Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS)

Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global atmospheric model predicated on meteorological measurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargue and averaged monthly models, the utility of the GDAS data is shown. (C) 2012 Elsevier B.V. All rights reserved.

Atmospheric pollution: influence on hospital admissions in paediatric rheumatic diseases

Objective: To investigate the lag structure effects from exposure to atmospheric pollution in acute outbursts in hospital admissions of paediatric rheumatic diseases (PRDs). Methods: Morbidity data were obtained from the Brazilian Hospital Information System in seven consecutive years, including admissions due to seven PRDs (juvenile idiopathic arthritis, systemic lupus erythematosus, dermatomyositis, Henoch-Schonlein purpura, polyarteritis nodosa, systemic sclerosis and ankylosing spondylitis). Cases with secondary diagnosis of respiratory diseases were excluded. Daily concentrations of inhaled particulate matter (PM10), sulphur dioxide (SO2) nitrogen dioxide (NO2), ozone (O-3) and carbon monoxide (CO) were evaluated. Generalized linear Poisson regression models controlling for short-term trend, seasonality, holidays, temperature and humidity were used. Lag structures and magnitude of air pollutants' effects were adopted to estimate restricted polynomial distributed lag models. Results: The total number of admissions due to acute outbursts PRD was 1,821. The SO2 interquartile range (7.79 mu g/m(3)) was associated with an increase of 1.98% (confidence interval 0.25-3.69) in the number of hospital admissions due to outcome studied after 14 days of exposure. This effect was maintained until day 17. Of note, the other pollutants, with the exception of O-3, showed an increase in the number of hospital admissions from the second week. Conclusion: This study is the first to demonstrate a delayed association between SO2 and PRD outburst, suggesting that oxidative stress reaction could trigger the inflammation of these diseases. Lupus (2012) 21, 526-533.

Impact of urban atmospheric environment on hospital admissions in the elderly

OBJECTIVE: To analyze the impact of intra-urban atmospheric conditions on circulatory and respiratory diseases in elder adults. METHODS: Cross-sectional study based on data from 33,212 hospital admissions in adults over 60 years in the city of Sao Paulo, southeastern Brazil, from 2003 to 2007. The association between atmospheric variables from Congonhas airport and bioclimatic index, Physiological Equivalent Temperature, was analyzed according to the district's socioenvironmental profile. Descriptive statistical analysis and regression models were used. RESULTS: There was an increase in hospital admissions due to circulatory diseases as average and lowest temperatures decreased. The likelihood of being admitted to the hospital increased by 12% with 1 degrees C decrease in the bioclimatic index and with 1 degrees C increase in the highest temperatures in the group with lower socioenvironmental conditions. The risk of admission due to respiratory diseases increased with inadequate air quality in districts with higher socioenvironmental conditions. CONCLUSIONS: The associations between morbidity and climate variables and the comfort index varied in different groups and diseases. Lower and higher temperatures increased the risk of hospital admission in the elderly. Districts with lower socioenvironmental conditions showed greater adverse health impacts.

Methane airborne measurements and comparison to global models during BARCA

Tropical regions, especially the Amazon region, account for large emissions of methane (CH4). Here, we present CH4 observations from two airborne campaigns conducted within the BARCA (Balanco Atmosferico Regional de Carbono na Amazonia) project in the Amazon basin in November 2008 (end of the dry season) and May 2009 (end of the wet season). We performed continuous measurements of CH4 onboard an aircraft for the first time in the Amazon region, covering the whole Amazon basin with over 150 vertical profiles between altitudes of 500 m and 4000 m. The observations support the finding of previous ground-based, airborne, and satellite measurements that the Amazon basin is a large source of atmospheric CH4. Isotope analysis verified that the majority of emissions can be attributed to CH4 emissions from wetlands, while urban CH4 emissions could be also traced back to biogenic origin. A comparison of five TM5 based global CH4 inversions with the observations clearly indicates that the inversions using SCIAMACHY observations represent the BARCA observations best. The calculated CH4 flux estimate obtained from the mismatch between observations and TM5-modeled CH4 fields ranges from 36 to 43 mg m(-2) d(-1) for the Amazon lowland region.

Analysis of the impact of rainfall assimilation during LBA atmospheric mesoscale missions in Southwest Amazon

The assimilation of satellite estimated precipitation data can be used as an efficient tool to improve the analysis of rainfall generated by numerical models of weather forecast. The system of data assimilation used in this study is cumulus parameterization inversion based on the Kuo scheme. Reanalysis were performed using the field experiment data of the LBA Project (WETAMC and DRYtoWET-AMC), where it was possible to verify an improvement in the simulations results, since the data assimilation corrects the position and the intensity of rainfall in the numerical model. (C) 2012 Elsevier B.V. All rights reserved.

Validation and comparison of two soil-vegetation-atmosphere transfer models for tropical Africa

This study aims to compare and validate two soil-vegetation-atmosphere-transfer (SVAT) schemes: TERRA-ML and the Community Land Model (CLM). Both SVAT schemes are run in standalone mode (decoupled from an atmospheric model) and forced with meteorological in-situ measurements obtained at several tropical African sites. Model performance is quantified by comparing simulated sensible and latent heat fluxes with eddy-covariance measurements. Our analysis indicates that the Community Land Model corresponds more closely to the micrometeorological observations, reflecting the advantages of the higher model complexity and physical realism. Deficiencies in TERRA-ML are addressed and its performance is improved: (1) adjusting input data (root depth) to region-specific values (tropical evergreen forest) resolves dry-season underestimation of evapotranspiration; (2) adjusting the leaf area index and albedo (depending on hard-coded model constants) resolves overestimations of both latent and sensible heat fluxes; and (3) an unrealistic flux partitioning caused by overestimated superficial water contents is reduced by adjusting the hydraulic conductivity parameterization. CLM is by default more versatile in its global application on different vegetation types and climates. On the other hand, with its lower degree of complexity, TERRA-ML is much less computationally demanding, which leads to faster calculation times in a coupled climate simulation.

Impact of urban atmospheric environment on hospital admissions in the elderly

OBJECTIVE: To analyze the impact of intra-urban atmospheric conditions on circulatory and respiratory diseases in elder adults. METHODS: Cross-sectional study based on data from 33,212 hospital admissions in adults over 60 years in the city of São Paulo, southeastern Brazil, from 2003 to 2007. The association between atmospheric variables from Congonhas airport and bioclimatic index, Physiological Equivalent Temperature, was analyzed according to the district's socioenvironmental profile. Descriptive statistical analysis and regression models were used. RESULTS: There was an increase in hospital admissions due to circulatory diseases as average and lowest temperatures decreased. The likelihood of being admitted to the hospital increased by 12% with 1ºC decrease in the bioclimatic index and with 1ºC increase in the highest temperatures in the group with lower socioenvironmental conditions. The risk of admission due to respiratory diseases increased with inadequate air quality in districts with higher socioenvironmental conditions. CONCLUSIONS: The associations between morbidity and climate variables and the comfort index varied in different groups and diseases. Lower and higher temperatures increased the risk of hospital admission in the elderly. Districts with lower socioenvironmental conditions showed greater adverse health impacts.