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- Author or Editor: Peter J. Sousounis x
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Abstract
The impact of initializing a mesoscale model with additional sounding data over the Great Lakes region is investigated. As part of the Lake-Induced Convection Experiment (Lake-ICE) field study during the winter of 1997/98, six supplementary Cross-chain Loran Atmospheric Sounding System (CLASS) units and three Integrated Sounding System (ISS) units were used in addition to those from the standard synoptic upper-air network. The three ISS units were in the vicinity of Lake Michigan, and the six CLASS units were in the data-sparse region of central and northeastern Ontario and western Quebec.
The Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model running on a doubly nested grid is used to simulate the lake-effect snow event of 4–5 December 1997. This model output from a 30-km horizontal resolution grid shows that the six CLASS soundings capture a warm layer below 850 hPa that appears to be the result of diabatic heating from the Great Lakes. This leads to an improved simulation of the surface pressure fields over the course of the simulation. A nested 10-km horizontal resolution grid shows that the initialization data from the CLASS sites seemed to have a greater influence on the propagation of a mesoalpha-scale trough that caused significant snowfall to the lee of Lake Michigan than data from the ISS sites. The inclusion of the CLASS sounding data changes the track of the precipitation maximum by approximately 25 km and agrees better with reflectivity data from the Weather Surveillance Radar-1988 Doppler. Implications for forecasters in the Great Lakes region are discussed.
Abstract
The impact of initializing a mesoscale model with additional sounding data over the Great Lakes region is investigated. As part of the Lake-Induced Convection Experiment (Lake-ICE) field study during the winter of 1997/98, six supplementary Cross-chain Loran Atmospheric Sounding System (CLASS) units and three Integrated Sounding System (ISS) units were used in addition to those from the standard synoptic upper-air network. The three ISS units were in the vicinity of Lake Michigan, and the six CLASS units were in the data-sparse region of central and northeastern Ontario and western Quebec.
The Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model running on a doubly nested grid is used to simulate the lake-effect snow event of 4–5 December 1997. This model output from a 30-km horizontal resolution grid shows that the six CLASS soundings capture a warm layer below 850 hPa that appears to be the result of diabatic heating from the Great Lakes. This leads to an improved simulation of the surface pressure fields over the course of the simulation. A nested 10-km horizontal resolution grid shows that the initialization data from the CLASS sites seemed to have a greater influence on the propagation of a mesoalpha-scale trough that caused significant snowfall to the lee of Lake Michigan than data from the ISS sites. The inclusion of the CLASS sounding data changes the track of the precipitation maximum by approximately 25 km and agrees better with reflectivity data from the Weather Surveillance Radar-1988 Doppler. Implications for forecasters in the Great Lakes region are discussed.
Abstract
Despite improvements in numerical weather prediction models, statistical models, forecast decision trees, and forecasting rules of thumb, human interpretation of meteorological information for a particular forecast situation can still yield a forecast that is superior to ones based solely on automated output. While such time-intensive activities may not be cost effective for routine operational forecasts, they may be crucial for the success of costly field experiments. The Lake-Induced Convection Experiment (Lake-ICE) and the Snowband Dynamics Experiment (SNOWBANDS) were conducted over the Great Lakes region during the 1997/98 winter. Project forecasters consisted of members of the academic as well as the operational forecast communities. The forecasters relied on traditional operationally available data as well as project-tailored information from special project soundings and locally run mesoscale models. The forecasting activities during Lake-ICE/SNOWBANDS are a prime example of how the man–machine mix of the forecast process can contribute significantly to forecast improvements over what is available from raw model output or even using traditional operational forecast techniques.
Abstract
Despite improvements in numerical weather prediction models, statistical models, forecast decision trees, and forecasting rules of thumb, human interpretation of meteorological information for a particular forecast situation can still yield a forecast that is superior to ones based solely on automated output. While such time-intensive activities may not be cost effective for routine operational forecasts, they may be crucial for the success of costly field experiments. The Lake-Induced Convection Experiment (Lake-ICE) and the Snowband Dynamics Experiment (SNOWBANDS) were conducted over the Great Lakes region during the 1997/98 winter. Project forecasters consisted of members of the academic as well as the operational forecast communities. The forecasters relied on traditional operationally available data as well as project-tailored information from special project soundings and locally run mesoscale models. The forecasting activities during Lake-ICE/SNOWBANDS are a prime example of how the man–machine mix of the forecast process can contribute significantly to forecast improvements over what is available from raw model output or even using traditional operational forecast techniques.
