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Lidar-based hazard detection and avoidance will enable safe landing in scientifically interesting terrain with higher hazard abundance. ASC GoldenEye flash lidar was tested at JPL as part of EDL technology development for Mars 2018
The lidar data obtained by the Langley Aircraft Lidar in October 1986 in Wisconsin is being reduced in a transparent, simple fashion and will be published in its reduced form in a NASA Reference Publication (RP). This reduced data will also be submitted to the FIRE data archives. Some of this reduced data will be presented at the FIRE FSET Workshop to acquaint the science team with the data format to be used in the archive and the upcoming catalog contained in the RP. A new method was utilized in Wisconsin for obtain the depolarization ratio of aerosols. This method involves using a half-wave plate to calibrate the lidar under field conditions. The theory behind this technique will be presented at this workshop as well as some of the lidar calibration results. The lidar calibration will be utilized in interpreting some of the dual polarization lidar data obtained during the IFO in Wisconsin. Some of these data are also discussed. A continuous wave laser lab-type lidar simulator was constructed during the previous year. One of the primary reasons for the construction of the simulator was to attempt dual-polarization lidar-like calibrations under laboratory, rather than field conditions. The data collected by this system was used to experimentally check and thus, inspire confidence in the algorithms being used to interpret the lidar data obtained in the field. A computer program which simulates noisy lidar data was used as a part of this effort in order to obtain some feel for the noise in the inversion parameters as a function of noise in the actual measurements. The lidar simulation will be described in addition to presenting some of the lab-generated calibration data.
A full analysis of the combined effects of all the noise sources of optical heterodyne receiver and the interaction between the competing control parameters of the receiver detector and pre-amplifier will be presented. This analysis provides the mean for true optimization of the coherent lidar receiver. The significance of the optimization of heterodyne receiver is shown for 2-micron coherent lidar.
Two different noise whitening methods in airborne wind profiling with a pulsed 2-micron coherent Doppler lidar system at NASA Langley Research Center in Virginia are presented. In order to provide accurate wind parameter estimates from the airborne lidar data acquired during the NASA Genesis and Rapid Intensification Processes (GRIP) campaign in 2010, the adverse effects of background instrument noise must be compensated properly in the early stage of data processing. The results of the two methods are presented using selected GRIP data and compared with the dropsonde data for verification purposes.
During FY-84, the Doppler Lidar data from the San Gorgonio Pass experiments were analyzed, evaluated, and interpreted with regard to signal strength, signal width, magnitude and direction of velocity component and a goodness parameter associated with the expected noise level of the signal. From these parameters, a screening criteria was developed to eliminate questionable data. For the most part analysis supports the validity of Doppler Lidar data obtained at San Gorgonio Pass with respect to the mean velocity magnitude and direction. The question as to whether the Doppler width could be interpreted as a measure of the variance of the turbulence within the Doppler Lidar System (DLS) focal volume was not resolved. The stochastic nature of the Doppler broadening from finite residence time of the particles in the beam as well as other Doppler broadening phenomenon tend to mask the Doppler spread associated with small scale turbulence. Future tests with longer pulses may assist in better understanding.
The performance of mean-frequency estimators for Doppler radar and lidar measurements of winds is presented in terms of two basic parameters: Phi, the ratio of the average signal energy per estimate to the spectral noise level; and Omega, which is proportional to the number of independent samples per estimate. For fixed Phi and Omega, the Cramer-Rao bound (CRB) (theoretical best performance) for unbiased estimators of mean frequency (normalized by the spectral width of the signal), signal power, and spectral width are essentially independent of the number of data samples M. For large Phi, the estimators of mean frequency are unbiased and the performance is independent of M. The spectral domain estimators and covariance based estimators are bounded by the approximate period of M. The spectral domain estimators and covariance based estimators are bounded by the approximate periodogram CRB. The standard deviation of the maximum-likelihood estimator approaches the exact CRB, which can be more than a factor of 2 better than the performance of the spectral domain estimators or covariance-based estimators for typical Omega. For small Phi, the estimators are biased due to the effects of the uncorrelated noise (white noise), which results in uniformly distributed 'bad' estimates. The fraction of bad estimates is a function of Phi and M with weak dependence on the parameter Omega. Simple empirical models describe the standard deviation of the good estimates and the fraction of bad estimates. For Doppler lidar and for large Phi, better performance is obtained by using many low-energy pulses instead of one pulse with the same total energy. For small Phi, the converse is true.
The Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission has been selected by NASA as a Decadal Survey mission, to be launched in 2016. Mission objectives are to measure land ice elevation, sea ice freeboard/ thickness and changes in these variables and to collect measurements over vegetation that will facilitate determination of canopy height, with an accuracy that will allow prediction of future environmental changes and estimation of sea-level rise. The importance of the ICESat-2 project in estimation of biomass and carbon levels has increased substantially, following the recent cancellation of all other planned NASA missions with vegetation-surveying lidars. Two innovative components will characterize the ICESat-2 lidar: (1) Collection of elevation data by a multi-beam system and (2) application of micropulse lidar (photon counting) technology. A micropulse photon-counting altimeter yields clouds of discrete points, which result from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of returned points to reflectors of interest including canopy and ground in forested areas. The objective of this paper is to derive and validate an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2-type data. Data are based on airborne observations with a Sigma Space micropulse lidar and vary with respect to signal strength, noise levels, photon sampling options and other properties. A mathematical algorithm is developed, using spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors and geostatistical classification parameters and hyperparameters. Validation shows that the algorithm works very well and that ground and canopy elevation, and hence canopy height, can be expected to be observable with a high accuracy during the ICESat-2 mission. A result relevant for instrument design is that even the two weaker beam classes considered can be expected to yield useful results for vegetation measurements (93.01-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp9) and 72.85% - 98.68% for 0.48 msp (msp4)). Resampling options affect results more than noise levels. The algorithm derived here is generally applicable for analysis of micropulse lidar altimeter data collected over forested areas as well as other surfaces, including land ice, sea ice and land surfaces.
The new development of a one-sided nonlinear adaptive shift estimation technique (NADSET) is introduced. The background of the algorithm and a brief overview of NADSET are presented. The new technique is applied to the wind parameter estimates from a 2-micron wavelength coherent Doppler lidar system called VALIDAR located in NASA Langley Research Center in Virginia. The new technique enhances wind parameters such as Doppler shift and power estimates in low Signal-To-Noise-Ratio (SNR) regimes using the estimates in high SNR regimes as the algorithm scans the range bins from low to high altitude. The original NADSET utilizes the statistics in both the lower and the higher range bins to refine the wind parameter estimates in between. The results of the two different approaches of NADSET are compared.
The signal processing aspect of a 2-m wavelength coherent Doppler lidar system under development at NASA Langley Research Center in Virginia is investigated in this paper. The lidar system is named VALIDAR (validation lidar) and its signal processing program estimates and displays various wind parameters in real-time as data acquisition occurs. The goal is to improve the quality of the current estimates such as power, Doppler shift, wind speed, and wind direction, especially in low signal-to-noise-ratio (SNR) regime. A novel Nonlinear Adaptive Doppler Shift Estimation Technique (NADSET) is developed on such behalf and its performance is analyzed using the wind data acquired over a long period of time by VALIDAR. The quality of Doppler shift and power estimations by conventional Fourier-transform-based spectrum estimation methods deteriorates rapidly as SNR decreases. NADSET compensates such deterioration in the quality of wind parameter estimates by adaptively utilizing the statistics of Doppler shift estimate in a strong SNR range and identifying sporadic range bins where good Doppler shift estimates are found. The authenticity of NADSET is established by comparing the trend of wind parameters with and without NADSET applied to the long-period lidar return data.
The lidar system model was described, and the statistics of the signal and noise at the receiver output were derived. Scattering media effects were discussed along with polarization and atmospheric turbulence. The major equations were summarized and evaluated for some typical parameters.
The main uncertainty in the stratospheric retrievals from GPS radio occultation (RO) measurements comes from the lack of reliable measurements in the upper stratosphere and above where the bending due to the neutral atmosphere is weak and residual ionospheric effects are strong. In this work, we quantify the bias and uncertainty of the refractivity and temperature retrievals due to different upper boundary strategies using a simulation study. We use lidar refractivity and temperature profiles as the input states in generating the synthetic occultations. Random noise levels commensurate with the CHAMP RO measurements are then added to the simulated data. Through this study, the sensitivity of stratospheric retrievals to upper boundary methods and parameters are examined. Such error characterizations are important prerequisites towards the effective use of GPS RO data in climate monitoring.
This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94 % of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.
Since 1981 the Royal Signals and Radar Establishment and the Royal Aircraft Establishment, United Kindom, have made vertical and horizontal sounding measurements of aerosol backscatter coefficients at 10.6 microns, using an airborne continuous-wave-focused CO2 Doppler lidar, the Laser True Airspeed System (LATAS). In this paper, the heterodyne signal from the LATAS detector is spectrally analyzed. Then, in conjunction with aircraft flight parameters, the data are processed in a six-stage computer algorithm: set search window, search for peak signal, test peak signal, measure total signal, calculate signal-to-noise ratio, and calculate backscatter coefficient.
We analyze the potential of combined backscatter and depolarization information for deriving the optical parameters of Polar Stratospheric Clouds (PSCs) together with the size and physical phase of the cloud particles. In particular, we investigate the influence of particle shape and orientation on the parallel and perpendicular backscattered intensities and on the depolarization ratio under experimental noise conditions, and how the first three moments of the particle size distribution (i.e., particle concentration, mean radius, and standard deviation) may be used to introduce further distinction between type Ia and type Ib PSCs.
There has been a renewed interest in the application of remote sensor technology to operational aviation and airport-related activities such as Aircraft Vortex Spacing System (AVOSS). Radio Acoustic Sounding Systems (RASS), Doppler-acoustic sodars, Ultrahigh Frequencies (UHF) profilers and lidars have many advantages in measuring wind and temperature profiles in the lower atmospheric boundary layer since they can operate more or less continuously and unattended; however, there are limitations in their operational use at airports. For example, profilers deteriorate (limited altitude coverage or missing) in moderate or greater rain and can be affected by airplane targets in their field of view. Sodars can handle precipitation better but are affected by the high noise environments of airports and strong winds. Morning temperature inversions typically limit performance of RASS, sodars and profilers. Fog affects sonic anemometers. Lidars can have difficulties in clouds, fog or heavy precipitation. Despite their limitations these sensors have proven useful to provide wind and temperature profiles for AVOSS. Capabilities and limitations of these and other sensors used in the AVOSS program are discussed, parameter settings for the sensor systems are documented, and recommendations are made for the most cost-effective group of sensors for the future. The potential use of specially tuned dynamic forecast models and measurements from landing and departing aircraft are addressed.
An algorithm for linear estimation of aerosol bulk properties such as particle volume, effective radius and complex refractive index from multiwavelength lidar measurements is presented. The approach uses the fact that the total aerosol concentration can well be approximated as a linear combination of aerosol characteristics measured by multiwavelength lidar. Therefore, the aerosol concentration can be estimated from lidar measurements without the need to derive the size distribution, which entails more sophisticated procedures. The definition of the coefficients required for the linear estimates is based on an expansion of the particle size distribution in terms of the measurement kernels. Once the coefficients are established, the approach permits fast retrieval of aerosol bulk properties when compared with the full regularization technique. In addition, the straightforward estimation of bulk properties stabilizes the inversion making it more resistant to noise in the optical data. Numerical tests demonstrate that for data sets containing three aerosol backscattering and two extinction coefficients (so called 3 + 2 ) the uncertainties in the retrieval of particle volume and surface area are below 45% when input data random uncertainties are below 20 %. Moreover, using linear estimates allows reliable retrievals even when the number of input data is reduced. To evaluate the approach, the results obtained using this technique are compared with those based on the previously developed full inversion scheme that relies on the regularization procedure. Both techniques were applied to the data measured by multiwavelength lidar at NASA/GSFC. The results obtained with both methods using the same observations are in good agreement. At the same time, the high speed of the retrieval using linear estimates makes the method preferable for generating aerosol information from extended lidar observations. To demonstrate the efficiency of the method, an extended time series of observations acquired in Turkey in May 2010 was processed using the linear estimates technique permitting, for what we believe to be the first time, temporal-height distributions of particle parameters.
The contribution of the multiple scattering to the lidar signal is dependent on the optical depth tau. Therefore, the radar analysis, based on the assumption that the multiple scattering can be neglected is limited to cases characterized by low values of the optical depth (tau less than or equal to 0.1) and hence it exclude scattering from most clouds. Moreover, all inversion methods relating lidar signal to number densities and particle size must be modified since the multiple scattering affects the direct analysis. The essential requests of a realistic model for lidar measurements which include the multiple scattering and which can be applied to practical situations follow. (1) Requested are not only a correction term or a rough approximation describing results of a certain experiment, but a general theory of multiple scattering tying together the relevant physical parameter we seek to measure. (2) An analytical generalization of the lidar equation which can be applied in the case of a realistic aerosol is requested. A pure analytical formulation is important in order to avoid the convergency and stability problems which, in the case of numerical approach, are due to the large number of events that have to be taken into account in the presence of large depth and/or a strong experimental noise.
A Detector Characterization Facility (DCF), capable of measuring 2-micron detection devices and evaluating heterodyne receivers, was developed at the Marshall Space Flight Center. The DCF is capable of providing all the necessary detection parameters for design, development, and calibration of coherent and incoherent solid state laser radar (lidar) systems. The coherent lidars in particular require an accurate knowledge of detector heterodyne quantum efficient, nonlinearity properties, and voltage-current relationship as a function of applied optical power. At present, no detector manufacturer provides these qualities or adequately characterizes their detectors for heterodyne detection operation. In addition, the detector characterization facility measures the detectors DC and AC quantum efficiencies noise equivalent power and frequency response up to several GHz. The DCF is also capable of evaluating various heterodyne detection schemes such as balanced detectors and fiber optic interferometers. The design and analyses of measurements for the DCF were preformed over the previous year and a detailed description of its design and capabilities was provided in the NASA report NAS8-38609/DO77. It should also be noted that the DCF design was further improved to allow for the characterization of diffractive andholographical optical elements and other critical components of coherent lidar systems.