{"title":"Variability of Extreme Temperature in the Arctic Observation and RCM","authors":"H. Matthes, A. Rinke, K. Dethloff","doi":"10.2174/1874282301004010126","DOIUrl":"https://doi.org/10.2174/1874282301004010126","url":null,"abstract":"This paper discusses results of a simulation with the regional climate model HIRHAM for 1958-2001, driven by the ECMWF reanalysis (ERA40) data over the Arctic domain. The aim is to analyze the ability of the model to capture certain features of climate extremes derived from daily mean, maximum and minimum temperatures. For this purpose, a range of climate indices (frost days, cold and warm spell days, growing degree days and growing season length) was calculated from the model output as well as from ERA40 data and region-specific station data for Eastern and Western Russian Arctic for comparison. It is demonstrated that the model captures the main features in the spatial distribution and temporal development of most indices well. Though systematic deviations in the seasonal means occur in various indices (frost days, growing degree days), variability and trends are well reproduced. Seasonal mean patterns in frost days are reproduced best, though the model persistently calculates too many frost days. Seasonal means of cold and warm spell days are reproduced without systematic biases, though deviations occur in summer for cold spells and in spring and summer for warm spells due to an early spring warming in the regional climate model and a low variability of the daily maximum temperature over sea ice.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113993881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiying Chen, Xin‐Zhong Liang, Min Xu, Tiejun Ling, J. Wang
{"title":"Improvement of Cloud Radiative Forcing and Its Impact on Weather Forecasts","authors":"Qiying Chen, Xin‐Zhong Liang, Min Xu, Tiejun Ling, J. Wang","doi":"10.2174/1874282301307010001","DOIUrl":"https://doi.org/10.2174/1874282301307010001","url":null,"abstract":"The global numerical weather prediction model GRAPES at the National Meteorological Center of the China Meteorological Administration is subject to substantial systematic discrepancies from satellite-retrieved cloud cover, cloud water contents, and radiative fluxes. In particular, GRAPES produces insufficient total cloud cover and liquid water amounts and, consequently, greatly underestimates cloud radiative forcings and causes substantial radiation budget errors. Along with updates of several physics components, new parameterization schemes are incorporated in this study to more realistically represent cloud-radiation interactions. These schemes include predictions for cloud cover, liquid water, and effective radius as well as radiative effects of partial clouds and in-cloud inhomogeneity. As a result, radiation fluxes and cloud radiative forcings at both the surface and top of the atmosphere agree much better with the best available satellite data. The global mean model biases in most radiation fluxes using the new physics are approximately three times smaller than using the original physics. These improvements enhance the model weather forecast skills for key surface variables, including precipitation and 2 m temperature, and for height and temperature in the lower troposphere. Although non- trivial biases still exist, this study nonetheless represents the first essential step toward correcting the radiation imbalance before tackling other formulation deficiencies so that significantly enhanced GRAPES weather forecast skills can eventually be achieved.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128861020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The First Decade of the New Century: A Cooling Trend for Most of Alaska","authors":"G. Wendler, Liangbiao Chen, B. Moore","doi":"10.2174/1874282301206010111","DOIUrl":"https://doi.org/10.2174/1874282301206010111","url":null,"abstract":"During the first decade of the 21st century most of Alaska experienced a cooling shift, modifying the long-term warming trend, which has been about twice the global change up to this time. All of Alaska cooled with the exception of Northern Regions. This trend was caused by a change in sign of the Pacific Decadal Oscillation (PDO), which became dominantly negative, weakening the Aleutian Low. This weakening results in less relatively warm air being advected from the Northern Pacific. This transport is especially important in winter when the solar radiation is weak. It is during this period that the strongest cooling was observed. In addition, the cooling was especially pronounced in Western Alaska, closest to the area of the center of the Aleutian Low. The changes seen in the reanalyzed data were confirmed from surface observations, both in the decrease of the North-South atmospheric pressure gradient, as well as the decrease in the mean wind speeds for stations located in the Bering Sea area.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128905159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of WRF-Forecasts Over Siberia: Air Mass Formation, Clouds and Precipitation","authors":"D. Paimazumder, D. Henderson, N. Mölders","doi":"10.2174/1874282301206010093","DOIUrl":"https://doi.org/10.2174/1874282301206010093","url":null,"abstract":"The Weather Research and Forecasting (WRF) model was run as a regional model without data assimilation or nudging (31 36h-simulations) for July and December 2005 over a limited area domain covering Siberia to examine weather formation in an air-mass source region. The WRF-results were compared to NCEP1/NCAR-reanalysis, International Satellite Cloud Climatology Project, Global Precipitation Climatology Centre and Canadian Meteorological Centre data to assess model performance and identify shortcomings. WRF is capable of predicting air-mass formation. Simulation errors are within the error range of other models. The timing of best/worst agreement differs among quantities depending on their sensitivity to systematic (model deficiencies) and/or unsystematic errors (e.g. initial conditions). Overall, the WRF-results agree better with reanalysis for July than December. WRF-results and reanalysis agree best under persistent high pressure and worst during frontal passages and transition from one pressure regime to another. In July, WRF provides smaller diurnal amplitudes of 2m-temperature with up to 5.4 K lower, and 3.5 K higher values at 0000 and 1200 UTC than the reanalysis. In December, WRF overestimates 2m-temperature by 1.4 K. WRF-temperatures excellently agree with the reanalysis from 700 hPa to 300 hPa. Except during frontal passages, wind-speed shows positive bias. Typically root-mean-square errors and standard deviation of errors of wind-speed (temperature) increase (decrease) with height. In December, WRF has difficulty predicting the position and strength of the polar jet. WRF underestimates cloudiness and snow-depth, but overestimates precipitation. In July, predicted convective precipitation is related strongly to boundaries between different land-cover. WRF-predicted snow-depth strongly correlates with terrain and misses the observed fine features.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126188402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spatial Variation in Personal Exposure of Parking Attendants to Traffic Emissions in an Urban Conurbation","authors":"A. Tiwary","doi":"10.2174/1874282301206010078","DOIUrl":"https://doi.org/10.2174/1874282301206010078","url":null,"abstract":"This study presents temporal and spatial variations in personal exposure of parking attendants in a busy conurbation in the northern part of the UK. Two traffic related pollutants - carbon monoxide (CO) and ultrafine particulates (UFP), mainly associated with urban drives, have been considered for two distinct locations- one, in the city centre and the other in a suburban area of Leeds, a prominent city in West Yorkshire. The monitoring of pollutants was conducted while parking attendants carried out their duty along the streets during different times of the year to capture the seasonal fluctuations. \u0000 \u0000Our results show a wide variation in exposure levels for both CO and UFP, marked both by seasonal and daily characteristics. There seems to be considerable variations in exposure levels depending on the location of the parking attendants with respect to traffic activity. Specifically, the level of exposure closer to market areas within the city centre, despite located in open spaces but closer to congested streets were found to be much higher owing to frequent stopping, stopping and idling of cars in search of parking spaces. This demonstrates the merit in setting up transport interchanges and park-and-ride schemes away from busy streets to ameliorate the exposure levels.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116536051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Editorial - Urban Air Pollution Exposure – Measurement, Modeling and Assessment","authors":"S. Gokhale","doi":"10.2174/1874282301206010061","DOIUrl":"https://doi.org/10.2174/1874282301206010061","url":null,"abstract":"Exposure to air pollution in city centers is of major concern because of its health-risks to urban dwellers, trafficcorridor users and even to in-transit commuters. The steady growth in traffic is the major source of toxic air pollutants in urban centers these days. Therefore, studying exposure to air pollutants in urban centers is of special interests to environmental health scientists. This special issue is aimed to bring together studies on current practices of measurement and quantification of exposure levels and assessment of the associated risks to humans. The receptor-oriented approach, in which exposure is assessed at the receptor, is of particular importance compare with the conventional source-oriented approach. The articles of this issue engulf the four important aspects of this important topic.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130903793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Particulate Air Pollution and Daily Mortality in Kathmandu Valley, Nepal: Associations and Distributed Lag","authors":"S. Shrestha","doi":"10.2174/1874282301206010062","DOIUrl":"https://doi.org/10.2174/1874282301206010062","url":null,"abstract":"The distributed lag effect of ambient particulate air pollution that can be attributed to all cause mortality in Kathmandu valley, Nepal is estimated through generalized linear model (GLM) and generalized additive model (GAM) with autoregressive count dependent variable. Models are based upon daily time series data on mortality collected from the leading hospitals and exposure collected from the 6 six strategically dispersed fixed stations within the valley. The distributed lag effect is estimated by assigning appropriate weights governed by a mathematical model in which weights increased initially and decreased later forming a long tail. A comparative assessment revealed that autoregressive semi- parametric GAM is a better fit compared to autoregressive GLM. Model fitting with autoregressive semi-parametric GAM showed that a 10 μg m -3 rise in PM10 is associated with 2.57 % increase in all cause mortality accounted for 20 days lag effect which is about 2.3 times higher than observed for one day lag and demonstrates the existence of extended lag effect of ambient PM10 on all cause deaths. The confounding variables included in the model were parametric effects of seasonal differences measured by Fourier series terms, lag effect of mortality, and nonparametric effect of temperature represented by loess smoothing. The lag effects of ambient PM10 remained constant beyond 20 days.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132221814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Assessment of Two-Wheeler CO and PM10 Exposures Along ArterialMain Roads in Bangalore City, India","authors":"Ashwin Sabapathy, K. Ragavan, S. Saksena","doi":"10.2174/1874282301206010071","DOIUrl":"https://doi.org/10.2174/1874282301206010071","url":null,"abstract":"This study investigated 2-wheeler exposures to CO and PM10 along six standardized arterial main road stretches in Bangalore city in India during morning peak (9:00-11:00) and afternoon non-peak hours (13:00-15:00) using personal samplers. Background levels on a local street carrying no traffic and away from main roads were also monitored to determine the actual contributions of vehicular traffic to exposure. Road stretches were selected to compare exposures on two types of routes - inner arterials and outer arterials with different built form characteristics. Results indicate that average background PM10 and CO concentrations were much lower than the respective averages of the 2-wheeler exposures as expected. While PM10 exposures for inner arterials were higher than for outer arterials (p=0.007), differences were much larger for CO (p<0.001). Since the average run speeds were comparable for the stretches, the variations in PM10 and CO could be attributed to different vehicular compositions and built form characteristics of the stretches, but this needs to be verified through further investigation. PM10 exposures during non- peaks were lower than during morning peaks (p=0.02). However, CO exposures were not very different between non-peak hours and morning peak hours (p=0.138) despite comparable average run speeds and shows that even lower traffic volumes during non-peak hours result in high exposures. Results of various bivariate models indicate that average run speed is a good predictor of CO exposures (R 2 =0.56) but is only a minor predictor of PM10 exposures (R 2 =0.18) in Bangalore.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131089758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Personal Exposure to Air Pollution for Various Modes of Transport in Auckland, New Zealand","authors":"K. Dirks, P. Sharma, J. Salmond, S. Costello","doi":"10.2174/1874282301206010084","DOIUrl":"https://doi.org/10.2174/1874282301206010084","url":null,"abstract":"This paper investigates the carbon monoxide (CO) doses received while commuting by different modes (car, bus, train, motorcycle, bicycle and running), taking into account the commute time as well as the level of physical activity required. While the participants were constrained to travel at specific peak traffic times and between designated start and end points, they were free to choose a route appropriate for their mode of transport. The results of this study suggest that the lowest exposures (concentrations of pollutants) are experienced by train commuters, largely a reflection of the routes being removed from any significant road traffic. Motorcyclists experienced significantly higher average concentrations as a result of high-concentration and very-short-duration peaks not seen in the traces of car and bus commuters travelling on the same road. Travel by bus along a dedicated busway was also found to be effective in reducing commuter air pollution exposure compared to travel by car on a congested stretch of motorway. The average concentrations to which cyclists and runners were exposed were found to be not significantly different for those travelling by car or bus (except when on dedicated pedestrian/cycleways). However, when the increased physical activity that is required is taken into account (leading to higher volumes of air breathed) along with the increased commuting time (especially in the case of runners), the air pollution doses (as estimated by the product of the concentration, commute time and breathing factor) were found to be significantly higher than for the motorised modes. The results suggest that separate pedestrian/cycleways go some way towards providing healthier options for cyclists and pedestrians.","PeriodicalId":122982,"journal":{"name":"The Open Atmospheric Science Journal","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120955536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}