful for propagating uncertainty and implementation of global optimization algorithms.

High resolution photon time-tagging lidar for atmospheric point cloud generation.

The application of time-correlated single photon counting hardware and techniques to atmospheric lidar is presented. The results establish the viability of adapting photon time-tagging techniques to atmospheric lidar systems, facilitating high-range resolution (millimeter-level precision) and dynamic system observing capabilities that address the variety of atmospheric scatterers often present in atmospheric lidar profiles. The technique is demonstrated through measurements made by a high repetition rate, low pulse energy, elastic scattering, photon counting lidar. Detection probabilities with a non-zero system dead-time are derived and tested using acquired data. Atmospheric point cloud generation and the statistical implications on data retrievals utilizing this approach are presented. The results show an ability to preserve backscattered intensities while generating photon detections at picosecond resolution from a variety atmospheric scatterers.

Perturbative solution to the two-component atmosphere DIAL equation for improving the accuracy of the retrieved absorption coefficient.

Thermodynamic profiling using ground-based remote sensing instruments such as differential absorption lidar (DIAL) has the potential to fill observational needs for climate and weather-related research and improve weather forecasting. The DIAL technique uses the return signal resulting from atmospherically scattered light at two closely spaced wavelengths to determine the range-resolved absorption coefficient for a molecule of interest. Temperature profiles can be retrieved using a temperature-sensitive absorption feature of a molecule with a known mixing ratio such as oxygen. In order to obtain accuracies of less than 1 K, the narrowband DIAL equation must be expanded to account for Doppler broadening of molecular backscatter, and its relative contribution to the total signal, the backscatter ratio, must be known. While newly developed low-cost high spectral resolution lidar (HSRL) can measure backscatter ratio with sufficient accuracy, the frequency-resolved DIAL equation, even with this information, remains transcendental, and solving it for temperature can be computationally expensive. In this paper, we present a perturbative solution to the frequency-resolved DIAL equation when we have an HSRL providing the required ancillary measurements. This technique leverages perturbative techniques commonly employed in quantum mechanics and has the ability to obtain accurate temperature profiles (better than 1 K) with low computational cost. The perturbative solution is applied to a modeled atmosphere as an initial demonstration of this retrieval technique. An initial estimate of the error in the temperature retrieval for a diode-laser-based O2 DIAL is presented, indicating that temperature retrievals with an error of less than ±1 K can be achieved in the lower troposphere. While this paper focuses on temperature measurements, the perturbative solution to the DIAL equation can also be used to improve the accuracy of retrieved number density profiles.