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Newman, Jennifer F., and Andrew Clifton. "An error reduction algorithm to improve lidar turbulence estimates for wind energy." Wind Energy Science 2, no. 1 (February 10, 2017): 77–95. http://dx.doi.org/10.5194/wes-2-77-2017.
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Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.
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Goodin, Christopher, Justin Carrillo, J. Gabriel Monroe, Daniel W. Carruth, and Christopher R. Hudson. "An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation." Sensors 21, no. 9 (May 5, 2021): 3211. http://dx.doi.org/10.3390/s21093211.
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Negative obstacles have long been a challenging aspect of autonomous navigation for ground vehicles. However, as terrestrial lidar sensors have become lighter and less costly, they have increasingly been deployed on small, low-flying UAV, affording an opportunity to use these sensors to aid in autonomous navigation. In this work, we develop an analytical model for predicting the ability of UAV or UGV mounted lidar sensors to detect negative obstacles. This analytical model improves upon past work in this area because it takes the sensor rotation rate and vehicle speed into account, as well as being valid for both large and small view angles. This analytical model is used to predict the influence of velocity on detection range for a negative obstacle and determine a limiting speed when accounting for vehicle stopping distance. Finally, the analytical model is validated with a physics-based simulator in realistic terrain. The results indicate that the analytical model is valid for altitudes above 10 m and show that there are drastic improvements in negative obstacle detection when using a UAV-mounted lidar. It is shown that negative obstacle detection ranges for various UAV-mounted lidar are 60–110 m, depending on the speed of the UAV and the type of lidar used. In contrast, detection ranges for UGV mounted lidar are found to be less than 10 m.
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Sannino, Alessia, Antonella Boselli, Domenico Maisto, Alberto Porzio, Changbo Song, Nicola Spinelli, and Xuan Wang. "Development of a High Spectral Resolution Lidar for day-time measurements of aerosol extinction." EPJ Web of Conferences 197 (2019): 02009. http://dx.doi.org/10.1051/epjconf/201919702009.
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Lidar technique is the most performing way to obtain the atmospheric vertical profile of aerosol optical properties with high space-time resolution. With elastic scattering lidars, the retrieval of aerosol optical properties (as the extinction profile) is realizable only with assumptions on aerosol extinction-to-backscatter ratio or with Raman measurement achievable in night-time. In order to overcome these problems, the High Spectral Resolution Lidar (HSRL) technique has been examined. In this paper we present an innovative prototype of High Spectral Resolution Lidar realized at Physics Department of University “Federico II” of Naples for the LISA (LIdar for Space study of the Atmosphere) project in the framework of the China-Italy international cooperation between CNISM and BRIT. The prototype which represents a first step of a spaceborne HSRL, is based on a laser source at 1064nm and 532nm with high spectral resolution ability at 532nm. The separation between the molecular and the aerosol components was obtained through the use of a confocal Fabry-Perot interferometer (CFPI) cavity.
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Zhang, Jincheng, and Xiaowei Zhao. "Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements." Applied Energy 288 (April 2021): 116641. http://dx.doi.org/10.1016/j.apenergy.2021.116641.
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Knobelspiesse, Kirk, Henrique M. J. Barbosa, Christine Bradley, Carol Bruegge, Brian Cairns, Gao Chen, Jacek Chowdhary, et al. "The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) airborne field campaign." Earth System Science Data 12, no. 3 (September 14, 2020): 2183–208. http://dx.doi.org/10.5194/essd-12-2183-2020.
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Abstract. In the fall of 2017, an airborne field campaign was conducted from the NASA Armstrong Flight Research Center in Palmdale, California, to advance the remote sensing of aerosols and clouds with multi-angle polarimeters (MAP) and lidars. The Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign was jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON). Six instruments were deployed on the ER-2 high-altitude aircraft. Four were MAPs: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary EXploration (SPEX airborne), and the Research Scanning Polarimeter (RSP). The remainder were lidars, including the Cloud Physics Lidar (CPL) and the High Spectral Resolution Lidar 2 (HSRL-2). The southern California base of ACEPOL enabled observation of a wide variety of scene types, including urban, desert, forest, coastal ocean, and agricultural areas, with clear, cloudy, polluted, and pristine atmospheric conditions. Flights were performed in coordination with satellite overpasses and ground-based observations, including the Ground-based Multiangle SpectroPolarimetric Imager (GroundMSPI), sun photometers, and a surface reflectance spectrometer. ACEPOL is a resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. Data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion. They are freely available to the public. The DOI for the primary database is https://doi.org/10.5067/SUBORBITAL/ACEPOL2017/DATA001 (ACEPOL Science Team, 2017), while for AirMSPI it is https://doi.org/10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/ELLIPSOID_V006 and https://doi.org/10.5067/AIRCRAFT/AIRMSPI/ACEPOL/RADIANCE/TERRAIN_V006 (ACEPOL AirMSPI 75 Science Team, 2017a, b). GroundMSPI data are at https://doi.org/10.5067/GROUND/GROUNDMSPI/ACEPOL/RADIANCE_v009 (GroundMSPI Science Team, 2017). Table 3 lists further details of these archives. This paper describes ACEPOL for potential data users and also provides an outline of requirements for future field missions with similar objectives.
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Goodin, Christopher, Matthew Doude, Christopher Hudson, and Daniel Carruth. "Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation." Electronics 7, no. 9 (August 21, 2018): 154. http://dx.doi.org/10.3390/electronics7090154.
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Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challenge autonomous ground vehicles. Many off-road navigation systems rely on LIDAR to sense and classify the environment, but LIDAR sensors often fail to distinguish navigable vegetation from non-navigable solid obstacles. While other areas of autonomy have benefited from the use of simulation, there has not been a real-time LIDAR simulator that accounted for LIDAR–vegetation interaction. In this work, we outline the development of a real-time, physics-based LIDAR simulator for densely vegetated environments that can be used in the development of LIDAR processing algorithms for off-road autonomous navigation. We present a multi-step qualitative validation of the simulator, which includes the development of an improved statistical model for the range distribution of LIDAR returns in grass. As a demonstration of the simulator’s capability, we show an example of the simulator being used to evaluate autonomous navigation through vegetation. The results demonstrate the potential for using the simulation in the development and testing of algorithms for autonomous off-road navigation.
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Baumgarten, G., F. J. Lübken, and K. H. Fricke. "First observation of one noctilucent cloud by a twin lidar in two different directions." Annales Geophysicae 20, no. 11 (November 30, 2002): 1863–68. http://dx.doi.org/10.5194/angeo-20-1863-2002.
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Abstract. In the early morning hours of 14 July 1999, a noctilucent cloud (NLC) was observed simultaneously by the two branches of a twin lidar system located at the ALOMAR observatory in northern Norway (69° N). The telescopes of the two lidars were pointing vertical (L^) and off the zenith by 30° (L30°). The two lidars detected an enhancement in the altitude profile of backscattered light (relative to the molecular background) for more than 5 h, starting approximately at 01:00 UT. These measurements constitute the detection of one NLC by two lidars under different directions and allow for a detailed study of the morphology of the NLC layer. A cross-correlation analysis of the NLC signals demonstrates that the main structures seen by both lidars are practically identical. This implies that a temporal evolution of the microphysics within the NLC during its drift from one lidar beam to the other is negligible. From the time delay of the NLC structures, a drift velocity of 55–65 m/s is derived which agrees nicely with radar wind measurements. During the observation period, the mean NLC altitude decreases by ~0.5 km/h (=14 cm/s) at both observation volumes. Further-more, the NLC is consistently observed approximately 500 m lower in altitude at L30° compared to L^. Supplementing these data by observations from rocket-borne and ground-based instruments, we show that the general downward progression of the NLC layer through the night, as seen by both lidars, is caused by a combination of particle sedimentation by 4–5 cm/s and a downward directed vertical wind by 9–10 cm/s, whereas a tilt of the layer in drift direction can be excluded.Key words. Atmospheric composition and structure (cloud physics and chemistry; aerosols and particles) Meteorology and atmospheric dynamics (middle atmosphere dynamics)
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Pauly, Rebecca M., John E. Yorks, Dennis L. Hlavka, Matthew J. McGill, Vassilis Amiridis, Stephen P. Palm, Sharon D. Rodier, et al. "Cloud-Aerosol Transport System (CATS) 1064 nm calibration and validation." Atmospheric Measurement Techniques 12, no. 11 (November 28, 2019): 6241–58. http://dx.doi.org/10.5194/amt-12-6241-2019.
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Abstract. The Cloud-Aerosol Transport System (CATS) lidar on board the International Space Station (ISS) operated from 10 February 2015 to 30 October 2017 providing range-resolved vertical backscatter profiles of Earth's atmosphere at 1064 and 532 nm. The CATS instrument design and ISS orbit lead to a higher 1064 nm signal-to-noise ratio than previous space-based lidars, allowing for direct atmospheric calibration of the 1064 nm signals. Nighttime CATS version 3-00 data were calibrated by scaling the measured data to a model of the expected atmospheric backscatter between 22 and 26 km a.m.s.l. (above mean sea level). The CATS atmospheric model is constructed using molecular backscatter profiles derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data and aerosol scattering ratios measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The nighttime normalization altitude region was chosen to simultaneously minimize aerosol loading and variability within the CATS data frame, which extends from 28 to −2 km a.m.s.l. Daytime CATS version 3-00 data were calibrated through comparisons with nighttime measurements of the layer-integrated attenuated total backscatter (iATB) from strongly scattering, rapidly attenuating opaque cirrus clouds. The CATS nighttime 1064 nm attenuated total backscatter (ATB) uncertainties for clouds and aerosols are primarily related to the uncertainties in the CATS nighttime calibration technique, which are estimated to be ∼9 %. Median CATS V3-00 1064 nm ATB relative uncertainty at night within cloud and aerosol layers is 7 %, slightly lower than these calibration uncertainty estimates. CATS median daytime 1064 nm ATB relative uncertainty is 21 % in cloud and aerosol layers, similar to the estimated 16 %–18 % uncertainty in the CATS daytime cirrus cloud calibration transfer technique. Coincident daytime comparisons between CATS and the Cloud Physics Lidar (CPL) during the CATS-CALIPSO Airborne Validation Experiment (CCAVE) project show good agreement in mean ATB profiles for clear-air regions. Eight nighttime comparisons between CATS and the PollyXT ground-based lidars also show good agreement in clear-air regions between 3 and 12 km, with CATS having a mean ATB of 19.7 % lower than PollyXT. Agreement between the two instruments (∼7 %) is even better within an aerosol layer. Six-month comparisons of nighttime ATB values between CATS and CALIOP also show that iATB comparisons of opaque cirrus clouds agree to within 19 %. Overall, CATS has demonstrated that direct calibration of the 1064 nm channel is possible from a space-based lidar using the atmospheric normalization technique.
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Mereu, Luigi, Simona Scollo, Antonella Boselli, Giuseppe Leto, Ricardo Zanmar Sanchez, Costanza Bonadonna, and Frank Silvio Marzano. "Dual-Wavelength Polarimetric Lidar Observations of the Volcanic Ash Cloud Produced during the 2016 Etna Eruption." Remote Sensing 13, no. 9 (April 29, 2021): 1728. http://dx.doi.org/10.3390/rs13091728.
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Lidar observations are very useful to analyse dispersed volcanic clouds in the troposphere mainly because of their high range resolution, providing morphological as well as microphysical (size and mass) properties. In this work, we analyse the volcanic cloud of 18 May 2016 at Mt. Etna, in Italy, retrieved by polarimetric dual-wavelength Lidar measurements. We use the AMPLE (Aerosol Multi-Wavelength Polarization Lidar Experiment) system, located in Catania, about 25 km from the Etna summit craters, pointing at a thin volcanic cloud layer, clearly visible and dispersed from the summit craters at the altitude between 2 and 4 km and 6 and 7 km above the sea level. Both the backscattering and linear depolarization profiles at 355 nm (UV, ultraviolet) and 532 nm (VIS, visible) wavelengths, respectively, were obtained using different angles at 20°, 30°, 40° and 90°. The proposed approach inverts the Lidar measurements with a physically based inversion methodology named Volcanic Ash Lidar Retrieval (VALR), based on Maximum-Likelihood (ML). VALRML can provide estimates of volcanic ash mean size and mass concentration at a resolution of few tens of meters. We also compared those results with two methods: Single-variate Regression (SR) and Multi-variate Regression (MR). SR uses the backscattering coefficient or backscattering and depolarization coefficients of one wavelength (UV or VIS in our cases). The MR method uses the backscattering coefficient of both wavelengths (UV and VIS). In absence of in situ airborne validation data, the discrepancy among the different retrieval techniques is estimated with respect to the VALR ML algorithm. The VALR ML analysis provides ash concentrations between about 0.1 μg/m3 and 1 mg/m3 and particle mean sizes of 0.1 μm and 6 μm, respectively. Results show that, for the SR method differences are less than <10%, using the backscattering coefficient only and backscattering and depolarization coefficients. Moreover, we find differences of 20–30% respect to VALR ML, considering well-known parametric retrieval methods. VALR algorithms show how a physics-based inversion approaches can effectively exploit the spectral-polarimetric Lidar AMPLE capability.
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Midzak, Natalie, John E. Yorks, Jianglong Zhang, Bastiaan van Diedenhoven, Sarah Woods, and Matthew McGill. "A Classification of Ice Crystal Habits Using Combined Lidar and Scanning Polarimeter Observations during the SEAC4RS Campaign." Journal of Atmospheric and Oceanic Technology 37, no. 12 (December 2020): 2185–96. http://dx.doi.org/10.1175/jtech-d-20-0037.1.
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AbstractUsing collocated NASA Cloud Physics Lidar (CPL) and Research Scanning Polarimeter (RSP) data from the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign, a new observational-based method was developed which uses a K-means clustering technique to classify ice crystal habit types into seven categories: column, plates, rosettes, spheroids, and three different type of irregulars. Intercompared with the collocated SPEC, Inc., Cloud Particle Imager (CPI) data, the frequency of the detected ice crystal habits from the proposed method presented in the study agrees within 5% with the CPI-reported values for columns, irregulars, rosettes, and spheroids, with more disagreement for plates. This study suggests that a detailed ice crystal habit retrieval could be applied to combined space-based lidar and polarimeter observations such as CALIPSO and POLDER in addition to future missions such as the Aerosols, Clouds, Convection, and Precipitation (A-CCP).
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von Savigny, Christian, and Christoph G. Hoffmann. "Issues related to the retrieval of stratospheric-aerosol particle size information based on optical measurements." Atmospheric Measurement Techniques 13, no. 4 (April 16, 2020): 1909–20. http://dx.doi.org/10.5194/amt-13-1909-2020.
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Abstract. Stratospheric-sulfate aerosols play an important role in the physics and chemistry of the atmosphere. The radiative and chemical effects of stratospheric-sulfate aerosols depend critically on the aerosol particle size distribution and its variability. Despite extensive research spanning several decades, the scientific understanding of the particle size distribution of stratospheric aerosols is still incomplete. Particle size estimates (often represented by the median radius of an assumed monomodal log-normal distribution with a fixed width or by the effective radius) reported in different studies cover a wide range, even under background stratospheric conditions, and particle size estimates retrieved from satellite solar-occultation measurements in the optical spectral range show a tendency to be systematically larger than retrievals based on other optical methods. In this contribution we suggest a potential reason for these systematic differences. Differences between the actual aerosol particle size distribution and the size distribution function assumed for aerosol size retrievals may lead to systematic differences in retrieved aerosol size estimates. We demonstrate that these systematic differences may differ significantly for different measurement techniques, which is related to the different sensitivities of these measurement techniques to specific parts of the aerosol particle population. In particular, stratospheric-aerosol size retrievals based on solar-occultation observations may yield systematically larger particle size estimates (median or effective radii) compared to, e.g., lidar backscatter measurements. Aerosol concentration, on the other hand, may be systematically smaller in retrievals based on occultation measurements compared to lidar measurements. The results indicate that stratospheric-aerosol size retrievals based on occultation or lidar measurements have to be interpreted with caution, as long as the actual aerosol particle size distribution is not well known.
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Bedka, S. T., W. F. Feltz, A. J. Schreiner, and R. E. Holz. "Satellite‐derived cloud top pressure product validation using aircraft‐based cloud physics lidar data from the ATReC field campaign." International Journal of Remote Sensing 28, no. 10 (May 2007): 2221–39. http://dx.doi.org/10.1080/01431160500391965.
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Kölling, Tobias, Tobias Zinner, and Bernhard Mayer. "Aircraft-based stereographic reconstruction of 3-D cloud geometry." Atmospheric Measurement Techniques 12, no. 2 (February 22, 2019): 1155–66. http://dx.doi.org/10.5194/amt-12-1155-2019.
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Abstract. This work describes a method to retrieve the location and geometry of clouds using RGB images from a video camera on an aircraft and data from the aircraft's navigation system. Opposed to ordinary stereo methods for which two cameras with fixed relative position at a certain distance are used to match images taken at the exact same moment, this method uses only a single camera and the aircraft's movement to provide the needed parallax. Advantages of this approach include a relatively simple installation on a (research) aircraft and the possibility to use different image offsets that are even larger than the size of the aircraft. Detrimental effects are the evolution of observed clouds during the time offset between two images as well as the background wind. However we will show that some wind information can also be recovered and subsequently used for the physics-based filtering of outliers. Our method allows the derivation of cloud top geometry which can be used, e.g., to provide location and distance information for other passive cloud remote sensing products. In addition it can also improve retrieval methods by providing cloud geometry information useful for the correction of 3-D illumination effects. We show that this method works as intended through comparison to data from a simultaneously operated lidar system. The stereo method provides lower heights than the lidar method; the median difference is 126 m. This behavior is expected as the lidar method has a lower detection limit (leading to greater cloud top heights for the downward view), while the stereo method also retrieves data points on cloud sides and lower cloud layers (leading to lower cloud heights). Systematic errors across the measurement swath are less than 50 m.
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Pérez-Ramírez, D., D. N. Whiteman, I. Veselovskii, A. Kolgotin, M. Korenskiy, and L. Alados-Arboledas. "Effects of systematic and random errors on the retrieval of particle microphysical properties from multiwavelength lidar measurements using inversion with regularization." Atmospheric Measurement Techniques Discussions 6, no. 3 (May 24, 2013): 4607–44. http://dx.doi.org/10.5194/amtd-6-4607-2013.
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Abstract. In this work we study the effects of systematic and random errors on the inversion of multi-wavelength (MW) lidar data, using the well-known regularization technique, to obtain vertically-resolved aerosol microphysical properties. The software implementation used here was developed at the Physics Instrumentation Center (PIC) in Troitsk (Russia) in conjunction with NASA/Goddard Space Flight Center. Its applicability to Raman lidar systems based on backscattering measurements at three wavelengths (355, 532 and 1064 nm) and extinction measurements at two wavelengths (355 and 532 nm) has been demonstrated widely. The systematic error sensitivity is quantified by first determining the retrieved parameters for a given set of optical input data consistent with two different sets of aerosol physical parameters. Then each optical input is perturbed by varying amounts and the inversion is repeated. We find a generally linear dependence of the retrieved errors in the microphysical properties on the induced systematic errors in the optical data. For the retrievals of effective radius, number/surface/volume concentrations and fine mode radius and volume, we found that these results are not significantly affected by the range of the constraints used in inversions. But significant sensitivity was found to the allowed range of the imaginary part of the particle refractive index to reach. Our results also indicate that exist an additive property for the deviations induced by the biases induced in the individual optical data. This permits the results here to be used to predict deviations in retrieved parameters when multiple input optical data are biased as well as to study the influence of random errors on the retrievals. The above results can be applied to questions regarding lidar design, as for example the space-borne multi-wavelength lidar to be deployed in the upcoming ACE mission anticipated to provide optical data with 15% accuracy in each of the lidar channels.
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Müller, Detlef, Christine Böckmann, Alexei Kolgotin, Lars Schneidenbach, Eduard Chemyakin, Julia Rosemann, Pavel Znak, and Anton Romanov. "Microphysical particle properties derived from inversion algorithms developed in the framework of EARLINET." Atmospheric Measurement Techniques 9, no. 10 (October 12, 2016): 5007–35. http://dx.doi.org/10.5194/amt-9-5007-2016.
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Abstract. We present a summary on the current status of two inversion algorithms that are used in EARLINET (European Aerosol Research Lidar Network) for the inversion of data collected with EARLINET multiwavelength Raman lidars. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. Development of these two algorithms started in 2000 when EARLINET was founded. The algorithms are based on a manually controlled inversion of optical data which allows for detailed sensitivity studies. The algorithms allow us to derive particle effective radius as well as volume and surface area concentration with comparably high confidence. The retrieval of the real and imaginary parts of the complex refractive index still is a challenge in view of the accuracy required for these parameters in climate change studies in which light absorption needs to be known with high accuracy. It is an extreme challenge to retrieve the real part with an accuracy better than 0.05 and the imaginary part with accuracy better than 0.005–0.1 or ±50 %. Single-scattering albedo can be computed from the retrieved microphysical parameters and allows us to categorize aerosols into high- and low-absorbing aerosols. On the basis of a few exemplary simulations with synthetic optical data we discuss the current status of these manually operated algorithms, the potentially achievable accuracy of data products, and the goals for future work. One algorithm was used with the purpose of testing how well microphysical parameters can be derived if the real part of the complex refractive index is known to at least 0.05 or 0.1. The other algorithm was used to find out how well microphysical parameters can be derived if this constraint for the real part is not applied. The optical data used in our study cover a range of Ångström exponents and extinction-to-backscatter (lidar) ratios that are found from lidar measurements of various aerosol types. We also tested aerosol scenarios that are considered highly unlikely, e.g. the lidar ratios fall outside the commonly accepted range of values measured with Raman lidar, even though the underlying microphysical particle properties are not uncommon. The goal of this part of the study is to test the robustness of the algorithms towards their ability to identify aerosol types that have not been measured so far, but cannot be ruled out based on our current knowledge of aerosol physics. We computed the optical data from monomodal logarithmic particle size distributions, i.e. we explicitly excluded the more complicated case of bimodal particle size distributions which is a topic of ongoing research work. Another constraint is that we only considered particles of spherical shape in our simulations. We considered particle radii as large as 7–10 µm in our simulations where the Potsdam algorithm is limited to the lower value. We considered optical-data errors of 15 % in the simulation studies. We target 50 % uncertainty as a reasonable threshold for our data products, though we attempt to obtain data products with less uncertainty in future work.
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Rizza, Umberto, Francesca Barnaba, Mario Marcello Miglietta, Cristina Mangia, Luca Di Liberto, Davide Dionisi, Francesca Costabile, Fabio Grasso, and Gian Paolo Gobbi. "WRF-Chem model simulations of a dust outbreak over the central Mediterranean and comparison with multi-sensor desert dust observations." Atmospheric Chemistry and Physics 17, no. 1 (January 3, 2017): 93–115. http://dx.doi.org/10.5194/acp-17-93-2017.
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Abstract. In this study, the Weather Research and Forecasting model with online coupled chemistry (WRF-Chem) is applied to simulate an intense Saharan dust outbreak event that took place over the Mediterranean in May 2014. Comparison of a simulation using a physics-based desert dust emission scheme with a numerical experiment using a simplified (minimal) emission scheme is included to highlight the advantages of the former. The model was found to reproduce well the synoptic meteorological conditions driving the dust outbreak: an omega-like pressure configuration associated with a cyclogenesis in the Atlantic coasts of Spain. The model performances in reproducing the atmospheric desert dust load were evaluated using a multi-platform observational dataset of aerosol and desert dust properties, including optical properties from satellite and ground-based sun photometers and lidars, plus in situ particulate matter mass concentration (PM) data. This comparison allowed us to investigate the model ability in reproducing both the horizontal and the vertical displacement of the dust plume, as well as its evolution in time. The comparison with satellite (MODIS-Terra) and sun photometers (AERONET) showed that the model is able to reproduce well the horizontal field of the aerosol optical depth (AOD) and its evolution in time (temporal correlation coefficient with AERONET of 0.85). On the vertical scale, the comparison with lidar data at a single site (Rome, Italy) confirms that the desert dust advection occurs in several, superimposed "pulses" as simulated by the model. Cross-analysis of the modeled AOD and desert dust emission fluxes further allowed for the source regions of the observed plumes to be inferred. The vertical displacement of the modeled dust plume was in rather good agreement with the lidar soundings, with correlation coefficients among aerosol extinction profiles up to 1 and mean discrepancy of about 50 %. The model–measurement comparison for PM10 and PM2.5 showed a good temporal matching, although it revealed a marked overestimation of PM10 and PM2.5 (of the order of 70 % during the dust peak). For PM10, it was also possible to investigate the accordance between the model- and the measurement-based dust PM10, this confirming the model PM10 overestimation to be related to over-predicted dust mass up to a factor of 140 %. In all the model–measurement comparisons performed, the enhanced capabilities of the physics-based emission scheme with respect to its simplified, minimal version were evident and are documented.
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Müller, D., C. Böckmann, A. Kolgotin, L. Schneidenbach, E. Chemyakin, J. Rosemann, P. Znak, and A. Romanov. "Microphysical particle properties derived from inversion algorithms developed in the framework of EARLINET." Atmospheric Measurement Techniques Discussions 8, no. 12 (December 8, 2015): 12823–85. http://dx.doi.org/10.5194/amtd-8-12823-2015.
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Abstract. We present a summary on the current status of two inversion algorithms that are used in EARLINET for the inversion of data collected with EARLINET multiwavelength Raman lidars. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. Development of these two algorithms started in 2000 when EARLINET was founded. The algorithms are based on manually controlled inversion of optical data which allows for detailed sensitivity studies and thus provides us with comparably high quality of the derived data products. The algorithms allow us to derive particle effective radius, and volume and surface-area concentration with comparably high confidence. The retrieval of the real and imaginary parts of the complex refractive index still is a challenge in view of the accuracy required for these parameters in climate change studies in which light-absorption needs to be known with high accuracy. Single-scattering albedo can be computed from the retrieved microphysical parameters and allows us to categorize aerosols into high and low absorbing aerosols. We discuss the current status of these manually operated algorithms, the potentially achievable accuracy of data products, and the goals for future work on the basis of a few exemplary simulations with synthetic optical data. The optical data used in our study cover a range of Ångström exponents and extinction-to-backscatter (lidar) ratios that are found from lidar measurements of various aerosol types. We also tested aerosol scenarios that are considered highly unlikely, e.g., the lidar ratios fall outside the commonly accepted range of values measured with Raman lidar, even though the underlying microphysical particle properties are not uncommon. The goal of this part of the study is to test robustness of the algorithms toward their ability to identify aerosol types that have not been measured so far, but cannot be ruled out based on our current knowledge of aerosol physics. We computed the optical data from monomodal logarithmic particle size distributions, i.e., we explicitly excluded the more complicated case of bimodal particle size distributions which is a topic of ongoing research work. Another constraint is that we only considered particles of spherical shape in our simulations. We considered particle radii as large as 7–10 μm in our simulations. That particle size does not only cover the size range of particles in the fine-mode fraction of naturally occurring particle size distributions but also covers a considerable part of the coarse-mode fraction of particle size distributions. We considered optical-data errors of 15 % in the simulation studies. We target 50 % uncertainty as a reasonable threshold for our data products, though we attempt to obtain data products with less uncertainty in future work.
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Chiriaco, M., H. Chepfer, P. Minnis, M. Haeffelin, S. Platnick, D. Baumgardner, P. Dubuisson, et al. "Comparison of CALIPSO-Like, LaRC, and MODIS Retrievals of Ice-Cloud Properties over SIRTA in France and Florida during CRYSTAL-FACE." Journal of Applied Meteorology and Climatology 46, no. 3 (March 1, 2007): 249–72. http://dx.doi.org/10.1175/jam2435.1.
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Abstract This study compares cirrus-cloud properties and, in particular, particle effective radius retrieved by a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-like method with two similar methods using Moderate-Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS), and Geostationary Operational Environmental Satellite imagery. The CALIPSO-like method uses lidar measurements coupled with the split-window technique that uses the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands to infer the microphysical properties of cirrus clouds. The two other methods, using passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (using 20 spectral bands from visible to infrared, referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the Clouds and the Earth’s Radiant Energy System (CERES) team at Langley Research Center (LaRC) in support of CERES algorithms (using 0.65-, 3.75-, 10.8-, and 12.05-μm bands); the two algorithms will be referred to as the MOD06 and LaRC methods, respectively. The three techniques are compared at two different latitudes. The midlatitude ice-clouds study uses 16 days of observations at the Palaiseau ground-based site in France [Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA)], including a ground-based 532-nm lidar and the MODIS overpasses on the Terra platform. The tropical ice-clouds study uses 14 different flight legs of observations collected in Florida during the intensive field experiment known as the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE), including the airborne cloud-physics lidar and the MAS. The comparison of the three methods gives consistent results for the particle effective radius and the optical thickness but discrepancies in cloud detection and altitudes. The study confirms the value of an active remote sensing method (CALIPSO like) for the study of subvisible ice clouds, in both the midlatitudes and Tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds, because of their particular microphysical properties.
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Pérez-Ramírez, D., D. N. Whiteman, I. Veselovskii, A. Kolgotin, M. Korenskiy, and L. Alados-Arboledas. "Effects of systematic and random errors on the retrieval of particle microphysical properties from multiwavelength lidar measurements using inversion with regularization." Atmospheric Measurement Techniques 6, no. 11 (November 7, 2013): 3039–54. http://dx.doi.org/10.5194/amt-6-3039-2013.
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Abstract. In this work we study the effects of systematic and random errors on the inversion of multiwavelength (MW) lidar data using the well-known regularization technique to obtain vertically resolved aerosol microphysical properties. The software implementation used here was developed at the Physics Instrumentation Center (PIC) in Troitsk (Russia) in conjunction with the NASA/Goddard Space Flight Center. Its applicability to Raman lidar systems based on backscattering measurements at three wavelengths (355, 532 and 1064 nm) and extinction measurements at two wavelengths (355 and 532 nm) has been demonstrated widely. The systematic error sensitivity is quantified by first determining the retrieved parameters for a given set of optical input data consistent with three different sets of aerosol physical parameters. Then each optical input is perturbed by varying amounts and the inversion is repeated. Using bimodal aerosol size distributions, we find a generally linear dependence of the retrieved errors in the microphysical properties on the induced systematic errors in the optical data. For the retrievals of effective radius, number/surface/volume concentrations and fine-mode radius and volume, we find that these results are not significantly affected by the range of the constraints used in inversions. But significant sensitivity was found to the allowed range of the imaginary part of the particle refractive index. Our results also indicate that there exists an additive property for the deviations induced by the biases present in the individual optical data. This property permits the results here to be used to predict deviations in retrieved parameters when multiple input optical data are biased simultaneously as well as to study the influence of random errors on the retrievals. The above results are applied to questions regarding lidar design, in particular for the spaceborne multiwavelength lidar under consideration for the upcoming ACE mission.
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Perrone, M. R., F. De Tomasi, and G. P. Gobbi. "Vertically resolved aerosol properties by multi wavelengths lidar measurements." Atmospheric Chemistry and Physics Discussions 13, no. 7 (July 10, 2013): 18535–79. http://dx.doi.org/10.5194/acpd-13-18535-2013.
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Abstract. A new approach is introduced to characterize the dependence on altitude of the aerosol fine mode radius (Rf) and of the fine mode contribution (η) to the aerosol optical thickness (AOT) by three-wavelength lidar measurements. The introduced approach is based on the graphical method of Gobbi et al. (2007), which was applied to AERONET spectral extinction observations and relies on the combined analysis of the Ångstrom exponent (å) and its spectral curvature Δå. Lidar measurements at 355, 532 and 1064 nm were used in this study to retrieve the vertical profiles of å and Δå and to determine the dependence on altitude of Rf and η (532 nm) from the å–Δå combined analysis. Lidar measurements were performed at the Mathematics and Physics Department of Universita' del Salento, in south eastern Italy. Aerosol from continental Europe, the Atlantic, northern Africa, and the Mediterranean Sea are often advected over south eastern Italy and as a consequence, mixed advection patterns leading to aerosol properties varying with altitude are dominant. The proposed approach was applied to eleven measurement days to demonstrate its feasibility in different aerosol load conditions. The selected-days were characterized by AOTs spanning the 0.23–0.67, 0.15–0.41, and 0.04–0.25 range at 355, 532, and 1064 nm, respectively. Lidar ratios varied within the 28–80, 30–70, and 30–55 sr range at 355, 532, and 1064 nm, respectively, for the high variability of the aerosol optical and microphysical properties. å(355 nm, 1064 nm) values retrieved from lidar measurements ranged between 0.12 and 2.5 with mean value ±1 standard deviation equal to 1.4 ± 0.5. Δå varied within the −0.10–0.87 range with mean value equal to 0.1 ± 0.4. Rf and η (532 nm) values spanning the 0.02–0.30 μm and the 0.30–0.99 range, respectively were associated to the å–Δå data points. Rf and η values showed no dependence on the altitude. 72% of the data points were in the Δå–å space delimited by the η and Rf curves varying within 0.70–0.95 and 0.15–0.05 μm, respectively for the dominance of fine mode particles in driving the AOT over south eastern Italy. Volume depolarization vertical profiles retrieved from lidar measurements, aerosol products from AERONET sunphotometer measurements collocated in space and time, the BSC-DREAM model, analytical back trajectories, and satellite images were used to demonstrate the robustness of the proposed method.
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Perrone, M. R., F. De Tomasi, and G. P. Gobbi. "Vertically resolved aerosol properties by multi-wavelength lidar measurements." Atmospheric Chemistry and Physics 14, no. 3 (February 3, 2014): 1185–204. http://dx.doi.org/10.5194/acp-14-1185-2014.
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Abstract. An approach based on the graphical method of Gobbi and co-authors (2007) is introduced to estimate the dependence on altitude of the aerosol fine mode radius (Rf) and of the fine mode contribution (η) to the aerosol optical thickness (AOT) from three-wavelength lidar measurements. The graphical method of Gobbi and co-authors (2007) was applied to AERONET (AErosol RObotic NETwork) spectral extinction observations and relies on the combined analysis of the Ångstrom exponent (å) and its spectral curvature Δå. Lidar measurements at 355, 532 and 1064 nm were used in this study to retrieve the vertical profiles of å and Δå and to estimate the dependence on altitude of Rf and η(532 nm) from the å–Δå combined analysis. Lidar measurements were performed at the Department of Mathematics and Physics of the Universita' del Salento, in south-eastern Italy. Aerosol from continental Europe, the Atlantic, northern Africa, and the Mediterranean Sea are often advected over south-eastern Italy and as a consequence, mixed advection patterns leading to aerosol properties varying with altitude are dominant. The proposed approach was applied to ten measurement days to demonstrate its feasibility in different aerosol load conditions. The selected days were characterized by AOTs spanning the 0.26–0.67, 0.15–0.39, and 0.04–0.27 range at 355, 532, and 1064 nm, respectively. Mean lidar ratios varied within the 31–83, 32–84, and 11–47 sr range at 355, 532, and 1064 nm, respectively, for the high variability of the aerosol optical and microphysical properties. å values calculated from lidar extinction profiles at 355 and 1064 nm ranged between 0.1 and 2.5 with a mean value &pm; 1 standard deviation equal to 1.3 ± 0.7. Δå varied within the −0.1–1 range with mean value equal to 0.25 ± 0.43. Rf and η(532 nm) values spanning the 0.05–0.3 μm and the 0.3–0.99 range, respectively, were associated with the å–Δå data points. Rf and η values showed no dependence on the altitude. 60% of the data points were in the Δå–å space delimited by the η and Rf curves varying within 0.80–0.99 and 0.05–0.15 μm, respectively, for the dominance of fine-mode particles in driving the AOT over south-eastern Italy. Vertical profiles of the linear particle depolarization ratio retrieved from lidar measurements, aerosol products from AERONET sun photometer measurements collocated in space and time, analytical back trajectories, satellite true colour images, and dust concentrations from the BSC–DREAM (Barcelona Super Computing Center-Dust REgional Atmospheric Model) model were used to demonstrate the robustness of the proposed method.
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McLean, William G. K., Guangliang Fu, Sharon P. Burton, and Otto P. Hasekamp. "Retrieval of aerosol microphysical properties from atmospheric lidar sounding: an investigation using synthetic measurements and data from the ACEPOL campaign." Atmospheric Measurement Techniques 14, no. 6 (July 1, 2021): 4755–71. http://dx.doi.org/10.5194/amt-14-4755-2021.
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Abstract. This study presents an investigation of aerosol microphysical retrievals from high spectral resolution lidar (HSRL) measurements. Firstly, retrievals are presented for synthetically generated lidar measurements, followed by an application of the retrieval algorithm to real lidar measurements. Here, we perform the investigation for an aerosol state vector that is typically used in multi-angle polarimeter (MAP) retrievals, so that the results can be interpreted in relation to a potential combination of lidar and MAP measurements. These state vectors correspond to a bimodal size distribution, where column number, effective radius, and effective variance of both modes are treated as fit parameters, alongside the complex refractive index and particle shape. The focus is primarily on a lidar configuration based on that of the High Spectral Resolution Lidar-2 (HSRL-2), which participated in the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, a combined project between NASA and SRON (Netherlands Institute for Space Research). The measurement campaign took place between October and November 2017, over the western region of the USA. Six different instruments were mounted on the aeroplane: four MAPs and two lidar instruments, HSRL-2 and the Cloud Physics Lidar (CPL). Most of the flights were carried out over land, passing over scenes with a low aerosol load. One of the flights passed over a prescribed forest fire in Arizona on 9 November, with a relatively higher aerosol optical depth (AOD), and it is the data from this flight that are focussed on in this study. A retrieval of the aerosol microphysical properties of the smoke plume mixture was attempted with the data from HSRL-2 and compared with a retrieval from the MAPs carried out in previous work pertaining to the ACEPOL data. The synthetic HSRL-2 retrievals resulted for the fine mode in a mean absolute error (MAE) of 0.038 (0.025) µm for the effective radius (with a mean truth value of 0.195 µm), 0.052 (0.037) for the real refractive index, 0.010 (7.20×10-3) for the imaginary part of the refractive index, 0.109 (0.071) for the spherical fraction, and 0.054 (0.039) for the AOD at 532 nm, where the retrievals inside brackets indicate the MAE for noise-free retrievals. For the coarse mode, we find the MAE is 0.459 (0.254) µm for the effective radius (with a mean truth value of 1.970 µm), 0.085 (0.075) for the real refractive index, 2.06×10-4 (1.90×10-4) for the imaginary component, 0.120 (0.090) for the spherical fraction, and 0.051 (0.039) for the AOD. A study of the sensitivity of retrievals to the choice of prior and first guess showed that, on average, the retrieval errors increase when the prior deviates too much from the truth value. These experiments revealed that the measurements primarily contain information on the size and shape of the aerosol, along with the column number. Some information on the real component of the refractive index is also present, with the measurements providing little on absorption or on the effective variance of the aerosol distribution, as both of these were shown to depend heavily on the choice of prior. Retrievals using the HSRL-2 smoke-plume data yielded, for the fine mode, an effective radius of 0.107 µm, a real refractive index of 1.561, an imaginary component of refractive index of 0.010, a spherical fraction of 0.719, and an AOD at 532 nm of 0.505. Additionally, the single-scattering albedo (SSA) from the HSRL-2 retrievals was 0.940. Overall, these results are in good agreement with those from the Spectropolarimeter for Planetary Exploration (SPEX) and Research Scanning Polarimeter (RSP) retrievals.
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Lolli, Simone, Wei Ying Khor, Mohd Zubir Matjafri, and Hwee San Lim. "Monsoon Season Quantitative Assessment of Biomass Burning Clear-Sky Aerosol Radiative Effect at Surface by Ground-Based Lidar Observations in Pulau Pinang, Malaysia in 2014." Remote Sensing 11, no. 22 (November 14, 2019): 2660. http://dx.doi.org/10.3390/rs11222660.
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Direct and indirect aerosol effects are still one of the largest uncertainties related to the Earth energy budget, especially in a wild and remote region like South-East Asia, where ground-based measurements are still difficult and scarce, while endemic cloudy skies make difficult active and passive satellite observations. In this preliminary study, we analyzed and quantitatively assessed the differences between monsoon and inter-monsoon seasons, in terms of radiative effects at surface and columnar heating rate, of clear-sky biomass burning aerosols (no clouds) using ground-based lidar observations obtained with a 355 nm elastic lidar instrument, deployed since 2012 at the Physics Department of Universiti Sains Malaysia (USM). The model-based back-trajectory analysis put in evidence that, during the monsoon seasons (November–March and June–September), the air masses advected towards the observational site transit over active fire hotspot regions, in contrast with the inter-monsoon season. In between the monsoon seasons (April–May, October), the atmosphere over Penang is constituted by local background urban aerosols that originate from road traffic emissions, domestic cooking, and industrial plants emissions. The analysis was carried out using the vertically-resolved profiles of the seasonal averaged aerosol optical properties (monsoon vs. inter-monsoon seasons), e.g., the atmospheric extinction coefficient, to evaluate the seasonal surface aerosol radiative effect and column heating rate differences through the Fu–Liou–Gu (FLG) radiative transfer model. The results put in evidence that the biomass burning advection during the monsoon season (especially during the South West monsoon from June to September) lowers the noon daytime incoming solar shortwave solar radiation reaching the Earth surface with respect to the local background conditions by 91.5 W/m2 (114–69 W/m2). The aerosols also lead to an averaged heating in the first kilometer of the atmosphere of about 4.9 K/day (6.4–3.4 W/m2). The two combined effects, i.e., less absorbed energy by Earth surface and warming of the first kilometer of the boundary layer, increase the low-level stability during monsoon seasons, with a possible reduction in cloud formation and precipitation. The net effect is to exacerbate the haze episodes, as the pollutants rest trapped into the boundary layer. Besides these considerations, the lidar measurements are of great interest in this particular world region and might be used for cal/val of the future space missions, e. g., Earthcare.
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Borchevkina, Olga P., Sergey O. Adamson, Yurii A. Dyakov, Ivan V. Karpov, Gennady V. Golubkov, Pao-Kuan Wang, and Maxim G. Golubkov. "The Influence of Tropospheric Processes on Disturbances in the D and E Ionospheric Layers." Atmosphere 12, no. 9 (August 30, 2021): 1116. http://dx.doi.org/10.3390/atmos12091116.
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Determination of the physical mechanisms of the energy transfer of tropospheric disturbances to the ionosphere is one of the fundamental problems of atmospheric physics. This article presents the observational results of tropospheric and ionospheric disturbances during the passages of the solar terminator and solar eclipse. Lidar observations showed the occurrence of tropospheric regions with noticeably increased amplitudes of density, pressure, and temperature variations with periods corresponding to acoustic and internal gravity waves, which were generated in the troposphere during the development of these events. Simultaneous satellite measurements demonstrate the response of the ionosphere to these tropospheric disturbances. Based on the experimental data, we determine the typical periods and spatial scales of variations. It is shown that the response time of the ionosphere to tropospheric disturbances is 30–40 min.
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Zhang, Kai, Yi Yang, Mengyin Fu, and Meiling Wang. "Traversability Assessment and Trajectory Planning of Unmanned Ground Vehicles with Suspension Systems on Rough Terrain." Sensors 19, no. 20 (October 10, 2019): 4372. http://dx.doi.org/10.3390/s19204372.
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This paper presents a traversability assessment method and a trajectory planning method. They are key features for the navigation of an unmanned ground vehicle (UGV) in a non-planar environment. In this work, a 3D light detection and ranging (LiDAR) sensor is used to obtain the geometric information about a rough terrain surface. For a given SE(2) pose of the vehicle and a specific vehicle model, the SE(3) pose of the vehicle is estimated based on LiDAR points, and then a traversability is computed. The traversability tells the vehicle the effects of its interaction with the rough terrain. Note that the traversability is computed on demand during trajectory planning, so there is not any explicit terrain discretization. The proposed trajectory planner finds an initial path through the non-holonomic A*, which is a modified form of the conventional A* planner. A path is a sequence of poses without timestamps. Then, the initial path is optimized in terms of the traversability, using the method of Lagrange multipliers. The optimization accounts for the model of the vehicle’s suspension system. Therefore, the optimized trajectory is dynamically feasible, and the trajectory tracking error is small. The proposed methods were tested in both the simulation and the real-world experiments. The simulation experiments were conducted in a simulator called Gazebo, which uses a physics engine to compute the vehicle motion. The experiments were conducted in various non-planar experiments. The results indicate that the proposed methods could accurately estimate the SE(3) pose of the vehicle. Besides, the trajectory cost of the proposed planner was lower than the trajectory costs of other state-of-the-art trajectory planners.
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Fernandez, Susana, Rolf Rüfenacht, Niklaus Kämpfer, Thierry Portafaix, Françoise Posny, and Guillaume Payen. "Results from the validation campaign of the ozone radiometer GROMOS-C at the NDACC station of Réunion island." Atmospheric Chemistry and Physics 16, no. 12 (June 20, 2016): 7531–43. http://dx.doi.org/10.5194/acp-16-7531-2016.
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Abstract. Ozone performs a key role in the middle atmosphere and its monitoring is thus necessary.At the Institute of Applied Physics of the University of Bern, Switzerland, we built a new ground-based microwave radiometer, GROMOS-C (GRound based Ozone MOnitoring System for Campaigns). It has a compact design and can be operated remotely with very little maintenance requirements, being therefore suitable for remote deployments. It has been conceived to measure the vertical distribution of ozone in the middle atmosphere, by observing pressure-broadened emission spectra at a frequency of 110.836 GHz. In addition, meridional and zonal wind profiles can be retrieved, based on the Doppler shift of the ozone line measured in the four directions of observation (north, east, south and west).In June 2014 the radiometer was installed at the Maïdo observatory, on Réunion island (21.2° S, 55.5° E). High-resolution ozone spectra were recorded continuously over 7 months. Vertical profiles of ozone have been retrieved through an optimal estimation inversion process, using the Atmospheric Radiative Transfer Simulator ARTS2 as the forward model. The validation is performed against ozone profiles from the Microwave Limb Sounder (MLS) on the Aura satellite, the ozone lidar located at the observatory and with ozone profiles from weekly radiosondes. Zonal and meridional winds retrieved from GROMOS-C data are validated against another wind radiometer located in situ, WIRA. In addition, we compare both ozone and winds with ECMWF (European Centre for Medium-Range Weather Forecasts) model data. Results show that GROMOS-C provides reliable ozone profiles between 30 and 0.02 hPa. The comparison with lidar profiles shows a very good agreement at all levels. The accordance with the MLS data set is within 5 % for pressure levels between 25 and 0.2 hPa. GROMOS-C's wind profiles are in good agreement with the observations by WIRA and with the model data, differences are below 5 m s−1 for both.
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Buzlov, N. A. "Scan Matching for Navigation of a Mobile Robot in Semi-Structured Terrain Conditions." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 5 (May 17, 2021): 246–53. http://dx.doi.org/10.17587/mau.22.246-253.
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To ensure unmanned autonomous movement of ground robotic means, it is required to accurately determine the position and orientation of the robot. The present study is related to the estimation of coordinates by comparing the scans of a laser scanning rangefinder in conditions of semi-structed infrastructure and the absence of a global satellite communications signal. The existing methods of comparing scans have significant drawbacks in the conditions of movement over a semi-structured terrain, associated both with the processing time of data from the laser scanning rangefinder, and with the quality of the results obtained. The scan is preliminarily placed in a map consisting of cells. Each cell of around point scan is described by forces represented by the laws of physics or probability theory. In the cells of the map, we take into account the mutual influence of all forces from each point of the scan and thus we obtain the resulting artificial potential field of the scan. The position of the robot is estimated by the change in the number of acting forces of one scan per points of the next scan taking into account their direction. We estimate the orientation of the robot based on the sum of the vector products of the forces and distances to the given forces acting on the points of the next scan. This method allows you to calculate the displacement of the robot between scans regardless of road conditions and terrain. This article presents the results of an experimental verification of the method on a mock-up of a mobile robot equipped with a Velodyne HDL-32 LIDAR. We indicate the operating conditions of the method for a given LIDAR, as well as the time spent on calculating the bias estimate. Given the peculiarities of the LIDAR, we present a method for eliminating the Doppler Effect (distortion) for the original point cloud. A comparative analysis of the developed method for integrating wheel odometry data, inertial and satellite navigation using the Extended Kalman Filter shows the applicability of this method to assess the position and orientation of the robot in conditions of its movement over rough terrain.
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Chipengo, Ushemadzoro, Peter M. Krenz, and Shawn Carpenter. "From Antenna Design to High Fidelity, Full Physics Automotive Radar Sensor Corner Case Simulation." Modelling and Simulation in Engineering 2018 (December 27, 2018): 1–19. http://dx.doi.org/10.1155/2018/4239725.
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Advanced driver assistance systems (ADAS) have recently been thrust into the spotlight in the automotive industry as carmakers and technology companies pursue effective active safety systems and fully autonomous vehicles. Various sensors such as lidar (light detection and ranging), radar (radio detection and ranging), ultrasonic, and optical cameras are employed to provide situational awareness to vehicles in a highly dynamic environment. Radar has emerged as a primary sensor technology for both active/passive safety and comfort-advanced driver-assistance systems. Physically building and testing radar systems to demonstrate reliability is an expensive and time-consuming process. Simulation emerges as the most practical solution to designing and testing radar systems. This paper provides a complete, full physics simulation workflow for automotive radar using finite element method and asymptotic ray tracing electromagnetic solvers. The design and optimization of both transmitter and receiver antennas is presented. Antenna interaction with vehicle bumper and fascia is also investigated. A full physics-based radar scene corner case is modelled to obtain high-fidelity range-Doppler maps. Finally, this paper investigates the effects of inclined roads on late pedestrian detection and the effects of construction metal plate radar returns on false target identification. Possible solutions are suggested and validated. Results from this study show how pedestrian radar returns can be increased by over 16 dB for early detection along with a 27 dB reduction in road construction plate radar returns to suppress false target identification. Both solutions to the above corner cases can potentially save pedestrian lives and prevent future accidents.
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Ocko, Ilissa B., and Paul A. Ginoux. "Comparing multiple model-derived aerosol optical properties to spatially collocated ground-based and satellite measurements." Atmospheric Chemistry and Physics 17, no. 7 (April 3, 2017): 4451–75. http://dx.doi.org/10.5194/acp-17-4451-2017.
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Abstract. Anthropogenic aerosols are a key factor governing Earth's climate and play a central role in human-caused climate change. However, because of aerosols' complex physical, optical, and dynamical properties, aerosols are one of the most uncertain aspects of climate modeling. Fortunately, aerosol measurement networks over the past few decades have led to the establishment of long-term observations for numerous locations worldwide. Further, the availability of datasets from several different measurement techniques (such as ground-based and satellite instruments) can help scientists increasingly improve modeling efforts. This study explores the value of evaluating several model-simulated aerosol properties with data from spatially collocated instruments. We compare aerosol optical depth (AOD; total, scattering, and absorption), single-scattering albedo (SSA), Ångström exponent (α), and extinction vertical profiles in two prominent global climate models (Geophysical Fluid Dynamics Laboratory, GFDL, CM2.1 and CM3) to seasonal observations from collocated instruments (AErosol RObotic NETwork, AERONET, and Cloud–Aerosol Lidar with Orthogonal Polarization, CALIOP) at seven polluted and biomass burning regions worldwide. We find that a multi-parameter evaluation provides key insights on model biases, data from collocated instruments can reveal underlying aerosol-governing physics, column properties wash out important vertical distinctions, and improved models does not mean all aspects are improved. We conclude that it is important to make use of all available data (parameters and instruments) when evaluating aerosol properties derived by models.
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Tiago Damas, MARTINS, VIEIRA Bianca Carvalho, FERNANDES Nelson Ferreira, OKAFIORI Chisato, and MONTGOMERY David R. "Application of the SHALSTAB model for the identification of areas susceptible to landslides: Brazilian case studies." Revista de Geomorfologie 19, no. 1 (December 30, 2017): 136–44. http://dx.doi.org/10.21094/rg.2017.015.
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Since the 1960s, catastrophic and generalized events of hazardous mass movements caused millions of dollars in economic losses and resulted in thousands of fatalities and homelessness in Brazil. To understand these processes and attempt to predict them, mathematical models have been utilized world-wide describing the physics of the process through mathematical equations. The objective of this study was to present two areas widely affected by shallow landslides where the SHALSTAB model was applied to understand the process and to predict potentially unstable areas in several hydrographic basins. Simulations utilized the types of distinct data that were available in each area. From both areas, geotechnical data collected in the field, topographical data from digital topographical maps and Digital Terrain Models (DTM) from Light Detection and Ranging (LiDAR) were utilized. Susceptibility maps were validated using two indexes, scar concentration (SC) and landslide potential (LP), based on landslides that occurred in 1985 and 2011. Both indexes showed satisfactory results given that the unconditionally unstable category described more than 45% of the landslide events, and the LP index displayed the highest values for the most unstable categories.
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Knepp, Travis N., James J. Szykman, Russell Long, Rachelle M. Duvall, Jonathan Krug, Melinda Beaver, Kevin Cavender, et al. "Assessment of mixed-layer height estimation from single-wavelength ceilometer profiles." Atmospheric Measurement Techniques 10, no. 10 (October 25, 2017): 3963–83. http://dx.doi.org/10.5194/amt-10-3963-2017.
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Abstract. Differing boundary/mixed-layer height measurement methods were assessed in moderately polluted and clean environments, with a focus on the Vaisala CL51 ceilometer. This intercomparison was performed as part of ongoing measurements at the Chemistry And Physics of the Atmospheric Boundary Layer Experiment (CAPABLE) site in Hampton, Virginia and during the 2014 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) field campaign that took place in and around Denver, Colorado. We analyzed CL51 data that were collected via two different methods (BLView software, which applied correction factors, and simple terminal emulation logging) to determine the impact of data collection methodology. Further, we evaluated the STRucture of the ATmosphere (STRAT) algorithm as an open-source alternative to BLView (note that the current work presents an evaluation of the BLView and STRAT algorithms and does not intend to act as a validation of either). Filtering criteria were defined according to the change in mixed-layer height (MLH) distributions for each instrument and algorithm and were applied throughout the analysis to remove high-frequency fluctuations from the MLH retrievals. Of primary interest was determining how the different data-collection methodologies and algorithms compare to each other and to radiosonde-derived boundary-layer heights when deployed as part of a larger instrument network. We determined that data-collection methodology is not as important as the processing algorithm and that much of the algorithm differences might be driven by impacts of local meteorology and precipitation events that pose algorithm difficulties. The results of this study show that a common processing algorithm is necessary for light detection and ranging (lidar)-based MLH intercomparisons and ceilometer-network operation, and that sonde-derived boundary layer heights are higher (10–15 % at midday) than lidar-derived mixed-layer heights. We show that averaging the retrieved MLH to 1 h resolution (an appropriate timescale for a priori data model initialization) significantly improved the correlation between differing instruments and differing algorithms.
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Mettig, Nora, Mark Weber, Alexei Rozanov, Carlo Arosio, John P. Burrows, Pepijn Veefkind, Anne M. Thompson, et al. "Ozone profile retrieval from nadir TROPOMI measurements in the UV range." Atmospheric Measurement Techniques 14, no. 9 (September 16, 2021): 6057–82. http://dx.doi.org/10.5194/amt-14-6057-2021.
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Abstract. The TOPAS (Tikhonov regularised Ozone Profile retrievAl with SCIATRAN) algorithm to retrieve vertical profiles of ozone from space-borne observations in nadir-viewing geometry has been developed at the Institute of Environmental Physics (IUP) of the University of Bremen and applied to the TROPOspheric Monitoring Instrument (TROPOMI) L1B spectral data version 2. Spectral data between 270 and 329 nm are used for the retrieval. A recalibration of the measured radiances is done using ozone profiles from MLS/Aura. Studies with synthetic spectra show that individual profiles in the stratosphere can be retrieved with an uncertainty of about 10 %. In the troposphere, the retrieval errors are larger depending on the a priori profile used. The vertical resolution above 18 km is about 6–10 km, and it degrades to 15–25 km below. The vertical resolution in the troposphere is strongly dependent on the solar zenith angle (SZA). The ozone profiles retrieved from TROPOMI with the TOPAS algorithm were validated using data from ozonesondes and stratospheric ozone lidars. Above 18 km, the comparison with sondes shows excellent agreement within less than ±5 % for all latitudes. The standard deviation of mean differences is about 10 %. Below 18 km, the relative mean deviation in the tropics and northern latitudes is still quite good, remaining within ±20 %. At southern latitudes, larger differences of up to +40 % occur between 10 and 15 km. The standard deviation is about 50 % between 7–18 km and about 25 % below 7 km. The validation of stratospheric ozone profiles with ground-based lidar measurements also shows very good agreement. The relative mean deviation is below ±5 % between 18–45 km, with a standard deviation of 10 %. TOPAS retrieval results for 1 d of TROPOMI observations were compared to ozone profiles from the Microwave Limb Sounder (MLS) on the Aura satellite and the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP). The relative mean difference was found to be largely below ±5 % between 20–50 km, except at very high latitudes.
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Strandgren, Johan, Jennifer Fricker, and Luca Bugliaro. "Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI." Atmospheric Measurement Techniques 10, no. 11 (November 14, 2017): 4317–39. http://dx.doi.org/10.5194/amt-10-4317-2017.
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Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate large-scale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary. In this paper the CiPS (Cirrus Properties from SEVIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m−2. For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.
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Nowottnick, E., P. Colarco, A. da Silva, D. Hlavka, and M. McGill. "The fate of Saharan dust across the Atlantic and implications for a Central American dust barrier." Atmospheric Chemistry and Physics Discussions 11, no. 3 (March 11, 2011): 8337–84. http://dx.doi.org/10.5194/acpd-11-8337-2011.
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Abstract. Saharan dust was observed over the Caribbean basin during the summer 2007 NASA Tropical Composition, Cloud, and Climate Coupling (TC4) field experiment. Airborne Cloud Physics Lidar (CPL) and satellite observations from MODIS suggest a barrier to dust transport across Central America into the eastern Pacific. We use the NASA GEOS-5 atmospheric transport model with online aerosol tracers to perform simulations of the TC4 time period in order to understand the nature of this barrier. Our simulations are driven by the Modern Era Retrospective-Analysis for Research and Applications (MERRA) meteorological analyses. We evaluate our baseline simulated dust distributions using MODIS and CALIOP satellite and ground-based AERONET sun photometer observations. GEOS-5 reproduces the observed location, magnitude, and timing of major dust events, but our baseline simulation does not develop as strong a barrier to dust transport across Central America as observations suggest. Analysis of the dust transport dynamics and lost processes suggest that while both mechanisms play a role in defining the dust transport barrier, loss processes by wet removal of dust are about twice as important as transport. Sensitivity analyses with our model showed that the dust barrier would not exist without convective scavenging over the Caribbean. The best agreement between our model and the observations was obtained when dust wet removal was parameterized to be more aggressive, treating the dust as we do hydrophilic aerosols.
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Booth, James F., Catherine M. Naud, and Anthony D. Del Genio. "Diagnosing Warm Frontal Cloud Formation in a GCM: A Novel Approach Using Conditional Subsetting." Journal of Climate 26, no. 16 (August 6, 2013): 5827–45. http://dx.doi.org/10.1175/jcli-d-12-00637.1.
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Abstract This study analyzes characteristics of clouds and vertical motion across extratropical cyclone warm fronts in the NASA Goddard Institute for Space Studies general circulation model. The validity of the modeled clouds is assessed using a combination of satellite observations from CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. The analysis focuses on developing cyclones, to test the model's ability to generate their initial structure. To begin, the extratropical cyclones and their warm fronts are objectively identified and cyclone-local fields are mapped into a vertical transect centered on the surface warm front. To further isolate specific physics, the cyclones are separated using conditional subsetting based on additional cyclone-local variables, and the differences between the subset means are analyzed. Conditional subsets are created based on 1) the transect clouds and 2) vertical motion; 3) the strength of the temperature gradient along the warm front, as well as the storm-local 4) wind speed and 5) precipitable water (PW). The analysis shows that the model does not generate enough frontal cloud, especially at low altitude. The subsetting results reveal that, compared to the observations, the model exhibits a decoupling between cloud formation at high and low altitudes across warm fronts and a weak sensitivity to moisture. These issues are caused in part by the parameterized convection and assumptions in the stratiform cloud scheme that are valid in the subtropics. On the other hand, the model generates proper covariability of low-altitude vertical motion and cloud at the warm front and a joint dependence of cloudiness on wind and PW.
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M�jean, G., J. Kasparian, E. Salmon, J. Yu, J. P. Wolf, R. Bourayou, R. Sauerbrey, et al. "Towards a supercontinuum-based infrared lidar." Applied Physics B: Lasers and Optics 77, no. 2-3 (September 1, 2003): 357–59. http://dx.doi.org/10.1007/s00340-003-1183-x.
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Deng, Shijie, Alan P. Morrison, Yong Guo, Chuanxin Teng, Ming Chen, Yu Cheng, Houquan Liu, Xianming Xiong, and Libo Yuan. "Design of a Real-Time Breakdown Voltage and On-Chip Temperature Monitoring System for Single Photon Avalanche Diodes." Electronics 10, no. 1 (December 27, 2020): 25. http://dx.doi.org/10.3390/electronics10010025.
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The design and implementation of a real-time breakdown voltage and on-chip temperature monitoring system for single photon avalanche diodes (SPADs) is described in this work. In the system, an on-chip shaded (active area of the detector covered by a metal layer) SPAD is used to provide a dark count rate for the breakdown voltage and temperature calculation. A bias circuit was designed to provide a bias voltage scanning for the shaded SPAD. A microcontroller records the pulses from the anode of the shaded SPAD and calculates its real-time dark count rate. An algorithm was developed for the microcontroller to calculate the SPAD’s breakdown voltage and the on-chip temperature in real time. Experimental results show that the system is capable of measuring the SPAD’s breakdown voltage with a mismatch of less than 1.2%. Results also show that the system can provide real-time on-chip temperature monitoring for the range of −10 to 50 °C with errors of less than 1.7 °C. The system proposed can be used for the real-time SPAD’s breakdown voltage and temperature estimation for dual-SPADs or SPAD arrays chip where identical detectors are fabricated on the same chip and one or more dummy SPADs are shaded. With the breakdown voltage and the on-chip temperature monitoring, intelligent control logic can be developed to optimize the performance of the SPAD-based photon counting system by adjusting the parameters such as excess bias voltage and dead-time. This is particularly useful for SPAD photon counting systems used in complex working environments such as the applications in 3D LIDAR imaging for geodesy, geology, geomorphology, forestry, atmospheric physics and autonomous vehicles.
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Muckenhuber, Stefan, Hannes Holzer, and Zrinka Bockaj. "Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities." Sensors 20, no. 11 (June 10, 2020): 3309. http://dx.doi.org/10.3390/s20113309.
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Development and validation of reliable environment perception systems for automated driving functions requires the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, a perception sensor is replaced by a sensor model. A major challenge for state-of-the-art sensor models is to represent the large variety of material properties of the surrounding objects in a realistic manner. Since lidar sensors are considered to play an essential role for upcoming automated vehicles, this paper presents a new lidar modelling approach that takes material properties and corresponding lidar capabilities into account. The considered material property is the incidence angle dependent reflectance of the illuminated material in the infrared spectrum and the considered lidar property its capability to detect a material with a certain reflectance up to a certain range. A new material classification for lidar modelling in the automotive context is suggested, distinguishing between 7 material classes and 23 subclasses. To measure angle dependent reflectance in the infrared spectrum, a new measurement device based on a time of flight camera is introduced and calibrated using Lambertian targets with defined reflectance values at
10
%
,
50
%
, and
95
%
. Reflectance measurements of 9 material subclasses are presented and 488 spectra from the NASA ECOSTRESS library are considered to evaluate the new measurement device. The parametrisation of the lidar capabilities is illustrated by presenting a lidar measurement campaign with a new Infineon lidar prototype and relevant data from 12 common lidar types.
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Salvoni, D., M. Ejrnaes, L. Parlato, A. Sannino, A. Boselli, G. P. Pepe, R. Cristiano, and X. Wang. "Lidar techniques for a SNSPD-based measurement." Journal of Physics: Conference Series 1182 (February 2019): 012014. http://dx.doi.org/10.1088/1742-6596/1182/1/012014.
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Su, Jia, M. Patrick McCormick, Zhaoyan Liu, Kevin H. Leavor, Robert B. Lee, Jasper Lewis, and Michael T. Hill. "Obtaining a ground-based lidar geometric form factor using coincident spaceborne lidar measurements." Applied Optics 49, no. 1 (December 21, 2009): 108. http://dx.doi.org/10.1364/ao.49.000108.
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Zhang, Qiang, Zihao Wang, Jianwen Shao, Libo Weng, and Fei Gao. "Measuring Vehicle Profile Size: Lidar-Based System and K-Frame-Based Methodology." Sensors 21, no. 18 (September 16, 2021): 6206. http://dx.doi.org/10.3390/s21186206.
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At present, light curtain is a widely-used method to measure the vehicle profile size. However, it is sensitive to temperature, humidity, dust and other weather factors. In this paper, a lidar-based system with a K-frame-based algorithm is proposed for measuring vehicle profile size. The system is composed of left lidar, right lidar, front lidar, control box and industry controlling computer. Within the system, a K-frame-based methodology is investigated, which include several probable algorithm combinations. Three groups of experiments are conducted. An optimal algorithm combination, A16, is determined through the first group experiments. In the second group experiments, various types of vehicles are chosen to verify the generality and repeatability of the proposed system and methodology. The third group experiments are implemented to compare with vision-based methods and other lidar-based methods. The experimental results show that the proposed K-frame-based methodology is far more accurate than the comparative methods.
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Alekseev, V. N. "Lidar based on intracavity radiation scanning." Journal of Optical Technology 68, no. 4 (April 1, 2001): 274. http://dx.doi.org/10.1364/jot.68.000274.
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Høgstedt, Lasse, Andreas Fix, Martin Wirth, Christian Pedersen, and Peter Tidemand-Lichtenberg. "Upconversion-based lidar measurements of atmospheric CO_2." Optics Express 24, no. 5 (March 1, 2016): 5152. http://dx.doi.org/10.1364/oe.24.005152.
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Martin, Aude, Peter Verheyen, Peter De Heyn, Philippe Absil, Patrick Feneyrou, Jerome Bourderionnet, Delphin Dodane, et al. "Photonic Integrated Circuit-Based FMCW Coherent LiDAR." Journal of Lightwave Technology 36, no. 19 (October 2018): 4640–45. http://dx.doi.org/10.1109/jlt.2018.2840223.
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Shi, J., G. Li, W. Gong, J. Bai, Y. Huang, Y. Liu, S. Li, and D. Liu. "A lidar system based on stimulated Brillouin scattering." Applied Physics B 86, no. 1 (July 1, 2006): 177–79. http://dx.doi.org/10.1007/s00340-006-2305-z.
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Veselovskii, I., and B. Barchunov. "Excimer-laser-based lidar for tropospheric ozone monitoring." Applied Physics B: Lasers and Optics 68, no. 6 (June 1, 1999): 1131–37. http://dx.doi.org/10.1007/s003400050756.
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Wu, Shaofeng, and Jingyu Lin. "A novel SLAM framework based on 2D LIDAR." Journal of Physics: Conference Series 1650 (October 2020): 022104. http://dx.doi.org/10.1088/1742-6596/1650/2/022104.
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Díaz-Vilariño, L., H. González-Jorge, J. Martínez-Sánchez, and H. Lorenzo. "Automatic LiDAR-based lighting inventory in buildings." Measurement 73 (September 2015): 544–50. http://dx.doi.org/10.1016/j.measurement.2015.06.009.
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Vondrak, T., J. M. C. Plane, S. Broadley, and D. Janches. "A chemical model of meteoric ablation." Atmospheric Chemistry and Physics 8, no. 23 (December 5, 2008): 7015–31. http://dx.doi.org/10.5194/acp-8-7015-2008.
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Abstract. Most of the extraterrestrial dust entering the Earth's atmosphere ablates to produce metal vapours, which have significant effects on the aeronomy of the upper mesosphere and lower thermosphere. A new Chemical Ablation Model (CAMOD) is described which treats the physics and chemistry of ablation, by including the following processes: sputtering by inelastic collisions with air molecules before the meteoroid melts; evaporation of atoms and oxides from the molten particle; diffusion-controlled migration of the volatile constituents (Na and K) through the molten particle; and impact ionization of the ablated fragments by hyperthermal collisions with air molecules. Evaporation is based on thermodynamic equilibrium in the molten meteoroid (treated as a melt of metal oxides), and between the particle and surrounding vapour phase. The loss rate of each element is then determined assuming Langmuir evaporation. CAMOD successfully predicts the meteor head echo appearance heights, observed from incoherent scatter radars, over a wide range of meteoroid velocities. The model also confirms that differential ablation explains common-volume lidar observations of K, Ca and Ca+ in fresh meteor trails. CAMOD is then used to calculate the injection rates into the atmosphere of a variety of elements as a function of altitude, integrated over the meteoroid mass and velocity distributions. The most abundant elements (Fe, Mg and Si) have peak injection rates around 85 km, with Na and K about 8 km higher. The more refractory element Ca ablates around 82 km with a Na:Ca ratio of 4:1, which does therefore not explain the depletion of atomic Ca to Na, by more than 2 orders of magnitude, in the upper mesosphere. Diffusion of the most volatile elements (Na and K) does not appear to be rate-limiting except in the fastest meteoroids. Non-thermal sputtering causes ~35% mass loss from the fastest (~60–70 km s−1) and smallest (10−17–10−13 g) meteoroids, but makes a minor contribution to the overall ablation rate.
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Vondrak, T., J. M. C. Plane, S. Broadley, and D. Janches. "A chemical model of meteoric ablation." Atmospheric Chemistry and Physics Discussions 8, no. 4 (July 30, 2008): 14557–606. http://dx.doi.org/10.5194/acpd-8-14557-2008.
Full textAbstract:
Abstract. Most of the extraterrestrial dust entering the Earth's atmosphere ablates to produce metal vapours, which have significant effects on the aeronomy of the upper mesosphere and lower thermosphere. A new Chemical Ablation Model (CAMOD) is described which treats the physics and chemistry of ablation, by including the following processes: sputtering by inelastic collisions with air molecules before the meteoroid melts; evaporation of atoms and oxides from the molten particle; diffusion-controlled migration of the volatile constituents (Na and K) through the molten particle; and impact ionization of the ablated fragments by hyperthermal collisions with air molecules. Evaporation is based on thermodynamic equilibrium in the molten meteoroid (treated as a melt of metal oxides), and between the particle and surrounding vapour phase. The loss rate of each element is then determined assuming Langmuir evaporation. CAMOD successfully predicts the meteor head echo appearance heights, observed from incoherent scatter radars, over a wide range of meteoroid velocities. The model also confirms that differential ablation explains common-volume lidar observations of K, Ca and Ca+ in fresh meteor trails. CAMOD is then used to calculate the injection rates into the atmosphere of a variety of elements as a function of altitude, integrated over the meteoroid mass and velocity distributions. The most abundant elements (Fe, Mg and Si) have peak injection rates around 85 km, with Na and K about 8 km higher. The more refractory element Ca ablates around 82 km with a Na:Ca ratio of 4:1, which does therefore not explain the depletion of atomic Ca to Na, by more than 2 orders of magnitude, in the upper mesosphere. Diffusion of the most volatile elements (Na and K) does not appear to be rate-limiting except in the fastest meteoroids. Non-thermal sputtering causes ~35% mass loss from the fastest (~60–70 km s−1) and smallest (10−17–10−13g) meteoroids, but makes a minor contribution to the overall ablation rate.