This exercise is designed to present the realistic problems of forecasting weather. Lake effect snows are hard to forecast because they depend on information that isn't part of the regular set of information and involve some pretty specific things that integrate the location of the site with surrounding environment. Even places close by can get totally different forecasts. When you have a regional forecast, it doesn't really address lake effect snows, unless the forecaster really focuses. So the exercise aims to show the value of broad critical thinking in meteorology, and it is very dramatic, because the difference between 36 inches and whiteout and clear blue sky is undeniable. The exercise comes when students are 8 weeks into the class. The class is an AMS based class, which has already been described well in this workshop by Julie Snow from Slippery Rock. Our class is given in the fall semester and lake effect snow starts in October and is quite an issue in forecasts until April. The skills of a forecaster are tested, and you cannot use forecasts from nearby areas reliably. Finally, we live in a fantastic snow belt, so lake effect snow happens a lot. In a good year we get over 300 inches of snow, mostly at times that places nearby do not. You can drive to Houghton in the bright sun and be met by a wall of very active blizzard just a few miles out of town. There are some excellent tutorials available from COMET, and outreach of the National Weather Service. I use one done by Greg Byrd, which is available online or in a power point format. There are a number of things that must be learned before forecasting. These include some fluid dynamics of plumes, latent heat, remote sensing, upper air mapping, and the use of models. We cannot cover all them completely. I try to introduce all these things and give people entry points into the juicy parts of these topics, but do not expect students to understand completely. One thing you can spend a long time on are the satellite images. Here is one, just to whet your interest: http://serc.carleton.edu/details/images/13586.html I have the students make a list of the critical parameters they think might be needed for a successful lake effect forecast. This is a challenge to prepare, but the idea is to include things that are even marginally useful and to collect data to see what is most important. We get a list of parameters like this: 850 mb wind direction 850 mb temperature Lake Superior surface temperature fetch length opposing bay? Inversion layer height topographic lift factor wind shear evidence upstream lake upstream moisture factor snow/ice cover issues This list is pretty good, but deliberately not complete, and we encourage students to add other things they think might be important. The next step is to find where you can get this information. I have web data sources for most (see below), and some of them are interrelated. You can do this exercise for any site around Lake Superior or probably many other lakes as well. For specific sites, the fetch length, upstream lake and opposing bay information are obtainable directly from the wind direction if you have a good map (Google Earth). So a spreadsheet for parameters related to wind direction can be prepared in advance and these parameters can be immediately available from the wind direction. Nonetheless the issue of sources for all this stuff must be addressed in an effort that spans several hours. The use of models is needed to look into the future where possible. Once students know what they are looking for and how to find it, the exercise starts its data collection. Every day or every 6 or 12 hours beginning when conditions get close to "LES favorable" students collect information on these LES predictors. They also make LES forecasts for each period and include that information in the spreadsheet. The next day the real snowfall data is added to the spreadsheet, and this can be used as validation data for the forecast. This data collection needs to be done for several weeks (November and December in my case, usually a good time for LES). The data analysis is the most challenging part. Spreadsheet plots which test the sensitivity of various parameters singly and together are possible. There is a lot of sophistication possible if there is enough LES to analyze. Overall, results should be a good experience with imperfect data addressed to a real-time problem. Models and real data, remote sensing, and balloons are all integrated and there are quite obvious weaknesses. On the final day of class student groups will compete by doing forecasting which employs the LES techniques. This might reflect the most recent snow event. A more important element of this submission will be their evaluation of LES prediction parameters. Not only do we consider the actual forecast, but we discuss which parameters were successful? Which are inconclusive? What suggestions for improved forecasts are possible from the experience? The format of this will be short presentations with time for discussion.


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