Introduction:  Student will take on the roll of a consultant to the Storm Prediction Center.  Their task is to analyze real time weather data  in order to predict the movement of severe weather.  As a consultant, the student must predict the town or city most likely to be affected by the storm 24 hours from present.  This lesson will provide the basis for the student to create a safety plan specified for a particular town and its' population.

Objectives:

1. Students will  identify a radar loop map and the date and time that this map was created.
2. Students will follow written directions.
3. Students will calculate the lapsed time of the radar loop map.
4. Students will locate states on the map using their abbreviations.
5. Students will  use the distance chart to calculate mileage.
6. Students will use the formula for speed to find miles per hour.(Speed =Distance over Time traveled)
7. Students will utilize wind pattern data to predict the storm path in the next twenty-four hours.
8. Students will combine information attained to forecast storm location in twenty-four hours.
9. Students will compare the prediction to available storm forecast.

This is an exercise in predicting the movement of severe weather. Severe storms are the basis for severe weather. These storms, when taken to extreme cases, sometimes form severe thunderstorms, tornadoes, and hurricanes.  Severe weather strikes all over the earth, and if not taken seriously, can be as dangerous as other violent events that take place from within the earth.  Severe storms form when warm, moist air collides with cooler, drier air. The atmosphere is unstable when cooler air is situated on top of warmer air because cold air is more dense than warm air. The warmer less dense air rises through the cooler more dense air. The rising air causes updrafts to form, while air which is higher in the atmosphere cools off and begins to sink, causing down drafts to form.  These can become very strong and even deadly at times.  To facilitate this exercise, it is a good idea to review the radar loop map before you begin the lesson.  This can be done a day or two before you begin the lesson. Locate severe weather patterns to make sure your students will find them the day of your lesson.

Materials:

1. weather journal
2. worksheet #2 for Real Time Data Project #2 (Click Here for a copy of  worksheet #2)
3. US Map with town and cities noted.  We suggest a large wall map for your classroom.
4. US Map for each student (Click Here for a printable on-line map of the U.S.)
5. For more detailed calculations you may choose to create your own map using Map Browser or photocopying region from Atlas.

1. Real Time Radar Loop Map
2. Distance Chart
3. Real Time Wind Map
4. Thunderstorm Probability Map

Students should have prior experience in reading a radar map and calculating distance using the distance chart.  The wind map and thunderstorm probability map will be new to them.  The teacher should act as a facilitator as each student follows the written directions available in Real Data Project #2.  Depending on the ability of the student to extract information and data from the real time data pages, it maybe necessary to review each one.

Please remember, that when utilizing real time data in any fashion, what you saw yesterday may not be there today.  Since this is real scientific data and weather is a dynamic system, predicting and forecasting is not easy. Upon completion of the lesson, a discussion of variables affecting weather system will be appropriate.

Follow-up:

(According to the Classroom Implementation Strategy)
Day #9,  The students will record in their weather journal how their prediction compared to the professional thunderstorm forecast that they viewed in "Does Your Forecast Agree with the Professionals?"
Day #10, The students will record/discuss the following items in their weather journal:

• The position of the storm according to the Real Time Data Radar Map.
• How well did you predict the storm's location?
• What environmental changes may have occurred to cause your predictions not to match?
• What possible calculation or human errors occurred to cause your predictions not to match?