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In this study, we used the Disaggregation based on Physical And Theoretical scale Change (DISPATCH) algorithm under very wet soil conditions in Western Canada for the disaggregation of coarse resolution 40-km soil moisture derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The algorithm relies on the Soil Evaporative Efficiency (SEE), which was estimated using the 1-km resolution data from the MODerate resolution Imaging Spectoradiometer (MODIS). The study aimed to: (i) evaluate DISPATCH under wet soil conditions, (ii) test the linearity/non-linearity of the relationship between soil moisture and SEE, and (iii) propose a more robust procedure to calibrate the SEE model under very wet soil conditions. The disaggregated soil moisture values were compared to 0–5 cm in situ measurements and the soil moisture derived from the L-MEB (L-band Microwave Emission of the Biosphere) model from airborne brightness temperature at 1.4 GHz collected during the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) field campaign. The results show a correlation between 0.7 and 0.8 and bias values ~ 0 m 3 /m 3 . The DISPATCH algorithm shows better disaggregation results under very wet soil conditions when a non-linear relationship is considered between SEE and soil moisture instead of a linear model. This is mainly due to the small variability of surface temperature inside the area covered by the SMOS pixel under very wet soil conditions, and the difficulty in accurately estimating the maximum soil temperature ( Ts max ), which is a driving factor for SEE. A sensitivity analysis was conducted and it shows that the linear model performs well only if Ts max can be determined more accurately. The possibility to determine Ts max using high resolution MODIS data over a larger area than the SMOS pixel is examined and discussed in the paper. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes an algorithm that has been developed to process field data from two upward looking sonars, one high (546kHz) and one low (235kHz) frequency, a 2MHz current profiler and a monitoring station, to measure surface ice characteristics on the North Saskatchewan River in the north east of Edmonton, AB, Canada, during the 2009/2010 freeze-up season. The algorithm used to calculate pans/rafts drafts and lengths, and the surface ice concentration is described. The validity and the accuracy of this technique were tested and computed surface ice conditions are presented. For the purpose of examining the effects of hydro-meteorological conditions on measured pan characteristics, the freeze-up period was divided into three distinct stages depending on the measured surface ice concentration. Pan/raft drafts were found to range from 0.1 to 1.0m and pan/raft lengths from 0.6 to 8.0m. The sonar proved to be very accurate in detecting the exact surface ice conditions locally above the sonar beam. However, interpretation of the results can sometimes be challenging, especially when physical processes such as bridging affect local ice conditions. Therefore, additional visual observations (e.g. time lapse photography) of surface ice conditions are recommended to aid in the interpretation of sonar measurements. [Copyright &y& Elsevier]

Abstract: Performance and economical efficiency of the combined cooling, heating and power (CCHP) system mainly depend on the system structure, operation strategy and choice of facility capacity. This paper proposes a structural configuration of the CCHP system with hybrid chillers, consisting of a combined electric and absorption chiller, whose electric cooling to cool load ratio varies according to different electric and thermal loads in every hour. A new operation strategy, based on the variational electric cooling to cool load ratio, for the CCHP system with unlimited and limited power generation unit (PGU) capacity is investigated. Given the proposed operation strategy, an optimization algorithm is adopted to determine the optimal PGU capacity. In addition, a case study of a hypothetical hotel in Victoria, BC, Canada is conducted to verify the feasibility of the proposed CCHP system structure and the corresponding optimal operation strategy. [Copyright &y& Elsevier]

Abstract: To extract information about the Earth''s surface from Earth Observation data, a key processing step is the separation of pixels representing clear-sky observations of land or water surfaces from observations substantially influenced by clouds. This paper presents an algorithm used for this purpose specifically for data from the AATSR sensor on ENVISAT. The algorithm is based on the structure of the SPARC cloud detection scheme developed at CCRS for AVHRR data, then modified, calibrated and validated for AATSR data. It uses a series of weighted tests to calculate per-pixel cloud presence probability, and also produces an estimate of cloud top height and a cloud shadow flag. Algorithm parameters have been optimized for daytime use in Canada, and evaluation shows good performance with a mean daytime kappa coefficient of 0.76 for the ‘cloud’/‘clear’ classification when compared to independent validation data. Performance is independent of season, and is a dramatic improvement over the existing AATSR L1B cloud flag for Canada. The algorithm will be used at CCRS for processing AATSR data, and will form the basis of similar processing for data from the SLSTR sensors on Sentinel-3. [Copyright &y& Elsevier]

Abstract: To interpret the snowpack evolution, and in particular to estimate snow water equivalent (SWE), passive microwave remote sensing has proved to be a useful tool given its sensitivity to snow properties. However, the main uncertainties using existing SWE algorithms arise from snow metamorphism which evolves during the winter season, and changes the snow emissivity. To consider the evolution in snow emissivity a coupled snow evolution–emission model can be used to simulate the brightness temperature (TB) of the snowpack. During a dedicated campaign in the winter season, from November to April, of 2007–2008 two surface-based radiometers operating at 19GHz and 37GHz continuously measured the passive microwave radiation emitted through a seasonal snowpack in southern Quebec (Canada). This paper aims at modeling and interpreting this time series of TB over the whole season, with an hourly step, using a coupled multi-layer snow evolution–emission model. The thermodynamic snow evolution model, referred as to Crocus, was driven by local meteorological measurements. Results from this model provided, in turn, the input variables to run the Microwave Emission Model of Layered Snowpacks (MEMLS) in order to predict TB at 19GHz and 37GHz for both vertical (V) and horizontal (H) polarizations. The accuracy of TB predicted by the Crocus–MEMLS coupled model was evaluated using continuous measurements from the surface-based radiometers. The weather conditions observed during the winter season were diverse, including several warm periods with melting snow and rain-on-snow events, producing very complex variations in the time series of TB. To aid our analysis, we identified days with melting snow versus days with dry snow. The Crocus–MEMLS coupled model was able to accurately predict melt events with a success rate of 86%. The residual error was due to an overestimation of the duration of several melt events simulated by Crocus. This problem was explained by 1) a limitation of percolation, and 2) a very long-acting melt of lower layers due to geothermal flux. When the snowpack was completely dry, the global trend of TB during the season was characterized by a decrease of TB due to growth in the snow grain size. During most of the season, Crocus–MEMLS correctly predicted the evolution of TB resulting from temperature gradient metamorphism; the root mean square errors ranged between 2.8K for the 19GHz vertical polarization (19V) and 6.9K for the 37GHz horizontal polarization (37H). However, during dry periods near the end of the season, the values of TB were strongly overestimated. This overestimation was mainly due to a limitation of the growth of large snow grains in the wet snowpack simulated by Crocus. This effect was confirmed by estimating snow grain sizes from the observed TB and the coupled model. The estimated snow grain sizes were larger and more realistic than those initially predicted by the Crocus model. [Copyright &y& Elsevier]

Abstract: This paper will discuss TRIPLEX-Flux, a process-based model that estimates net ecosystem production (NEP) as well as analyzing the level of sensitivity of the model''s response by simulating CO2 flux in an existing old black spruce BOREAS site in central Canada. The research objectives were: (1) to test the TRIPLEX-Flux model simulations against flux tower measurements; and (2) to examine parameter and input variable effects on model response via sensitivity analysis. Validation of NEP data at 30min intervals derived from tower and chamber measurements showed that the NEP data from the model corresponded well with the measured NEP from the BOREAS site (R 2 >0.65). Sensitivity analysis demonstrated different levels of sensitivity between morning and noon periods and from the current to doubled atmospheric CO2 concentrations. Additionally, the comparison of different algorithms to calculate stomatal conductance showed that the NEP predicted by the model using the iteration algorithm was consistent with the results using a constant C i/C a of 0.74 for current and 0.81 for doubled CO2 concentrations. Varying parameter and input variable values by ±10% resulted in a similar model response between morning and noon periods (less than or equal to 27.6% and 27.4%, respectively). Most parameters were more sensitive at noon than they were in the morning except for those that were correlated with air temperature, suggesting that air temperature has considerable influence over model sensitivity for these parameters/variables. Air temperature effect was greater under doubled than it was under current atmospheric CO2 concentrations. In contrast, model sensitivity to CO2 decreased under doubled CO2 concentrations. [Copyright &y& Elsevier]

Abstract: Mehrotra-type predictor–corrector algorithms are the backbone of most Interior Point Methods based software packages. Salahi et al. [M. Salahi, J. Peng, T. Terlaky, On Mehrotra-type predictor–corrector algorithms, Technical Report 2005/4, Advanced Optimization Lab., McMaster University, Hamilton, ON, Canada. http://www.cas.mcmaster.ca/~oplab/publication] in their recent works have shown some ill behaviors of Mehrotra’s original algorithm which motivated them to modify it in order to achieve the polynomial iteration complexity while preserving its practical efficiency. In this paper we analyze the same algorithm from a different perspective and give a condition on the maximum feasible step size in the predictor step, violation of which might lead to a very small or even zero step size in the corrector step. If the maximum step size in the predictor step is above a certain threshold, then we cut it to satisfy the derived condition. This enables us to prove that the algorithm terminates finitely. [Copyright &y& Elsevier]

Abstract: Quantification of the magnitude of net terrestrial carbon (C) uptake, and how it varies inter-annually, is an important question with future potential sequestration influenced by both increased atmospheric CO2 and changing climate. However the assessment of differences in measured and modeled C accumulation is a challenging task due to the significant fine scale variation occurring in terrestrial productivity due to soil, climate and vegetation characteristics as well as difficulties in measuring carbon accumulation over large spatial areas. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a means of monitoring gross primary production (GPP), both spatially and temporally, routinely from space. However it is critical to compare and contrast the temporal dynamics of the C and water fluxes with those measured from ground-based networks, or estimated using physiological models. In this paper, using a number of approaches, our objective is to determine if any systematic biases exists in either the MODIS, or the modeled estimates of fluxes, relative to the measurements made over an evergreen, needleleaf temperate rainforest on Vancouver Island, Canada. Results indicate that 8-day GPP as predicted with a simple physiological model (3PGS), forced using local meteorology and canopy characteristics, matched measured fluxes very well (r 2 =0.86, p <0.001) with no significant difference between eddy covariance (EC) and modeled GPP (p <0.001). In addition, modeled water supply closely matched measured relative available soil water content at the site. Using canopy characteristics from the MODIS fraction of photosynthetically active radiation (fPAR) algorithm, slightly reduced the correspondence of the predictions due to a large number of unsuccessful retrievals (83%) due to sun angle, snow and cloud. Predictions of GPP based on the MODIS GPP algorithm, forced using local meteorology and canopy characteristics, were also highly correlated with EC measurements (r 2 =0.89, p <0.001) however these estimates were biased under predicting GPP. Estimates of GPP based on the most recent MODIS reprocessing (collection 4.5) remained highly correlated (r 2 =0.88, p <0.001) yet were also the most biased with the estimates being 30% less than the EC-measured GPP. Most of the variance in GPP at the site was explained by the absorbed photosynthetically active radiation. We also compared the nighttime respiration as measured over 2 years at the site with the minimum 8-day MODIS land surface temperature and found a significant relationship (r 2 =0.57), similar to other studies. [Copyright &y& Elsevier]

• A Survey is conducted on 693 EV owners in Canada pertaining to the EV usage. • Blockchain's smart contracts allow decentralized energy trading among EVs. • User preferences make the charging scheduling model adaptive to various conditions. • Peer-to-peer trading and ancillary services provision are increased by up to 90%. • Charging costs could be offset by external transactions revenue by up to 100%. Electric vehicles (EVs) are witnessing increased utilization throughout the world as an alternative to fossil–fueled vehicles. The extensive deployment of EVs can bring challenges to the grid if not properly integrated. Such challenges, however, could be exploited as opportunities if the huge unused capacity of the battery storage in millions of EVs are utilized for ancillary services to the grid and peer–to–peer (PtP) energy trade. Given that there is at least one human user per vehicle, human input must be considered to improve the scheduling process. To that end, this paper presents a new algorithm for bidirectional smart charging of EVs considering user preferences, PtP energy trade, and provision of ancillary services to the grid. The preferences of an EV user as input to the model are embedded into the scheduling process enabling the model to be adaptive to various conditions. Optimization slack variables are utilized for optimal management of EV battery SOC and energy allocation for multiple services. New indices are developed and introduced for quantification of the EV participation in ancillary services and PtP transactions. Real–world data has been collected and utilized for model specification and simulation to make the assumptions more realistic. The efficacy and feasibility of the proposed model are validated using numerical studies. The results indicate that incorporating users' preferences into the scheduling process would improve the aggregated revenue generated by the EV scheduling model which in turn could offset the charging costs by up to 100%. Further, an increase of about 90% in peer-to-peer energy transactions among EVs and 11% in ancillary services provision to the grid are achieved through the developed user-centric smart charging model. [ABSTRACT FROM AUTHOR]