Severe storms Doppler lidar signal processing
The recommended algorithm for the correction of higher order data errors in severe storms doppler lidar signal processing is discussed.
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The recommended algorithm for the correction of higher order data errors in severe storms doppler lidar signal processing is discussed.
Four aspects of the Doppler lidar are discussed: (1) error analysis of the system; (2) design of the first field program; (3) potential areas of application; and (4) verification of Doppler lidar data by independent measurements.
Differential absorption lidar and Raman lidar have been applied to the range-resolved measurements of water vapor density for more than 20 years. Results have been obtained using both lidar techniques that have led to improved understanding of water vapor distributions in the atmosphere. This paper reviews the theory of the measurements, including the sources of systematic and random error; the progress in lidar technology and techniques during that period, including a brief look at some of the lidar systems in development or proposed; and the steps being taken to improve such lidar systems.
Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously ("wall-to-wall") over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI plots being grouped in clusters) against corresponding estimates assuming two-stage sampling with the LiDAR and employing model-assisted estimators. For each of the two comparisons, the standard errors of the AGB estimates were consistently lower for the LiDAR-assisted designs. The overall reduction of the standard errors in the LiDAR-assisted estimation was around 40-60% compared to the pure field survey. We conclude that the previously proposed two-stage model-assisted estimators are inappropriate for surveys with unequal lengths of the LiDAR flight-lines and new estimators are needed. Some options for design of LiDAR-assisted sample surveys under REDD are also discussed, which capitalize on the flexibility offered when the field survey is designed as an integrated part of the overall survey design as opposed to previous LiDAR-assisted sample surveys in the boreal and temperate zones which have been restricted by the current design of an existing NFI.
This paper presents an evaluation of the random and systematic error sources associated with differential absorption lidar (DIAL) measurements of tropospheric water vapor profiles from airborne and spaceborne platforms. The results of this analysis are used in the development and performance evaluation of the Lidar Atmospheric Sensing Experiment (LASE) H2O DIAL system presently under development at the NASA Langley Research Center for operation on a high altitude ER-2 aircraft. The analysis shows that a less than 10-percent H2O profile measurement accuracy is possible for the LASE system with a vertical and horizontal resolution of 200 m and 10 km, respectively, at night and 300 m and 20 km during the day. Global measurements of H2O profiles from spaceborne DIAL systems can be made to a similar accuracy with a vertical resolution of 500 m and a horizontal resolution of 100 km.
Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.
The Geoscience Lidar Altimeter System (GLAS) is a laser altimeter designed to measure temporal changes in the topography and mass balance of the Earth's ice sheets. GLAS is scheduled to be launched as part of the Earth Observing System (EOS) in 2001. Due to the large pulse width of GLAS and the slope (and roughness) of the ice sheets, the mean elevation of the laser's surface spot will be estimated from the centroid of the return pulse. While the accuracy of individual altimetry measurements is expected to be 15 cm, a cross-over technique that averages the elevation differences at selected points on the ice sheets will allow GLAS to detect mean ice elevation changes as small as 2 cm per year. One factor that may degrade the proposed accuracy of the altimeter measurements is the effect of multiple scattering by thin clouds and aerosols over the ice sheets. In the return pulse, some photons from the lidar are slightly deflected by cloud particles but still return to the instrument detector. Since these photons travel a longer path than photons that pass directly to and from the surface, the mean travel time of the pulse is lengthened and the centroid of the return pulse is shifted toward a later time . This study intends to determine the importance of this source of altimetry error in the GLAS measurements. Two methods will be used to estimate path delays in the GLAS lidar for a range of cloud conditions. An analytic expression of a doubly scattered return signal (Eloranta, 1972) will be compared to Monte Carlo simulations that estimate path delays by cloud, aerosol and molecular scattering. The results from both methods will be discussed in relationship to current knowledge of Arctic cloud climatology, and the impact of multiple scattering on GLAS altimeter measurements will be evaluated.
Line of sight velocity and measurement position sensitivity analyses for an orbiting coherent Doppler lidar are developed and applied to two lidars, one with a nadir angle of 30 deg. in a 300 km altitude, 58 deg. inclination orbit and the second for a 45 deg. nadir angle instrument in a 833 km altitude, 89 deg. inclination orbit. The effect of orbit related effects on the backscatter sensitivity of a coherent Doppler lidar is also discussed. Draft performance estimate, error budgets and payload accommodation requirements for the SPARCLE (Space Readiness Coherent Lidar) instrument were also developed and documented.
Intercomparison of wind data from the airborne Doppler lidar, ground-based Doppler radars, the 444 m NSSL-KTVY tower, and rawinsonde were completed. The vertical profile of wind in the PBL measured by the radars compared favorably with the profiles measured by the tower and rawinsonde while the one obtained from lidar data differed from the other three by as much as 3m/sec in wind speed and 38 degrees in direction. The time dependence of differences in wind estimates from radar and lidar suggested that these discrepencies could be attributed to a Schuler resonance in the aircraft's inertial navigation system which caused an erroneous component of the aircraft's velocity vector to be subtracted from the lidar radial velocities, thus creating errors in the synthesized wind speed and direction. The vertical profile of turbulent fluctuations of the horizontal wind detected by the different systems compared well. Also, spectra from the different sensing systems compared well in both magnitude and shape, suggesting that the lidar and radar detected similar turbulent structure.
Random and systematic error sourses are presently analyzed for atmospheric H2O detection in the vicinity of the 720 nm absorption bands with Differential Absorption Lidar (DIAL). Attention is given to the random errors due to undertainties in the measurement of the lidar signal and noise that are associated with the background and detector dark currents. Systematic errors are related to Doppler broadening due to backscattering from air molecules, the pressure shift of H2O lines, laser line distortions, spectral impurities, and laser tuning errors. Global measurements of H2O profiles from spaceborne DIAL systems are achievable with 200 m vertical x 10 km horizontal during nighttime, and 250 m vertical x 20 km horizontal during daytime.
Techniques are presented to obtain vertical velocity (w) in cirrus clouds from in situ aircraft lateral wind measurements and from ground-based remote Doppler lidar measurements. In general, direct measurements of w from aircraft are not feasible because of offsets in the air velocity sensors. An alternative is to calculate w from the integral of the divergence of the horizontal velocity around a closed path. This technique is applied to a cirrus cloud investigated during the FIRE Cirrus Intensive Field Observation Program. The results indicate that the error in w is about + or - 14 cm/sec from the aircraft technique; this can be reduced to about + or - 2-3 cm/sec with technical improvements in both ground-speed and lateral-velocity measurements. The error in w from Doppler lidar measurements, which is about + or - 8 cm/sec can be reduced to about + or - 5 cm/sec by improvements in the Doppler velocity measurements with technology that is currently available.
Three major potential sources of errors that influence the performance of the proposed LIDAR wind profiling systems are accounted for in the following simulation studies. First, the coherent CO2 laser system proposed by NOAA/ERL for the WINDSAT requires distributions of 10 microns size aerosol concentrations throughout the atmosphere to determine the Doppler shifted backscatter signal. Second, atmospheric influences such as clouds and precipitation affect the LIDAR system's ability to obtain complete vertical wind profiles. In this study, experiments are reported on in which the complete wind profiles are only available in model generated cloud-free regions down to model cloud tops. Third, constraints on the available power required for the expected operational lifetime duration of a LIDAR system govern the number of pulses which can be used to sample a 300 km square area.
Stereogrammetry applied to globally available high resolution spaceborne imagery (HRSI; less than 5 m spatial resolution) yields fine-scaled digital surface models (DSMs) of elevation. These DSMs may represent elevations that range from the ground to the vegetation canopy surface, are produced from stereoscopic image pairs (stereo pairs) that have a variety of acquisition characteristics, and have been coupled with lidar data of forest structure and ground surface elevation to examine forest height. This work explores surface elevations from HRSI DSMs derived from two types of acquisitions in open canopy forests. We (1) apply an automated mass-production stereogrammetry workflow to along-track HRSI stereo pairs, (2) identify multiple spatially coincident DSMs whose stereo pairs were acquired under different solar geometry, (3) vertically co-register these DSMs using coincident spaceborne lidar footprints (from ICESat-GLAS) as reference, and(4) examine differences in surface elevations between the reference lidar and the co-registered HRSI DSMs associated with two general types of acquisitions (DSM types) from different sun elevation angles. We find that these DSM types, distinguished by sun elevation angle at the time of stereo pair acquisition, are associated with different surface elevations estimated from automated stereogrammetry in open canopy forests. For DSM values with corresponding reference ground surface elevation from spaceborne lidar footprints in open canopy northern Siberian Larix forests with slopes less than10, our results show that HRSI DSM acquired with sun elevation angles greater than 35deg and less than 25deg (during snow-free conditions) produced characteristic and consistently distinct distributions of elevation differences from reference lidar. The former include DSMs of near-ground surfaces with root mean square errors less than 0.68 m relative to lidar. The latter, particularly those with angles less than 10deg, show distributions with larger differences from lidar that are associated with open canopy forests whose vegetation surface elevations are captured. Terrain aspect did not have a strong effect on the distribution of vegetation surfaces. Using the two DSM types together, the distribution of DSM-differenced heights in forests (6.0 m, sigma = 1.4 m) was consistent with the distribution of plot-level mean tree heights (6.5m, sigma = 1.2 m). We conclude that the variation in sun elevation angle at time of stereo pair acquisition can create illumination conditions conducive for capturing elevations of surfaces either near the ground or associated with vegetation canopy. Knowledge of HRSI acquisition solar geometry and snow cover can be used to understand and combine stereogrammetric surface elevation estimates to co-register rand difference overlapping DSMs, providing a means to map forest height at fine scales, resolving the vertical structure of groups of trees from spaceborne platforms in open canopy forests.
In connection with studies concerning the use of an orbiting optical radar (lidar) to conduct aerosol and cloud measurements, attention has been given to the accuracy with which lidar return signals could be measured. However, signal-measurement error is not the only source of error which can affect the accuracy of the derived information. Other error sources are the assumed molecular-density and atmospheric-transmission profiles, and the lidar calibration factor (which relates signal to backscatter coefficient). The present investigation has the objective to account for the effects of all these errors sources for several realistic combinations of lidar parameters, model atmospheres, and background lighting conditions. In addition, a procedure is tested and developed for measuring density and temperature profiles with the lidar, and for using the lidar-derived density profiles to improve aerosol retrievals.
The spatiotemporal sampling differences between ground-based and satellite lidar data can contribute to significant errors for direct measurement comparisons. Improvement in sample correspondence is examined by the use of radiosonde wind velocity to vary the time average in ground-based lidar data to spatially match coincident satellite lidar measurements. Results are shown for the 26 February 2004 GLAS/ICESat overflight of a ground-based lidar stationed at NASA GSFC. Statistical analysis indicates that improvement in signal correlation is expected under certain conditions, even when a ground-based observation is mismatched in directional orientation to the satellite track.
We are demonstrating on a few cases the capability of CALIPSO to retrieve the 532 nm lidar ratio over the ocean when CloudSat surface scattering cross section is used as a constraint. We are presenting the algorithm used and comparisons with the column lidar ratio retrieved by the NASA airborne high spectral resolution lidar. For the three cases presented here, the agreement is fairly good. The average CALIPSO 532 nm column lidar ratio bias is 13.7% relative to HSRL, and the relative standard deviation is 13.6%. Considering the natural variability of aerosol microphysical properties, this level of accuracy is significant since the lidar ratio is a good indicator of aerosol types. We are discussing dependencies of the accuracy of retrieved aerosol lidar ratio on atmospheric aerosol homogeneity, lidar signal to noise ratio, and errors in the optical depth retrievals. We are obtaining the best result (bias 7% and standard deviation around 6%) for a nighttime case with a relatively constant lidar ratio (in the vertical) indicative of homogeneous aerosol type
We present, for the first time, a quantitative retrieval error-propagation study for a bistatic high spectral resolution lidar (HSRL) system intended for detailed quasi-global monitoring of aerosol properties from space. Our results demonstrate that supplementing a conventional monostatic HSRL with an additional receiver flown in formation at a scattering angle close to 165 degrees dramatically increases the information content of the measurements and allows for a sufficiently accurate characterization of tropospheric aerosols. We conclude that a bistatic HSRL system would far exceed the capabilities of currently flown or planned orbital instruments in monitoring global aerosol effects on the environment and on the Earth's climate. We also demonstrate how the commonly used a priori 'regularization' methodology can artificially reduce the propagated uncertainties and can thereby be misleading as to the real retrieval capabilities of a measurement system.
An error analysis of differential absorption lidar (DIAL) measurements of stratospheric ozone from a Space Shuttle is discussed. A transmitter system consisting of a KrF excimer laser pumping gas cells of H2 or D2 producing output wavelengths in the near UV is shown to be useful for the measurement of ozone in an altitude range from 15 to 50 km.