Source-oriented model for air pollutant effects on visibility

A source-oriented model for air pollutant effects on visibility has been developed that can compute light scattering, light extinction, and estimated visual range directly from data on gas phase and primary particle phase air pollutant emissions from sources. The importance of such a model is that it can be used to compute the effect of emission control proposals on visibility-related parameters in advance of the adoption of such control programs. The model has been assembled by embedding several aerosol process modules within the photochemical trajectory model previously developed for aerosol nitrate concentration predictions by Russell et al. [1983] and Russell and Cass [1986]. These modules describe the size distribution and chemical composition of primary particle emissions, the speciation of organic vapor emissions, atmospheric chemical reactions, transport of condensible material between the gas and the particle phases, fog chemistry, dry deposition, and atmospheric light scattering and light absorption. Model predictions have been compared to observed values using 48-hour trajectories arriving at Claremont, California, at each hour of August 28, 1987, during the Southern California Air Quality Study. The predicted fine particle concentration averages 62 μg m^(−3) compared to an observed value of 61 μg m^(−3), while predicted PM_(10) concentrations average 102 μg m^(−3) compared to an observed average of 97 μg m^(−3). The size distribution and chemical composition predictions for elemental carbon, sulfate, and sodium ion agree with observations to within plus or minus a few micrograms per cubic meter, while ammonium and nitrate concentrations are underpredicted by the base case model by 3 to 7 μg m^(−3) on average. Light-scattering coefficient values are calculated from the predicted aerosol size distribution and refractive index, and the model predictions agree with measured values on average to within 19%. The advantages and limitations of the modeling procedure are discussed

Aerosol optical properties during INDOEX based on measured aerosol particle size and composition

The light scattering and light absorption as a function of wavelength and relative humidity due to aerosols measured at the Kaashidhoo Climate Observatory in the Republic of the Maldives during the INDOEX field campaign has been calculated. Using size-segregated measurements of aerosol chemical composition, calculated light scattering and absorption has been evaluated against measurements of light scattering and absorption. Light scattering coefficients are predicted to within a few percent over relative humidities of 20–90%. Single scattering albedos calculated from the measured elemental carbon size distributions and concentrations in conjunction with other aerosol species have a relative error of 4.0% when compared to measured values. The single scattering albedo for the aerosols measured during INDOEX is both predicted and observed to be about 0.86 at an ambient relative humidity of 80%. These results demonstrate that the light scattering, light absorption, and hence climate forcing due to aerosols over the Indian Ocean are consistent with the chemical and physical properties of the aerosol at that location

Observation of Sulfate Aerosols and SO₂ From the Sarychev Volcanic Eruption Using Data From the Atmospheric Chemistry Experiment (ACE)

[1] Infrared spectra measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) on the SCISAT satellite were used to analyze the Sarychev volcanic aerosol after the eruption in June 2009. Evidence of the Sarychev eruptions was first detected in July 2009 from enhanced SO2 concentrations and atmospheric extinction. By February 2010, the atmosphere had returned to pre-Sarychev conditions. In July 2009, the volcanic plume was found between 8.5 km and 17.5 km in altitude at mid- and high latitudes (55°N–70°N). The first SO2 and sulfate aerosol retrievals carried out using the infrared solar occultation spectra recorded with the ACE-FTS are presented here. The size distribution parameters, the aerosol volume slant column and the composition of the sulfate aerosol were obtained by using a least squares algorithm. The maximum volume slant column of the aerosols was found to be 850 μm3 cm−3 km, which results in an approximate aerosol loading of 3 μm3 cm−3. One month after the eruption, the composition of the aerosols providing the best-fit is a 75% sulfuric acid-water solution with an effective radius (Reff) of 0.1–0.3 μm

Pearl Kendrick, Grace Eldering, and the Pertussis Vaccine

State health department laboratories are crucial to the development of public health research

How bias correction goes wrong: measurement of X_(CO_2) affected by erroneous surface pressure estimates

All measurements of X_(CO_2) from space have systematic errors. To reduce a large fraction of these errors, a bias correction is applied to X_(CO_2) retrieved from GOSAT and OCO-2 spectra using the ACOS retrieval algorithm. The bias correction uses, among other parameters, the surface pressure difference between the retrieval and the meteorological reanalysis. Relative errors in the surface pressure estimates, however, propagate nearly 1:1 into relative errors in bias-corrected X_(CO_2). For OCO-2, small errors in the knowledge of the pointing of the observatory (up to ∼130 arcsec) introduce a bias in X_(CO_2) in regions with rough topography. Erroneous surface pressure estimates are also caused by a coding error in ACOS version 8, sampling meteorological analyses at wrong times (up to 3 h after the overpass time). Here, we derive new geolocations for OCO-2's eight footprints and show how using improved knowledge of surface pressure estimates in the bias correction reduces errors in OCO-2's v9 X_(CO_2) data

The SFXC software correlator for Very Long Baseline Interferometry: Algorithms and Implementation

In this paper a description is given of the SFXC software correlator, developed and maintained at the Joint Institute for VLBI in Europe (JIVE). The software is designed to run on generic Linux-based computing clusters. The correlation algorithm is explained in detail, as are some of the novel modes that software correlation has enabled, such as wide-field VLBI imaging through the use of multiple phase centres and pulsar gating and binning. This is followed by an overview of the software architecture. Finally, the performance of the correlator as a function of number of CPU cores, telescopes and spectral channels is shown.Comment: Accepted by Experimental Astronom

Validation of northern latitude Tropospheric Emission Spectrometer stare ozone profiles with ARC-IONS sondes during ARCTAS: sensitivity, bias and error analysis

We compare Tropospheric Emission Spectrometer (TES) versions 3 and 4, V003 and V004, respectively, nadir-stare ozone profiles with ozonesonde profiles from the Arctic Intensive Ozonesonde Network Study (ARCIONS, http://croc.gsfc.nasa.gov/arcions/ during the Arctic Research on the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field mission. The ozonesonde data are from launches timed to match Aura's overpass, where 11 coincidences spanned 44° N to 71° N from April to July 2008. Using the TES "stare" observation mode, 32 observations are taken over each coincidental ozonesonde launch. By effectively sampling the same air mass 32 times, comparisons are made between the empirically-calculated random errors to the expected random errors from measurement noise, temperature and interfering species, such as water. This study represents the first validation of high latitude (>70°) TES ozone. We find that the calculated errors are consistent with the actual errors with a similar vertical distribution that varies between 5% and 20% for V003 and V004 TES data. In general, TES ozone profiles are positively biased (by less than 15%) from the surface to the upper-troposphere (~1000 to 100 hPa) and negatively biased (by less than 20%) from the upper-troposphere to the lower-stratosphere (100 to 30 hPa) when compared to the ozonesonde data. Lastly, for V003 and V004 TES data between 44° N and 71° N there is variability in the mean biases (from −14 to +15%), mean theoretical errors (from 6 to 13%), and mean random errors (from 9 to 19%)

Relating tropical ocean clouds to moist processes using water vapor isotope measurements

We examine the co-variations of tropospheric water vapor, its isotopic composition and cloud types and relate these distributions to tropospheric mixing and distillation models using satellite observations from the Aura Tropospheric Emission Spectrometer (TES) over the summertime tropical ocean. Interpretation of these process distributions must take into account the sensitivity of the TES isotope and water vapor measurements to variations in cloud, water, and temperature amount. Consequently, comparisons are made between cloud-types based on the International Satellite Cloud Climatology Project (ISSCP) classification; these are clear sky, non-precipitating (e.g., cumulus), boundary layer (e.g., stratocumulus), and precipitating clouds (e.g. regions of deep convection). In general, we find that the free tropospheric vapor over tropical oceans does not strictly follow a Rayleigh model in which air parcels become dry and isotopically depleted through condensation. Instead, mixing processes related to convection as well as subsidence, and re-evaporation of rainfall associated with organized deep convection all play significant roles in controlling the water vapor distribution. The relative role of these moisture processes are examined for different tropical oceanic regions

Alpha, Betti and the Megaparsec Universe: on the Topology of the Cosmic Web

We study the topology of the Megaparsec Cosmic Web in terms of the scale-dependent Betti numbers, which formalize the topological information content of the cosmic mass distribution. While the Betti numbers do not fully quantify topology, they extend the information beyond conventional cosmological studies of topology in terms of genus and Euler characteristic. The richer information content of Betti numbers goes along the availability of fast algorithms to compute them. For continuous density fields, we determine the scale-dependence of Betti numbers by invoking the cosmologically familiar filtration of sublevel or superlevel sets defined by density thresholds. For the discrete galaxy distribution, however, the analysis is based on the alpha shapes of the particles. These simplicial complexes constitute an ordered sequence of nested subsets of the Delaunay tessellation, a filtration defined by the scale parameter, $\alpha$. As they are homotopy equivalent to the sublevel sets of the distance field, they are an excellent tool for assessing the topological structure of a discrete point distribution. In order to develop an intuitive understanding for the behavior of Betti numbers as a function of $\alpha$, and their relation to the morphological patterns in the Cosmic Web, we first study them within the context of simple heuristic Voronoi clustering models. Subsequently, we address the topology of structures emerging in the standard LCDM scenario and in cosmological scenarios with alternative dark energy content. The evolution and scale-dependence of the Betti numbers is shown to reflect the hierarchical evolution of the Cosmic Web and yields a promising measure of cosmological parameters. We also discuss the expected Betti numbers as a function of the density threshold for superlevel sets of a Gaussian random field.Comment: 42 pages, 14 figure

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

The precision of the two-layer cloud height fields derived from the Atmospheric Infrared Sounder (AIRS) is explored and quantified for a five-day set of observations. Coincident profiles of vertical cloud structure by CloudSat, a 94 GHz profiling radar, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), are compared to AIRS for a wide range of cloud types. Bias and variability in cloud height differences are shown to have dependence on cloud type, height, and amount, as well as whether CloudSat or CALIPSO is used as the comparison standard. The CloudSat-AIRS biases and variability range from −4.3 to 0.5±1.2–3.6 km for all cloud types. Likewise, the CALIPSO-AIRS biases range from 0.6–3.0±1.2–3.6 km (−5.8 to −0.2±0.5–2.7 km) for clouds ≥7 km (<7 km). The upper layer of AIRS has the greatest sensitivity to Altocumulus, Altostratus, Cirrus, Cumulonimbus, and Nimbostratus, whereas the lower layer has the greatest sensitivity to Cumulus and Stratocumulus. Although the bias and variability generally decrease with increasing cloud amount, the ability of AIRS to constrain cloud occurrence, height, and amount is demonstrated across all cloud types for many geophysical conditions. In particular, skill is demonstrated for thin Cirrus, as well as some Cumulus and Stratocumulus, cloud types infrared sounders typically struggle to quantify. Furthermore, some improvements in the AIRS Version 5 operational retrieval algorithm are demonstrated. However, limitations in AIRS cloud retrievals are also revealed, including the existence of spurious Cirrus near the tropopause and low cloud layers within Cumulonimbus and Nimbostratus clouds. Likely causes of spurious clouds are identified and the potential for further improvement is discussed