Description: A genetic programming (GP)-based logistic regression method is proposed in the present study for the downscaling of extreme rainfall indices on the east coast of Peninsular Malaysia, which is considered one of the zones in Malaysia most vulnerable to climate change. A National Centre for Environmental Prediction reanalysis dataset at 42 grid points surrounding the study area was used to select the predictors. GP models were developed for the downscaling of three extreme rainfall indices: days with larger than or equal to the 90th percentile of rainfall during the north-east monsoon; consecutive wet days; and consecutive dry days in a year. Daily rainfall data for the time periods 1961–1990 and 1991–2000 were used for the calibration and validation of models, respectively. The results are compared with those obtained using the multilayer perceptron neural network (ANN) and linear regression-based statistical downscaling model (SDSM). It was found that models derived using GP can predict both annual and seasonal extreme rainfall indices more accurately compared to ANN and SDSM.

Description: Four synoptic regimes were identified as accompanying the widespread dust in central and eastern Saudi Arabia. The widespread cases of dust were classified based on the value and spread of the aerosol index data from the TOMS aerosol index (TOMS AI) satellite over the area of interest. The synoptic regimes of these dust cases were recognized using the Empirical Orthogonal Function (EOF) analysis of their mean sea level pressure (SLP), which was obtained from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) Reanalysis Project dataset. The variations of the analyzed SLP of these four regimes appeared as meridional distributions for the first two regimes and zonal distributions for the second two regimes. A surface synoptic study of the first two regimes showed that the most significant features were either a strong low-pressure system over the eastern region or a strong high-pressure system over the western region. The synoptic features for the less significant regimes (the second two regimes) were characterized by the interaction between the northern high-pressure belt, which shifted northward because of the significant regime decrease, and the southern low-pressure belt. In addition, the upper synoptic study showed that the upper synoptic systems support the surface systems. Moreover, the study showed that the surface northerly wind over the eastern Arabian Peninsula is the dominant wind during strong dust activity, whereas the surface southerly wind is dominant during weak dust activity.

Description: Satellite measurements of the spatiotemporal distributions of atmospheric CO2 concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO2 column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO2 data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO2 concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO2 concentrations is presented with the goal of generating global land CO2 distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO2 distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO2 distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO2 distribution with high accuracies and high spatiotemporal resolution.

Description: Drought & flood events, especially the drought & flood combination events (DFCEs) on the North China Plain (NCP), known as an important grain production region in China, constitute a serious threat to China’s food security. Studies on DFCEs in this region are of great significance for the rational allocation of water resources and the formulation of integrated response strategy for droughts and floods. In this study, L-moments theory and bivariate copula method were used to evaluate the probability characteristics of seasonal DFCEs (continuous drought, continuous flood, and alternation between drought and flood) on the NCP, based on the daily precipitation data (1960–2012) at 19 meteorological stations. Results indicate the following: (1) On the NCP, the precipitation in summer accounts for 56.45%–72.02% of mean annual precipitation, and the precipitation in autumn and spring come second. The winter precipitation is the smallest (less than 4%); (2) The best-fit distribution for precipitation anomaly percentages in spring, summer and autumn are Generalized Normal (GNO), Generalized Logistic (GLO) and Pearson III (P-III) in sub-region I, respectively. While in sub-region II, they are respectively the P-III, P-III and Generalized Extreme-Value (GEV); (3) Compared with the Gumbel copula and Clayton copula, Frank copula is more suitable for spring-summer and summer-autumn precipitation anomaly percentage sequences on the NCP; (4) On the time scale, continuous drought respectively dominate in spring-summer DFCEs and in summer-autumn DFCEs on the NCP. Summer-autumn DFCEs prevail in sub-region I with the average probability value 0.34, while spring-summer DFCEs dominate in sub-region II, of which average probability value is 0.42; (5) On the spatial scale, most areas where the probability of continuous drought in spring-summer and spring drought & summer flood is relatively high are located in the northwest, northeast, and coastal parts of sub-region II; all the events with high probability of continuous drought in summer-autumn and summer flood & autumn drought occurred at the central part in the northwest of sub-region II.

Description: The SO2 emissions from coal-fired power plants in China have been regulated since 2005 by a mandatory installation of flue gas desulfurization (FGD) devices. In order to verify the effectiveness of FGD systems applied in power plants, Shanxi (a province well-known for the largest coal reserves in China) was selected, and the characteristic and evolution of SO2 densities over 22 regions with large coal-fired power plants during 2005–2012 were investigated by using the satellite remote sensing data from the Ozone Monitoring Instrument (OMI). A unit-based inventory was also employed to study the trend of SO2 emissions from coal-fired power plants in Shanxi. The results show that the operation of FGD systems was successful in reducing SO2 emissions from power plants during 2005–2010: the mean SO2 densities satellite-observed over those regions with power plants operated before 2005 showed a notable decrease of approximate 0.4 DU; the mean SO2 densities over other regions with power plants newly built behind 2006 did not show a statistical increasing trend overall; the mean SO2 density over the whole Shanxi also showed a moderate decline from 2008 to 2010. However, the polluted conditions over Shanxi during 2011–2012 rebounded and the declining trend in mean SO2 density over the whole Shanxi disappeared again. In comparison of unit-based emission inventory, the emissions calculated show a similar trend with SO2 densities satellite-observed during 2005–2010 and still maintain at a lower volume during 2011–2012. By investigating the developments of other emission sources in Shanxi during 2005–2012, it is considered that the rapid expansion of industries with high coal-consumption has played an important role for the increment rise of SO2 emissions. Lack of an independent air quality monitoring network and the purposeful reduced operation rate of FGD systems occurring in some coal-fired power plants have reduced the effectiveness of SO2 emission reduction policy applied in Shanxi. In view that the SO2 pollution in Shanxi has not been well ameliorated, more reasonable and mandatory policies, such as a national-wide independent monitoring network and installation of FGD systems in other large emission sources, should be pushed out in the near future.

Description: Data from the annual, seasonal, and hourly behavior of the criteria air pollutants CO, NO2, SO2, O3, and PM10 in three Mexican metropolitan areas (the Mexico City Metropolitan Area (MCMA), Guadalajara Metropolitan Area (GMA), and Monterrey Metropolitan Area (MMA)) over the period 2000–2011 were analyzed; and compliance with Mexican air quality standards was evaluated, highlighting causes of specific episodes of high and low concentrations. Data analyzed were collected from automatic air-monitoring networks located in the MCMA (32 stations), GMA (8 stations), and MMA (5 stations). In the MCMA and MMA, correlations between wind direction and concentrations of SO2 suggest that there was a considerable contribution of trans-boundary transport from outside of these areas. Analysis of annual trends revealed large reductions of CO in the MCMA, and SO2 in the three metropolitan areas. However, the annual mean concentration of O3 increased by 47% and 42% in the GMA and MMA, respectively, from 2000 to 2011, but decreased by 13% in the MCMA from 2005 to 2010. The annual mean concentration of PM10 in the MMA was about 58% and 76% higher than that in the MCMA and GMA, respectively, from 2001 to 2010.

Description: We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro) and NFIS-WPM (Ave) are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

Description: Urban forest parks play important roles in improving environments, protecting biodiversity and even public welfare. Aerosols, including total suspended particles (TSP) and particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5), were simultaneously collected in an urban forest park (Dafushan) at Guangzhou, southern China, from January 2012 to December 2013. The concentrations of 12 metals (Al, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Se, and Zn) in both TSP and PM2.5 were quantified using an inductively coupled plasma-mass spectrometer. The origins and possible sources of the studied metals in the PM2.5 and TSP were evaluated using the crustal enrichment factors and the principal component analysis, respectively. The results showed that Dafushan urban forest park was polluted by PM2.5 rather than by TSP. The PM2.5 and TSP in the forest park exhibited seasonal patterns with significantly higher contents in the dry season compared with the rainy season. The metals Al, Zn, Pb were the most abundant, while Hg was the lowest metals in the aerosols. The ratios of PM2.5/TSP ratio indicated that the metals were predominant in the finer particles (PM2.5). The crustal enrichment factors indicated that Cd, Cu, Mo, Pb, Se and Zn in the aerosols originated from anthropogenic sources, while Al and Mn were mainly of crustal origin. The principal component analysis implied that industrial activities, traffic-related emissions, and soil dust were the main possible sources of the metals in both PM2.5 and TSP in Dafushan forest park.

Description: A recent re-evaluation of urban heat island (UHI) studies has suggested that the urban effect may be expressed more meaningfully as a difference between Local Climate Zones (LCZ), defined as areas with characteristic dimensions of between one and several kilometers that have distinct effects on climate at both micro-and local-scales (city streets to neighborhoods), rather than adopting the traditional method of comparing urban and rural air temperatures. This paper reports on a UHI study in Dublin (Ireland) which maps the urban area into LCZ and uses these as a basis for carrying out a UHI study. The LCZ map for Dublin is derived using a widely available land use/cover map as a basis. A small network of in-situ stations is deployed into different LCZ across Dublin and additional mobile temperature traverses carried out to examine the thermal characteristics of LCZ following mixed weather during a 1 week period in August 2010. The results show LCZ with high impervious/building coverage were on average >4 °C warmer at night than LCZ with high pervious/vegetated coverage during conditions conducive to strong UHI development. The distinction in mean LCZ nocturnal temperature allows for the generation of a heat map across the entire urban area.

Description: Potential evapotranspiration (PET) is an important indicator of atmospheric evaporation demand and has been widely used to characterize hydrological change. However, sparse observations of pan evaporation (EP) prohibit the accurate characterization of the spatial and temporal patterns of PET over large spatial scales. In this study, we have estimated PET of China using the Penman-Monteith (PM) method driven by gridded reanalysis datasets to analyze the spatial and decadal variations of PET in China during 1982–2010. The results show that the estimated PET has decreased on average by 3.3 mm per year (p 〈 0.05) over China during 1982–1993, while PET began to increase since 1994 by 3.4 mm per year (p 〈 0.05). The spatial pattern of the linear trend in PET of China illustrates that a widely significant increasing trend in PET appears during 1982–2010 in Northwest China, Central China, Northeast China and South China while there are no obvious variations of PET in other regions. Our findings illustrate that incident solar radiation (Rs) is the largest contributor to the variation of PET in China, followed by vapor pressure deficit (VPD), air temperature (Tair) and wind speed (WS). However, WS is the primary factor controlling inter-annual variation of PET over Northwest China.