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Atmospheric mineral dust aerosol constitutes a threat to aircraft engines from deterioration of internal components. Here we fulfil an outstanding need to quantify engine dust ingestion at worldwide airports. The vertical distribution of dust is of key importance since ascent/descent rates and engine power both vary with altitude and affect dust ingestion. We use representative jet engine power profile information combined with vertically and seasonally varying dust concentrations to calculate the 'dust dose' ingested by an engine over a single ascent or descent. Using the Copernicus Atmosphere Monitoring Service (CAMS) model reanalysis, we calculate climatological and seasonal dust dose at 10 airports for 2003–2019. Dust doses are mostly largest in summer for descent, with the largest at Delhi (6.6 g). Beijing's largest dose occurs in spring (2.9 g). Holding patterns at altitudes coincident with peak dust concentrations can lead to substantial quantities of dust ingestion, resulting in a larger dose than the take-off, climb and taxi phases. We compare dust dose calculated from CAMS to spaceborne lidar observations from two dust datasets derived from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). In general, seasonal and spatial patterns are similar between CAMS and CALIOP though large variations in dose magnitude are found, with CAMS producing lower doses by a mean factor of 2.4±0.5, particularly when peak dust concentration is very close to the surface. We show that mitigating action to reduce engine dust damage could be achieved, firstly by moving arrivals and departures to after sunset and secondly by altering the altitude of the holding pattern away from that of the local dust peak altitude, reducing dust dose by up to 44 % or 41 % respectively. [ABSTRACT FROM AUTHOR]

The accurate classification of aerosol types injected into the stratosphere is important to properly characterize their chemical and radiative impacts within the Earth climate system. The updated stratospheric aerosol subtyping algorithm used in the Version 4.5 (V4.5) release of the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) level 2 data products now delivers more comprehensive and accurate classifications than its predecessor. The original algorithm identified four aerosol subtypes for layers detected above the tropopause: volcanic ash, smoke, sulfate/other, and polar stratospheric aerosol (PSA). In the revised algorithm, sulfates are separately identified as a distinct, homogeneous subtype and the diffuse, weakly scattering layers previously assigned to the sulfate/other class are recategorized as a fifth “unclassified” subtype. By making two structural changes to the algorithm and revising two thresholds, the V4.5 algorithm improves the ability to discriminate between volcanic ash and smoke from pyrocumulonimbus injections, improves the fidelity of the sulfate subtype, and more accurately reflects the uncertainties inherent in the classification process. The 532 nm lidar ratio for volcanic ash was also revised to a value more consistent with the current state of knowledge. This paper briefly reviews the previous version of the algorithm (V4.1/V4.2), then fully details the rationale and impact of the V4.5 changes on subtype classification frequency for specific events where the dominant aerosol type is known based on literature. Classification accuracy is best for volcanic ash due to its characteristically high depolarization ratio. Smoke layers in the stratosphere are also classified with reasonable accuracy, though during the daytime a substantial fraction are misclassified as ash. It is also possible for mixtures of ash and sulfate to be misclassified as smoke. The V4.5 sulfate subtype accuracy is less than that for ash or smoke, with sulfates being misclassified as smoke about one-third of the time. However, because exceptionally tenuous layers are now assigned to the unclassified subtype and the revised algorithm levies more stringent criteria for identifying an aerosol as sulfate, it is more likely that layers labeled as this subtype are in fact sulfate. [ABSTRACT FROM AUTHOR]

We present observations of cirrus clouds from June 2006 to July 2007 performed by using a two-wavelength lidar located at Hampton University. For this time period, cirrus clouds were observed mostly in 7-13.5 km altitudes. Data analyses have been performed focusing on a color-ratio retrieval. In total, 86,369 samples from 1,689 profiles (1 min average and 15 m range resolution) containing cirrus clouds with attenuated backscatter ratio (ratio of attenuated total backscatter to the molecular backscatter) larger than 10 have been selected. The cirrus color ratio distribution shows a peak value at about 0.88 and a full width at half-maximum of 0.12. OCIS codes: 280.3640, 010.1615.

We discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photoncounting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poisson-distributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the root mean square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF, uncertainties can be reliably calculated from or for a single data sample. Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations lidar and tested using data from the Lidar In-space Technology Experiment. OCIS codes: 280.3640, 040.5160, 270.5290.

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This paper reviews recent advances regarding the retrieval of optical depths of semi-transparent cirrus clouds using synergetic analyses of perfectly collocated observations from the CALIOP lidar and the IIR infrared radiometer aboard the CALIPSO satellite.

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar, onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite, has been providing a near continuous record of high-resolution vertical profiles of clouds and aerosols properties since the summer of 2006. Key to the generation of these vertical profiles is proper calibration of the 532 nm and 1064 nm channels. This abstract summarizes improvements to the calibration techniques used to calibrate the 532 nm and 1064 nm signals for the recent version 4 (V4) Lidar Level 1 data release.

In a previous study we evaluated the above-cloud aerosol optical depth (AOD) retrieval at 532 nm in the CALIOP version 3 (V3) data products for two selected spatial domains along the Saharan dust transport pathway and African smoke transport pathway. In that study we rescaled the V3 level-1 (L1) data to compensate for known V3 calibration biases, and then derived aerosol intrinsic properties such as lidar ratio (Sa) and particulate depolarization ratio (PDR) for comparison with the CALIOP dust and smoke aerosol models. The calibration of the recently released version 4 (V4) CALIPSO L1 data product is significantly improved over V3. So in this paper we repeat our previous analysis using the new V4 L1 data. A comparison shows that our rescaled V3 and the new V4 data are different only by ~1% in the two selected spatial domains. The retrieved AOD values decrease by ~2.6% in both domains from V3 to V4. When the data is screened to exclude weakly scattering layers, the median Sa retrieved from the V4 L1 data in the dust transport region is reduced by 1.4 sr to 43.0±8.3 sr. The median Sa value in the smoke transport region is increased by 0.8 sr to 71.2±15.1 sr. The PDR values remain almost unchanged for the screened data.

Measurements of ice cloud lidar ratio from the University of Wisconsin High Spectral Resolution Lidar System (UW HSRL) are shown, for the period 2013/06/18-2013/11/04 in Hunstville, AL. These data were acquired as part of the SEAC4RS campaign. The layer-averaged median lidar ratio ,at 532 nm, for non-precipitating ice-clouds, 0.3 < OD < 3.0, observed during the experiment was determined to be 25.7 +/- 10.6 sr. As part of this work we’ve also developed an automated cloud and precipitation classification and detection algorithm.

Backscatter and extinction from water clouds are well-understood, both theoretically and experimentally, and thus changes to the expected measurement of layer-integrated attenuated backscatter can be used to infer the optical properties of overlying layers. In this paper we offer a first look at a new retrieval technique that uses CALIPSO measurements of opaque water clouds to derive optical depths and Ångström exponents for overlying aerosol layers.

Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor). On July 17, 2014, the CALIPSO satellite was tilted 30° off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraft-based high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.

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Reserach was conducted to show that a fluoroamino acid, which could serve as an 19F NMR probe of rotational freedom and internal packing in a protein, would be a powerful addition to the repertoire of biophysical probes that use the ease and simplicity of 19F NMR methods. Results demonstrate that L-difluoromethionine (L-S-(-difluoromethyl)homocysteine) could be used to probe amino acid packing in protein interiors but it could potentially be used in more specific applications in the study of methionine and its implications in protein structure and function.