Unit 9 - Essays - Atmospheric Disturbances UPDATED Flashcards
Assess the view that it is easier to predict and monitor large-scale atmospheric disturbances (cyclones, hurricanes, typhoons) than it is for small-scale atmospheric disturbances (tornadoes).
Paragraph 1: Why Large-Scale Storms Are Easier to Predict (Case Study: Cyclone Nargis)
Key Argument:
Large storms form in specific environments (warm ocean waters with low pressure) and follow seasonal and geographical patterns, making them easier to track.
Cyclone Nargis (2008) formed in the Bay of Bengal due to high sea surface temperatures (~30°C) and favorable wind conditions.
Scientists tracked its path and intensity using:
Satellites – Monitored cloud formation and temperature changes.
Weather buoys – Measured ocean conditions.
Computer models – Predicted trajectory towards Myanmar.
Prediction Success:
Scientists predicted wind speeds of 215 km/h and storm surges of 3–4 meters.
The prediction was fairly accurate, but Myanmar’s poor warning system meant people were unprepared.
138,000 deaths occurred, not due to incorrect forecasting but due to a lack of preparedness and communication.
Development:
Spatial factor: Large-scale storms affect massive areas (~500–1,500 km wide), giving meteorologists more data points to work with.
Temporal factor: They take days to develop, allowing forecasts to improve over time.
Limitation: Even with accurate predictions, impact depends on government response and infrastructure.
Paragraph 2: Why Small-Scale Disturbances Are Harder to Predict (Case Study: Tornadoes in Indiana)
Key Argument:
Tornadoes form rapidly and unpredictably due to complex mesoscale atmospheric interactions.
Unlike hurricanes, tornadoes do not have a clear formation zone and can develop suddenly from thunderstorms.
Case Study: Super Outbreak of 1974 (Indiana)
148 tornadoes in 13 states within 24 hours.
Some tornadoes reached EF4–EF5 strength, with wind speeds over 300 km/h.
Doppler radar detected tornado vortex signatures (TVS), but warnings were only 15–20 minutes in advance.
Challenges in Prediction:
Tornadoes form in random locations – impossible to track long-term.
Short temporal scale – Most tornadoes last a few minutes to an hour.
Even with Doppler radar, forecasters can only predict areas likely to experience tornadoes, not exact locations.
Development:
Spatial factor: Tornadoes are small (~1 km wide), meaning fewer data points for prediction.
Temporal factor: Tornadoes develop in minutes, leaving little time for warnings.
Technology limitation: Even with modern radar, predicting when and where a tornado will form remains difficult.
Paragraph 3: Technology Improves Large-Scale Predictions (Case Study: Hurricane Katrina)
Key Argument:
Technological advancements have made large-scale storm prediction much more accurate.
Case Study: Hurricane Katrina (2005)
National Hurricane Center (NHC) tracked Katrina five days before landfall.
Used satellites, aircraft reconnaissance, and buoys to monitor:
Wind speeds (280 km/h).
Pressure drops (902 mb).
Ocean temperatures (28–30°C).
Accurate prediction of storm surge height (up to 8 meters).
Impact of Good Predictions:
New Orleans was warned days in advance, allowing evacuations.
However, levee failures led to 1,800+ deaths.
Shows that even with accurate forecasts, human preparedness is crucial.
Development:
Spatial factor: Large storms have huge data coverage, improving accuracy.
Temporal factor: Days of tracking allow for refinement of predictions.
Limitation: Even perfect forecasts cannot force people to evacuate.
Paragraph 4: Why Tornadoes Remain Unpredictable (Revisiting Tornadoes in Indiana)
Key Argument:
Tornadoes form under very specific conditions within thunderstorms.
Case Study: 2012 Henryville Tornado (Indiana)
Radar detected a debris ball and strong rotation minutes before touchdown.
People had less than 10 minutes to seek shelter.
Unlike hurricanes, tornadoes don’t gradually intensify—they form and dissipate rapidly.
Difficulties in Prediction:
Tornado formation depends on wind shear and instability, which are hard to measure.
Even with the best radar, lead times rarely exceed 20 minutes.
Contrast with hurricanes:
Hurricanes have consistent intensification over days.
Tornadoes appear and disappear within minutes.
Development:
Spatial factor: Tornadoes are small and highly localized.
Temporal factor: Form and move too fast for long-term tracking.
Technology limitation: Radar can only detect a tornado once it’s forming, unlike hurricanes which are tracked from the start.
Paragraph 5: The Role of Scale in Monitoring (Case Study: Typhoon Haiyan)
Key Argument:
Large-scale storms are easier to track because they span thousands of kilometers.
Case Study: Typhoon Haiyan (2013)
Tracked days before landfall in the Philippines.
Wind speeds peaked at 315 km/h, making it a Category 5 super typhoon.
600 km-wide system meant more satellite and radar coverage.
Why It Was Easier to Predict:
Satellites covered a large area, allowing better tracking.
Computer models could estimate landfall zones accurately.
However, 6,000+ people died due to lack of shelters and slow response.
Comparison to Tornadoes:
Haiyan’s massive size made it easier to monitor than tornadoes, which are tiny and short-lived.
Development:
Spatial factor: Large storms cover wide areas, making tracking easier.
Temporal factor: Days of data improve forecasts.
Limitation: Accurate forecasts do not guarantee good emergency responses.
Conclusion
Judgment: Large-scale storms are easier to predict because they develop over days, follow clearer patterns, and have more data points.
Tornadoes are much harder to predict because they form suddenly, last only minutes, and lack clear warning signs.
Even the best predictions are useless without proper preparation.
