30 research outputs found
Retrieving UVâVis spectral single-scattering albedo of absorbing aerosols above clouds from synergy of ORACLES airborne and A-train sensors
Inadequate knowledge about the complex microphysical and optical processes of the aerosolâcloud system severely restricts our ability to quantify the resultant impact on climate. Contrary to the negative radiative forcing (cooling) exerted by aerosols in cloud-free skies over dark surfaces, the absorbing aerosols, when lofted over the clouds, can potentially lead to significant warming of the atmosphere. The sign and magnitude of the aerosol radiative forcing over clouds are determined mainly by the amount of aerosol loading, the absorption capacity of aerosols or single-scattering albedo (SSA), and the brightness of the underlying cloud cover. In satellite-based algorithms that use measurements from passive sensors, the assumption of aerosol SSA is known to be the largest source of uncertainty in quantifying above-cloud aerosol optical depth (ACAOD). In this paper, we introduce a novel synergy algorithm that combines direct airborne measurements of ACAOD and the top-of-atmosphere (TOA) spectral reflectance from Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors of NASA's A-train satellites to retrieve (1)Â SSA of light-absorbing aerosols lofted over the clouds and (2)Â aerosol-corrected cloud optical depth (COD). Radiative transfer calculations show a marked sensitivity of the TOA measurements to ACAOD, SSA, and COD, further suggesting that the availability of accurate ACAOD allows retrieval of SSA for above-cloud aerosol scenes using the âcolor ratioâ algorithm developed for satellite sensors carrying ultraviolet (UV) and visible-near-IR (VNIR) wavelength bands. The proposed algorithm takes advantage of airborne measurements of ACAOD acquired from the High Spectral Resolution Lidar-2 (HSRL-2) and Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) sun photometer operated during the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) field campaign (September 2016, August 2017, and October 2018) over the southeastern Atlantic Ocean and synergizes them with TOA reflectance from OMI and MODIS to derive spectral SSA in the near-UV (354â388ânm) and VNIR (470â860ânm), respectively. When compared against the ORACLES airborne remote sensing and in situ measurements and the inversion dataset of the ground-based Aerosol Robotic Network (AERONET) over land, the retrieved spectral SSAs from the satellites, on average, were found to be within agreement of âźâ0.01 â the difference well within the uncertainties involved in all these inversion datasets. The retrieved SSA above the clouds at UVâVis-NIR wavelengths shows a distinct increasing trend from August to October, which is consistent with the ORACLES in situ measurements, AERONET inversions, and previous findings. The sensitivity analysis quantifying theoretical uncertainties in the retrieved SSA shows that errors in the measured ACAOD, aerosol layer height, and the ratio of the imaginary part of the refractive index (spectral dependence) of aerosols by 20â%, 1âkm, and 10â%, respectively, produce an error in the retrieved SSA at 388ânm (470ânm) by 0.017 (0.015), 0.008 (0.002), and 0.03 (0.005). The development of the proposed aerosolâcloud algorithm implies a possible synergy of CloudâAerosol Lidar with Orthogonal Polarization (CALIOP) and OMIâMODIS passive sensors to deduce a global product of ACAOD and SSA. Furthermore, the presented synergy algorithm assumes implications for future missions, such as the Atmosphere Observing System (AOS) and the Earth Cloud Aerosol and Radiation Explorer (EarthCARE). The availability of the intended global dataset can help constrain climate models with the much-needed observational estimates of the radiative effects of aerosols in cloudy regions and expand our ability to study aerosol effects on clouds.</p
Clinical aspects of short-chain acyl-CoA dehydrogenase deficiency
Short-chain acyl-CoA dehydrogenase deficiency (SCADD) is an autosomal recessive inborn error of mitochondrial fatty acid oxidation. SCADD is biochemically characterized by increased C4-carnitine in plasma and ethylmalonic acid in urine. The diagnosis of SCADD is confirmed by DNA analysis showing SCAD gene mutations and/or variants. SCAD gene variants are present in homozygous form in approximately 6% of the general population and considered to confer susceptibility to development of clinical disease. Clinically, SCADD generally appears to present early in life and to be most frequently associated with developmental delay, hypotonia, epilepsy, behavioral disorders, and hypoglycemia. However, these symptoms often ameliorate and even disappear spontaneously during follow-up and were found to be unrelated to the SCAD genotype. In addition, in some cases, symptoms initially attributed to SCADD could later be explained by other causes. Finally, SCADD relatives of SCADD patients as well as almost all SCADD individuals diagnosed by neonatal screening remained asymptomatic during follow-up. This potential lack of clinical consequences of SCADD has several implications. First, the diagnosis of SCADD should never preclude extension of the diagnostic workup for other potential causes of the observed symptoms. Second, patients and parents should be clearly informed about the potential lack of relevance of the disorder to avoid unfounded anxiety. Furthermore, to date, SCADD is not an optimal candidate for inclusion in newborn screening programs. More studies are needed to fully establish the relevance of SCADD and solve the question as to whether SCADD is involved in a multifactorial disease or represents a nondisease
An AeroComâAeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation
To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1 degrees x 1 degrees daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach +/- 50 %, although a typical bias would be 15 %-25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1 degrees x 1 degrees grid cells. Up to similar to 50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed
Towards a satellite formaldehyde â in situ hybrid estimate for organic aerosol abundance
Organic aerosol (OA) is one of the main components of the global particulate
burden and intimately links natural and anthropogenic emissions with air
quality and climate. It is challenging to accurately represent OA in global
models. Direct quantification of global OA abundance is not possible with
current remote sensing technology; however, it may be possible to exploit
correlations of OA with remotely observable quantities to infer OA
spatiotemporal distributions. In particular, formaldehyde (HCHO) and OA share
common sources via both primary emissions and secondary production from
oxidation of volatile organic compounds (VOCs). Here, we examine OAâHCHO
correlations using data from summertime airborne campaigns investigating
biogenic (NASA SEAC4RS and DC3), biomass burning (NASA SEAC4RS), and
anthropogenic conditions (NOAA CalNex and NASA KORUS-AQ). In situ OA
correlates well with HCHO (r=0.59â0.97), and the slope and intercept
of this relationship depend on the chemical regime. For biogenic and
anthropogenic regions, the OAâHCHO slopes are higher in low NOx
conditions, because HCHO yields are lower and aerosol yields are likely
higher. The OAâHCHO slope of wildfires is over 9 times higher than that for
biogenic and anthropogenic sources. The OAâHCHO slope is higher for highly
polluted anthropogenic sources (e.g., KORUS-AQ) than less polluted (e.g.,
CalNex) anthropogenic sources. Near-surface OAs over the continental US are
estimated by combining the observed in situ relationships with HCHO column
retrievals from NASA's Ozone Monitoring Instrument (OMI). HCHO vertical
profiles used in OA estimates are from climatology a priori profiles in the
OMI HCHO retrieval or output of specific period from a newer version of
GEOS-Chem. Our OA estimates compare well with US EPA IMPROVE data obtained
over summer months (e.g., slope =0.60â0.62, r=0.56 for August 2013),
with correlation performance comparable to intensively validated GEOS-Chem
(e.g., slope =0.57, r=0.56) with IMPROVE OA and superior to the
satellite-derived total aerosol extinction (r=0.41) with IMPROVE OA. This
indicates that OA estimates are not very sensitive to these HCHO vertical
profiles and that a priori profiles from OMI HCHO retrieval have a similar
performance to that of the newer model version in estimating OA. Improving
the detection limit of satellite HCHO and expanding in situ airborne HCHO and
OA coverage in future missions will improve the quality and spatiotemporal
coverage of our OA estimates, potentially enabling constraints on global OA
distribution.</p
An Advanced Method to Assess the Diet of Free-Ranging Large Carnivores Based on Scats
<div><h3>Background</h3><p>The diet of free-ranging carnivores is an important part of their ecology. It is often determined from prey remains in scats. In many cases, scat analyses are the most efficient method but they require correction for potential biases. When the diet is expressed as proportions of consumed mass of each prey species, the consumed prey mass to excrete one scat needs to be determined and corrected for prey body mass because the proportion of digestible to indigestible matter increases with prey body mass. Prey body mass can be corrected for by conducting feeding experiments using prey of various body masses and fitting a regression between consumed prey mass to excrete one scat and prey body mass (correction factor 1). When the diet is expressed as proportions of consumed individuals of each prey species and includes prey animals not completely consumed, the actual mass of each prey consumed by the carnivore needs to be controlled for (correction factor 2). No previous study controlled for this second bias.</p> <h3>Methodology/Principal Findings</h3><p>Here we use an extended series of feeding experiments on a large carnivore, the cheetah (<em>Acinonyx jubatus</em>), to establish both correction factors. In contrast to previous studies which fitted a linear regression for correction factor 1, we fitted a biologically more meaningful exponential regression model where the consumed prey mass to excrete one scat reaches an asymptote at large prey sizes. Using our protocol, we also derive correction factor 1 and 2 for other carnivore species and apply them to published studies. We show that the new method increases the number and proportion of consumed individuals in the diet for large prey animals compared to the conventional method.</p> <h3>Conclusion/Significance</h3><p>Our results have important implications for the interpretation of scat-based studies in feeding ecology and the resolution of human-wildlife conflicts for the conservation of large carnivores.</p> </div
Structural, Spectroscopic, Thermal studies of Pure and DL-Methionine Doped ADP Crystals
International audienceThe growth of Nonlinear Optical crystals retains great number of attention nowadays. Ammonium Dihydrogen Phosphate (ADP) is an important NLO material used for electro-optical applications and LASER material for Nd: YAG and Nd: YLF etc. Amino acids due to their properties like molecular chirality and zwitter ionic structure attract many researchers to dope them in ADP for the improvement of its properties. The Pure and 0.1wt% DL-Methionine doped ADP crystals were grown using slow solvent evaporation technique at room temperature. The Powder XRD shows single phase nature of doped crystal with slight variation in unit cell parameters. The interaction of DL-Methionine with functional groups of ADP crystal was studied using FT-IR spectroscopy. The TGA curve of pure ADP sample indicates that it remain stable upto 200 o C and then decompose slowly, while the doped sample slowly decomposes right from beginning of the analysis. The DTA curves exhibits endothermic peaks at 209 o C and 212 o C for pure and doped sample, respectively. Introduction. Ammonium dihydrogen phosphate (ADP) is important isomorphs of the Potassium dihydrogen phosphate (KDP) type crystal, which is used for several nonlinear optical applications and higher SHG efficiency of fundamental laser with large NLO coefficients [1-2]. Amino acids possess properties like molecular chirality, absence of strongly conjugated bond and Zwitter ionic nature [3] attracted researcher to dope them in KDP [4] and ADP [5,6] crystals to improve the NLO performance and other properties. DL-Methionine consists of a 4-carbon aliphatic straight chain, the distal end of which is capped by a complex guanidinium group. The conjugation between the double bond and the nitrogen lone pairs, the positive charge is de-localized, enabling the formation of multiple H-bonds. In present context, the authors have doped amino acid DL-Methionine in ADP crystals to investigate its effect on structural, spectroscopic and thermal properties
Comparisons of spectral aerosol single scattering albedo in Seoul, South Korea
Quantifying aerosol absorption at ultraviolet (UV)
wavelengths is important for monitoring air pollution and aerosol amounts
using current (e.g., Aura/OMI) and future (e.g., TROPOMI, TEMPO, GEMS, and
Sentinel-4) satellite measurements. Measurements of column average
atmospheric aerosol single scattering albedo (SSA) are performed on the
ground by the NASA AERONET in the visible (VIS) and near-infrared (NIR)
wavelengths and in the UV-VIS-NIR by the SKYNET networks. Previous
comparison studies have focused on VIS and NIR wavelengths due to the lack
of co-incident measurements of aerosol and gaseous absorption properties in
the UV. This study compares the SKYNET-retrieved SSA in the UV with the SSA
derived from a combination of AERONET, MFRSR, and Pandora (AMP) retrievals
in Seoul, South Korea, in spring and summer 2016. The results show that
the spectrally invariant surface albedo assumed in the SKYNET SSA retrievals
leads to underestimated SSA compared to AMP values at near UV wavelengths.
Re-processed SKYNET inversions using spectrally varying surface albedo,
consistent with the AERONET retrieval improve agreement with AMP SSA. The
combined AMP inversions allow for separating aerosol and gaseous (NO2
and O3) absorption and provide aerosol retrievals from the shortest
UVB (305âŻnm) through VIS to NIR wavelengths (870âŻnm)
An AeroCom/AeroSat study: intercomparison of satellite AODDatasets for Aerosol Model Evaluation
To better understand current uncertainties in the important observational constraint to climate models of AOD
(Aerosol Optical Depth), we evaluate and intercompare fourteen satellite products, representing 9 different retrieval algorithm families using observations from 5 different sensors on 6 different platforms. The satellite products, super-observations
consisting of 1 o Ă 1 o daily aggregated retrievals drawn from the years 2006, 2008 and 2010, are evaluated with AERONET
(AErosol RObotic NETwork) and MAN (Maritime Aerosol Network) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach Âą50%, although a typical bias would be 15â25% (depending on product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that
aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the cells. Up to 50% of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products
shows clear spatial patterns and varies from 10% (parts of the ocean) to 100% (central Asia and Australia). More importantly,
we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This
provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations, and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed