Counseling Arab and Chaldean American Families

The last century has seen an increase in the population of Americans of Arab and Chaldean descent. In recent decades, clinicians have articulated the goal of enhancing their knowledge of cultural diversity for the purpose of improving their appreciation for diversity and the quality of their mental health interventions with diverse populations. However, there is currently little systematic empirical research regarding the counseling of Arab and Chaldean Americans, although awareness of the need for such research among mental health professionals has started to emerge. The purpose of this paper is to provide an integrative review of the values and socio-cultural forces that are relevant to the counseling of this population in North America, and to provide some culturally sensitive recommendations for working with American families of Arab and Chaldean ethnicity. In particular, we propose that effective interventions with clients of Arab and Chaldean ethnic backgrounds will need to be informed by an understanding of the everyday sociopolitical contextual background of target clients and the impact of values and acculturation processes on the family network

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

Spectroscopic Detection of COClF in the Tropical and Mid-Latitude Lower Stratosphere

We report retrievals of COClF (carbonyl chlorofluoride) based on atmospheric chemistry experiment (ACE) solar occultation spectra recorded at tropical and mid-latitudes during 2004-2005. The COClF molecule is a temporary reservoir of both chlorine and fluorine and has not been measured previously by remote sensing. A maximum COClF mixing ratio of 99.7+/-48.0 pptv (10(exp -12) per unit volume, 1 sigma) is measured at 28km for tropical and subtropical occultations (latitudes below 20deg in both hemispheres) with lower mixing ratios at both higher and lower altitudes. Northern hemisphere mid-latitude mixing ratios (30-50degN) resulted in an average profile with a peak mixing ratio of 51.7+/-32.1 pptv, 1 sigma, at 27 km, also decreasing above and below that altitude. We compare the measured average profiles with the one reported set of in situ lower stratospheric mid-latitude measurements from 1986 and 1987, a previous two-dimensional (2-D) model calculation for 1987 and 1993, and a 2-D-model prediction for 2004. The measured average tropical profile is in close agreement with the model prediction; the northern mid-latitude profile is also consistent, although the peak in the measured profile occurs at a higher altitude (2.5-4.5km offset) than in the model prediction. Seasonal average 2-D-model predictions of the COClF stratospheric distribution for 2004 are also reported

Carbon Monitoring System Flux Estimation and Attribution: Impact of ACOS-GOSAT X(CO2) Sampling on the Inference of Terrestrial Biospheric Sources and Sinks

Using an Observing System Simulation Experiment (OSSE), we investigate the impact of JAXA Greenhouse gases Observing SATellite 'IBUKI' (GOSAT) sampling on the estimation of terrestrial biospheric flux with the NASA Carbon Monitoring System Flux (CMS-Flux) estimation and attribution strategy. The simulated observations in the OSSE use the actual column carbon dioxide (X(CO2)) b2.9 retrieval sensitivity and quality control for the year 2010 processed through the Atmospheric CO2 Observations from Space algorithm. CMS-Flux is a variational inversion system that uses the GEOS-Chem forward and adjoint model forced by a suite of observationally constrained fluxes from ocean, land and anthropogenic models. We investigate the impact of GOSAT sampling on flux estimation in two aspects: 1) random error uncertainty reduction and 2) the global and regional bias in posterior flux resulted from the spatiotemporally biased GOSAT sampling. Based on Monte Carlo calculations, we find that global average flux uncertainty reduction ranges from 25% in September to 60% in July. When aggregated to the 11 land regions designated by the phase 3 of the Atmospheric Tracer Transport Model Intercomparison Project, the annual mean uncertainty reduction ranges from 10% over North American boreal to 38% over South American temperate, which is driven by observational coverage and the magnitude of prior flux uncertainty. The uncertainty reduction over the South American tropical region is 30%, even with sparse observation coverage. We show that this reduction results from the large prior flux uncertainty and the impact of non-local observations. Given the assumed prior error statistics, the degree of freedom for signal is approx.1132 for 1-yr of the 74 055 GOSAT X(CO2) observations, which indicates that GOSAT provides approx.1132 independent pieces of information about surface fluxes. We quantify the impact of GOSAT's spatiotemporally sampling on the posterior flux, and find that a 0.7 gigatons of carbon bias in the global annual posterior flux resulted from the seasonally and diurnally biased sampling when using a diagonal prior flux error covariance

Two years of satellite-based carbon dioxide emission quantification at the world's largest coal-fired power plants

Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (&lt;15 %) uncertainties. However, as the constellation of CO2-observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources.</p

Regional uncertainty of GOSAT XCO_2 retrievals in China: quantification and attribution

The regional uncertainty of the column-averaged dry air mole fraction of CO_2 (XCO_2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO_2 within a latitude band of 37–42° N segmented into 8 cells in a grid of 5° from west to east (80–120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO_2 retrievals by quantifying and attributing the consistency of XCO_2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO_2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO_2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO_2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7–1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0–1.6 ppm) with a high-brightness surface from the pairwise comparison results of XCO_2 retrievals. (2) Compared with XCO_2 simulated by GEOS-Chem (GEOS-XCO_2), the XCO_2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO_2. (3) Viewing attributions of XCO_2 in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO_2 emissions, which implies that XCO_2 from satellite observations could be reliably applied in the assessment of atmospheric CO_2 enhancements induced by anthropogenic CO_2 emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo

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

Intelligent pointing increases the fraction of cloud-free CO2 and CH4 observations from space

For most CO2 and CH4 satellites, only a small percentage (∼10%) of observations yield successful retrievals, with the remaining ∼90% rejected, primarily due to the effects of clouds. Discarding this large fraction of data is an inefficient strategy worth reconsidering due to the costs involved in developing, launching and operating the satellites to make these observations. However, if real-time cloud data are available together with pointing capability, cloud data can guide the instrument pointing in an “intelligent pointing” strategy for cloud avoidance. In this work, multiple intelligent pointing simulations were conducted, demonstrating the significant advantages of this approach for satellites in a highly elliptical orbit (HEO), from which nearly the whole Earth disk can be observed. Multiple factors are shown to contribute to intelligent pointing efficiency such as the size and shape (or aspect ratio) of the field of view (FOV). For the current baseline orbit and Imaging Fourier Transform Spectrometer (IFTS) observing characteristics for the proposed Arctic Observing Mission (AOM), the monthly fraction of cloud-free observations is roughly a factor of 2 (ranging from ∼1.5–2.5) more than obtained with standard pointing (in which cloud information is not used). A similar efficiency is expected in a geostationary orbit (GEO) with an IFTS, however, for a dispersive instrument in HEO or GEO, the gain is more modest. This result is primarily attributed to the ∼1:1 aspect ratio of the IFTS FOV, since it is more efficient for cloud avoidance and scanning irregularly-shaped land masses than the long and narrow slit projection of a typical dispersive spectrometer. These results have implications for the design of future CO2 or CH4 monitoring satellites and constellation architectures, as well as other fields of satellite earth observation in which clouds significantly impact observations

Tracking CO2 emission reductions from space: A case study at Europe’s largest fossil fuel power plant

We quantify CO2 emissions from Europe’s largest fossil fuel power plant, the Bełchatόw Power Station in Poland, using CO2 observations from NASA’s Orbiting Carbon Observatory (OCO) 2 and 3 missions on 10 occasions from March 2017 to June 2022. The space-based CO2 emission estimates reveal emission changes with a trend that is consistent with the independent reported hourly power generation trend that results from both permanent and temporary unit shutdowns. OCO-2 and OCO-3 emission estimates agree with the bottom-up emission estimates within their respective 1σ uncertainties for 9 of the 10 occasions. Different methods for defining background values and corresponding uncertainties are explored in order to better understand this important potential error contribution. These results demonstrate the ability of existing space-based CO2 observations to quantify emission reductions for a large facility when adequate coverage and revisits are available. The results are informative for understanding the expected capability and potential limitations of the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) and other future satellites to support monitoring and verification of CO2 emission reductions resulting from climate change mitigation efforts such as the Paris Agreement

Massive stars as thermonuclear reactors and their explosions following core collapse

Nuclear reactions transform atomic nuclei inside stars. This is the process of stellar nucleosynthesis. The basic concepts of determining nuclear reaction rates inside stars are reviewed. How stars manage to burn their fuel so slowly most of the time are also considered. Stellar thermonuclear reactions involving protons in hydrostatic burning are discussed first. Then I discuss triple alpha reactions in the helium burning stage. Carbon and oxygen survive in red giant stars because of the nuclear structure of oxygen and neon. Further nuclear burning of carbon, neon, oxygen and silicon in quiescent conditions are discussed next. In the subsequent core-collapse phase, neutronization due to electron capture from the top of the Fermi sea in a degenerate core takes place. The expected signal of neutrinos from a nearby supernova is calculated. The supernova often explodes inside a dense circumstellar medium, which is established due to the progenitor star losing its outermost envelope in a stellar wind or mass transfer in a binary system. The nature of the circumstellar medium and the ejecta of the supernova and their dynamics are revealed by observations in the optical, IR, radio, and X-ray bands, and I discuss some of these observations and their interpretations.Comment: To be published in " Principles and Perspectives in Cosmochemistry" Lecture Notes on Kodai School on Synthesis of Elements in Stars; ed. by Aruna Goswami & Eswar Reddy, Springer Verlag, 2009. Contains 21 figure