Up-to-date information on Snow Water Equivalent (SWE) plays a key role in the management of water resources in Hydro-power reservoirs during the spring runoff. In this study, we compared two different SWE products. The first one, called QSG (Quebec Snow Grid), was produced by the Quebec hydroelectricity agency (Hydro-Quebec). The second one was the well-known GlobSnow2 product, provided by the European Space Agency. Both products were generated by using spaceborne passive microwave measurements. The comparison was done over the La Grande and Manicouagan watersheds in northeast Quebec (Canada) during 2006 to 2010 winters (1stJanuary-31 March). The study showed better results for QSG products ( RMSE=31%) compared to GlobSnow ( RMSE=43%), for an average SWE of 215 mm over the entire study area. However, the performance decreased from north to south, where both products became quite similar, with RMSE=45% and a bias of -90 mm, for an average SWE of 253 mm. This suggested the key role of the average accumulated ( mm), and dense vegetation (forest ) in the SWE estimation.

Intelligent video surveillance systems (IVSS) that can track people, identify unattended objects, and recognize activity are constantly being developed, updated, and tested against standard test stimuli. The development of these systems, however, seems to focus on technological advancements and largely ignores how automation will affect the human closed-circuit television (CCTV) operator. This paper outlines the advantages of employing a cognitive system engineering approach to the design of IVSS, reviews the literature that has examined human factors issues related to CCTV automation, and introduces a testbed for investigating the effects of automated surveillance technology on human CCTV operators.

Snow water equivalent is a key variable for hydrological modeling. For Hydro-Québec, the water contains within the snowpack represents more than 30% of its annual energy reserve. This is even more important since 97% of Hydro-Québec's power production is hydroelectric. Knowing the maximum snow water equivalent value is crucial to determine the amount of potential energy that the snow melting will provide during the spring. In order to optimise the level water stock in the reservoirs and the hydropower generation for all Hydro-Québec's watersheds, the monitoring of spring runoff must be done adequately in near real time. To improve monitoring snow properties during both winter and springs seasons, a methodology based on a model inversion scheme is coupled with a simple density model. The first results show that the methodology can quantify adequately the snow accumulation and snowmelt.

Ongoing information on snow distribution is a crucial issue for Hydro-Quebec in terms of reservoir management late in the winter or in the early months of spring. A dynamic approach that use passive microwave data have been evaluated over province of Quebec (Canada). The method combines a K-mean algorithm and ensemble mapping approach. The first results show that the methods can perform a realistic snow mapping, and unlike standard approaches, can quantify some part of the uncertainty which is an advantage in forecasting.

For Hydro-Québec, the water contains within the snow-pack represents more than 30% of its annual energy reserve. Moreover, 97% of Hydro-Quebec's power production is hydroelectric. Thus, the knowledge of the maximum value of snow water equivalent is a crucial information to maximise hydropower generation for all Hydro-Québec's watersheds. To improve monitoring snow properties in forested areas, a model inversion scheme is adapted to incorporate the information provided by the total volume map produced by the Canadian Forest Service. The first results show that the inversion algorithm can quantify adequately the snow water equivalent during the winter period over a forested watershed.

Snow water equivalent (SWE) is critical information for hydrological studies over northern latitudes. This information is needed to adequately simulate spring runoff in order to optimize hydropower generation. In this paper, a new geophysical inversion methodology for monitoring SWE evolution on a daily basis is presented. The methodology is based on a modified version of the Helsinki University of Technology (HUT) snow emission model and assimilatesin situdata as ground truth. Previous studies used the mean snow grain size (?) as an optimization parameter to retrieve SWE with the HUT model. Therefore,a prioriinformation on ? was needed to estimate SWE, but this information is rarely measured in the field. In the HUT model, the mean snow grain size is used to compute the total extinction coefficient (?e) of the snowpack. We modified the model to eliminate the requireda prioriknowledge on mean snow grain size and used the total extinction coefficient as an optimization parameter instead. The methodology was applied in a subarctic territory in northern QuE?bec (Canada) using snow measurements acquired during the 2001 to 2006 winters (January 1 to March 31). A metric based on the brightness temperature difference between the special sensor microwave/imager 19- and 37-GHz (vertical polarization) was used for SWE retrieval. In the monitoring process, the result obtained from the previous day inversion was used to initialize the current day SWE retrieval. Whenin situmeasurements are available, they are assimilated and used to calibrate the model. The results obtained with this methodology show an improvement in the experimental context considered (high SWE values). For all winters considered in this paper, the results described in this paper show that SWE was estimated with a root-mean-square error (rmse) of less than 19% after the first process ofin situdata assimilation and with an rmse of 14% fornondenseforested areas and for SWE values below 300 mm.

This letter proposes an estimation of microwave transmissivity within the Canadian boreal forest. The aim is to correct the forest effect in snow water equivalent estimation from Special Sensor Microwave Imager and Advanced Microwave Scanning Radiometer microwave measurements. The estimation was carried out using ground-based radiometric measurements, at 19 and 37 GHz, and for both polarizations. The results show that the transmissivity is correlated with the stem volume and is independent of the tree species. For high stem volumes (>100 m/sup 3//ha), the transmissivity is found to be 0.4 and 0.3 for 19 and 37 GHz, respectively.

We present the technical implementation details for a prototype central atmospheric correction parameter server called NOMAD (Networked On-line Mapping of Atmospheric Data). Using a Web service, aerosol optical depth (AOD) values are transmitted via the network to a Web service aware atmospheric correction application. Information about the date and location of the image are extracted automatically from the image, allowing a best estimate of AOD at 500 nm to be retrieved from the central server database. A Web service approach was adopted to allow easy cross platform development in multiple software languages. Using the Web Services Definition Language (WSDL) description of the atmospheric correction parameter server Web service, application developers can easily make their atmospheric correction applications "Web aware". On the server side, researchers maintaining the central database are free to concentrate on providing the best quality data available.

Significant atmospheric correction improvements can only be achieved by accurately representing the dynamic variability of the intrinsic atmospheric optical parameters that serve as input to the radiative transfer model. The challenge is to render the existing array of ground and satellite based (input) atmospheric optical measurements coherent and standard. This is accomplished by assimilating the atmospheric optical measurements and their expected errors with a spatiotemporal atmospheric model driven by meteorological-scale wind fields. Such models act as intelligent interpolators in space and time, and as a tool for product QA and standardization. The concept of a meteorological scale optical parameter prediction system for atmospheric corrections (NOMAD for "Networked On-line Mapping of Atmospheric Data") is presented in this communication along with examples of ongoing developmental work. The system is viewed as multi-level in that it will output atmospheric optical parameter estimates ranging from the meteorological to climatological scales and from the synoptic scale to the lower mesoscale (AVHRR scale to SPOT scale). The operational concept is that it will reside on a central server that will be accessible to any atmospheric correction system. Aside from the obvious advantage of atmospheric corrections operationalization, this concept will transfer the responsibility of quality assuring atmospheric parameters from the user to the science and technical staff maintaining the atmospheric server.

No abstract available.