Est-ce qu'une forme presque sphérique est "LE NOUVEAU NOIR" pour la fumée?

International audienceWe present smoke lidar measurements from the Canadian fires of 2017. The advected smoke layers over Europe are detected at both tropospheric and stratospheric heights, with the latter presenting non-typical values of the Linear Particle Depolarization Ratio (LPDR) with strong wavelength dependence from the UV to the Near-IR. Specifically, the LPDR values are of the order of 22, 18 and 4% at 355, 532 and 1064 nm respectively. In an attempt to interpret these results, we apply the hypothesis that smoke particles have near-spherical and/or more complicated shapes. Scattering calculations with the T-matrix code revealed that the near-spherical shape is able to reproduce the observed LPDR and LR values of the stratospheric smoke particles at the three measurement wavelengths.Nous présentons les mesures du lidar de fumée des incendies canadiens de 2017. Les couches de fumée advectées sur l'Europe sont détectées à la fois à des hauteurs troposphériques et stratosphériques, ces dernières présentant des valeurs atypiques du rapport de dépolarisation linéaire des particules (LPDR) avec une forte dépendance de longueur d'onde de l'UV au proche IR. Plus précisément, les valeurs LPDR sont de l'ordre de 22, 18 et 4% à 355, 532 et 1064 nm respectivement. Pour tenter d'interpréter ces résultats, nous appliquons l'hypothèse que les particules de fumée ont des formes quasi sphériques et/ou plus complexes. Les calculs de diffusion avec le code de la matrice T ont révélé que la forme quasi-sphérique est capable de reproduire les valeurs LPDR et LR observées des particules de fumée stratosphériques aux trois longueurs d'onde de mesure

Image segmentation with implicit color standardization using spatially constrained expectation maximization: Detection of nuclei

Abstract. Color nonstandardness -the propensity for similar objects to exhibit different color properties across images -poses a significant problem in the computerized analysis of histopathology. Though many papers propose means for improving color constancy, the vast majority assume image formation via reflective light instead of light transmission as in microscopy, and thus are inappropriate for histological analysis. Previously, we presented a novel Bayesian color segmentation algorithm for histological images that is highly robust to color nonstandardness; this algorithm employed the expectation maximization (EM) algorithm to dynamically estimate -for each individual image -the probability density functions that describe the colors of salient objects. However, our approach, like most EM-based algorithms, ignored important spatial constraints, such as those modeled by Markov random field (MRFs). Addressing this deficiency, we now present spatially-constrained EM (SCEM), a novel approach for incorporating Markov priors into the EM framework. With respect to our segmentation system, we replace EM with SCEM and then assess its improved ability to segment nuclei in H&E stained histopathology. Segmentation performance is evaluated over seven (nearly) identical sections of gastrointestinal tissue stained using different protocols (simulating severe color nonstandardness). Over this dataset, our system identifies nuclear regions with an area under the receiver operator characteristic curve (AUC) of 0.838. If we disregard spatial constraints, the AUC drops to 0.748

Validation of OMI erythemal doses with multi-sensor ground-based measurements in Thessaloniki, Greece

The aim of this study is to validate the Ozone Monitoring Instrument (OMI) erythemal dose rates using ground-based measurements in Thessaloniki, Greece. In the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki, a Yankee Environmental System UVB-1 radiometer measures the erythemal dose rates every minute, and a Norsk Institutt for Luftforskning (NILU) multi-filter radiometer provides multi-filter based irradiances that were used to derive erythemal dose rates for the period 2005–2014. Both these datasets were independently validated against collocated UV irradiance spectra from a Brewer MkIII spectrophotometer. Cloud detection was performed based on measurements of the global horizontal radiation from a Kipp & Zonen pyranometer and from NILU measurements in the visible range. The satellite versus ground observation validation was performed taking into account the effect of temporal averaging, limitations related to OMI quality control criteria, cloud conditions, the solar zenith angle and atmospheric aerosol loading. Aerosol optical depth was also retrieved using a collocated CIMEL sunphotometer in order to assess its impact on the comparisons. The effect of total ozone columns satellite versus ground-based differences on the erythemal dose comparisons was also investigated. Since most of the public awareness alerts are based on UV Index (UVI) classifications, an analysis and assessment of OMI capability for retrieving UVIs was also performed. An overestimation of the OMI erythemal product by 3–6% and 4–8% with respect to ground measurements is observed when examining overpass and noontime estimates respectively. The comparisons revealed a relatively small solar zenith angle dependence, with the OMI data showing a slight dependence on aerosol load, especially at high aerosol optical depth values. A mean underestimation of 2% in OMI total ozone columns under cloud-free conditions was found to lead to an overestimation in OMI erythemal doses of 1–5%.While OMI overestimated the erythemal dose rates over the range of cloudiness conditions examined, its UVIs were found to be reliable for the purpose of characterising the ambient UV radiation impact

An EARLINET early warning system for atmospheric aerosol aviation hazards

A stand-alone lidar-based method for detecting airborne hazards for aviation in near real time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Research Lidar Network (EARLINET) delivers high-resolution preprocessed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally high resolution and thus provide the basis of the NRT early warning system (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna, Italy, in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network's preparedness to offer insight into natural hazards that affect the aviation sector. © 2020 Author(s)

An EARLINET early warning system for atmospheric aerosol aviation hazards

A stand-alone lidar-based method for detecting airborne hazards for aviation in near real time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Research Lidar Network (EARLINET) delivers high-resolution preprocessed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally high resolution and thus provide the basis of the NRT early warning system (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna, Italy, in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network’s preparedness to offer insight into natural hazards that affect the aviation sector.ACTRIS-2 654109ACTRIS preparatory phase 739530EUNADICS-AV 723986E-shape (EuroGEOSS Showcases: Applications Powered by Europe) 820852Ministry of Research and Innovation, Ontario 19PFE/17.10.2018Romanian National Core Program 18N/2019European Commission European Commission Joint Research Centre 72569

Validation of the TROPOMI/S5P aerosol layer height using EARLINET lidars

The purpose of this study is to investigate the ability of the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) to derive accurate geometrical features of lofted aerosol layers, selecting the Mediterranean Basin as the study area. Comparisons with ground-based correlative measurements constitute a key component in the validation of passive and active satellite aerosol products. For this purpose, we use ground-based observations from quality-controlled lidar stations reporting to the European Aerosol Research Lidar Network (EARLINET). An optimal methodology for validation purposes has been developed and applied using the EARLINET optical profiles and TROPOMI aerosol products, aiming at the in-depth evaluation of the TROPOMI aerosol layer height (ALH) product for the period 2018 to 2022 over the Mediterranean Basin. Seven EARLINET stations were chosen, taking into consideration their proximity to the sea, which provided 63 coincident aerosol cases for the satellite retrievals. In the following, we present the first validation results for the TROPOMI/S5P ALH using the optimized EARLINET lidar products employing the automated validation chain designed for this purpose. The quantitative validation at pixels over the selected EARLINET stations illustrates that the TROPOMI ALH product is consistent with the EARLINET lidar products, with a high correlation coefficient R=0.82 (R=0.51) and a mean bias of -0.51±0.77 km and -2.27±1.17 km over ocean and land, respectively. Overall, it appears that aerosol layer altitudes retrieved from TROPOMI are systematically lower than altitudes from the lidar retrievals. High-albedo scenes, as well as low-aerosol-load scenes, are the most challenging for the TROPOMI retrieval algorithm, and these results testify to the need to further investigate the underlying cause. This work provides a clear indication that the TROPOMI ALH product can under certain conditions achieve the required threshold accuracy and precision requirements of 1 km, especially when only ocean pixels are included in the comparison analysis. Furthermore, we describe and analyse three case studies in detail, one dust and two smoke episodes, in order to illustrate the strengths and limitations of the TROPOMI ALH product and demonstrate the presented validation methodology. The present analysis provides important additions to the existing validation studies that have been performed so far for the TROPOMI S5P ALH product, which were based only on satellite-to-satellite comparisons.</p

Representativeness of aerosol measurements: EARLINET-CALIPSO correlative study

The high variability of tropospheric aerosols, both in space and time, is the main cause of the high uncertainty about radiative forcing related to tropospheric aerosols and their interaction with clouds. Because of the lack of high resolution aerosol global vertical profiles, the vertical mixing has not been considered so far in studies of spatial and temporal variability. The CALIPSO mission provides the first opportunity to investigate the 4-D aerosol and cloud fields in detail. However, because of the CALIOP small footprint and the revisit time of 16 days, correlative ground-based lidar observations are necessary in order to investigate the representativeness of these satellite observations. EARLINET, the European Aerosol Research Lidar Network, started correlative measurements for CALIPSO in June 2006, right after the CALIPSO launch. An integrated study of CALIPSO and EARLINET correlative measurements opens new possibilities for spatial (both horizontal and vertical) and temporal representativeness investigation of polar-orbit satellite measurements also in terms of revisit time.Postprint (published version

Long-term aerosol and cloud database from correlative EARLINET-CALIPSO observations

The European Aerosol Research Lidar Network, EARLINET, performs correlative observations during CALIPSO overpasses based on a sophisticated measurement strategy since June 2006. Within a dedicated activity supported by the European Space Agency (ESA), sixteen EARLINET stations contributed about 1500 measurements during an intensive observational period from May 2008 to October 2009. From these measurements, we establish a long-term aerosol and cloud database of correlative EARLINET-CALIPSO observations. This database shall provide a basis for homogenizing long-term space-borne observations conducted with different lidar instruments operating at different wavelengths on various platforms over the next decade(s). The database is also used to study the quality and representativeness of satellite lidar cross sections along an orbit against long-term lidar network observations on a continental scale.Postprint (published version

Automated vector selection of SIVQ and parallel computing integration MATLAB™: Innovations supporting large-scale and high-throughput image analysis studies

Introduction: Spatially invariant vector quantization (SIVQ) is a texture and color-based image matching algorithm that queries the image space through the use of ring vectors. In prior studies, the selection of one or more optimal vectors for a particular feature of interest required a manual process, with the user initially stochastically selecting candidate vectors and subsequently testing them upon other regions of the image to verify the vector′s sensitivity and specificity properties (typically by reviewing a resultant heat map). In carrying out the prior efforts, the SIVQ algorithm was noted to exhibit highly scalable computational properties, where each region of analysis can take place independently of others, making a compelling case for the exploration of its deployment on high-throughput computing platforms, with the hypothesis that such an exercise will result in performance gains that scale linearly with increasing processor count. Methods: An automated process was developed for the selection of optimal ring vectors to serve as the predicate matching operator in defining histopathological features of interest. Briefly, candidate vectors were generated from every possible coordinate origin within a user-defined vector selection area (VSA) and subsequently compared against user-identified positive and negative "ground truth" regions on the same image. Each vector from the VSA was assessed for its goodness-of-fit to both the positive and negative areas via the use of the receiver operating characteristic (ROC) transfer function, with each assessment resulting in an associated area-under-the-curve (AUC) figure of merit. Results: Use of the above-mentioned automated vector selection process was demonstrated in two cases of use: First, to identify malignant colonic epithelium, and second, to identify soft tissue sarcoma. For both examples, a very satisfactory optimized vector was identified, as defined by the AUC metric. Finally, as an additional effort directed towards attaining high-throughput capability for the SIVQ algorithm, we demonstrated the successful incorporation of it with the MATrix LABoratory (MATLAB TM ) application interface. Conclusion: The SIVQ algorithm is suitable for automated vector selection settings and high throughput computation