Atmosphere2014, 5(4), 1020-1041; doi:10.3390/atmos5041020 - published 15 December 2014 Show/Hide Abstract
Abstract: The impact of adverse weather conditions (AWCs) on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI) related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical (“event scenario”) approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.
Atmosphere2014, 5(4), 1002-1019; doi:10.3390/atmos5041002 - published 5 December 2014 Show/Hide Abstract
Abstract: This study focused on the estimation of black carbon emissions from dry dipterocarp forest fires in Thailand. Field experiments were set up at the natural forest, Mae Nam Phachi wildlife sanctuary, Ratchaburi Province, Thailand. The dead leaves were the main component consumed of the surface biomass with coverage higher than 90% in volume and mass. The dead leaves load was 342 ± 190 g∙m−2 and followed by a little mass load of twig, 100 g∙m−2. The chemical analysis of the dead leaves showed that the carbon content in the experimental biomass fuel was 45.81 ± 0.04%. From the field experiments, it was found that 88.38 ± 2.02% of the carbon input was converted to carbon released to the atmosphere, while less than 10% were left in the form of residues, and returned to soil. The quantity of dead leaves consumed to produce each gram of carbon released was 2.40 ± 0.02 gdry biomass burned. From the study, the emissions factor of carbon dioxide, carbon monoxide, particulate matter (PM2.5) and black carbon amounted 1329, 90, 26.19 and 2.83 g∙kg−1dry biomass burned, respectively. In Thailand, the amount of black carbon emissions from dry dipterocarp forest fires amounted 17.43 tonnes∙y−1.
Atmosphere2014, 5(4), 973-1001; doi:10.3390/atmos5040973 - published 4 December 2014 Show/Hide Abstract
Abstract: An analysis of coastal meteorological mechanisms facilitating the transit pollution plumes emitted from sources in the Northeastern U.S. was based on observations from the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) 2004 field campaign. Particular attention was given to the relation of these plumes to coastal transport patterns in lower tropospheric layers throughout the Gulf of Maine (GOM), and their contribution to large-scale pollution outflow from the North American continent. Using measurements obtained during a series of flights of the National Oceanic & Atmospheric Administration (NOAA) WP-3D and the National Aeronautics and Space Administration (NASA) DC-8, a unique quasi-Lagrangian case study was conducted for a freshly emitted plume emanating from the New York City source region in late July 2004. The development of this plume stemmed from the accumulation of boundary layer pollutants within a coastal residual layer, where weak synoptic conditions allowed for its advection into the marine troposphere and transport by a mean southwesterly flow. Upon entering the GOM, analysis showed that the plume layer vertical structure evolved into an internal boundary layer form, with signatures of steep vertical gradients in temperature, moisture and wind speed often resulting in periodic turbulence. This structure remained well-defined during the plume study, allowing for the detachment of the plume layer from the surface and minimal plume-sea surface exchange. In contrast, shear driven turbulence within the plume layer facilitated lateral mixing with other low-level plumes during its transit. This turbulence was periodic and further contributed to the high spatial variability in trace gas mixing ratios. Further influences of the turbulent mixing were observed in the impact of the plume inland as observed by the Atmospheric Investigation, Regional Modeling, Analysis and Prediction (AIRMAP) air quality network. This impact was seen as extreme elevations of surface ozone and CO levels, equaling the highest observed that summer.
Atmosphere2014, 5(4), 959-972; doi:10.3390/atmos5040959 - published 3 December 2014 Show/Hide Abstract
Abstract: Solar surface irradiance is an important variable in many different fields, e.g., climate monitoring and solar energy. Remote sensing data are nowadays well established and the only observational data source in many regions of the world. Aerosols significantly affect the clear sky radiation and hence also the all sky radiation. In order to achieve the optimal accuracy for surface radiation, information of aerosols with low uncertainty is needed. In this study, the effect of four different aerosol climatologies on the solar surface radiation have been evaluated for the period 2006–2009 at nine BSRN stations. The use of the aerosol climatology from the European Center of Medium Weather Forecast (MACC) leads to the highest accuracy of solar radiation. The mean absolute bias is 6.8 Watt per square meter for global irradiance and 11.3 for direct irradiance. With the Max-Planck climatology (MAC-v1) 9.4 and 14.8 Watt per square meter and with GADS/OPAC (Global Aerosol Data Set/Optical Properties of Aerosols and Clouds) 10.0 and 14.6 Watt per square meter have been achieved, respectively. The improvement in the accuracy of solar radiation by using the MACC climatology is relatively large. Also remarkable is that the new MAC-v1 climatology and the older GADS/OPAC climatology performs on the same level with respect to the achieved accuracy in radiation. The effect of interannual variations of Aerosol Optical Depth (AOD) on the global irradiance is rather low for the investigated sites and period.
Atmosphere2014, 5(4), 937-958; doi:10.3390/atmos5040937 - published 2 December 2014 Show/Hide Abstract
Abstract: Ozone concentrations in the Mediterranean area regularly exceed the maximum levels set by the EU Air Quality Directive, 2008/50/CE, a maximum 8-h mean of 120 μg·m-3, in the summer, with consequences for both human health and agriculture. There are a number of reasons for this: the particular geographical and meteorological conditions in the Mediterranean play a part, as do anthropogenic ozone precursor emissions from around the Mediterranean and continental Europe. Ozone concentrations measured on-board the Italian Research Council’s R. V. Urania during summer oceanographic campaigns between 2000 and 2010 regularly exceeded 60 ppb, even at night. The WRF/Chem (Weather Research and Forecasting (WRF) model coupled with Chemistry) model has been used to simulate tropospheric chemistry during the periods of the measurement campaigns, and then, the same simulations were repeated, excluding the contribution of maritime traffic in the Mediterranean to the anthropogenic emissions inventory. The differences in the model output suggest that, in large parts of the coastal zone of the Mediterranean, ship emissions contribute to 3 and 12 ppb to ground level daily average ozone concentrations. Near busy shipping lanes, up to 40 ppb differences in the hourly average ozone concentrations were found. It seems that ship emissions could be a significant factor in the exceedance of the EU directive on air quality in large areas of the Mediterranean Basin.
Atmosphere2014, 5(4), 914-936; doi:10.3390/atmos5040914 - published 27 November 2014 Show/Hide Abstract
Abstract: 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.