Variations In Meteorological Conditions Research Articles

Hourly PM2.5 concentration forecast using stacked autoencoder model with emphasis on seasonality

Accurate PM2.5 forecasting provides a possibility for establishing an early warning system to notify the public and take precautionary measures to prevent negative effects on ambient air quality and public health. Considering strong seasonal variation in meteorological conditions, in this paper, a seasonal stacked autoencoder model combining seasonal analysis and deep feature learning is proposed for forecasting the hourly PM2.5 concentration, named DL-SSAE model. The original data are firstly decomposed into four seasonal subseries according to the Chinese calendar, and then the Kendall correlation coefficient method is employed to search inherent relationships between PM2.5 concentrations and meteorological parameters within 1-h ahead for each seasonal time series. The inherent relationships of each seasonal subseries are finally extracted, learned, and modeled by different deep neural networks (stacked autoencoders for regression), and the hourly PM2.5 forecasts are yielded. The addressed model is tested by the dataset collected from three environmental monitoring stations in Beijing, China. The results demonstrate that the proposed model outperforms all other considered models with/without seasonality consideration in this paper.

How have the characteristics of air quality in a typical large Chinese city changed between 2011 and 2017?

Great efforts have been made to control air pollution in China, and these have markedly changed air quality. Here, a detailed analysis of changes in air pollution in Nanjingbetween 2011 and 2017 based on hourly concentration measurements of six air pollutants is presented. The concentrations of most of these pollutants have decreased since 2013. This has resulted in the annual proportion of days meeting the new Chinese air quality standards increasing by 17.5%, as well as the proportion of days of severe or strong pollution decreasing by 8.0%. However, the ozone (O3) concentration increased between 2011 and 2017, and the number of days that O3 concentrations exceeded the Chinese standard dramatically increased from 8 days in 2011 to 60 days in 2017. Clearly, O3 has replaced fine particulate matter (PM2.5) as the most frequent dominant pollutant since 2016. Our results indicate that air pollution in Nanjing has evolved from being dominated by primary pollutants to being dominated by secondary pollutants, reflecting interaction of multiple emissions and chemical processes. Current policies and strategies need to be reassessed and modified to deal with interactions between emissions from different sources under variable meteorological conditions.

ModOdor: 3D numerical model for dispersion simulation of gaseous contaminants from waste treatment facilities

Pollution caused by gaseous contaminant from waste treatment facilities, such as wastewater plants, municipal solid waste landfills, transfer stations, has elicited public concern. The potential environmental impacts of gaseous emissions from waste sectors are regulated by legislations in many countries, and dispersion models are powerful tools.Modeling of Odor gas air dispersion software (ModOdor), a 3D unsteady-state numerical model, was developed in this study to simulate the dispersion of gaseous contaminants from waste sectors. ModOdor overcomes the limitations of Gaussian and Lagrangian models when applied to unsteady-state, local-scale gaseous dispersion from waste sectors, because it adopts the numerical Eulerian scheme and finite difference method. Particularly, ModOdor can generate potential flow solutions associated with complex topography, process different types of emission sources, simulate spatiotemporal variations in meteorological conditions (wind and dispersion), and implement physical/chemical reactions for tracking the temporal and spatial evolution of plume and managing gaseous pollution from waste sectors.

Impact of seasonal variation in meteorological conditions on dry eye severity.

PurposeTo compare dry eye (DE) diagnosis patterns by season in Miami vis-a-vis the US and examine differences in DE symptoms and signs by season in Miami.Patients and methodsUS veteran affairs (VA) patient visits with ICD-9 codes for DE (375.15) and routine medical examination (V70.0) from 2010 to 2013 were retrospectively analyzed to evaluate the seasonal pattern of DE diagnosis. A total of 365 patients with normal ocular anatomy were prospectively recruited from the Miami VA eye clinic from 2014 to 2016 for the assessment of symptoms and signs.ResultsWhile DE visit prevalence in Miami was about 10% lower than that of the rest of the country (22.5% vs 33.7%), Miami had roughly four times higher variability in DE visit prevalence throughout the year than the US. Peak values for DE symptoms in the Miami cohort aligned with peak DE prevalence seen in the retrospective sample, occurring in spring and fall. A similar, but less dramatic, pattern was noted with DE signs. The seasonal pattern in DE symptoms remained even after controlling for confounders including demographics and medication use.ConclusionDE symptoms, and to a lesser degree signs, varied by month, with the highest severity of symptoms occurring in spring and fall, which corresponded with peak allergy season and weather fluctuations, respectively. These findings have important implications for season-specific diagnosis, treatment, and management of DE.

Primary effects of changes in meteorology vs. anthropogenic emissions on mercury wet deposition: A modeling study

In this study we investigated the respective contributions of changing meteorology and anthropogenic emissions to Hg wet deposition in New York State (NYS), which illustrated quantitatively how the variations in meteorological conditions modified the response of Hg wet deposition to anthropogenic emission reductions in NYS. A regional chemical transport model, CMAQ-newHg-Br (Ye et al., 2018), was employed driven by four sets of meteorological fields (2004, 2005, 2007, and 2010) and two anthropogenic emission inventories (NEI, 2005 and 2011). Four major findings emerged from this study. First, simulated Hg wet deposition flux in NYS was greatly affected by variation in meteorological conditions, ranging from a 91% decrease to a factor of 5 increase in monthly Hg wet deposition, whereas it hardly responded (2%) to a near doubling of emissions in the northeastern U.S. Second, the precipitation amount dominated interannual and seasonal variations of Hg wet deposition over all other meteorological variables, with total (= convective + non-convective) precipitation explained 60% of Hg wet deposition variance in an urban environment compared to convective precipitation alone explaining 65% in a rural area. Third, large-scale atmospheric circulation influenced regional transport greatly leading to out-of-state anthropogenic emissions explaining at least 80% of Hg wet deposition changes in a NYS urban environment. Fourth, higher Hg concentrations in precipitation and higher atmospheric reactive mercury concentrations in urban areas were related to calm conditions due likely to regional build-up of emissions, whereas in rural areas photochemical oxidation of gaseous elemental mercury was a more important source of reactive mercury. Solar radiation variance was found to explain 41–55% of the variance in simulated reactive mercury concentrations. Given that more than 95% of the US is rural lands and the majority of water bodies occur in rural areas, a realistic chemical mechanism for photochemical gaseous elemental mercury oxidation is paramount to accurate simulation of reactive mercury formation and Hg wet deposition in the US.

A PARAMETRIC APPROACH FOR THE STUDY OF HEAT FLOW BETWEEN STREET CANYON AND THE ATMOSPHERE

This paper presents the investigation results of the convective heat flow behaviour among the top of an urban street canyon and overlying atmosphere using a numerical model together with available field measurement data in variable geographical and meteorological conditions. It finds that the heat flow structure characterizes the street canyon have a strong relationship with narrowness index, outside wind velocity and latitude of the study area. The increase of the narrowness index and consequently, reduction of the sky-view factor leads to the diminution of heat flow exchange. The temperature of canyon surfaces in smaller narrowness index (n) decreases quickly to the lower degree than the temperature of the surface with larger (n) one. The increase of wind velocity outside of canyon makes this convective heat exchange flow higher, and cooler the street canyon. A parametric approach was established to evaluate this convective heat exchange flow based on the narrowness index, the latitude of the city and outside wind velocity.

Comparison of short-term solar irradiance forecasting methods when weather conditions are complicated

Although the output of a photovoltaic power generation system is significantly positively correlated with solar irradiance, the latter variable is intermittent, random, and volatile. Volatility in solar irradiance is particularly marked when weather conditions are complex, and so, this factor has proved to be difficult to predict. A neural network (NN)-based approach is applied for short-term predictions in this study based on a timescale that encompasses the amount of irradiance each hour throughout the next day. Thus, a backpropagation NN (BPNN), a radial basis function NN (RBFNN), and an Elman NN (ENN) were selected for use in this analysis. A predictive model was established to evaluate the accuracy of different approaches, given variable meteorological conditions. To reduce the influence of solar irradiance, samples used for forecasts were subdivided into spring, summer, fall, and winter, and the forecast results of sunny and rainy as well as cloudy days in different seasons were investigated. The results of this study reveal that the predictive accuracies of the BPNN and RBFNN were poor on rainy and cloudy days, while the efficiency of the ENN was high and stable in variable meteorological conditions.

Measurement-based investigation of ozone deposition to vegetation under the effects of coastal and photochemical air pollution in the Eastern Mediterranean

Dry deposition of ozone (O3) to vegetation is an important pathway for its removal from the troposphere, and it can lead to adverse effects in plants and changes in climate. However, our mechanistic understanding of O3 dry deposition is insufficient to adequately account for it in global and regional models, primarily because this process is highly complicated by feedback mechanisms and sensitivity to specific characteristics of vegetative environment and atmospheric dynamics and composition. We hypothesized that measuring dry deposition of O3 to vegetation near the Eastern Mediterranean (EM) coast, where large variations in meteorological conditions and photochemical air pollution frequently occur, would enable identifying the mechanisms controlling O3 deposition to vegetation. Moreover, we have only limited knowledge of O3 deposition to vegetation occurring near a coastline, under air pollution, or in the EM. This study investigated O3 deposition to mixed Mediterranean vegetation between the summers of 2015 and 2017, 3.6 km away from the EM coast, using the eddy covariance technique to quantify vertical flux of O3 and its partitioning to stomatal and non-stomatal flux, concurrent with nitrogen oxide (NOx), sulfur dioxide and carbon monoxide. Surprisingly, nighttime O3-deposition velocity (Vd) was smaller than daytime Vd by only ~20–37% on average for all measurement periods, primarily related to moderate nighttime atmospheric stability due to proximity to the seashore. We provide evidence for the role of sea-salt aerosols in enhancing O3 deposition via surface-wetness buildup at low relative humidity near the coast, and for daytime enhancement of O3 deposition by the combined effects of biogenic volatile organic compound emission and surface-wetness buildup. We further show that NOx emitted from elevated emission sources can reduce O3 deposition, and even lead to a positive O3 flux, demonstrating the importance of adequately taking into account the impact of air pollution on O3 deposition to vegetation.

SPATIAL VARIABILITY OF THE MICROCLIMATE IN DIFFERENT SMALL RUMINANT HOUSE LOCATED IN THE SEMIARID OF PERNAMBUCO, BRAZIL

The spatial variability in meteorological conditions was evaluated in two small ruminant house with different constructive aspects, based on the variables of air temperature, relative humidity, wind speed and temperature humidity index (THI), through the generation of maps using the Kriging interpolation method. The experiment was conducted in two seasons, winter and summer, and the small ruminant house were denominated as FNRG (facility with no ridge vents at ground level) and FWRG (facility with ridge vents at ground level). The studied parameters were recorded by portable digital sensors, thermo-anemometers and thermo-hygrometers, with data collections at 9:00 am and 3:00 pm. With the analysis of the spatial variability of the data, it was observed that the pens positioned in the central region of the two facilities had the least stressful places to animals in two seasons and two times, where the FWRG presented larger areas with lower temperatures compared to the FNRG due to the presence of the ridge vents, were the least stressful places for the animals in the two seasons and at the two data collection times. At 9:00 am, in the winter and summer, both facilities presented comfortable conditions for the rearing of goats and sheep in the morning that were not observed at 3:00 pm.

Modeling the Distributions of Brightness Temperatures of a Cropland Study Area Using a Model that Combines Fast Radiosity and Energy Budget Methods

Land surface temperatures (LSTs) obtained from remote sensing data are crucial in monitoring the conditions of crops and urban heat islands. However, since retrieved LSTs represent only the average temperature states of pixels, the distributions of temperatures within individual pixels remain unknown. Such data cannot satisfy the requirements of applications such as precision agriculture. Therefore, in this paper, we propose a model that combines a fast radiosity model, the Radiosity Applicable to Porous IndiviDual Objects (RAPID) model, and energy budget methods to dynamically simulate brightness temperatures (BTs) over complex surfaces. This model represents a model-based tool that can be used to estimate temperature distributions using fine-scale visible as well as near-infrared (VNIR) data and temporal variations in meteorological conditions. The proposed model is tested over a study area in an artificial oasis in Northwestern China. The simulated BTs agree well with those measured with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The results reflect root mean squared errors (RMSEs) less than 1.6 °C and coefficients of determination (R2) greater than 0.7. In addition, compared to the leaf area index (LAI), this model displays high sensitivity to wind speed during validation. Although simplifications may be adopted for use in specific simulations, this proposed model can be used to support in situ measurements and to provide reference data over heterogeneous vegetation surfaces.

Differences in Meteorological Conditions between Days with Persistent and Non-Persistent Pollution in Beijing, China

We compared the regional synoptic patterns and local meteorological conditions during persistent and non-persistent pollution events in Beijing using US NCEP–Department of Energy reanalysis outputs and observations from meteorological stations. The analysis focused on the impacts of high-frequency (period < 90 days) variations in meteorological conditions on persistent pollution events (those lasting for at least 3 days). Persistent pollution events tended to occur in association with slow-moving weather systems producing stagnant weather conditions, whereas rapidly moving weather systems caused a dramatic change in the local weather conditions so that the pollution event was short-lived. Although Beijing was under the influence of anomalous southerly winds in all four seasons during pollution events, notable differences were identified in the regional patterns of sea-level pressure and local anomalies in relative humidity among persistent pollution events in different seasons. A region of lower pressure was present to the north of Beijing in spring, fall, and winter, whereas regions of lower and higher pressures were observed northwest and southeast of Beijing, respectively, in summer. The relative humidity near Beijing was higher in fall and winter, but lower in spring and summer. These differences may explain the seasonal dependence of the relationship between air pollution and the local meteorological variables. Our analysis showed that the temperature inversion in the lower troposphere played an important part in the occurrence of air pollution under stagnant weather conditions. Some results from this study are based on a limited number of events and thus require validation using more data.

Air quality and odour evaluation on terrains characterized by a complicated odour emission profiles

The complexity of describing the very phenomenon of odour nuisance is probably the cause of lack of regulations concerning this topic in the Polish law system as well as standardized odour law in EU. However, in European countries there are solutions regarding this matter. Moreover, methods enabling the evaluation of odour air quality are available. The problem of an authoritative assessment of odour nuisance appears especially when few emission sources characterized by changing conditions and emission profiles and, very often, a complex spatial/topographical structures, also showing locally variable meteorological conditions are located in „vulnerable” places (with many „odour complaints” being recorded there). In conditions similar to the aforementioned the odour situation analysis requires simultaneous usage of several studying methods. In this work are presented: field measurement results and sociological poll results, all carried out for area with several domineering odour emission sources. The research conducted made possible showing the concentration of various odour types and their sources for the researched area, which later on enabled to undertake action aiming at reducing odour nuisance.

Low flow controls on stream thermal dynamics

Water level fluctuations in surface water bodies, and in particular low flow drought conditions, are expected to become more frequent and more severe in the future due to the impacts of global environmental change. Variations in water level, and therefore in-channel water volume, not only have the potential to directly impact stream temperature, but also aquatic vegetation coverage which, in turn, may affect stream temperature patterns and dynamics. Manipulation experiments provide a systematic approach to investigate the multiple environmental controls on stream temperature patterns. This study aims to use temperature data loggers and fibre optic distributed temperature sensing (FO-DTS) to investigate potential drought impacts on patterns in surface water and streambed temperature as a function of change in water column depth. To quantify the joint impacts of water level and associated vegetation coverage on stream temperatures, investigations were conducted in outdoor flumes using identical pool-riffle-pool features, but with spatially variable water levels representative of different drought severity conditions. Naturally evolved vegetation growth in the flumes ranged from sparse vegetation coverage in the shallow flumes to dense colonization in the deepest. Observed surface water and streambed temperature patterns differed significantly within the range of water levels and degrees of vegetation coverage studied. Streambed temperature patterns were more pronounced in the shallowest flume, with minimum and maximum temperature values and diurnal temperature variation being more intensively affected by variation in meteorological conditions than daily average temperatures. Spatial patterns in streambed temperature correlated strongly with morphologic features in all flumes, with riffles coinciding with the highest temperatures, and pools representing areas with the lowest temperatures. In particular, the shallowest flume (comprising multiple exposed features) exhibited a maximum upstream-downstream temperature warming of 3.3°C (T in=10.3°C, T out=13.5°C), exceeding the warming observed in the deeper flumes by ∼2°C. Our study reveals significant streambed and water temperature variation caused by the combined impacts of water level and related vegetation coverage. These results highlight the importance of maintaining minimum water levels in lowland rivers during droughts for buffering the impacts of atmospheric forcing on both river and streambed water temperatures.

The levels, variation characteristics, and sources of atmospheric non-methane hydrocarbon compounds during wintertime in Beijing, China

Abstract. Atmospheric non-methane hydrocarbon compounds (NMHCs) were measured at a sampling site in Beijing city from 15 December 2015 to 14 January 2016 to recognize their pollution levels, variation characteristics, and sources. We quantified 53 NMHCs, and the proportions of alkanes, alkenes, acetylene, and aromatics to the total NMHCs were 49.8–55.8, 21.5–24.7, 13.5–15.9, and 9.3–10.7 %, respectively. The variation trends in the NMHC concentrations were basically identical and exhibited remarkable fluctuation, which was mainly ascribed to the variation in meteorological conditions, especially wind speed. The diurnal variations in NMHCs on clear days exhibited two peaks during the morning and evening rush hours, whereas the rush hours' peaks diminished or even disappeared on the haze days, implying that the relative contribution of the vehicular emissions to atmospheric NMHCs depended on the pollution status. Two evident peaks of the propane ∕ propene ratios appeared in the early morning before sun rise and at noontime on clear days, whereas only one peak occurred in the afternoon during the haze days, which were attributed to the relatively fast reactions of propene with OH, NO3, and O3. Based on the chemical kinetic equations, the daytime OH concentrations were calculated to be in the range of 3. 47 × 105–1. 04 × 106 molecules cm−3 on clear days and 6. 42 × 105–2. 35 × 106 molecules cm−3 on haze days. The nighttime NO3 concentrations were calculated to be in the range of 2. 82 × 109–4. 86 × 109 molecules cm−3 on clear days. The correlation coefficients of typical hydrocarbon pairs (benzene ∕ toluene, o-xylene ∕ m,p-xylene, isopentane ∕ n-pentane, etc.) revealed that vehicular emissions and coal combustion were important sources for atmospheric NMHCs in Beijing during the wintertime. Five major emission sources for atmospheric NMHCs in Beijing during the wintertime were further identified by positive matrix factorization (PMF), including gasoline-related emissions (gasoline exhaust and evaporation), coal combustion, diesel exhaust, acetylene-related emissions, and consumer and household products. Coal combustion (probably domestic coal combustion) was found to make the greatest contribution (29.6–33.4 %) to atmospheric NMHCs during haze days.

Features of transformation of degraded peat soils in Western Polissya of Ukraine

The results of the study of the regularities of the transformation of peat soils (Fibric Histosols Dystric) of Western Polissia of Ukraine are presented. The regional character of the development of peat soils are illustrated by the Kopaivska drainage system (the village of Pishcha, Shatskyi district, Volyn oblast). The criteria for assessing the agroecological sustainability of peatlands subjected to degradation are proposed. Indicators rang together with scoring system are suggested. Route field expeditions were conducted during 2012-2015.&#x0D; The specific features of changes in agroecological and agrochemical indicators of degraded peat soils are established. The following parametres were investigated bogging, state of the soil surface and cover crop, erosional feature, skeleton soil profile, gritty consistency, number of stumps and wood, depth of humus-accumulated horizons, organic carbon content, mineral nitrogen, labile phosphorus and potassium compounds, radionuclide contamination, ignition loss, soil reaction.&#x0D; The change of the main diagnostic signs of the agroecological state of degraded peat soils is revealed. The results indicate a change in the degree of degradation from moderate to severe. According to the results of observations, during the years under investigation, there have been significant changes in agrochemical parameters of fire-induced soil. This is due to the variability of meteorological conditions. In particular, there was an increase in the content of organic carbon and mineral nitrogen compounds, in the content of mobile potassium compounds, an increase in the acidity of the soil and radionuclide contamination. Based on the results obtained, measures are proposed to restore the processes of peat formation, their water regimes, and the elimination of fire-induced occurrence. We propose to carry out renaturation and creation of a raw material zone for the production of alternative fuel and energy resources and solid types of fertilizers.

Habitat suitability and demography, a time-dependent relationship.

The habitat suitability index, which reflects spatial variability in species occurrence probability, has been shown to exhibit various contrasting relationships with local demographic performances (survival, productivity) in several species. One proposed explanation for these discrepancies is that the link between the habitat suitability index and demography is influenced by density‐dependent, temporally variable processes. Based on the survival rates of more than 3,000 nests monitored over 12 years in the North African Houbara Bustard, we investigated whether the habitat suitability index computed over the species breeding range is related to nest survival throughout the breeding season, accounting for variation in meteorological conditions. We found that the relationship between the habitat suitability index and nest survival progressively changes along the breeding season and that this intra‐annual variation is consistent between years. Our results support the hypothesis that variation in space use occurs intra‐annually and that biotic interactions throughout the breeding season strongly influence the habitat suitability index–demography relationship.

Dispersion of volatile organic compounds (VOCs) emissions from a biofilter at an electronic manufacturing facility

Printed circuit board (PCB) manufacturing industries, known as PCB facilities, emit odorous and toxic volatile organic compounds (VOCs). Such compounds not only harm the health but also create a nuisance environment for people who live in the neighborhood of PCB facilities. Biofilter technology which is based on the bio‐oxidation of pollutants has been used by many industries to remove odorous VOCs. When the removal efficiency drops due to a design or operational deficiency, odorous VOCs are released from the biofilter and this becomes evident by the increased odor complaints. Poor biofilter performance, geographical location of the facility, and meteorological conditions contribute to increased dispersion of VOCs. This research investigates dispersion of a VOC pollutant known as propylene glycol monomethyl ether acetate (PGMEA) which is released from a commercial biofilter unit installed at a PCB facility in Ontario, Canada. A Gaussian dispersion model and California puff model (CALPUFF) were used to examine the dispersion effects due to changes in wind speed, wind direction, temperature, mixing height, atmospheric stability classes as well as biofilter performance. Simulations were done for the three modeling periods (January, May, September) to account for seasonal effects on PGEMEA dispersion and predictions from both models are compared. The contour hourly wind and concentration plots confirm that the seasonal changes have a direct impact on the PGMEA concentration and plume path. The results presented under variable meteorological conditions show that the average daily concentration of PGMEA is the highest in the month of September. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1100–1107, 2017

Satellite-sensed tropospheric NO2 patterns and anomalies over Indus, Ganges, Brahmaputra, and Meghna river basins

ABSTRACTThis study presents trends, seasonality, hot spots, and anomalies of tropospheric NO2 pollution over four basins of Indus, Ganges, Brahmaputra, and Meghna rivers in South Asia using observations from Ozone Monitoring Instrument (OMI) on-board Aura satellite during 2004–2015. For the first time this area, a highly populated and industrialized region with significant emissions of air pollutants, has been discussed collectively. OMI data reveal significantly elevated NO2 column over the region averaged at (1.9 ± 0.1) × 1015 molecules cm–2 (average ± standard deviation of observations) with an increase of 21.12% (slope (0.036 ± 0.004) × 1015 molecules cm–2, y-intercept (1.705 ± 0.024) × 1015 molecules cm–2, R2 = 0.92) during the study period. According to MACCity anthropogenic emissions inventory transportation, energy, residential, and industrial sectors are the major contributors of high NOx emissions. NO2 pollution hot spots are identified and their tendencies have been discussed. The hot spots of megacities Lahore (Pakistan) and Dhaka (Bangladesh) are found to be strengthening and expanding over the time. Eastern Ganges Basin shows the highest NO2 concentration at (2.63 ± 0.22) × 1015 molecules cm–2 and growth rate of 3.22% per year mainly linked to power generation, fossil fuel extraction, mining activities, and biomass burning. NO2 over Indus–Ganges–Brahmaputra–Meghna Basin exhibits seasonal maximum in winter and minimum in monsoon. The highest seasonality is found over Meghna Basin due to large variations in meteorological conditions and large-scale crop-residue burning. Some anomalies in NO2 levels have been detected linked to intense crop-residue burning events. During these anomalies, exceptionally high levels of daily NO2 reaching up to 76.23 × 1015 molecules cm–2 have been observed over some places in Indus and Meghna Basins.

The impact of meteorological conditions on snow and ice thickness in an Arctic lake

Inter-annual variation of meteorological conditions and their effects on snow and ice thickness in an Arctic lake Unari (67.14° N, 25.73° E) were investigated for winters 1980/1981–2012/2013. The lake snow and ice thicknesses were modelled applying a thermodynamic model, and the results were compared with observations. Regression equations were derived for the relationships between meteorological parameters and modelled snow and ice properties. The composite differences of large-scale atmospheric circulation patterns between seasons of thin and thick ice were analysed. The air temperature had an increasing trend (statistical significance p<0.05) during the freezing season (1.0° C/decade), associated with an increasing trend of liquid precipitation (p<0.05) in winter. Both observed and modelled average and maximum ice thicknesses showed a decreasing trend (p<0.05). The model results were statistically more reliable (1) for lake ice than snow and (2) for seasonal means than maxima. Low temperature with less precipitation prompted the formation of columnar ice, whereas strong winds and heavy snowfall were in favour of granular ice formation. The granular (columnar) ice thickness was positively (negatively) correlated with precipitation. The seasonal mean and maximum modelled lake ice and snow thicknesses were controlled by precipitation and temperature history, with 58–86 % of the inter-annual variance explained. Using regression equations derived from data from 1980 to 2013, snow and ice thickness for the following winter seasons was statistically forecasted, yielding errors of 9–12 %. Among large-scale climate indices, the Pacific Decadal Oscillation was the only one that correlated with inter-annual variations in the seasonal average ice thickness in Lake Unari.

Validation of a simple dynamic thermal performance characterization model based on the piston flow concept for flat-plate solar collectors

A simple dynamic characterization model of flat-plate solar collectors based on the piston flow concept is used both to identify the collector characteristic parameters and to predict the dynamic thermal performance. The heat transport time originally defined as (1−e−1)−1τC by Amrizal et al. (2012) for the model turns out to be the collector static response time constant τC by analytical derivation. The nonlinear least squares method is applied to determine the characteristic parameters of a flat-plate solar air collector previously tested by the authors. Then the obtained parameters are used to predict the dynamic behavior of the collector outlet temperature. The model coefficients particularly c3 in the simple dynamic characterization model are examined by the collector dynamic prediction under variable meteorological conditions. Meanwhile, the prediction accuracy of the simple dynamic model based on the first-order difference method is compared to that of the numerical solution of the collector ordinary differential equation (ODE) model using the fourth-order Runge-Kutta method. The improved thermal inertia model (TIM) on the basis of closed-form solution presented by Deng et al. (2016a) is also considered. The results show that the prediction performance of the simple dynamic model is nearly as accurate as the ODE numerical solution and the TIM by Deng et al. (2016a) except some special conditions such as sharply changed solar irradiance and collector inlet temperature.