Search Results
1. Introduction With the growing number of satellite platforms and improvements in the processing and transmission of digital data obtained from them, it has become possible to obtain frequent snow-cover information in near–real time through a variety of different sources. Retrieving snow products from satellite data is still a challenging task. Topography, heterogeneity in snow distribution, the effects of slope, aspect, land use, wind, and other factors in the accumulation and melting periods
1. Introduction With the growing number of satellite platforms and improvements in the processing and transmission of digital data obtained from them, it has become possible to obtain frequent snow-cover information in near–real time through a variety of different sources. Retrieving snow products from satellite data is still a challenging task. Topography, heterogeneity in snow distribution, the effects of slope, aspect, land use, wind, and other factors in the accumulation and melting periods
1. Introduction The relative snow cover duration n within a season is a parameter that can be used to describe the snow cover. It is defined here as the sum of days with a snow depth (SD) above a threshold s = 0 cm with respect to the total number of days in the considered period and, therefore, can take values from 0 to 1. Knowledge of the duration of the snow-covered period provides different applications. The variation of the snow cover duration can serve as a measure for climate change
1. Introduction The relative snow cover duration n within a season is a parameter that can be used to describe the snow cover. It is defined here as the sum of days with a snow depth (SD) above a threshold s = 0 cm with respect to the total number of days in the considered period and, therefore, can take values from 0 to 1. Knowledge of the duration of the snow-covered period provides different applications. The variation of the snow cover duration can serve as a measure for climate change
1. Introduction a. Manual snow depth measurements In Canada, for all meteorological stations prior to the 1960s, and for most nonsynoptic stations over their entire history, snowboards were used to measure new snow within a specified time period ( Potter 1965 ). The depth of snow on the board was measured with a ruler that, since 1978, has been 1 m long and graduated every 0.2 cm. The board was then reset on the surface of the snow cover to prepare for the next snowfall (SF) event. A limitation
1. Introduction a. Manual snow depth measurements In Canada, for all meteorological stations prior to the 1960s, and for most nonsynoptic stations over their entire history, snowboards were used to measure new snow within a specified time period ( Potter 1965 ). The depth of snow on the board was measured with a ruler that, since 1978, has been 1 m long and graduated every 0.2 cm. The board was then reset on the surface of the snow cover to prepare for the next snowfall (SF) event. A limitation
1. Introduction Snow cover and snow depth are important parameters for numerical weather prediction (NWP) and hydrological models, especially in springtime during the melting period. Essential characteristics include snow water equivalent (SWE), snow depth, and snow covered area. For hydrology, the maximum SWE prior to the onset of spring snowmelt is typically the most important snow characteristic for operational runoff and river discharge forecasts. In NWP models, the snow cover affects the
1. Introduction Snow cover and snow depth are important parameters for numerical weather prediction (NWP) and hydrological models, especially in springtime during the melting period. Essential characteristics include snow water equivalent (SWE), snow depth, and snow covered area. For hydrology, the maximum SWE prior to the onset of spring snowmelt is typically the most important snow characteristic for operational runoff and river discharge forecasts. In NWP models, the snow cover affects the
1. Introduction Snow is an extremely important component of the land surface system, substantially affecting the radiative and hydrological properties of the surface and consequently the way it interacts with the atmosphere. Most important is that snow cover dramatically increases the land surface albedo from between 0.05 and 0.4 (typical for bare soil and vegetation) to up to 0.9 for pure snow ( Nolin and Liang 2000 ), which has a huge effect on diabatic heating. In hydrological terms, water
1. Introduction Snow is an extremely important component of the land surface system, substantially affecting the radiative and hydrological properties of the surface and consequently the way it interacts with the atmosphere. Most important is that snow cover dramatically increases the land surface albedo from between 0.05 and 0.4 (typical for bare soil and vegetation) to up to 0.9 for pure snow ( Nolin and Liang 2000 ), which has a huge effect on diabatic heating. In hydrological terms, water
in March and April of 2012 to investigate impacts of Arctic sea ice reduction on chemical processes, transport, and distribution of bromine, ozone, and mercury from snow-covered sea ice and land surfaces ( Nghiem et al. 2013a , b ; Moore et al. 2014 ). Temperature is a key factor in chemical reactions ( Tarasick and Bottenheim 2002 ), and an increase in temperature fluctuations may lead to more episodes of the halogen chemical process known as bromine explosion ( Wennberg 1999 ) and more
in March and April of 2012 to investigate impacts of Arctic sea ice reduction on chemical processes, transport, and distribution of bromine, ozone, and mercury from snow-covered sea ice and land surfaces ( Nghiem et al. 2013a , b ; Moore et al. 2014 ). Temperature is a key factor in chemical reactions ( Tarasick and Bottenheim 2002 ), and an increase in temperature fluctuations may lead to more episodes of the halogen chemical process known as bromine explosion ( Wennberg 1999 ) and more
predictor data for seasonal forecasts, other persistent surface conditions may also contribute skill. In particular, autumn Eurasian snow cover has been found to provide skill in statistical forecasts of North American winter temperatures ( Foster et al. 1983 ; Cohen and Fletcher 2007 ; Lin and Wu 2011 ; Brands 2013 ). The physical basis for this predictive skill appears to be the connection between Eurasian snow cover and the Arctic Oscillation ( Cohen and Entekhabi 1999 ; Cohen et al. 2007 ; Gong
predictor data for seasonal forecasts, other persistent surface conditions may also contribute skill. In particular, autumn Eurasian snow cover has been found to provide skill in statistical forecasts of North American winter temperatures ( Foster et al. 1983 ; Cohen and Fletcher 2007 ; Lin and Wu 2011 ; Brands 2013 ). The physical basis for this predictive skill appears to be the connection between Eurasian snow cover and the Arctic Oscillation ( Cohen and Entekhabi 1999 ; Cohen et al. 2007 ; Gong
1. Introduction Snow is a very important component for the predictability of weather and climate and for the hydrological cycle. Modeling and empirical studies of the effect of snow cover revealed the influence of snow cover on atmospheric circulation patterns (e.g., Clark et al. 1999 ; Clark and Serreze 2000 ). Despite significant efforts (e.g., Groisman et al. 1994 ; Fallot et al. 1997 ; Cohen and Entekhabi 1999 ; Hall and Qu 2006 ), the understanding of the influence of snow cover on
1. Introduction Snow is a very important component for the predictability of weather and climate and for the hydrological cycle. Modeling and empirical studies of the effect of snow cover revealed the influence of snow cover on atmospheric circulation patterns (e.g., Clark et al. 1999 ; Clark and Serreze 2000 ). Despite significant efforts (e.g., Groisman et al. 1994 ; Fallot et al. 1997 ; Cohen and Entekhabi 1999 ; Hall and Qu 2006 ), the understanding of the influence of snow cover on
and climatological aspects of this phenomenon. The majority of urban climate research has been conducted in cities with warm-to-moderate climates (e.g., Yow 2007 ). There have been fewer investigations of UHIs in cities with significant seasonal temperature variation and/or seasonal snow cover [exceptions include Minneapolis, Minnesota ( Todhunter 1996 ; Winkler et al. 1981 ); Barrow, Alaska ( Hinkel et al. 2003 ); Lódź, Poland ( Offerle et al. 2006 ); and Hamburg, Germany ( Schlünzen et al
and climatological aspects of this phenomenon. The majority of urban climate research has been conducted in cities with warm-to-moderate climates (e.g., Yow 2007 ). There have been fewer investigations of UHIs in cities with significant seasonal temperature variation and/or seasonal snow cover [exceptions include Minneapolis, Minnesota ( Todhunter 1996 ; Winkler et al. 1981 ); Barrow, Alaska ( Hinkel et al. 2003 ); Lódź, Poland ( Offerle et al. 2006 ); and Hamburg, Germany ( Schlünzen et al
models and forecasting. Current remote sensing satellites used for snow detection are either in polar or geostationary orbits, which have their advantages and disadvantages. Most of the seasonal snow is in high latitudes, which are poorly covered by geostationary satellites. Whereas instruments aboard geostationary satellites provide excellent temporal resolution, polar satellite instruments have a better spatial resolution and a better polar coverage, making them often a better option in snow
models and forecasting. Current remote sensing satellites used for snow detection are either in polar or geostationary orbits, which have their advantages and disadvantages. Most of the seasonal snow is in high latitudes, which are poorly covered by geostationary satellites. Whereas instruments aboard geostationary satellites provide excellent temporal resolution, polar satellite instruments have a better spatial resolution and a better polar coverage, making them often a better option in snow
