Aerosols of biological origin play a vital role in the Earth system, particularly in the interactions between atmosphere, biosphere, climate, and public health. Airborne bacteria, fungal spores, pollen, and other bioparticles are essential for the reproduction and spread of organisms across various ecosystems, and they can cause or enhance human, animal, and plant diseases. Moreover, they can serve as nuclei for cloud droplets, ice crystals, and precipitation, thus influencing the hydrological cycle and climate. The sources, abundance, composition, and effects of biological aerosols and the atmospheric microbiome are, however, not yet well characterized and constitute a large gap in the scientific understanding of the interaction and co-evolution of life and climate in the Earth system. This review presents an overview of the state of bioaerosol research, highlights recent advances, and outlines future perspectives in terms of bioaerosol identification, characterization, transport, and transformation processes, as well as their interactions with climate, health, and ecosystems, focusing on the role bioaerosols play in the Earth system.
Gridded climatologies of total lightning flash rates observed by the spaceborne Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) instruments have been updated. OTD collected data from May 1995 to March 2000. LIS data (equatorward of about 38°) adds the years 1998–2010. Flash counts from each instrument are scaled by the best available estimates of detection efficiency. The long LIS record makes the merged climatology most robust in the tropics and subtropics, while the high latitude data is entirely from OTD. The gridded climatologies include annual mean flash rate on a 0.5° grid, mean diurnal cycle of flash rate on a 2.5° grid with 24 hour resolution, mean annual cycle of flash rate on a 0.5° or 2.5° grid with daily, monthly, or seasonal resolution, mean annual cycle of the diurnal cycle on a 2.5° grid with two hour resolution for each day, and time series of flash rate over the sixteen year record with roughly three-month smoothing. For some of these (e.g., annual cycle of the diurnal cycle), more smoothing is necessary for results to be robust. The mean global flash rate from the merged climatology is 46 flashes s . This varies from around 35 flashes s in February (austral summer) to 60 flashes s in August (boreal summer). The peak annual flash rate at 0.5° scale is 160 fl km yr in eastern Congo. The peak monthly average flash rate at 2.5° scale is 18 fl km mo from early April to early May in the Brahmaputra Valley of far eastern India. Lightning decreases in this region during the monsoon season, but increases further north and west. An August peak in northern Pakistan also exceeds any monthly averages from Africa, despite central Africa having the greatest yearly average.
In spite of considerable progresses in recent years, a quantitative and predictive understanding of atmospheric aerosol sources, chemical composition, transformation processes and environmental effects is still rather limited, and therefore represents a major research challenge in atmospheric science. This review begins with a historical perspective on the scientific questions regarding atmospheric aerosols over the past centuries, followed by a description of the distribution, sources, transformation processes, and chemical and physical properties as they are currently understood. The major open questions and suggestions for future research priorities are outlined to narrow the gap between the present understanding of the contribution of both anthropogenic and biogenic aerosols to radiative forcing resulting from the spatial non-uniformity, intermittency of sources, unresolved composition and reactivity. ► Historical perspective of atmospheric aerosols over the past centuries ► Survey of recent literature regarding aerosol sources and chemical composition ► Open questions and suggestions for future research priorities
Drought forecasting using standardized metrics of rainfall is a core task in hydrology and water resources management. Standardized Precipitation Index (SPI) is a rainfall-based metric that caters for different time-scales at which the drought occurs, and due to its standardization, is well-suited for forecasting drought at different periods in climatically diverse regions. This study advances drought modelling using multivariate adaptive regression splines (MARS), least square support vector machine (LSSVM), and M5Tree models by forecasting SPI in eastern Australia. MARS model incorporated rainfall as mandatory predictor with month (periodicity), Southern Oscillation Index, Pacific Decadal Oscillation Index and Indian Ocean Dipole, ENSO Modoki and Nino 3.0, 3.4 and 4.0 data added gradually. The performance was evaluated with root mean square error ( ), mean absolute error ( ), and coefficient of determination ( ). Best MARS model required different input combinations, where rainfall, sea surface temperature and periodicity were used for all stations, but ENSO Modoki and Pacific Decadal Oscillation indices were not required for Bathurst, Collarenebri and Yamba, and the Southern Oscillation Index was not required for Collarenebri. Inclusion of periodicity increased the value by 0.5–8.1% and reduced by 3.0–178.5%. Comparisons showed that MARS superseded the performance of the other counterparts for three out of five stations with lower by 15.0–73.9% and 7.3–42.2%, respectively. For the other stations, M5Tree was better than MARS/LSSVM with lower by 13.8–13.4% and 25.7–52.2%, respectively, and for Bathurst, LSSVM yielded more accurate result. For droughts identified by SPI ≤ − 0.5, accurate forecasts were attained by MARS/M5Tree for Bathurst, Yamba and Peak Hill, whereas for Collarenebri and Barraba, M5Tree was better than LSSVM/MARS. Seasonal analysis revealed disparate results where MARS/M5Tree was better than LSSVM. The results highlight the importance of periodicity in drought forecasting and also ascertains that model accuracy scales with geographic/seasonal factors due to complexity of drought and its relationship with inputs and data attributes that can affect the evolution of drought events.
Heatwaves impose disastrous impacts over human, natural and industrial systems across the globe. Over a relatively short period of time, there has been considerable advancement in the scientific understanding of heatwaves. Such advancements include how heatwaves are measured, their driving mechanisms, observed and projected changes, and quantifying the anthropogenic influence behind these changes. This paper reviews these developments. There are however gaps in the scientific literature that should be filled in order to gain a more complete understanding of the changing nature of heatwaves. The conclusions of this paper propose that the global community should work toward a unified framework in which to measure heatwaves, reduce spatial and temporal gaps by increasing the global observation network, further research on how physical mechanisms interact for heatwave manifestation, and continual work and expansion of methods used for attribution studies on observed heatwaves and their trends.
Daily PM samples were collected in Guangzhou — the largest megacity in South China, for a period of one month in each season during 2009–2010. Mass concentrations of water-soluble inorganic ions, organic carbon (OC) and elemental carbon (EC) in PM were determined, and aerosol scattering coefficient (b ) was synchronously measured. The daily PM mass concentrations ranged from 21.0 to 213.6 μg m with an annual average of 76.8 ± 41.5 μg m . The highest seasonal average PM was observed in winter (103.3 ± 50.1 μg m ) and the lowest in summer (38.6 ± 15.7 μg m ). Annual average PM mass scattering efficiency (MSE) was 3.5 ± 0.9 m g , with obvious seasonal variations in sequence of autumn (4.5 ± 0.2 m g ) > winter (3.9 ± 0.5 m g ) > spring (3.0 ± 0.4 m g ) > summer (2.3 ± 0.3 m g ). To determine the relationship between b and the chemical components of PM , b was reconstructed in each season using the original IMPROVE formula with a modification of including sea salt aerosols. The estimated b using this method was 22 ± 28% smaller on annual average compared to the measurements. Multiple linear regression of measured b against (NH ) SO , NH NO , OM (Organic Mass), SS (Sea Salt), FS (Fine Soil), and CM (Coarse Mass) were also performed in all the four seasons. The estimated b from using the regression equation was 4 ± 12% larger than the measured values. On average, (NH ) SO , NH NO , OM, SS, FS and CM accounted for 50 ± 11%, 18 ± 10%, 19 ± 5%, 5 ± 4%, 3 ± 2% and 5 ± 6%, respectively, of the estimated b .
Southeast Asia (SEA) hosts one of the most complex aerosol systems in the world, with convoluted meteorological scales, sharp geographic and socioeconomic features, high biological productivity, mixtures of a wide range of atmospheric pollutants, and likely a significant susceptibility to global climate change. This physical complexity of SEA is coupled with one of the world's most challenging environments for both in situ and remote sensing observation. The 7-Southeast Asian Studies (7SEAS) program was formed to facilitate interdisciplinary research into the integrated SEA aerosol environment via grass roots style collaboration. In support of the early 7SEAS program and the affiliated Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC RS), this review was created to outline the network of connections linking aerosol particles in SEA with meteorology, climate and the total earth system. In this review, we focus on and repeatedly link back to our primary data source: satellite aerosol remote sensing and associated observability issues. We begin with a brief rationale for the program, outlining key aerosol impacts and, comparing their magnitudes to the relative uncertainty of observations. We then discuss aspects of SEA's physical, socio-economic and biological geography relevant to meteorology and observability issues associated with clouds and precipitation. We show that not only does SEA pose significant observability challenges for aerosol particles, but for clouds and precipitation as well. With the fundamentals of the environment outlined, we explore SEA's most studied aerosol issue: biomass burning. We summarize research on bulk aerosol properties for SEA, including a short synopsis of recent AERONET observations. We describe long range transport patterns. Finally, considerable attention is paid to satellite aerosol observability issues, with a face value comparison of common aerosol products in the region including passive and active aerosol products as well as fluxes. We show that satellite data products diverge greatly due to a host of known artifacts. These artifacts have important implications for how research is conducted, and care must be taken when using satellite products to study aerosol problems. The paper ends with a discussion of how the community can approach this complex and important environment. ► The complex relationships between Southeast Asia’s geographic, meteorological and aerosol systems are reviewed. ► There are few aerosol measurements in Southeast Asia, and those that do exist suggest significant regional variability. ► Southeast Asia hosts one of the world’s most challenging aerosol observing environments. ► Satellite derived products for clouds, precipitation, fire and aerosol diverge significantly in Southeast Asia. ► If used properly, satellite products can provide significant insight into the regional aerosol environment.
Two post-real time precipitation products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) are systematically evaluated over China with China daily Precipitation Analysis Product (CPAP) as reference. The IMERG products include the gauge-corrected IMERG product (IMERG_Cal) and the version of IMERG without direct gauge correction (IMERG_Uncal). The post-research TRMM Multisatellite Precipitation Analysis version 7 (TMPA-3B42V7) is also evaluated concurrently with IMERG for better perspective. In order to be consistent with CPAP, the evaluation and comparison of selected products are performed at 0.25° and daily resolutions from 12 March 2014 through 28 February 2015. The results show that: Both IMERG and 3B42V7 show similar performances. Compared to IMERG_Uncal, IMERG_Cal shows significant improvement in overall and conditional bias and in the correlation coefficient. Both IMERG_Cal and IMERG_Uncal perform relatively poor in winter and over-detect slight precipitation events in northwestern China. As an early validation of the GPM-era IMERG products that inherit the TRMM-era global satellite precipitation products, these findings will provide useful feedbacks and insights for algorithm developers and data users over China and beyond.
This review aims to update our understanding on molecular distributions of water-soluble dicarboxylic acids and related compounds in atmospheric aerosols with a focus on their geographical variability, size distribution, sources and formation pathways. In general, molecular distributions of diacids in aerosols from the continental sites and over the open ocean waters are often characterized by the predominance of oxalic acid (C ) followed by malonic acid (C ) and/or succinic acid (C ), while those sampled over the polar regions often follow the order of C ≥ C and C . The most abundant and ubiquitous diacid is oxalic acid, which is principally formed via atmospheric oxidation of its higher homologues of long chain diacids and other pollution-derived organic precursors (e.g., olefins and aromatic hydrocarbons). However, its occurrence in marine aerosols is mainly due to the transport from continental outflows (e.g., East Asian outflow during winter/spring to the North Pacific) and/or governed by photochemical/aqueous phase oxidation of biogenic unsaturated fatty acids (e.g., oleic acid) and isoprene emitted from the productive open ocean waters. The long-range atmospheric transport of pollutants from mid latitudes to the Arctic in dark winter facilitates to accumulate the reactants prior to their intense photochemical oxidation during springtime polar sunrise. Furthermore, the relative abundances of C in total diacid mass showed similar temporal trends with downward solar irradiation and ambient temperatures, suggesting the significance of atmospheric photochemical oxidation processing. Compound-specific isotopic analyses of oxalic acid showed the highest δ C among diacids whereas azelaic acid showed the lowest value, corroborating the significance of atmospheric aging of oxalic acid. On the other hand, other diacids gave intermediate values between these two diacids, suggesting that aging of oxalic acid is associated with C enrichment.
The primary objective of this study is to investigate the formation and evolution mechanism of the regional haze in Beijing by analyzing the process of a severe haze episode that occurredfrom1 to 31 January 2013. The mass concentration of PM and its chemical components were simultaneously measured at the Beijing urban atmospheric environmental monitoring station. The haze was characterized by a high frequency, a long duration, a large influential region and an extremely high PM values (> 500 μg/m ). The primary factors driving the haze formation were stationary atmospheric flows (in both vertical and horizontal directions), while a temperature inversion, a lower planetary boundary layer and a higher RH accelerated the formation of the regional haze. In one incident, the temperature inversion layer occurred at a height of 130 m above ground level, which prevented the air pollutants from being dispersed vertically. The regional transport of pollutants also played an important role in the formation of the haze. Wind from the south of Beijing increased from 58% in January 2012 to 63% in January 2013. Because the area to the south of Beijing is characterized by high industrial development, the unusual wind direction favored the regional transport of pollutants and severely exacerbated the haze. SO , NO and NH are the three major water-soluble ions that contributed to the formation of the haze. The high variability in Cl and K indicated that large quantities of coal combustion and biomass burning occurred during the haze.
Daily PM samples were collected in Chengdu, a megacity in southwest China, for a period of one month in every season during 2009–2010. Mass concentrations of water-soluble inorganic ions, organic carbon (OC), elemental carbon (EC), levoglucosan (LG), water soluble organic carbon (WSOC), and elements were determined to identify the chemical characteristics and potential sources of PM . The data obtained in spring were discussed in detail to explore the impacts of dust storms and biomass burning on the chemical aerosol properties. The daily PM mass concentrations ranged from 49.2 to 425.0 μg m with an annual average of 165.1 ± 85.1 μg m . The highest seasonal average of PM concentrations was observed in the winter (225.5 ± 73.2 μg m ) and the lowest in the summer (113.5 ± 39.3 μg m ). Dust storm influence was observed only during the spring, while biomass burning activities occurred frequently in late spring and early summer. In the spring season, water-soluble ions, total carbonaceous aerosols, and the sum of the dominant elements (Al, Si, Ca, Ti, Fe, Mn, Zn, Pb, and Cu) accounted for 30.0 ± 9.3%, 38.6 ± 11.4%, and 6.2 ± 5.3%, respectively, of the total PM mass. Crustal element levels evidently increased during the dust storm episode and LG, OC, WSOC, Cl and K concentrations increased by a factor of 2-7 during biomass burning episodes. Using the Positive Matrix Factorization (PMF) receptor model, four sources for spring aerosols were identified, including secondary sulfate and nitrate, motor vehicle emissions, soil dust, and biomass burning. The four sources were estimated to contribute 24.6%, 18.8%, 23.6% and 33.0%, respectively, to the total PM mass. ► Major chemical components of PM were identified in each season. ► Dust storms occasionally affected PM composition in spring. ► Biomass burning significantly changed PM composition in late spring to early summer. ► Major sources for PM include secondary sulfate/nitrate, motor vehicles and road dust, soil dust and others, and biomass burning.
To investigate the chemical properties of PM and put forward reasonable control measures, daily samples of PM were collected at an urban site in Beijing from August 4 to September 3 of 2012 using two 2-channel samplers. Chemical analysis was conducted for eight water soluble inorganic ions (WSII, including Na , NH , K , Mg , Ca , Cl , NO , and SO ), organic carbon (OC) and elementary carbon (EC). PM concentrations ranged from 8.8 to 218.6 μg m , with an average concentration of 80.6 ± 57.3 μg m . WSII, the most dominant PM constituents contributing 60 ± 18% of its mass, ranged from 3.1 to 172.2 μg m . SO , NO , and NH dominated WSII (90 ± 28%) and their concentrations were 1.3–105.7 μg m , 0.5–52.7 μg m and 0.3–33.5 μg m , respectively. The concentrations of OC and EC were 3.0–28.8 μgC m and 0.8–7.4 μgC m , constituting 17.6% and 4.9% of PM , respectively. Three serious pollution episodes (haze days) occurred during the campaign. PM and its chemical species showed substantial increases during haze episodes. The greater enhancement factors for SO (4.5), NO (4.0), and NH (4.2) during haze days compared to non-haze days were obtained, suggesting that these secondary inorganic ions play important roles in the formation of haze. The average ratio of NO /SO was 0.52. Ion balance calculations showed that PM samples were acidic during haze periods and close to neutral during non-haze days. Correlation analysis between the major ions was conducted and the results suggested that the main forms of NH might be (NH ) SO . In addition, the variations between haze days and non-haze days for OC, EC, and the ratio of OC/EC were discussed.
Trend analysis of the mean (monsoon season, non-monsoon season and annual) and extreme annual daily rainfall and temperature at the spatial and temporal scales was carried out for all the 33 urban centers of the arid and semi-arid state of Rajasthan, India. Statistical trend analysis techniques, namely the Mann–Kendall test and Sen's slope estimator, were used to examine trends (1971–2005) at the 10% level of significance. Both positive and negative trends were observed in mean and extreme events of rainfall and temperature in the urban centers of Rajasthan State. The magnitude of the significant trend of monsoon rainfall varied from (−) 6.00 mm/hydrologic year at Nagaur to (−) 8.56 mm/hydrologic year at Tonk. However, the magnitude of the significant negative trends of non-monsoon rainfall varied from (−) 0.66 mm/hydrologic year at Dungarpur to (−) 1.27 mm/hydrologic year at Chittorgarh. The magnitude of positive trends of non-monsoon rainfall varied from 0.93 mm/hydrologic year at Churu to 1.70 mm/hydrologic year at Hanumangarh. The magnitude of the significant negative trends of annual rainfall varied from (−) 6.47 mm/year at Nagaur to (−) 10.0 mm/year at Tonk. The minimum, average and maximum temperature showed significant increasing warming trends on an annual and seasonal scale in most of the urban centers in Rajasthan State. The magnitude of statistically significant annual extreme daily rainfall varied from 2.00 mm at Jhalawar to (−) 1.64 mm at Tonk, while the magnitude of statistically significant extreme annual daily minimum and maximum temperature varied from 0.03 °C at Ganganagar to 0.05 °C at Jhalawar, respectively. The spatial variations of the trends in mean (monsoon season, non-monsoon season and annual) and extreme annual daily rainfall and temperature were also determined using the inverse-distance-weighted (IDW) interpolation technique. IDW results are helpful to identify trends and variability in mean and extreme rainfall and temperature in space and time for the study locations where the data is not available and the quality of data is not good. These spatial maps of temperature and rainfall can help local stakeholders and water managers to understand the risks and vulnerabilities related to climate change in terms of mean and extreme events in the region.
This paper explores the many aspects of precipitation measurement that are relevant to providing an accurate global assessment of this important environmental parameter. Methods discussed include ground data, satellite estimates and numerical models. First, the methods for measuring, estimating, and modeling precipitation are discussed. Then, the most relevant datasets gathering precipitation information from those three sources are presented. The third part of the paper illustrates a number of the many applications of those measurements and databases, namely hydropower, data assimilation and validation of Regional Climate Models (RCM). The aim of the paper is to organize the many links and feedbacks between precipitation measurement, estimation and modeling, indicating the uncertainties and limitations of each technique in order to identify areas requiring further attention, and to show the limits within which datasets can be used. Special emphasis is put on the central role of the upcoming Global Precipitation Measurement (GPM) mission in precipitation science.
The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957–2008 and the monthly EDI predicted over the period 2009–2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg–Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.
The abundance, behavior, and source of chemical species in size-fractionated atmospheric particle were studied with a 13-stage low pressure impactor (ELPI) during high polluted winter episode in Beijing. Thirty three elements (Al, Ca, Fe, K, Mg, Na, Si, Sc, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Sr, Zr, Mo, Ag, Cd, In, Sn, Sb, Cs, Ba, Hg, Tl, and Pb) and eight water soluble ions (Cl , NO , SO , NH , Na , K , Ca , and Mg ) were determined by ICP/MS and IC, respectively. The size distribution of TC (OC + EC) was reconstructed. Averagely, 51.5 ± 5.3% and 74.1 ± 3.7% of the total aerosol mass was distributed in the sub-micron (PM ) and fine particle (PM ), respectively. A significant shift to larger fractions during heavy pollution episode was observed for aerosol mass, NH , SO , NO , K, Fe, Cu, Zn, Cd, and Pb. The mass size distributions of NH , SO , NO , and K were dominated by accumulation mode. Size distributions of elements were classified into four main types: (I) elements were enriched within the accumulation mode (< 1 μm, Ge, Se, Ag, Sn, Sb, Cs, Hg, Ti, and Pb); (II) those mass (K, Cr, Mn, Cu, Zn, As, Mo, and Cd) was resided mainly within the accumulation mode, ranged from 1 to 2 μm; (III) Na, V, Co, Ni, and Ga were distributed among fine, intermediate, and coarse modes; and (IV) those which were mainly found within particles larger than 2.7 μm (Al, Mg, Si, Ca, Sc, Tl, Fe, Sr, Zr, and Ba). [H ] showed an accumulation mode at 600–700 nm and the role of Ca should be fully considered in the estimation of acidity. The acidity in accumulation mode particles suggested that generally gaseous NH was not enough to neutralize sulfate completely. PMF method was applied for source apportionment of elements combined with water soluble ions. Dust, vehicle, aged coal combustion, and sea salt were identified, and the size resolved source apportionments were discussed. Aged coal combustion was the important source of fine particles and dust contributed most to coarse particle.
The 24-h PM 2.5 samples were collected at the site of East China University of Science and Technology (ECUST) in Shanghai from 2011 to 2012, representing winter, spring, summer and autumn, respectively. And PM 2.5 and its chemical components including organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), humic-like substance carbon (HULIS-C) and water-soluble ions were analyzed. The results suggested that the average PM 2.5 concentrations were (70.35±43.75) μg/m 3 , (69.76±38.67) μg/m 3 , (51.26±28.25) μg/m 3 and (82.37±48.70) μg/m 3 in winter, spring, summer and autumn, respectively. Secondary inorganic ions (sulfate, nitrate and ammonium) were the dominant pollutants of PM 2.5 in the four seasons. Total carbon (TC) was an important component explaining above 15% of PM 2.5 . OC/EC ratios were all above 2 ranging from 4.31 to 6.35; particularly in winter it reached the highest 6.35 which demonstrated that secondary organic carbon (SOC) should be a significant composition of PM 2.5 . The SOC calculated based on the OC/EC ratio method had stronger correlation with WSOC in summer and autumn (summer: R 2 =0.73 and autumn: R 2 =0.75). The HULIS-C and SOC most significantly correlated in autumn (R 2 =0.83). The data showed that PM 2.5 atmospheric aerosols were more acidic in autumn and the concentrations of PM 2.5 and its chemical components were much higher. Factor analysis (FA), cluster analysis of air mass back trajectories, potential source contribution function (PSCF) model and concentration weighted trajectory (CWT) model were used to investigate the transport pathways and identify potential source areas of PM 2.5 in different seasons. FA identified various sources of PM 2.5 : secondary aerosol reactions, the aged sea salts and road dusts. The results of cluster analysis, PSCF model and CWT model demonstrated that the local sources in the Yangtze River Delta Region (YRDR) made significant contributions to PM 2.5 . During winter and autumn long-time transport from the Circum-Bohai-Sea Region (CBSR) and northwestern China including the Inner Mongol had adverse effects.
The Geostationary Operational Environmental Satellite R-series (GOES-R) is the next block of four satellites to follow the existing GOES constellation currently operating over the Western Hemisphere. Advanced spacecraft and instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES capabilities include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved cloud and moisture imagery with the 16-channel Advanced Baseline Imager (ABI). The GLM will map total lightning activity continuously day and night with near-uniform storm-scale spatial resolution of 8 km with a product refresh rate of less than 20 s over the Americas and adjacent oceanic regions in the western hemisphere. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, an Algorithm Working Group (AWG) Lightning Detection Science and Applications Team developed the Level 2 (stroke and flash) algorithms from the Level 1 lightning event (pixel level) data. Proxy data sets used to develop the GLM operational algorithms as well as cal/val performance monitoring tools were derived from the NASA Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) instruments in low Earth orbit, and from ground-based lightning networks and intensive prelaunch field campaigns. The GLM will produce the same or similar lightning flash attributes provided by the LIS and OTD, and thus extend their combined climatology over the western hemisphere into the coming decades. Science and application development along with preoperational product demonstrations and evaluations at NWS forecast offices and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and checkout of GOES-R in late 2015. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings. ► GLM provides operational total lightning data for the global observing system. ► GLM will improve forecasts and increase warning lead times. ► Observations of total lightning day and night will help to save lives.
The forecasting of drought based on cumulative influence of rainfall, temperature and evaporation is greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture, ecosystems, wildlife, tourism, recreation, crop health and hydrologic engineering. Predictive models of drought indices help in assessing water scarcity situations, drought identification and severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network (ANN) as a data-driven model for predicting the monthly Standardized Precipitation and Evapotranspiration Index ( ) for eight candidate stations in eastern Australia using predictive variable data from 1915 to 2005 (training) and simulated data for the period 2006–2012. The predictive variables were: monthly rainfall totals, mean temperature, minimum temperature, maximum temperature and evapotranspiration, which were supplemented by large-scale climate indices (Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and Indian Ocean Dipole) and the Sea Surface Temperatures (Nino 3.0, 3.4 and 4.0). A total of 30 ANN models were developed with 3-layer ANN networks. To determine the best combination of learning algorithms, hidden transfer and output functions of the optimum model, the Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithms were utilized to train the network, tangent and logarithmic sigmoid equations used as the activation functions and the linear, logarithmic and tangent sigmoid equations used as the output function. The best ANN architecture had 18 input neurons, 43 hidden neurons and 1 output neuron, trained using the Levenberg–Marquardt learning algorithm using tangent sigmoid equation as the activation and output functions. An evaluation of the model performance based on statistical rules yielded time-averaged Coefficient of Determination, Root Mean Squared Error and the Mean Absolute Error ranging from 0.9945–0.9990, 0.0466–0.1117, and 0.0013–0.0130, respectively for individual stations. Also, the Willmott's Index of Agreement and the Nash–Sutcliffe Coefficient of Efficiency were between 0.932–0.959 and 0.977–0.998, respectively. When checked for the severity ( ), duration ( ) and peak intensity ( ) of drought events determined from the simulated and observed , differences in drought parameters ranged from − 1.41–0.64%, − 2.17–1.92% and − 3.21–1.21%, respectively. Based on performance evaluation measures, we aver that the Artificial Neural Network model is a useful data-driven tool for forecasting monthly and its drought-related properties in the region of study.
Haze pollution in Beijing is rather deteriorated. Long-term measurement of PM from 2005 to 2010 at an urban site in Beijing showed very high concentration level with an annual average 74 ± 55 μg/m . The contribution of regional sources is one of the most important factors; thus, transport and regional sources of PM in Beijing are investigated using the trajectory cluster and receptor models (potential source contribution function and trajectory sector analysis). The results indicated that the highest concentrations of PM (76–120 μg/m ) were associated with south, southeast, and short northwest trajectories, and moderate concentrations (46–67 μg/m ) with long northwest and short north trajectories, and the lowest concentrations (20–33 μg/m ) with long north trajectories. During the relatively polluted periods, the probable locations of regional emission sources were mainly in the south and the west of Beijing and varied according to different seasons. Between 2005 and 2010, the annual mean contribution of 35.5% (32.8 μg/m ) for PM was attributed to long-distance transportation. The transported contribution percentages from 2005 to 2010 for PM showed an increasing tendency with a linear rate of 1.2/year.