Showing papers in "Atmosphere in 2016"
TL;DR: The Holiday Climate Index (HCI) as mentioned in this paper was developed to more accurately assess the suitability of destinations for leisure tourism, and its variable rating scales and the component weighting system are based on this aforementioned literature of tourists' stated climatic preferences.
Abstract: Much research has been devoted to quantifying optimal or unacceptable climate conditions both generally and for specific tourism segments or activities over the last 10 years. This knowledge is not incorporated in the Tourism Climate Index (TCI), which has also been subject to other substantial critiques. To more accurately assess the climatic suitability of destinations for leisure tourism, the Holiday Climate Index (HCI) was developed. A major advancement of the HCI is that its variable rating scales and the component weighting system are based on this aforementioned literature of tourists’ stated climatic preferences. This paper will discuss the design of the HCI and how the limitations of the TCI were overcome. It then presents an inter-comparison of the results from HCI:Urban and TCI for geographically diverse urban destinations across Europe. The results illustrate how the HCI:Urban rates the climate of many cities higher than the TCI, particularly in shoulder seasons and the winter months, which is more consistent with observed visitation patterns. The results empirically demonstrate that use of the TCI should be discontinued.
112 citations
TL;DR: In this paper, the teleconnections from the tropical Atlantic to the Indo-Pacific region from inter-annual to centennial time scales are reviewed and further explored in a century-long pacemaker coupled ocean-atmosphere simulation ensemble.
Abstract: In this paper, the teleconnections from the tropical Atlantic to the Indo-Pacific region from inter-annual to centennial time scales will be reviewed. Identified teleconnections and hypotheses on mechanisms at work are reviewed and further explored in a century-long pacemaker coupled ocean-atmosphere simulation ensemble. There is a substantial impact of the tropical Atlantic on the Pacific region at inter-annual time scales. An Atlantic Nino (Nina) event leads to rising (sinking) motion in the Atlantic region, which is compensated by sinking (rising) motion in the central-western Pacific. The sinking (rising) motion in the central-western Pacific induces easterly (westerly) surface wind anomalies just to the west, which alter the thermocline. These perturbations propagate eastward as upwelling (downwelling) Kelvin-waves, where they increase the probability for a La Nina (El Nino) event. Moreover, tropical North Atlantic sea surface temperature anomalies are also able to lead La Nina/El Nino development. At multidecadal time scales, a positive (negative) Atlantic Multidecadal Oscillation leads to a cooling (warming) of the eastern Pacific and a warming (cooling) of the western Pacific and Indian Ocean regions. The physical mechanism for this impact is similar to that at inter-annual time scales. At centennial time scales, the Atlantic warming induces a substantial reduction of the eastern Pacific warming even under CO2 increase and to a strong subsurface cooling.
78 citations
TL;DR: In the extremely low frequency (ELF) range below 100 Hz, the global Schumann Resonance (SR) are excited at frequencies of 8 Hz, 14 Hz, 20 Hz, etc as mentioned in this paper.
Abstract: Lightning produces electromagnetic fields and waves in all frequency ranges. In the extremely low frequency (ELF) range below 100 Hz, the global Schumann Resonances (SR) are excited at frequencies of 8 Hz, 14 Hz, 20 Hz, etc. This review is aimed at the reader generally unfamiliar with the Schumann Resonances. First some historical context to SR research is given, followed by some theoretical background and examples of the extensive use of Schumann resonances in a variety of lightning-related studies in recent years, ranging from estimates of the spatial and temporal variations in global lighting activity, connections to global climate change, transient luminous events and extraterrestrial lightning. Both theoretical and experimental results of the global resonance phenomenon are presented. It is our hope that this review will increase the interest in SR among researchers previously unfamiliar with this phenomenon.
68 citations
TL;DR: In this article, the authors compared an ensemble of numerical simulations including volcanic aerosols and their radiative effects (VE) and a reference simulations ensemble (this article) with no radiative impact of the volcanic aerosol.
Abstract: Large explosive volcanic eruptions are capable of injecting considerable amounts of particles and sulfur gases above the tropopause, causing large increases in stratospheric aerosols. Five major volcanic eruptions after 1960 (i.e., Agung, St. Helens, El Chichon, Nevado del Ruiz and Pinatubo) have been considered in a numerical study conducted with a composition-climate coupled model including an aerosol microphysics code for aerosol formation and growth. Model results are compared between an ensemble of numerical simulations including volcanic aerosols and their radiative effects (VE) and a reference simulations ensemble (REF) with no radiative impact of the volcanic aerosols. Differences of VE-REF show enhanced diabatic heating rates; increased stratospheric temperatures and mean zonal westerly winds; increased planetary wave amplitude; and tropical upwelling. The impact on stratospheric upwelling is found to be larger when the volcanically perturbed stratospheric aerosol is confined to the tropics, as tends to be the case for eruptions which were followed by several months with easterly shear of the quasi-biennial oscillation (QBO), e.g., the Pinatubo case. Compared to an eruption followed by a period of westerly QBO, such easterly QBO eruptions are quite different, with meridional transport to mid- and high-latitudes occurring later, and at higher altitude, with a consequent decrease in cross-tropopause removal from the stratosphere, and therefore longer decay timescale. Comparing the model-calculated e-folding time of the volcanic aerosol mass during the first year after the eruptions, an increase is found from 8.1 and 10.3 months for El Chichon and Agung (QBO westerly shear), to 14.6 and 30.7 months for Pinatubo and Ruiz (QBO easterly shear). The corresponding e-folding time of the global-mean radiative flux changes goes from 9.1 and 8.0 months for El Chichon and Agung, to 28.7 and 24.5 months for Pinatubo and Ruiz.
41 citations
TL;DR: In this paper, the seasonal variations and sources of trace metal elements in atmospheric fine aerosols (PM2.5) were investigated for a year-long field campaign from July 2012 to June 2013, conducted in suburban Nanjing, eastern China, at a site adjacent to an industry zone.
Abstract: In this work, the seasonal variations and sources of trace metal elements in atmospheric fine aerosols (PM2.5) were investigated for a year-long field campaign from July 2012 to June 2013, conducted in suburban Nanjing, eastern China, at a site adjacent to an industry zone. The PM2.5 samples collected across four seasons were analyzed for 17 metal elements, namely, Sodium (Na), Magnesium (Mg), Aluminum (Al), Vanadium (V), Chromium (Cr), Manganese (Mn), Nickel (Ni), Copper (Cu), Zinc (Zn), Arsenic (As), Selenium (Se), Strontium (Sr), Cadmium (Cd), Barium (Ba), Lead (Pb), Molybdenum (Mo), and Antimony (Sb) using an inductively coupled plasma mass spectrometry (ICP-MS). We found that the total concentration of all 17 metal elements was 1.23 μg/m3, on average accounting for 1.0% of the total PM2.5 mass. For our data, mass concentrations of Al, Cd, Ba were highest in summer, Mg, Cu, Zn, Se, Pb peaked in autumn, Cr, Mn, Ni, As, Sr, Sb increased significantly in winter, while the concentrations of Na, V, Mo were at their highest levels in spring. Air mass back trajectory analysis suggested that air parcels that arrived at the site originated from four dominant regions (Japan, yellow sea and bohai; Southeast of China, the Pacific Ocean; Southwest of Jiangsu and Anhui province; Northern Asia inland and Mongolia region), in particular, the one from Northern Asia inland and Mongolia contained the highest concentrations of As, Sb, Sr, and was predominant in winter. Positive matrix factorization (PMF) analyses revealed that the industrial emission is the largest contributor (34%) of the observed metal elements, followed by traffic (25%), soil dust (19%), coal combustion (10%), incineration of electronic waste (9%), and a minor unknown source (3%). In addition, we have also investigated the morphology and composition of particles by using the scanning electron microscopy (SEM)/energy-dispersive spectrometry (EDS) techniques, and identified particles from coal burning sources, etc., similar to the PMF results.
40 citations
TL;DR: In this paper, results of an in-situ survey of mountain summer tourists (n = 733) in the Alps in Southern Germany are presented, and respondents rated "rain" as the most important aspect of weather during their holiday.
Abstract: Weather and climate are important factors for travel decision-making and overall tourist satisfaction. As central motivators for destination choice, they directly and indirectly influence demand patterns and can be a resource and limitation for tourism at the same time. In this paper, results of an in-situ survey of mountain summer tourists (n = 733) in the Alps in Southern Germany are presented. Respondents rated ‘rain’ as the most important aspect of weather during their holiday. During a 7-day holiday, 2.1 days of continuous rain are accepted, and 3.1 days of days with thunderstorms. The ideal temperature range is between 21 and 25 °C, thus lying 4–7 degrees lower than for beach tourism. Temperatures below 15 °C and above 30 °C are perceived as unacceptable. Statistically significant differences were found for several tourist types: Older tourists are more sensitive to heat, tourists with sports activities are more tolerant to cool temperatures, first-time visitors are more sensitive to rain and families with children prefer higher temperatures. From the results, some implications for mountain destinations arise: mountain destinations could be promoted as a heat refuge, and attracting sports tourists might be a promising way to reduce weather sensitivity; however, some variety of well-promoted weather independent attractions seems to be mandatory.
40 citations
TL;DR: In this paper, the George Mason University Libraries Open Access Publishing Fund (OAPP) was used to fund the publication of a paper by the authors. But the work was limited in scope.
Abstract: Funding provided in part by NSF Project #0947982. Publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund.
37 citations
TL;DR: In this article, the average mass concentration of the eight ions was 40.96 µg/m3, which accounted for 62% of the entire mass concentration, and the order of the ion concentrations was SO42− > NO3− > NH4+ > Cl− > K+ > Ca2+ > Na+ > Mg2+.
Abstract: Daily PM2.5 and water-soluble inorganic ions (NH4+, SO42−, NO3−, Cl−, Ca2+, Na+, K+, Mg2+) were collected at the Hongshan Air Monitoring Station at the China University of Geosciences (Wuhan) (30°31′N, 114°23′E), Wuhan, from 1 January to 30 December 2013. A total of 52 effective PM2.5 samples were collected using medium flow membrane filter samplers, and the anionic and cationic ions were determined by ion chromatography and ICP, respectively. The results showed that the average mass concentration of the eight ions was 40.96 µg/m3, which accounted for 62% of the entire mass concentration. In addition, the order of the ion concentrations was SO42− > NO3− > NH4+ > Cl− >K+ > Ca2+ > Na+ > Mg2+. The secondary inorganic species SO42−, NO3− and NH4+ were the major components of water-soluble ions in PM2.5, with a concentration of 92% of the total ions of PM2.5, and the total concentrations of the three ions in the four seasons in descending order as follows: winter, spring, autumn, and summer. NH4+ had a significant correlation with SO42− and NO3−, and the highest correlation coefficients were 0.943 and 0.923 (in winter), while the minimum coefficients were 0.683 and 0.610 (in summer). The main particles were (NH4)2SO4 and NH4NO3 in PM2.5. The charge of the water-soluble ions was nearly balanced in PM2.5, and the pertinence coefficients of water-soluble anions and cations were more than 0.9. The highest pertinence coefficients were in the spring (0.9887), and the minimum was in summer (0.9459). That is, there were more complicated ions in PM2.5 in the summer. The mean value of NO3−/SO42− was 0.64, indicating that stationary sources of PM2.5 had a greater contribution in Wuhan.
37 citations
TL;DR: Wang et al. as mentioned in this paper proposed an efficient visualization method to represent directly, quickly, and clearly the spatio-temporal information contained in air pollution data, which allows the data to be easily understood by the public and inspire or aid further studies in other fields.
Abstract: In recent years, frequent occurrences of significant air pollution events in China have routinely caused panic and are a major topic of discussion by the public and air pollution experts in government and academia. Therefore, this study proposed an efficient visualization method to represent directly, quickly, and clearly the spatio-temporal information contained in air pollution data. Data quality check and cleansing during a preliminary visual analysis is presented in tabular form, heat matrix, or line chart, upon which hypotheses can be deduced. Further visualizations were designed to verify the hypotheses and obtain useful findings. This method was tested and validated in a year-long case study of the air quality index (AQI of PM2.5) in Beijing, China. We found that PM2.5, PM10, and NO2 may be emitted by the same sources, and strong winds may accelerate the spread of pollutants. The average concentration of PM2.5 in Beijing was greater than the AQI value of 50 over the six-year study period. Furthermore, arable lands exhibited considerably higher concentrations of air pollutants than vegetation-covered areas. The findings of this study showed that our visualization method is intuitive and reliable through data quality checking and information sharing with multi-perspective air pollution graphs. This method allows the data to be easily understood by the public and inspire or aid further studies in other fields.
36 citations
TL;DR: In this paper, an exploratory study addressed a sample of 50 tourists from three globally important source markets: Austria, Germany and Switzerland, and found that weather events do not dominate long-term memories of tourist experiences.
Abstract: The importance of weather for tourism is now widely recognized. However, no research has so far addressed weather events from retrospective viewpoints, and, in particular, the role of “extreme” events in longer-term holiday memories. To better understand the character of ex post weather experiences and their importance in destination image perceptions and future travel planning behavior, this exploratory study addressed a sample of 50 tourists from three globally important source markets: Austria, Germany and Switzerland. Results indicate that weather events do not dominate long-term memories of tourist experiences. Yet, weather events are important in shaping destination image, with “rain” being the single most important weather variable negatively influencing perceptions. Results also suggest that weather events perceived as extreme can involve considerable emotions. The study of ex post traveler memories consequently makes a valuable contribution to the understanding of the complexity of “extreme weather” events for tourist demand responses.
33 citations
TL;DR: The results suggest an elevated risk of adverse health effects on younger children from airborne bacteria and fungi populations present in rural nursery schools in the Upper Silesia region of Poland during winter and spring seasons through quantification and identification procedures.
Abstract: This study aimed to characterize airborne bacteria and fungi populations present in rural nursery schools in the Upper Silesia region of Poland during winter and spring seasons through quantification and identification procedures. Bacterial and fungal concentration levels and size distributions were obtained by the use of a six-stage Andersen cascade impactor. Results showed a wide range of indoor bioaerosols levels. The maximum level of viable bacterial aerosols indoors was about 2600 CFU·m−3, two to three times higher than the outdoor level. Fungi levels were lower, from 82 to 1549 CFU·m−3, with indoor concentrations comparable to or lower than outdoor concentrations. The most prevalent bacteria found indoors were Gram-positive cocci (>65%). Using the obtained data, the nursery school exposure dose (NSED) of bioaerosols was estimated for both the children and personnel of nursery schools. The highest dose for younger children was estimated to range: 327–706 CFU·kg−1 for bacterial aerosols and 31–225 CFU·kg−1 for fungal aerosols. These results suggest an elevated risk of adverse health effects on younger children. These findings may contribute to the promotion and implementation of preventative public health programs and the formulation of recommendations aimed at providing healthier school environments.
TL;DR: In this article, the authors analyzed the composition of resuspended road dust particles in different environments: local streets, paved roads inside traffic tunnels, and high traffic streets, to quantify the concentrations of trace elements and black carbon.
Abstract: Many studies have been performed in order to characterize the sources of airborne particles in the Metropolitan Area of Sao Paulo (MASP), in Brazil. Those studies have been based on receptor modeling and most of the uncertainties in their results are related to the emission profile of the resuspended road dust particles. In this study, we analyzed the composition of resuspended road dust particles in different environments: local streets, paved roads inside traffic tunnels, and high traffic streets. We analyzed the samples to quantify the concentrations of trace elements and black carbon. On the basis of that analysis, we developed emission profiles of the resuspended road dust that are representative of the different types of urban pavement in the MASP. This study is important given the international efforts in improving emissions factors with local characteristics, mainly in South America and other regions for which there is a lack of related information. This work presents emission profiles derived from resuspended road dust samples that are representative of the different types of urban pavement in the Metropolitan Area of Sao Paulo.
TL;DR: In this paper, the authors investigated the sources of moisture (and moisture for precipitation) over the Danube River Basin (DRB) by means of a Lagrangian approach using the FLEXPART V9.0 particle dispersion model together with ERA-Interim reanalysis data to track changes in atmospheric moisture over 10-day trajectories.
Abstract: In this study, we investigate the sources of moisture (and moisture for precipitation) over the Danube River Basin (DRB) by means of a Lagrangian approach using the FLEXPART V9.0 particle dispersion model together with ERA-Interim reanalysis data to track changes in atmospheric moisture over 10-day trajectories. This approach computes the budget of evaporation-minus-precipitation by calculating changes in specific humidity along forward and backward trajectories. We considered a time period of 34 years, from 1980 to 2014, which allowed for the identification of climatological sources and moisture transport towards the basin. Results show that the DRB mainly receives moisture from seven different oceanic, maritime, and terrestrial moisture source regions: North Atlantic Ocean, North Africa, the Mediterranean Sea, Black Sea, Caspian Sea, the Danube River Basin, and Central and Eastern Europe. The contribution of these sources varies by season. During winter (October–March) the main moisture source for the DRB is the Mediterranean Sea, while during summer (April–September) the dominant source of moisture is the DRB itself. Moisture from each source has a different contribution to precipitation in the DRB. Among the sources studied, results show that the moisture from the Mediterranean Sea provides the greatest contribution to precipitation in the basin in both seasons, extending to the whole basin for the winter, but being more confined to the western side during the summer. Moisture from the Caspian and Black Seas contributes to precipitation rather less.
TL;DR: In this article, the authors focused on the 2012-2015 winter fog episodes over the Pakistan region using the Moderate Resolution Image Spectrometer (MODIS), the Ozone Monitoring Instrument and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) products.
Abstract: Fog is a meteorological/environmental phenomenon which happens across the Indo-Gangetic Plains (IGP) and leads to significant social and economic problems, especially posing significant threats to public health and causing disruptions in air and road traffic. Meteorological stations in Pakistan provide limited information regarding fog episodes as these provide only point observations. Continuous monitoring, as well as a spatially coherent picture of fog distribution, is possible through the use of satellite observations. This study focuses on the 2012–2015 winter fog episodes over the Pakistan region using the Moderate Resolution Image Spectrometer (MODIS), the Ozone Monitoring Instrument and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) products. The main objective of the study was to map the spatial distribution of aerosols, their types, and to identify the aerosol origins during special weather conditions like fog in Pakistan. The study also included ground monitoring of particulate matter (PM) concentrations, which were conducted during the 2014–2015 winter period only. Overall, this study is part of a multi-country project supported by the International Centre for Integrated Mountain Development (ICIMOD), started in 2014–2015 winter period, whereby scientists from Bangladesh, India and Nepal have also conducted measurements at their respective sites. A significant correlation between MODIS (AOD) and AERONET Station (AOD) data from Lahore was identified. Mass concentration of PM10 at all sampling sites within Lahore city exceeded the National Environmental Quality Standards (NEQS) levels on most of the occasions. Smoke and absorbing aerosol were found to be major constituents of winter fog in Pakistan. Furthermore, an extended span of winter fog was also observed in Lahore city during the winter of 2014–2015. The Vertical Feature Mask (VFM) provided by CALIPSO satellite confirmed the low-lying aerosol layers, instead of clouds for the vertical profiles of selected case studies.
TL;DR: In this paper, the specific molecular compounds as well as bulk structural properties of total suspended particulates in ambient organic aerosols were evaluated using ultrahigh resolution mass spectrometry (UHR-MS) and proton nuclear magnetic resonance spectroscopy (1H NMR).
Abstract: Organic aerosols (OA) are universally regarded as an important component of the atmosphere that have far-ranging impacts on climate forcing and human health. Many of these impacts are related to OA molecular characteristics. Despite the acknowledged importance, current uncertainties related to the source apportionment of molecular properties and environmental impacts make it difficult to confidently predict the net impacts of OA. Here we evaluate the specific molecular compounds as well as bulk structural properties of total suspended particulates in ambient OA collected from key emission sources (marine, biomass burning, and urban) using ultrahigh resolution mass spectrometry (UHR-MS) and proton nuclear magnetic resonance spectroscopy (1H NMR). UHR-MS and 1H NMR show that OA within each source is structurally diverse, and the molecular characteristics are described in detail. Principal component analysis (PCA) revealed that (1) aromatic nitrogen species are distinguishing components for these biomass burning aerosols; (2) these urban aerosols are distinguished by having formulas with high O/C ratios and lesser aromatic and condensed aromatic formulas; and (3) these marine aerosols are distinguished by lipid-like compounds of likely marine biological origin. This study provides a unique qualitative approach for enhancing the chemical characterization of OA necessary for molecular source apportionment.
TL;DR: In this paper, the authors used a camera network to estimate the visibility and spatial extent of measured dust events in southwestern Iceland and calculated the total dust flux from the sources as 180,000 and 280,000 tons for each storm.
Abstract: Particulate matter mass concentrations and size fractions of PM1, PM2.5, PM4, PM10, and PM15 measured in transversal horizontal profile of two dust storms in southwestern Iceland are presented. Images from a camera network were used to estimate the visibility and spatial extent of measured dust events. Numerical simulations were used to calculate the total dust flux from the sources as 180,000 and 280,000 tons for each storm. The mean PM15 concentrations inside of the dust plumes varied from 10 to 1600 µg·m−3 (PM10 = 7 to 583 µg·m−3). The mean PM1 concentrations were 97–241 µg·m−3 with a maximum of 261 µg·m−3 for the first storm. The PM1/PM2.5 ratios of >0.9 and PM1/PM10 ratios of 0.34–0.63 show that suspension of volcanic materials in Iceland causes air pollution with extremely high PM1 concentrations, similar to polluted urban areas in Europe or Asia. Icelandic volcanic dust consists of a higher proportion of submicron particles compared to crustal dust. Both dust storms occurred in relatively densely inhabited areas of Iceland. First results on size partitioning of Icelandic dust presented here should challenge health authorities to enhance research in relation to dust and shows the need for public dust warning systems.
TL;DR: In this paper, a semi-physical geographically weighted regression (GWR) model was established to estimate nationwide mass concentrations of PM10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters.
Abstract: The estimation of ambient particulate matter with diameter less than 10 µm (PM10) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM10 more accessible to relevant departments and scientific research institutions, a semi-physical geographically weighted regression (GWR) model was established in this study to estimate nationwide mass concentrations of PM10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters. The results demonstrated that applying physics-based corrections could remarkably improve the quality of the dataset for better model performance with the adjusted R2 between PM10 and AOD increasing from 0.08 to 0.43, and the fitted results explained approximately 81% of the variability in the corresponding PM10 mass concentrations. Annual average PM10 concentrations estimated by the semi-physical GWR model indicated that many residential regions suffer from severe particle pollution. Moreover, the deviation in estimation, which primarily results from the frequent changes in elevation, the spatially heterogeneous distribution of monitoring sites, and the limitations of AOD retrieval algorithm, was acceptable. Therefore, the semi-physical GWR model provides us with an effective and efficient method to estimate PM10 at large scale. The results could offer reasonable estimations of health impacts and provide guidance on emission control strategies in China.
TL;DR: In this article, the characteristics of greenhouse gas (GHG) emissions from heavy-duty trucks in the Beijing-Tianjin-Hebei (BTH) region, which is located in Northern China, were analyzed.
Abstract: This paper aims to study the characteristics of greenhouse gas (GHG) emissions from heavy-duty trucks in the Beijing-Tianjin-Hebei (BTH) region, which is located in Northern China. The multiyear emissions of GHG (CO2, CH4 and N2O) from heavy-duty trucks fueled by diesel and natural gas during the period of 2006–2015 were compared and analyzed. The results show that the GHG emissions from heavy-duty trucks increase with time, which is consistent with the trend of the population growth. The total amount of carbon dioxide equivalence (CO2e) emissions in the BTH region was about 5.12 × 106 t in 2015. Among the three sub-regions, Hebei possesses the largest number of heavy-duty trucks due to the size of its heavy-duty industries. As a consequence, the GHG emissions are about 10 times compared to Beijing and Tianjin. Tractor trailers account for the major proportion of heavy-duty trucks and hence contribute to about 74% of GHG emissions. Diesel- and liquefied natural gas (LNG)-powered heavy-duty trucks can reduce GHG emissions more effectively under current national standard IV than can the previous standard. The widespread utilization of the alternative fuel of LNG to mitigate emissions must be accompanied with engine technology development in China. This study has provided new insight on management methods and the policy-making as regards trucks in terms of environmental demand.
TL;DR: Many ski resorts worldwide are going through deteriorating snow cover conditions due to anthropogenic warming trends as mentioned in this paper, as the natural and the artificially supported, i.e., technical, snow reliabilit...
Abstract: Many ski resorts worldwide are going through deteriorating snow cover conditions due to anthropogenic warming trends. As the natural and the artificially supported, i.e., technical, snow reliabilit ...
TL;DR: In this paper, an objectively trained model for tropical cyclone intensity estimation from routine satellite infrared images over the Northwestern Pacific Ocean is presented, where the intensity is correlated to some critical signals extracted from the infrared images, by training the 325 cases from 1996 to 2007 typhoon seasons.
Abstract: An objectively trained model for tropical cyclone intensity estimation from routine satellite infrared images over the Northwestern Pacific Ocean is presented in this paper. The intensity is correlated to some critical signals extracted from the satellite infrared images, by training the 325 tropical cyclone cases from 1996 to 2007 typhoon seasons. To begin with, deviation angles and radial profiles of infrared images are calculated to extract as much potential predicators for intensity as possible. These predicators are examined strictly and included into (or excluded from) the initial predicator pool for regression manually. Then, the “thinned” potential predicators are regressed to the intensity by performing a stepwise regression procedure, according to their accumulated variance contribution rates to the model. Finally, the regressed model is verified using 52 cases from 2008 to 2009 typhoon seasons. The R2 and Root Mean Square Error are 0.77 and 12.01 knot in the independent validation tests, respectively. Analysis results demonstrate that this model performs well for strong typhoons, but produces relatively large errors for weak tropical cyclones.
TL;DR: In this paper, high-precision differential air pressure measurements were conducted in the below-canopy space of a Scots pine forest and in the forest soil to investigate small air pressure fluctuations and their effect on soil gas flux.
Abstract: High-precision differential air pressure measurements were conducted in the below-canopy space of a Scots pine forest and in the forest soil to investigate small air pressure fluctuations and their effect on soil gas flux. In addition to air pressure measurements, tracer gas concentration in the soil and airflow characteristics above and below the canopy were measured. Results suggest that air pressure fluctuations in the frequency range of 0.01 Hz–0.1 Hz are strongly dependent on above-canopy wind speed. While amplitudes of the observed air pressure fluctuations (<10 Pa) increase significantly with increasing above-canopy wind speed, the periods decrease significantly with increasing above-canopy wind speed. These air pressure fluctuations are associated with the pressure-pumping effect in the soil. A pressure-pumping coefficient was defined, which describes the strength of the pressure-pumping effect. During the measurement period, pressure-pumping coefficients up to 0.44 Pa·s−1 were found. The dependence of the pressure-pumping coefficient on mean above-canopy wind speed can be described well with a polynomial fit of second degree. The knowledge of this relation simplifies the quantification of the pressure-pumping effect in a Scots pine forest considerably, since only the mean above-canopy wind speed has to be measured. In addition, empirical modeling revealed that the pressure-pumping coefficient explains the largest fraction of the variance of tracer gas concentration in the topsoil.
TL;DR: In this paper, the authors examined the overall performance of data assimilation outputs, including temperature and water vapor profiles from the Korea Meteorological Administration (KMA) and the United Kingdom Met Office (UKMO) Unified Model (UM) and from reanalysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) using collocated radiosonde observations from the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) for January-December 2012.
Abstract: Temperature and water vapor profiles from the Korea Meteorological Administration (KMA) and the United Kingdom Met Office (UKMO) Unified Model (UM) data assimilation systems and from reanalysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) were assessed using collocated radiosonde observations from the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) for January–December 2012. The motivation was to examine the overall performance of data assimilation outputs. The difference statistics of the collocated model outputs versus the radiosonde observations indicated a good agreement for the temperature, amongst datasets, while less agreement was found for the relative humidity. A comparison of the UM outputs from the UKMO and KMA revealed that they are similar to each other. The introduction of the new version of UM into the KMA in May 2012 resulted in an improved analysis performance, particularly for the moisture field. On the other hand, ECMWF reanalysis data showed slightly reduced performance for relative humidity compared with the UM, with a significant humid bias in the upper troposphere. ECMWF reanalysis temperature fields showed nearly the same performance as the two UM analyses. The root mean square differences (RMSDs) of the relative humidity for the three models were larger for more humid conditions, suggesting that humidity forecasts are less reliable under these conditions.
TL;DR: It is shown that a combination of the IFS model and the neural network model further improves the accuracy of the forecasts and this work focuses on day-ahead and intra-day solar forecasting.
Abstract: This paper aims at assessing the accuracy of different solar forecasting methods in the case of an insular context. Two sites of La Reunion Island, Le Tampon and Saint-Pierre, are chosen to do the benchmarking exercise. Reunion Island is a tropical island with a complex orography where cloud processes are mainly governed by local dynamics. As a consequence, Reunion Island exhibits numerous micro-climates. The two aforementioned sites are quite representative of the challenging character of solar forecasting in the case of a tropical island with complex orography. Hence, although distant from only 10 km, these two sites exhibit very different sky conditions. This work focuses on day-ahead and intra-day solar forecasting. Day-ahead solar forecasts are provided by the European Center for Medium-Range Weather Forecast (ECMWF). This organization maintains and runs the Numerical Weather Prediction (NWP) model named Integrated Forecast System (IFS). In this work, post-processing techniques are applied to refine the output of the IFS model for day-ahead forecasting. Statistical models like a recursive linear model or a nonlinear model such as an artificial neural network are used to produce the intra-day solar forecasts. It is shown that a combination of the IFS model and the neural network model further improves the accuracy of the forecasts.
TL;DR: In this paper, the authors assessed the dekadal rainfall patterns and rain days to determine intra-seasonal rainfall variability during the March-May season using the Mann-Kendall (MK) trend test and simple linear regression (S L R ) over the period 2000-2015.
Abstract: Understanding variations in rainfall in tropical regions is important due to its impacts on water resources, health and agriculture. This study assessed the dekadal rainfall patterns and rain days to determine intra-seasonal rainfall variability during the March–May season using the Mann–Kendall ( M K ) trend test and simple linear regression ( S L R ) over the period 2000–2015. Results showed an increasing trend of both dekadal rainfall amount and rain days (third and seventh dekads). The light rain days ( S L R = 0.181; M K = 0.350) and wet days ( S L R = 0.092; M K = 0.118) also depict an increasing trend. The rate of increase of light rain days and wet days during the third dekad (light rain days: S L R = 0.020; M K = 0.279 and wet days: S L R = 0.146; M K = 0.376) was slightly greater than during the seventh dekad (light rain days: S L R = 0.014; M K = 0.018 and wet days: S L R = 0.061; M K = 0.315) dekad. Seventy-four percent accounted for 2–4 consecutive dry days, but no significant trend was detected. The extreme rainfall was increasing over the third ( M K = 0.363) and seventh ( M K = 0.429) dekads. The rainfall amount and rain days were highly correlated (r: 0.43–0.72).
TL;DR: Wang et al. as mentioned in this paper studied the occurrence of high extremes of warm days and hot days (TX95p and TX99p) and cold nights and very cold nights (TN05p and TN01p), based on the 95th and 99th (5th and 1st) percentiles of the daily maximum (minimum) temperature data at a certain station in the period 1971-2000, which have more direct impacts on society and the ecosystem.
Abstract: Studies based on the 10th (90th) percentiles as thresholds have been presented to assess moderate extremes in China and globally. However, there has been notably little research on the occurrences of high extremes of warm days and hot days (TX95p and TX99p) and cold nights and very cold nights (TN05p and TN01p), based on the 95th and 99th (5th and 1st) percentiles of the daily maximum (minimum) temperature data at a certain station in the period 1971–2000, which have more direct impacts on society and the ecosystem. The trends analyses of cool nights or warm days are based upon the hypothesis that expects a linear trend and no abrupt change. However, abrupt changes in the climate, especially in extreme temperatures, have been pointed to as a major threat to ecosystem services. This study demonstrates that (1) the mean frequencies of TX95p and TX99p increased by 1.80 day/10 year and 0.62 day/10 year, respectively, and that those of TN05p and TN01p decreased by 3.18 day/10 year and 1.01 day/10 year, respectively, in mainland China. Additionally, the TX95p and TX99p increased significantly by 50.42% and 58.21%, respectively, while the TN05p and TN01p of all of the stations decreased significantly by 83.76% and 76.48%, respectively. Finally, (2) the TX95p and TX99p trends underwent abrupt changes in the 1990s or 2000s, but the trends of TN05p and TN01p experienced abrupt changes in the late 1970s and early 1980s. After the abrupt change points, the trend of warm and hot days increased more rapidly than before in most regions, but the trend of cold days and very cold days decreased more slowly than before in most regions, which indicates a greater risk of heat waves in the future.
TL;DR: Li et al. as mentioned in this paper investigated the characteristics and the source of the errors contained in IMERG, and a bias-decomposition scheme was employed to evaluate the hourly IMERG over the eastern part of Mainland China during the warm season.
Abstract: Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) is an important satellite precipitation product of Global Precipitation Measurement (GPM) mission. Quantitative information about the errors of IMERG has great significance for the data developers and end users. In order to investigate the characteristics and the source of the errors contained in IMERG, a bias-decomposition scheme was employed to evaluate the hourly IMERG over the eastern part of Mainland China during the warm season. First, the total bias of IMERG before and after calibration (termed as precipitationUncal and precipitationCal) was calculated using rain gauge measurements as reference. Then the bias was decomposed into three independent components including false bias, missed bias, and hit bias. Finally, the hit bias was further decomposed according to the rainfall intensity measured by rain gauges. The results indicate that (1) the bias of precipitationUncal over the north part is dominated by hit bias and false bias, leading to the serious overestimation for the precipitation over this area, but it underestimates the precipitation over the south part with the false bias and missed bias acting as major contributors; (2) the precipitationCal overestimates the precipitation over more than 80% of the study areas mainly as a result of a large amplitude of false bias; (3) the calibration algorithm used by IMERG could not reduce the missed bias and enlarges the false bias over some regions, revealing a shortcoming of this algorithm in that it could not effectively alleviate the bias resulting from the rain areas delineation; (4) the hit bias of IMERG is strongly related with the rainfall intensity of rain gauge measurements, which should be beneficial for reducing the errors of IMERG. This study provides a deep insight into the characteristics and sources of the biases inherent in IMERG, which is significant for its utilization and possible correction in future.
TL;DR: In this article, the authors analyzed the inter-annual hydrological variability (precipitation and streamflow) in the basins of the High Atlas in Morocco and determined how climate fluctuations (represented by the North Atlantic Oscillation (NAO) climate index) are expressed in the Hydrological system.
Abstract: The aim of this study is to understand the inter-annual hydrological variability (precipitation and streamflow) in the basins of the High Atlas in Morocco and to determine how climate fluctuations (represented by the North Atlantic Oscillation (NAO) climate index) are expressed in the hydrological system. To reach this objective, time series of precipitation and streamflow are processed as standardized anomalies and studied by continuous wavelet analysis and wavelet coherence analysis, which are particularly suitable for the study of unsteady processes. Wet and dry periods vary from one basin to another between three and five years. The wavelet analysis shows the existence of many bands of energy in most of the sub-basins, from annual to inter-annual scales regarding the precipitation and streamflow time series. These bands correspond to intervals of one year, 2–4 years, 4–8 years and 8–12 years. The wavelet coherence analysis shows a strong coherence between NAO/streamflow and precipitation/NAO identified at the inter-annual scale. Non-stationarity can be observed in the late 1980s, 1990s and 2000s. The contribution of the NAO is different from one basin to another ranging between 67% and 77%.
TL;DR: In this article, two multi-decadal time-slice runs of a climate-chemistry-aerosol model have been designed for studying these chemical-radiative effects.
Abstract: SO2 and H2S are the two most important gas-phase sulfur species emitted by volcanoes, with a global amount from non-explosive emissions of the order 10 Tg-S/yr. These gases are readily oxidized forming SO42− aerosols, which effectively scatter the incoming solar radiation and cool the surface. They also perturb atmospheric chemistry by enhancing the NOx to HNO3 heterogeneous conversion via hydrolysis on the aerosol surface of N2O5 and Br-Cl nitrates. This reduces formation of tropospheric O3 and the OH to HO2 ratio, thus limiting the oxidation of CH4 and increasing its lifetime. In addition to this tropospheric chemistry perturbation, there is also an impact on the NOx heterogeneous chemistry in the lower stratosphere, due to vertical transport of volcanic SO2 up to the tropical tropopause layer. Furthermore, the stratospheric O3 formation and loss, as well as the NOx budget, may be slightly affected by the additional amount of upward diffused solar radiation and consequent increase of photolysis rates. Two multi-decadal time-slice runs of a climate-chemistry-aerosol model have been designed for studying these chemical-radiative effects. A tropopause mean global net radiative flux change (RF) of −0.23 W·m−2 is calculated (including direct and indirect aerosol effects) with a 14% increase of the global mean sulfate aerosol optical depth. A 5–15 ppt NOx decrease is found in the mid-troposphere subtropics and mid-latitudes and also from pole to pole in the lower stratosphere. The tropospheric NOx perturbation triggers a column O3 decrease of 0.5–1.5 DU and a 1.1% increase of the CH4 lifetime. The surface cooling induced by solar radiation scattering by the volcanic aerosols induces a tropospheric stabilization with reduced updraft velocities that produce ice supersaturation conditions in the upper troposphere. A global mean 0.9% decrease of the cirrus ice optical depth is calculated with an indirect RF of −0.08 W·m−2.
TL;DR: In this paper, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the VISible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite.
Abstract: In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March–May 2015. A test is carried out by selecting surface PM2.5 data from 12 PM2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 μg/m3 with the average of 59.39 μg/m3. Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM2.5 concentration exceeds 40 μg/m3. The study provides a superiority approach for monitoring PM2.5 air quality from space with visible light remote sensing data at night.
TL;DR: In this article, the authors characterized residential biomass burning contributions to fine particle concentrations via multiple methods at Fyfe Elementary School in Las Vegas, Nevada, during January 2008: with levoglucosan on quartz fiber filters; with water soluble potassium (K+) measured using a particle-into-liquid system with ion chromatography (PILS-IC); and with the fragment C2H4O2+ from an Aerodyne High Resolution Aerosol Mass Spectrometer (HR-AMS).
Abstract: We characterized residential biomass burning contributions to fine particle concentrations via multiple methods at Fyfe Elementary School in Las Vegas, Nevada, during January 2008: with levoglucosan on quartz fiber filters; with water soluble potassium (K+) measured using a particle-into-liquid system with ion chromatography (PILS-IC); and with the fragment C2H4O2+ from an Aerodyne High Resolution Aerosol Mass Spectrometer (HR-AMS). A Magee Scientific Aethalometer was also used to determine aerosol absorption at the UV (370 nm) and black carbon (BC, 880 nm) channels, where UV-BC difference is indicative of biomass burning (BB). Levoglucosan and AMS C2H4O2+ measurements were strongly correlated (r2 = 0.92); K+ correlated well with C2H4O2+ (r2 = 0.86) during the evening but not during other times. While K+ may be an indicator of BB, it is not necessarily a unique tracer, as non-BB sources appear to contribute significantly to K+ and can change from day to day. Low correlation was seen between UV-BC difference and other indicators, possibly because of an overwhelming influence of freeway emissions on BC concentrations. Given the sampling location—next to a twelve-lane freeway—urban-scale biomass burning was found to be a surprisingly large source of aerosol: overnight BB organic aerosol contributed between 26% and 33% of the organic aerosol mass.