High Spectral Resolution Lidar Research Articles

The Airborne Cloud–Aerosol Transport System: Overview and Description of the Instrument and Retrieval Algorithms

AbstractThe Airborne Cloud–Aerosol Transport System (ACATS) is a Doppler wind lidar system that has recently been developed for atmospheric science capabilities at the NASA Goddard Space Flight Center (GSFC). ACATS is also a high-spectral-resolution lidar (HSRL), uniquely capable of directly resolving backscatter and extinction properties of a particle from a high-altitude aircraft. Thus, ACATS simultaneously measures optical properties and motion of cloud and aerosol layers. ACATS has flown on the NASA ER-2 during test flights over California in June 2012 and science flights during the Wallops Airborne Vegetation Experiment (WAVE) in September 2012. This paper provides an overview of the ACATS method and instrument design, describes the ACATS HSRL retrieval algorithms for cloud and aerosol properties, and demonstrates the data products that will be derived from the ACATS data using initial results from the WAVE project. The HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions, such as the Cloud–Aerosol Transport System (CATS) to be installed on the International Space Station (ISS). Furthermore, the direct extinction and particle wind velocity retrieved from the ACATS data can be used for science applications such as dust or smoke transport and convective outflow in anvil cirrus clouds.

Arrange and average algorithm for the retrieval of aerosol parameters from multiwavelength high-spectral-resolution lidar/Raman lidar data.

We present the results of a feasibility study in which a simple, automated, and unsupervised algorithm, which we call the arrange and average algorithm, is used to infer microphysical parameters (complex refractive index, effective radius, total number, surface area, and volume concentrations) of atmospheric aerosol particles. The algorithm uses backscatter coefficients at 355, 532, and 1064nm and extinction coefficients at 355 and 532nm as input information. Testing of the algorithm is based on synthetic optical data that are computed from prescribed monomodal particle size distributions and complex refractive indices that describe spherical, primarily fine mode pollution particles. We tested the performance of the algorithm for the "3 backscatter (β)+2 extinction (α)" configuration of a multiwavelength aerosol high-spectral-resolution lidar (HSRL) or Raman lidar. We investigated the degree to which the microphysical results retrieved by this algorithm depends on the number of input backscatter and extinction coefficients. For example, we tested "3β+1α," "2β+1α," and "3β" lidar configurations. This arrange and average algorithm can be used in two ways. First, it can be applied for quick data processing of experimental data acquired with lidar. Fast automated retrievals of microphysical particle properties are needed in view of the enormous amount of data that can be acquired by the NASA Langley Research Center's airborne "3β+2α" High-Spectral-Resolution Lidar (HSRL-2). It would prove useful for the growing number of ground-based multiwavelength lidar networks, and it would provide an option for analyzing the vast amount of optical data acquired with a future spaceborne multiwavelength lidar. The second potential application is to improve the microphysical particle characterization with our existing inversion algorithm that uses Tikhonov's inversion with regularization. This advanced algorithm has recently undergone development to allow automated and unsupervised processing; the arrange and average algorithm can be used as a preclassifier to further improve its speed and precision. First tests of the performance of arrange and average algorithm are encouraging. We used a set of 48 different monomodal particle size distributions, 4 real parts and 15 imaginary parts of the complex refractive index. All in all we tested 2880 different optical data sets for 0%, 10%, and 20% Gaussian measurement noise (one-standard deviation). In the case of the "3β+2α" configuration with 10% measurement noise, we retrieve the particle effective radius to within 27% for 1964 (68.2%) of the test optical data sets. The number concentration is obtained to 76%, the surface area concentration to 16%, and the volume concentration to 30% precision. The "3β" configuration performs significantly poorer. The performance of the "3β+1α" and "2β+1α" configurations is intermediate between the "3β+2α" and the "3β."

Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US

Abstract. We present measurements acquired by the world's first airborne 3 backscatter (β) + 2 extinction (α) High Spectral Resolution Lidar (HSRL-2). HSRL-2 measures particle backscatter coefficients at 355, 532, and 1064 nm, and particle extinction coefficients at 355 and 532 nm. The instrument has been developed by the NASA Langley Research Center. The instrument was operated during Phase 1 of the Department of Energy (DOE) Two-Column Aerosol Project (TCAP) in July 2012. We observed pollution outflow from the northeastern coast of the US out over the western Atlantic Ocean. Lidar ratios were 50–60 sr at 355 nm and 60–70 sr at 532 nm. Extinction-related Ångström exponents were on average 1.2–1.7, indicating comparably small particles. Our novel automated, unsupervised data inversion algorithm retrieved particle effective radii of approximately 0.2 μm, which is in agreement with the large Ångström exponents. We find good agreement with particle size parameters obtained from coincident in situ measurements carried out with the DOE Gulfstream-1 aircraft.

Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing

Abstract. A novel approach for the detection of cirrus clouds and the retrieval of optical thickness and top altitude based on the measurements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented. Trained with 8 000 000 co-incident measurements of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission the new "cirrus optical properties derived from CALIOP and SEVIRI algorithm during day and night" (COCS) algorithm utilizes a backpropagation neural network to provide accurate measurements of cirrus optical depth τ at λ = 532 nm and top altitude z every 15 min covering almost one-third of the Earth's atmosphere. The retrieved values are validated with independent measurements of CALIOP and the optical thickness derived by an airborne high spectral resolution lidar.

Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set – DISCOVER-AQ 2011

Abstract. Retrievals of aerosol microphysical properties (effective radius, volume and surface-area concentrations) and aerosol optical properties (complex index of refraction and single-scattering albedo) were obtained from a hybrid multiwavelength lidar data set for the first time. In July 2011, in the Baltimore–Washington DC region, synergistic profiling of optical and microphysical properties of aerosols with both airborne (in situ and remote sensing) and ground-based remote sensing systems was performed during the first deployment of DISCOVER-AQ. The hybrid multiwavelength lidar data set combines ground-based elastic backscatter lidar measurements at 355 nm with airborne High-Spectral-Resolution Lidar (HSRL) measurements at 532 nm and elastic backscatter lidar measurements at 1064 nm that were obtained less than 5 km apart from each other. This was the first study in which optical and microphysical retrievals from lidar were obtained during the day and directly compared to AERONET and in situ measurements for 11 cases. Good agreement was observed between lidar and AERONET retrievals. Larger discrepancies were observed between lidar retrievals and in situ measurements obtained by the aircraft and aerosol hygroscopic effects are believed to be the main factor in such discrepancies.

Stratospheric temperature measurement with scanning Fabry-Perot interferometer for wind retrieval from mobile Rayleigh Doppler lidar.

Temperature detection remains challenging in the low stratosphere, where the Rayleigh integration lidar is perturbed by aerosol contamination and ozone absorption while the rotational Raman lidar is suffered from its low scattering cross section. To correct the impacts of temperature on the Rayleigh Doppler lidar, a high spectral resolution lidar (HSRL) based on cavity scanning Fabry-Perot Interferometer (FPI) is developed. By considering the effect of the laser spectral width, Doppler broadening of the molecular backscatter, divergence of the light beam and mirror defects of the FPI, a well-behaved transmission function is proved to show the principle of HSRL in detail. Analysis of the statistical error of the HSRL is carried out in the data processing. A temperature lidar using both HSRL and Rayleigh integration techniques is incorporated into the Rayleigh Doppler wind lidar. Simultaneous wind and temperature detection is carried out based on the combined system at Delhi (37.371°N, 97.374°E; 2850 m above the sea level) in Qinghai province, China. Lower Stratosphere temperature has been measured using HSRL between 18 and 50 km with temporal resolution of 2000 seconds. The statistical error of the derived temperatures is between 0.2 and 9.2 K. The temperature profile retrieved from the HSRL and wind profile from the Rayleigh Doppler lidar show good agreement with the radiosonde data. Specifically, the max temperature deviation between the HSRL and radiosonde is 4.7 K from 18 km to 36 km, and it is 2.7 K between the HSRL and Rayleigh integration lidar from 27 km to 34 km.

Effects of spectral discrimination in high-spectral-resolution lidar on the retrieval errors for atmospheric aerosol optical properties.

This paper presents detailed analysis about the effects of spectral discrimination on the retrieval errors for atmospheric aerosol optical properties in high-spectral-resolution lidar (HSRL). To the best of our knowledge, this is the first study that focuses on this topic comprehensively, and our goal is to provide some heuristic guidelines for the design of the spectral discrimination filter in HSRL. We first introduce a theoretical model for retrieval error evaluation of an HSRL instrument with a general three-channel configuration. The model only takes the error sources related to the spectral discrimination parameters into account, while other error sources not associated with these focused parameters are excluded on purpose. Monte Carlo (MC) simulations are performed to validate the correctness of the theoretical model. Results from both the model and MC simulations agree very well, and they illustrate one important, although not well realized, fact: a large molecular transmittance and a large spectral discrimination ratio (SDR, i.e., ratio of the molecular transmittance to the aerosol transmittance) are beneficial to promote the retrieval accuracy. More specifically, we find that a large SDR can reduce retrieval errors conspicuously for atmosphere at low altitudes, while its effect on the retrieval for high altitudes is very limited. A large molecular transmittance contributes to good retrieval accuracy everywhere, particularly at high altitudes, where the signal-to-noise ratio is small. Since the molecular transmittance and SDR are often trade-offs, we suggest considering a suitable SDR for higher molecular transmittance instead of using unnecessarily high SDR when designing the spectral discrimination filter. These conclusions are expected to be applicable to most of the HSRL instruments, which have similar configurations as the one discussed here.

Polarization lidar observations of backscatter phase matrices from oriented ice crystals and rain.

Oriented particles can exhibit different polarization properties than randomly oriented particles. These properties cannot be resolved by conventional polarization lidar systems and are capable of corrupting the interpretation of depolarization ratio measurements. Additionally, the typical characteristics of backscatter phase matrices from atmospheric oriented particles are not well established. The National Center for Atmospheric Research High Spectral Resolution Lidar was outfitted in spring of 2012 to measure the backscatter phase matrix, allowing it to fully characterize the polarization properties of oriented particles. The lidar data analyzed here considers operation at 4°, 22° and 32° off zenith in Boulder, CO, USA (40.0°N,105.2°W). The HSRL has primarily observed oriented ice crystal signatures at lidar tilt angles near 32° off zenith which corresponds to an expected peak in backscatter from horizontally oriented plates. The maximum occurrence frequency of oriented ice crystals is measured at 5 km, where 2% of clouds produced significant oriented ice signatures by exhibiting diattenuation in their scattering matrices. The HSRL also observed oriented particle characteristics of rain at all three tilt angles. Oriented signatures in rain are common at all three tilt angles. As many as 70% of all rain observations made at 22° off zenith exhibited oriented signatures. The oriented rain signatures exhibit significant linear diattenuation and retardance.

Comparison of mixed layer heights from airborne high spectral resolution lidar, ground-based measurements, and the WRF-Chem model during CalNex and CARES

Abstract. The California Research at the Nexus of Air Quality and Climate Change (CalNex) and Carbonaceous Aerosol and Radiative Effects Study (CARES) field campaigns during May and June 2010 provided a data set appropriate for studying the structure of the atmospheric boundary layer (BL). The NASA Langley Research Center (LaRC) airborne high spectral resolution lidar (HSRL) was deployed to California onboard the NASA LaRC B-200 aircraft to aid in characterizing aerosol properties during these two field campaigns. Measurements of aerosol extinction (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) profiles during 31 flights, many in coordination with other research aircraft and ground sites, constitute a diverse data set for use in characterizing the spatial and temporal distribution of aerosols, as well as the depth and variability of the daytime mixed layer (ML) height. The paper describes the modified Haar wavelet covariance transform method used to derive the ML heights from HSRL backscatter profiles. HSRL ML heights are validated using ML heights derived from two radiosonde profile sites during CARES. Comparisons between ML heights from HSRL and a Vaisala ceilometer operated during CalNex were used to evaluate the representativeness of a fixed measurement over a larger region. In the Los Angeles basin, comparisons of ML heights derived from HSRL measurements and ML heights derived from the ceilometer result in a very good agreement (mean bias difference of 10 m and correlation coefficient of 0.89) up to 30 km away from the ceilometer site, but are essentially uncorrelated for larger distances, indicating that the spatial variability of the ML height is significant over these distances and not necessarily well captured by limited ground stations. The HSRL ML heights are also used to evaluate the performance in simulating the temporal and spatial variability of ML heights from the Weather Research and Forecasting Chemistry (WRF-Chem) community model. When compared to aerosol ML heights from HSRL, thermodynamic ML heights from WRF-Chem were underpredicted in the CalNex and CARES regions, shown by a bias difference value of −157 m and −29 m, respectively. Better agreement over the Central Valley than in mountainous regions suggests that some variability in the ML height is not well captured at the 4 km grid resolution of the model. A small but significant number of cases have poor agreement when WRF-Chem consistently overestimates the ML height in the late afternoon. Additional comparisons with WRF-Chem aerosol mixed layer heights show no significant improvement over thermodynamic ML heights, confirming that any differences between measurement and model are not due to the methodology of ML height determination.

Influence of local surface albedo variability and ice crystal shape on passive remote sensing of thin cirrus

Abstract. Airborne measurements of solar spectral radiance reflected by cirrus are performed with the HALO-Solar Radiation (HALO-SR) instrument onboard the High Altitude and Long Range Research Aircraft (HALO) in November 2010. The data are used to quantify the influence of surface albedo variability on the retrieval of cirrus optical thickness and crystal effective radius. The applied retrieval of cirrus optical properties is based on a standard two-wavelength approach utilizing measured and simulated reflected radiance in the visible and near-infrared spectral region. Frequency distributions of the surface albedos from Moderate resolution Imaging Spectroradiometer (MODIS) satellite observations are used to compile surface-albedo-dependent lookup tables of reflected radiance. For each assumed surface albedo the cirrus optical thickness and effective crystal radius are retrieved as a function of the assumed surface albedo. The results for the cirrus optical thickness are compared to measurements from the High Spectral Resolution Lidar (HSRL). The uncertainty in cirrus optical thickness due to local variability of surface albedo in the specific case study investigated here is below 0.1 and thus less than that caused by the measurement uncertainty of both instruments. It is concluded that for the retrieval of cirrus optical thickness the surface albedo variability is negligible. However, for the retrieval of crystal effective radius, the surface albedo variability is of major importance, introducing uncertainties up to 50%. Furthermore, the influence of the bidirectional reflectance distribution function (BRDF) on the retrieval of crystal effective radius was investigated and quantified with uncertainties below 10%, which ranges below the uncertainty caused by the surface albedo variability. The comparison with the independent lidar data allowed for investigation of the role of the crystal shape in the retrieval. It is found that if assuming aggregate ice crystals, the HSRL observations fit best with the retrieved optical thickness from HALO-SR.

Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data

Abstract. Knowledge of aerosol type is important for determining the magnitude and assessing the consequences of aerosol radiative forcing, and can provide useful information for source attribution studies. However, atmospheric aerosol is frequently not a single pure type, but instead occurs as a mixture of types, and this mixing affects the optical and radiative properties of the aerosol. This paper extends the work of earlier researchers by using the aerosol intensive parameters measured by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL-1) to develop a comprehensive and unified set of rules for characterizing the external mixing of several key aerosol intensive parameters: extinction-to-backscatter ratio (i.e., lidar ratio), backscatter color ratio, and depolarization ratio. We present the mixing rules in a particularly simple form that leads easily to mixing rules for the covariance matrices that describe aerosol distributions, rather than just single values of measured parameters. These rules can be applied to infer mixing ratios from the lidar-observed aerosol parameters, even for cases without significant depolarization. We demonstrate our technique with measurement curtains from three HSRL-1 flights which exhibit mixing between two aerosol types, urban pollution plus dust, marine plus dust, and smoke plus marine. For these cases, we infer a time-height cross-section of extinction mixing ratio along the flight track, and partition aerosol extinction into portions attributed to the two pure types.

An evaluation of CALIOP/CALIPSO's aerosol‐above‐cloud detection and retrieval capability over North America

Assessing the accuracy of the aerosol‐above‐cloud (AAC) properties derived by CALIOP (the Cloud‐Aerosol Lidar with Orthogonal Polarization) is challenged by the shortage of accurate global validation measurements. We have used measurements of aerosol vertical profiles from the NASA Langley airborne High Spectral Resolution Lidar (HSRL‐1) in 86 CALIOP‐coincident flights to evaluate CALIOP AAC detection, classification, and retrieval. Our study shows that CALIOP detects ~23% of the HSRL‐detected AAC. According to our CALIOP‐HSRL data set, the majority of AAC aerosol optical depth (AOD) values are < 0.1 at 532 nm over North America. Our analyses show that the standard CALIOP retrieval algorithm substantially underestimates the occurrence frequency of AAC when optical depths are less than ~0.02. Those aerosols with low AOD values can still have a consequent radiative forcing effect depending on the underlying cloud cover and overlying aerosol absorption properties. We find essentially no correlation between CALIOP and HSRL AAC AOD (R2 = 0.27 and N = 151). We show that the CALIOP underestimation of AAC is mostly due to tenuous aerosol layers with backscatter less than the CALIOP detection threshold. The application of an alternate CALIOP AAC retrieval method (depolarization ratio) to our data set yields very few coincident cases. We stress the need for more extensive suborbital CALIOP validation campaigns to acquire a process‐level understanding of AAC implications and further evaluate CALIOP's AAC detection and retrieval capability, especially over the ocean and in different parts of the world where AAC are more frequently observed and show higher values of AOD.

基于扫描F-P标准具的高光谱分辨低平流层温度探测

A high spectral resolution lidar(HSRL) for measurement of low-stratosphere temperature by scanning Fabry-Perot interferometer (FPI) is proposed and demonstrated. The transmission of Rayleigh backscatter through the FPI is obtained by scanning the cavity spacing of the FPI, and then fitted to Gaussian function using the nonlinear fitting algorithm. Temperature is calculated from the fitted bandwidth of the measured transmission. To reduce systematic error due to frequency instability of the laser, another solid FPI is incorporated into the optical receiver to monitor the frequency drift, which compensates in the data processing. The statistical error is calculated based on a maximum likelihood estimator, which is less than 1.9 K/9.8 K below 30 km/50 km. In the comparison experiment, the max temperature deviation between the high spectral resolution lidar (HSRL) and radiosonde is 4.7 K from 18 km to 36 km, and it is 2.7 K between the HSRL and Rayleigh integration lidar (RIL) from 27 km to 34 km. The temperature profile from Rayleigh integration lidar deviates from the results from HSRL and radiosonde obviously from 15 km to 27 km, with a max deviation of 22.8 K, which may due to the aerosol contamination.

基于视场展宽迈克尔孙干涉仪的高光谱分辨率激光雷达滤光器设计研究

基于视场展宽迈克尔孙干涉仪的高光谱分辨率激光雷达滤光器设计研究

Interferometric filters for spectral discrimination in high-spectral-resolution lidar: performance comparisons between Fabry–Perot interferometer and field-widened Michelson interferometer

Thanks to wavelength flexibility, interferometric filters such as Fabry-Perot interferometers (FPIs) and field-widened Michelson interferometers (FWMIs) have shown great convenience for spectrally separating the molecule and aerosol scattering components in the high-spectral-resolution lidar (HSRL) return signal. In this paper, performance comparisons between the FPI and FWMI as a spectroscopic discrimination filter in HSRL are performed. We first present a theoretical method for spectral transmission analysis and quantitative evaluation on the spectral discrimination. Then the process in determining the parameters of the FPI and FWMI for the performance comparisons is described. The influences from the incident field of view (FOV), the cumulative wavefront error induced by practical imperfections, and the frequency locking error on the spectral discrimination performance of the two filters are discussed in detail. Quantitative analyses demonstrate that FPI can produce higher transmittance while the remarkable spectral discrimination is one of the most appealing advantages of FWMI. As a result of the field-widened design, the FWMI still performs well even under the illumination with large FOV while the FPI is only qualified for a small incident angle. The cumulative wavefront error attaches a great effect on the spectral discrimination performance of the interferometric filters. We suggest if a cumulative wavefront error is less than 0.05 waves RMS, it is beneficial to employ the FWMI; otherwise, FPI may be more proper. Although the FWMI shows much more sensitivity to the frequency locking error, it can outperform the FPI given a locking error less than 0.1 GHz is achieved. In summary, the FWMI is very competent in HSRL applications if these practical engineering and control problems can be solved, theoretically. Some other estimations neglected in this paper can also be carried out through the analytical method illustrated herein.

Aerosol airmass type mapping over the Urban Mexico City region from space-based multi-angle imaging

Abstract. Using Multi-angle Imaging SpectroRadiometer (MISR) and sub-orbital measurements from the 2006 INTEX-B/MILAGRO field campaign, in this study we explore MISR's ability to map different aerosol air mass types over the Mexico City metropolitan area. The aerosol air mass distinctions are based on shape, size and single scattering albedo retrievals from the MISR Research Aerosol Retrieval algorithm. In this region, the research algorithm identifies dust-dominated aerosol mixtures based on non-spherical particle shape, whereas spherical biomass burning and urban pollution particles are distinguished by particle size. Two distinct aerosol air mass types based on retrieved particle microphysical properties, and four spatially distributed aerosol air masses, are identified in the MISR data on 6 March 2006. The aerosol air mass type identification results are supported by coincident, airborne high-spectral-resolution lidar (HSRL) measurements. Aerosol optical depth (AOD) gradients are also consistent between the MISR and sub-orbital measurements, but particles having single-scattering albedo of ≈0.7 at 558 nm must be included in the retrieval algorithm to produce good absolute AOD comparisons over pollution-dominated aerosol air masses. The MISR standard V22 AOD product, at 17.6 km resolution, captures the observed AOD gradients qualitatively, but retrievals at this coarse spatial scale and with limited spherical absorbing particle options underestimate AOD and do not retrieve particle properties adequately over this complex urban region. However, we demonstrate how AOD and aerosol type mapping can be accomplished with MISR data over complex urban regions, provided the retrieval is performed at sufficiently high spatial resolution, and with a rich enough set of aerosol components and mixtures.

Evaluation of surface and upper air fine scale WRF meteorological modeling of the May and June 2010 CalNex period in California

Prognostic meteorological models such as Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) are often used to supply inputs for retrospective air quality modeling done to support ozone and PM2.5 emission control demonstrations. In this study, multiple configurations of the WRF model are applied at 4 km grid resolution and compared to routine meteorological measurements and special study measurements taken in California during May–June 2010. One configuration is routinely used by US EPA to generate meteorological inputs for regulatory air quality modeling and another that is used by research scientists for evaluating meteorology and air quality. Mixing layer heights estimated from airborne High Spectral Resolution Lidar (HSRL) measurements of aerosol backscatter are compared with WRF modeled planetary boundary layer (PBL) height estimates. Both WRF configurations generally capture the variability in HSRL mixing height between days, hour-to-hour, and between surface features such as terrain and land–water interfaces. Fractional bias over all flights and both model configurations range from −38% to 32% and fractional error ranges from 22% to 58%. Surface and upper level measurements of temperature, water mixing ratio, and winds are generally well characterized by both WRF model configurations, often more closely matching surface observations than the input analysis data (12-NAM). The WRF model generally captures orographic and mesoscale meteorological features in the central Valley (bifurcation of wind flow from the San Francisco bay into the Sacramento and San Joaquin valleys) and Los Angeles air basin (ocean-land flows) during this summer period.

From CloudSat‐CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar‐lidar observations

This paper presents the implementation of a new version of the DARDAR (radar lidar) classification derived from CloudSat and CALIPSO data. The resulting target classification called DARDAR v2 is compared to the first version called DARDAR v1. Overall DARDAR v1 reports more cloud or rain pixels than DARDAR v2. In the low troposphere this is because v1 detects too many liquid cloud pixels, and in the higher troposphere this is because v2 is more restrictive in lidar detection than v1. Nevertheless, the spatial distribution of different types of hydrometeors show similar patterns in both classifications. The French airborne Radar‐Lidar (RALI) platform carries a CloudSat/CALIPSO instrument configuration (lidar at a wavelength of 532nm and a 95GHz cloud radar) as well as an EarthCare instrument configuration (high spectral resolution lidar at 355nm and a 95GHz Doppler cloud radar). It therefore represents an ideal go‐between for A‐Train and EarthCare. The DARDAR v2 classification algorithm is adapted to RALI data for A‐Train overpasses during dedicated airborne field experiments using the lidar at 532nm and the radar Doppler measurements. The results from the RALI classification are compared with the DARDAR v2 classification to identify where the classification should still be interpreted with caution. Finally, the RALI classification algorithm with lidar at 532nm is adapted to RALI with high spectral resolution lidar data at 355nm in preparation for EarthCare.

Spatial and seasonal distribution of Arctic aerosols observed by the CALIOP satellite instrument (2006–2012)

Abstract. We use retrievals of aerosol extinction from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite to examine the vertical, horizontal and temporal variability of tropospheric Arctic aerosols during the period 2006–2012. We develop an empirical method that takes into account the difference in sensitivity between daytime and nighttime retrievals over the Arctic. Comparisons of the retrieved aerosol extinction to in situ measurements at Barrow (Alaska) and Alert (Canada) show that CALIOP reproduces the observed seasonal cycle and magnitude of surface aerosols to within 25 %. In the free troposphere, we find that daytime CALIOP retrievals will only detect the strongest aerosol haze events, as demonstrated by a comparison to aircraft measurements obtained during NASA's ARCTAS mission during April 2008. This leads to a systematic underestimate of the column aerosol optical depth by a factor of 2–10. However, when the CALIOP sensitivity threshold is applied to aircraft observations, we find that CALIOP reproduces in situ observations to within 20% and captures the vertical profile of extinction over the Alaskan Arctic. Comparisons with the ground-based high spectral resolution lidar (HSRL) at Eureka, Canada, show that CALIOP and HSRL capture the evolution of the aerosol backscatter vertical distribution from winter to spring, but a quantitative comparison is inconclusive as the retrieved HSRL backscatter appears to overestimate in situ observations by a factor of 2 at all altitudes. In the High Arctic (>70° N) near the surface (<2 km), CALIOP aerosol extinctions reach a maximum in December–March (10–20 Mm−1), followed by a sharp decline and a minimum in May–September (1–4 Mm−1), thus providing the first pan-Arctic view of Arctic haze seasonality. The European and Asian Arctic sectors display the highest wintertime extinctions, while the Atlantic sector is the cleanest. Over the Low Arctic (60–70° N) near the surface, CALIOP extinctions reach a maximum over land in summer due to boreal forest fires. During summer, we find that smoke aerosols reach higher altitudes (up to 4 km) over eastern Siberia and North America than over northern Eurasia, where they remain mostly confined below 2 km. In the free troposphere, the extinction maximum over the Arctic occurs in March–April at 2–5 km altitude and April–May at 5–8 km. This is consistent with transport from the midlatitudes associated with the annual maximum in cyclonic activity and blocking patterns in the Northern Hemisphere. A strong gradient in aerosol extinction is observed between 60° N and 70° N in the summer. This is likely due to efficient stratocumulus wet scavenging at high latitudes combined with the poleward retreat of the polar front. Interannual variability in the middle and upper troposphere is associated with biomass burning events (high extinctions observed by CALIOP in spring 2008 and summer 2010) and volcanic eruptions (Kasatochi in August 2008 and Sarychev in June 2009). CALIOP displays below-average extinctions observed from August 2009 through May 2010, which appear to be linked with a strongly negative Arctic Oscillation index.

MODIS 3 km aerosol product: applications over land in an urban/suburban region

Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites have provided a rich dataset of aerosol information at a 10 km spatial scale. Although originally intended for climate applications, the air quality community quickly became interested in using the MODIS aerosol data. However, 10 km resolution is not sufficient to resolve local scale aerosol features. With this in mind, MODIS Collection 6 includes a global aerosol product with a 3 km resolution. Here, we evaluate the 3 km product over the Baltimore–Washington D.C., USA, corridor during the summer of 2011 by comparing with spatially dense aerosol data measured by airborne High Spectral Resolution Lidar (HSRL) and a network of 44 sun photometers (SP) spaced approximately 10 km apart, collected as part of the DISCOVER-AQ field campaign. The HSRL instrument shows that AOD can vary by over 0.2 within a single 10 km MODIS pixel, meaning that higher resolution satellite retrievals may help to better characterize aerosol spatial distributions in this region. Different techniques for validating a high-resolution aerosol product against SP measurements are considered. Although the 10 km product is more statistically reliable than the 3 km product, the 3 km product still performs acceptably with nearly two-thirds of MODIS/SP collocations falling within an expected error envelope with high correlation (R > 0.90), although with a high bias of ~ 0.06. The 3 km product can better resolve aerosol gradients and retrieve closer to clouds and shorelines than the 10 km product, but tends to show more noise, especially in urban areas. This urban degradation is quantified using ancillary land cover data. Overall, we show that the MODIS 3 km product adds new information to the existing set of satellite derived aerosol products and validates well over the region, but due to noise and problems in urban areas, should be treated with some degree of caution.