Application of high-dimensional fuzzy <i>k</i>-means cluster analysis to CALIOP/CALIPSO version 4.1 cloud–aerosol discrimination

This study applies fuzzy k-means (FKM) cluster analyses to a subset of the parameters reported in the CALIPSO lidar level 2 data products in order to classify the layers detected as either clouds or aerosols. The results obtained are used to assess the reliability of the cloud–aerosol discrimination (CAD) scores reported in the version 4.1 release of the CALIPSO data products. FKM is an unsupervised learning algorithm, whereas the CALIPSO operational CAD algorithm (COCA) takes a highly supervised approach. Despite these substantial computational and architectural differences, our statistical analyses show that the FKM classifications agree with the COCA classifications for more than 94&thinsp;% of the cases in the troposphere. This high degree of similarity is achieved because the lidar-measured signatures of the majority of the clouds and the aerosols are naturally distinct, and hence objective methods can independently and effectively separate the two classes in most cases. Classification differences most often occur in complex scenes (e.g., evaporating water cloud filaments embedded in dense aerosol) or when observing diffuse features that occur only intermittently (e.g., volcanic ash in the tropical tropopause layer). The two methods examined in this study establish overall classification correctness boundaries due to their differing algorithm uncertainties. In addition to comparing the outputs from the two algorithms, analysis of sampling, data training, performance measurements, fuzzy linear discriminants, defuzzification, error propagation, and key parameters in feature type discrimination with the FKM method are further discussed in order to better understand the utility and limits of the application of clustering algorithms to space lidar measurements. In general, we find that both FKM and COCA classification uncertainties are only minimally affected by noise in the CALIPSO measurements, though both algorithms can be challenged by especially complex scenes containing mixtures of discrete layer types. Our analysis results show that attenuated backscatter and color ratio are the driving factors that separate water clouds from aerosols; backscatter intensity, depolarization, and mid-layer altitude are most useful in discriminating between aerosols and ice clouds; and the joint distribution of backscatter intensity and depolarization ratio is critically important for distinguishing ice clouds from water clouds.</p

Comparison of Two Different Cloud Climatologies Derived from CALIOP-Attenuated Backscattered Measurements (Level 1): The CALIPSO-ST and the CALIPSO-GOCCP

International audienceTwo different cloud climatologies have been derived from the same NASA-Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)-measured attenuated backscattered profile (level 1, version 3 dataset). The first climatology, named Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations-Science Team (CALIPSO-ST), is based on the standard CALIOP cloud mask (level 2 product, version 3), with the aim to document clouds with the highest possible spatiotemporal resolution, taking full advantage of the CALIOP capabilities and sensitivity for a wide range of cloud scientific studies. The second climatology, named GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP), is aimed at a single goal: evaluating GCM prediction of cloudiness. For this specific purpose, it has been designed to be fully consistent with the CALIPSO simulator included in the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) used within version 2 of the CFMIP (CFMIP-2) experiment and phase 5 of the Coupled Model Intercomparison Project (CMIP5). The differences between the two datasets in the global cloud cover maps-total, low level (P > 680 hPa), midlevel (680 < P < 440 hPa), and high level (P < 440 hPa)-are frequently larger than 10% and vary with region. The two climatologies show significant differences in the zonal cloud fraction profile (which differ by a factor of almost 2 in some regions), which are due to the differences in the horizontal and vertical averaging of the measured attenuated backscattered profile CALIOP profile before the cloud detection and to the threshold used to detect clouds (this threshold depends on the resolution and the signal-to-noise ratio)

The global 3-D distribution of tropospheric aerosols as characterized by CALIOP

The CALIOP lidar, carried on the CALIPSO satellite, has been acquiring global atmospheric profiles since June 2006. This dataset now offers the opportunity to characterize the global 3-D distribution of aerosol as well as seasonal and interannual variations, and confront aerosol models with observations in a way that has not been possible before. With that goal in mind, a monthly global gridded dataset of daytime and nighttime aerosol extinction profiles has been constructed, available as a Level 3 aerosol product. Averaged aerosol profiles for cloud-free and all-sky conditions are reported separately. This 6-yr dataset characterizes the global 3-dimensional distribution of tropospheric aerosol. Vertical distributions are seen to vary with season, as both source strengths and transport mechanisms vary. In most regions, clear-sky and all-sky mean aerosol profiles are found to be quite similar, implying a lack of correlation between high semi-transparent cloud and aerosol in the lower troposphere. An initial evaluation of the accuracy of the aerosol extinction profiles is presented. Detection limitations and the representivity of aerosol profiles in the upper troposphere are of particular concern. While results are preliminary, we present evidence that the monthly-mean CALIOP aerosol profiles provide quantitative characterization of elevated aerosol layers in major transport pathways. Aerosol extinction in the free troposphere in clean conditions, where the true aerosol extinction is typically 0.001 km⁻¹ or less, is generally underestimated, however. The work described here forms an initial global 3-D aerosol climatology which we plan to extend and improve over time

CALIPSO lidar calibration at 1064&thinsp;nm: version 4 algorithm

Radiometric calibration of space-based elastic backscatter lidars is accomplished by comparing the measured backscatter signals to theoretically expected signals computed for some well-characterized calibration target. For any given system and wavelength, the choice of calibration target is dictated by several considerations, including signal-to-noise ratio (SNR) and target availability. This paper describes the newly implemented procedures used to calibrate the 1064&thinsp;nm measurements acquired by CALIOP (i.e., the Cloud-Aerosol Lidar with Orthogonal Polarization), the two-wavelength (532 and 1064&thinsp;nm) elastic backscatter lidar currently flying on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission. CALIOP's 532&thinsp;nm channel is accurately calibrated by normalizing the molecular backscatter from the uppermost aerosol-free altitudes of the CALIOP measurement region to molecular model data obtained from NASA's Global Modeling and Assimilation Office. However, because CALIOP's SNR for molecular backscatter measurements is prohibitively lower at 1064&thinsp;nm than at 532&thinsp;nm, the direct high-altitude molecular normalization method is not a viable option at 1064&thinsp;nm. Instead, CALIOP's 1064&thinsp;nm channel is calibrated relative to the 532&thinsp;nm channel using the backscatter from a carefully selected subset of cirrus cloud measurements. In this paper we deliver a full account of the revised 1064&thinsp;nm calibration algorithms implemented for the version 4.1 (V4) release of the CALIPSO lidar data products, with particular emphases on the physical basis for the selection of “calibration quality” cirrus clouds and on the new averaging scheme required to characterize intra-orbit calibration variability. The V4 procedures introduce latitudinally varying changes in the 1064&thinsp;nm calibration coefficients of 25&thinsp;% or more, relative to previous data releases, and are shown to substantially improve the accuracy of the V4 1064&thinsp;nm attenuated backscatter coefficients. By evaluating calibration coefficients derived using both water clouds and ocean surfaces as alternate calibration targets, and through comparisons to independent, collocated measurements made by airborne high spectral resolution lidar, we conclude that the CALIOP V4 1064&thinsp;nm calibration coefficients are accurate to within 3&thinsp;%.</p

CALIOP Calibration: Version 4.0 Algorithm Updates

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

CALIPSO lidar calibration at 532&thinsp;nm: version 4 daytime algorithm

The Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission released version 4.00 of their lidar level 1 data set in April of 2014, and subsequently updated this to version 4.10 in November of 2016. The primary difference in the newly released version 4 (V4) data is a suite of updated calibration coefficients calculated using substantially revised calibration algorithms. This paper describes the revisions to the V4 daytime calibration procedure for the 532&thinsp;nm parallel channel. As in earlier releases, the V4 daytime calibration coefficients are derived by scaling the raw daytime signals to the calibrated nighttime signals acquired within a calibration transfer region, and thus the new V4 daytime calibration benefits from improvements made to the V4 532&thinsp;nm nighttime calibration. The V4 calibration transfer region has been moved upward from the upper troposphere to the more stable lower stratosphere. The identification of clear-air columns by an iterative thresholding scheme, crucial to selecting the observation regions used for calibration, now uses uncalibrated 1064&thinsp;nm data rather than recursively using the calibrated 532&thinsp;nm data, as was done in version 3 (V3). A detailed account of the rationale and methodology for this new calibration approach is provided, along with results demonstrating the improvement of this calibration over the previous version. Extensive validation data acquired by NASA's airborne high spectral resolution lidar (HSRL) shows that during the daytime the average difference between collocated CALIPSO and HSRL measurements of 532&thinsp;nm attenuated backscatter coefficients is reduced from 3.3 % ± 3.1&thinsp;% in V3 to 1.0 % ± 3.5&thinsp;% in V4.</p

Cloud-Aerosol Interactions: Retrieving Aerosol Ångström Exponents from Calipso Measurements of Opaque Water Clouds

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

CALIPSO lidar calibration at 532 nm: version 4 nighttime algorithm

Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4) calibration algorithms for all of the Level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOP's other radiometric calibration procedures – i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime – depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration has been significantly improved by raising the molecular normalization altitude from 30–34 km to the upper possible signal acquisition range of 36–39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio (SNR) at these higher altitudes, the signal is now averaged over a larger number of samples using data from multiple adjacent granules. Additionally, an enhanced strategy for filtering the radiation-induced noise from high-energy particles was adopted. Further, the meteorological model used in the earlier versions has been replaced by the improved Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), model. An aerosol scattering ratio of 1.01 ± 0.01 is now explicitly used for the calibration altitude. These modifications lead to globally revised calibration coefficients which are, on average, 2–3 % lower than in previous data releases. Further, the new calibration procedure is shown to eliminate biases at high altitudes that were present in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) are reduced from 3.6 % ± 2.2 % in the version 3 data set to 1.6 % ± 2.4 % in the version 4 release

Auditory (dis-)fluency triggers sequential processing adjustments

An increasing amount of studies indicates that experiencing increased task demands, triggered for example by conflicting stimulus features or low perceptual fluency, lead to processing adjustments. While these demand triggered processing adjustments have been shown for different paradigms (e.g., response conflict tasks, perceptual disfluency, task switching, dual tasking), most of them are restricted to the visual modality. The present study investigated as to whether the challenge to understand speech signals in normal-hearing subjects would also lead to sequential processing adjustments if the processing fluency of the respective auditory signals changes from trial to trial. To that end, we used spoken number words (one to nine) that were either presented with high (clean speech) or low perceptual fluency (i.e., vocoded speech as used in cochlear implants-Experiment 1; speech embedded in multi-speaker babble noise as typically found in bars-Experiment 2). Participants had to judge the spoken number words as smaller or larger than five. Results show that the fluency effect (performance difference between high and low perceptual fluency) in both experiments was smaller following disfluent words. Thus, if it's hard to understand, you try harder