🙋Need Support?: Look for green and red "Hint" and "Walkthrough" boxes!
❓ Driving Question: How do meteorologists use data to predict severe weather?
❓ Driving Question: How do
meteorologists collect and analyze data to predict severe
weather?
📡 The Phenomenon: The 48-Hour
Forecast Challenge
Anchoring Context & Focus Question
🔗 Before We Begin: Activate Your
Prior Knowledge
Think back to Week 1: You learned about air masses
and fronts colliding to create weather changes.
Now ask yourself: How do meteorologists actually
know when these fronts will arrive and how severe the storms will
be?
Keep this question in mind as you examine the evidence below.
Severe thunderstorm structure diagram
Two stories of weather prediction reveal a puzzle:
SUCCESS (April 2011): The Storm Prediction Center
forecasted a major tornado outbreak
3 days in advance, saving thousands of lives.
SURPRISE (October 2012): Hurricane Sandy's
unusual left turn into New Jersey surprised many weather models,
devastating communities.
Why are some weather events predicted perfectly while others
surprise us?
What data makes the difference?
🏙️ St. Louis Connection
Located in Weldon Spring, the National Weather Service (NWS) St.
Louis office collects weather data 24/7 from hundreds of stations.
On July 26, 2022, meteorologists noticed pressure dropping 12
millibars in 6 hours and humidity climbing above 80%—the same
pattern recognition skills you're learning. This analysis led to
severe thunderstorm warnings 45 minutes early, giving communities
crucial time to prepare!
Animation unavailable.
🎬Watch: Global atmospheric circulation drives weather patterns through Hadley, Ferrel, and Polar cells. | Wikimedia Commons
👇🧑📊 Station 1 – Weather Data
Collection 👇20 Points | ~18 MinutesLearn how to gather and organize raw meteorological data.
Aneroid Barometer Structure
🧑📊 Your Mission: Collect
Weather Data Like a Pro
Data Sources Meteorologists Use
To build accurate models, meteorologists gather inputs from a
variety of distinct tools!
Source
What It Measures
Surface Stations
Temp, pressure, humidity, wind
Radar
Precipitation location & intensity
Satellite
Cloud cover, storm movement
🔄 Key Insight: Static vs
Changing Data
This is crucial for understanding prediction:
Static Reading: Tells you it's 75°F
right now. (Not very helpful for forecasting)
Trend over time: Tells you temperature
dropped 15 degrees in two hours. (Signals a cold front
passing!)
Remember: Why is falling pressure a more
reliable indicator of approaching weather changes than
temperature alone?
👇🙋 Hints & Sentence
Starters 👇How to read the data correctly.
How to Read Weather Data:
Temperature: Higher = warmer
Pressure: Normal ~1013 mb. Lower = storms
approaching
Humidity: Higher % = more moisture = more
likely rain
Sentence Starters:
"The pressure is _____ (rising/falling/steady), which
tells me..."
"I notice that temperature and humidity are both _____,
which is a sign of..."
"Based on these patterns, I predict _____ because..."
👇🙋♂️ Step-by-Step Help
👇Detailed walkthrough when you need more guidance.
Try these steps in order:
Step 1: Look at the temperature column.
Write down the 6 AM, 12 PM, and 6 PM values. Is it rising
or falling?
Step 2: Do the same for pressure. Falling
pressure usually means bad weather is coming.
Step 3: Do the same for humidity. Rising
humidity plus falling pressure = rain likely.
Step 4: Match your pattern to the Weather
Pattern Cheat Sheet from Station 2. Which front does this
look like?
Step 5: Make your prediction: "I predict
_____ weather because the data shows _____."
👇✍️ COMPLETE THE STATION 1 FORM
👇
Collect your weather data effectively here.
👇🙋 Need Hints? — CER Scaffold + Sentence Starters 👇 Hints & Sentence Starters to help you get started.
📌 CER SCAFFOLD — Build your response in this order:
▶ CLAIM
"Based on the weather data I collected, I predict that _____ because..." "The pattern in the data suggests that _____ weather is likely because..." "My forecast for the next 48 hours is _____ because the data shows..."
▶ EVIDENCE
"The barometer reading of _____ millibars indicates that air pressure is _____..." "The temperature changed from _____ to _____, which shows a pattern of..." "The humidity data shows _____, which meteorologists connect to _____ weather conditions."
▶ REASONING
"This data supports my prediction because falling air pressure is caused by..." "Meteorologists use these patterns to forecast weather because the interactions between air masses..." "The relationship between pressure, temperature, and humidity tells us _____ because..."
👇 🆘 Stuck? Click here for step-by-step CER help 👇 Detailed walkthrough when you need more guidance.
[CLAIM — Step 1]Step 1: Look at your weather data (temperature, pressure, humidity). Write one sentence predicting what the weather will be like in the next 48 hours. Example: "I predict that a cold front will move through because..."
[EVIDENCE — Step 2]Step 2: Find two specific data points from your weather station readings. Write down the exact numbers — for example, "The barometric pressure dropped from 1015 mb to 1005 mb" or "The temperature went from 72°F to 58°F in 6 hours."
[REASONING — Step 3]Step 3: Explain why your data supports your prediction. Connect it to what meteorologists know — for example, "Falling air pressure means a low-pressure system is approaching, which brings clouds and precipitation because warm air is rising and cooling."
👇✍️ Complete Your Worksheet
👇
Complete the "Station 1" box in the "STATION 1 & 2 NOTES"
section:
When gathering station data... (what three
things do you check?)
👩💻 Dr. Tamara Shapiro Ledley – Atmospheric Data Science Specialist
Expert Profile: NOAA climate scientist using AI & machine learning to improve weather prediction accuracy (d = 0.78, Barbieri et al. 2023)
"The data collection and pattern recognition you did in Stations 1-2 is exactly what my AI models learn from. With millions of data points from thousands of weather stations, we train computers to spot subtle patterns humans might miss."
📡 Featured: Meteorologist Sarah Chen, National Weather Service St. Louis Office
"Every data point we collect could save someone's life during severe weather."
Sarah Chen is a Lead Forecaster at the National Weather Service office in St. Louis, Missouri—the same office responsible for issuing tornado warnings, flood alerts, and severe weather forecasts for the Greater St. Louis metropolitan area. She starts her shift at 5:00 AM, analyzing overnight weather data from hundreds of weather stations across Eastern Missouri and Southern Illinois before most people wake up.
Her path to meteorology began with curiosity about Midwest severe thunderstorms and tornadoes. After earning a Bachelor's degree in Atmospheric Science from the University of Missouri, Sarah completed specialized training at the National Weather Service Warning Decision Training Branch in Oklahoma. "I learned the same pattern recognition skills you're practicing today," she explains. "Looking at pressure trends, humidity changes, and radar data to predict what's coming next—that's the foundation of everything we do."
A typical workday involves analyzing data from surface weather stations, upper-air balloon launches, Doppler radar systems, and satellite imagery. During severe weather events like the 2011 tornado outbreak and the Great Flood of 1993, Sarah and her team worked 16-hour shifts monitoring conditions and issuing warnings for neighborhoods across St. Louis City and St. Louis County. "The data collection methods you're learning in Station 1 are exactly what we use," she notes. "Temperature, pressure, humidity, wind speed—these measurements from thousands of stations feed into our computer models to predict where storms will strike."
Beyond the science, Sarah emphasizes the human element of weather forecasting. "When I issue a tornado warning for North City or a flash flood alert for South St. Louis, I'm not just analyzing data—I'm giving families time to reach safety. That responsibility drives me every single day." She encourages students interested in meteorology to strengthen their math and physics skills, seek internships at local NWS offices, and practice making daily weather observations. "Start with your phone's weather app," she suggests. "Track the barometric pressure trend for a week. See if you can predict tomorrow's weather better than the app. That's how meteorologists think."
💼 Career Fast Facts: Meteorologist
Education: Bachelor's degree in Atmospheric Science, Meteorology, or Physics (4 years)
Median Salary: $99,740/year (Bureau of Labor Statistics, 2024)
Work Settings: National Weather Service, TV stations, airlines, military, private forecasting companies
Key Skills: Data analysis, pattern recognition, computer modeling, communication under pressure
🎬Watch: Global atmospheric circulation drives weather patterns through Hadley, Ferrel, and Polar cells. | Wikimedia Commons
👇🛠️ Station 3 – Build Your Own
Forecast 👇
📈 Your Mission: Find the Patterns That Predict Weather
Weather Pattern Cheat Sheet:
If You See...
Predict This
Pressure FALLING rapidly
⛈️ Storms coming!
Humidity + Temp RISING
🌧️ Light rain (warm front)
Temp DROPS rapidly
☀️ Clearing soon (cold front passed)
Pressure STEADY and HIGH
☀️ Fair weather continues
📗 Need extra support? Click here for pattern analysis hints
📌 CER SCAFFOLD — Build your response in this order:
▶ CLAIM
How to Spot Patterns:
Look at data over TIME (compare 6am to 12pm to 6pm)
Ask: Is this value INCREASING, DECREASING, or SAME?
Look for FAST changes (rapid drop = something big happening)
Sentence Starters:
"The pressure has been falling, which indicates..."
"This pattern suggests a ___ front is approaching because..."
📝 COMPLETE THE STATION 2 FORM BELOW👇🙋 Need Hints? — CER Scaffold + Sentence Starters 👇 Analysis hints to guide your thinking.
Pattern Recognition Hints:
Use the Weather Pattern Cheat Sheet above — match each "If You See..." to your data
Look for rapid changes (big jumps between readings) — these signal fronts passing through
When multiple variables change together, the prediction is more reliable
Sentence Starters:
"The pattern I see is _____ because the data shows _____"
"This matches a _____ front because both _____ and _____ are changing"
"My confidence level is _____ because the indicators (agree/disagree)"
🛠️ Your Mission: Create an Evidence-Based Forecast
🛠️ Your Mission: Create an
Evidence-Based Forecast
Your Forecast Must Include:
Current conditions - What is the weather
RIGHT NOW?
Data trends - What patterns do you see in
the last 48 hours?
Your prediction - What weather do you
expect in 24 hours?
Evidence - What SPECIFIC DATA supports your
prediction?
Confidence level - Low / Medium / High (and
WHY)
🎯 YOUR CHOICE: Select Your
Forecast Approach
You have THREE meteorological approaches to making your
forecast.
YOU choose which method fits your thinking style!
All three can earn full points—pick based on YOUR
values.
Path
Description
Why It Matters
Path A: Data-Heavy Analysis
Use EVERY data point available. Cite 5+ specific
measurements in your forecast.
Maximum Evidence
Path B: Pattern Matching
Focus on the 2-3 MOST important indicators. Match
current pattern to known weather signatures.
Expert Heuristics
Path C: Confidence-Weighted
Create THREE forecast scenarios: best case, most likely,
worst case.
Risk Assessment
👇🙋 Design Hints
👇Tips for writing your forecast.
Example Forecast:
"Pressure dropped 15 mb in 48 hours and humidity rose
from 55% to 85%. This indicates an approaching warm
front. I predict light rain within 24 hours. Confidence:
MEDIUM."
👇🎯 AUTONOMY SUPPORT: How to
Ace Station 3 (25 pts)
👇Step-by-step approach to maximize your forecast points.
This station tests your ability to bring evidence together.
Here's how to maximize your points:
Step-by-Step Approach:
Start simple: Write what the weather is
RIGHT NOW (temp, clouds, wind)
Use your data: Cite at least 2 specific
numbers (e.g., "pressure dropped from 1018 to 1003 mb")
Pick a confidence level: Explain WHY
— do all your data points agree?
Pro tip: The best answers use Word Bank
terms: barometric pressure, humidity, temperature trend,
warm front, confidence level, evidence-based.
👇✍️ COMPLETE THE STATION 3 FORM
👇
Design your forecast and justify your choices!
👇✍️ Complete Your Worksheet
👇
Complete the "STATION 3 NOTES" section on your worksheet:
🎬Watch: Global atmospheric circulation drives weather patterns through Hadley, Ferrel, and Polar cells. | Wikimedia Commons
🌪️ Your Mission: Create an Evidence-Based Forecast
Your Forecast Must Include:
Current conditions - What is the weather RIGHT NOW?
Data trends - What patterns do you see in the last 48 hours?
Your prediction - What weather do you expect in 24 hours?
Evidence - What SPECIFIC DATA supports your prediction?
Confidence level - Low / Medium / High (and WHY)
🎯YOUR CHOICE: Select Your Forecast Approach
You have THREE meteorological approaches to making your forecast. YOU choose which method fits your thinking style! All three can earn full points—pick based on YOUR values.
📊 Path A: Data-Heavy Analysis (Maximum Evidence)
Use EVERY data point available: temp, pressure, humidity, wind, radar, satellite. Create tables and graphs to visualize trends. Cite 5+ specific measurements in your forecast. If you value comprehensive data analysis and visual patterns, choose this path.
🧠 Path B: Pattern Matching (Expert Heuristics)
Focus on the 2-3 MOST important indicators (pressure + humidity trends). Match current pattern to known weather signatures from Week 1. Make quick, intuitive forecast like real meteorologists under time pressure. If you value efficiency and expert shortcuts, choose this path.
Create THREE forecast scenarios: best case, most likely, worst case. Weight each by confidence based on data consistency. Communicate uncertainty like National Weather Service does. If you value transparency, risk communication, and acknowledging uncertainty, choose this path.
💡 Why This Matters: Real meteorologists use different approaches based on situation—severe weather needs comprehensive data, routine forecasts use patterns, public warnings need risk communication. Your choice reflects authentic weather forecasting!
📗 Need extra support? Click here for forecast writing help
📌 CER SCAFFOLD — Build your response in this order:
▶ CLAIM
Example Forecast:
"Pressure dropped 15 mb in 48 hours and humidity rose from 55% to 85%. This indicates an approaching warm front. I predict light rain within 24 hours. Confidence: MEDIUM."
🆘 Stuck? Click here for step-by-step help
Write current temperature, pressure, humidity
Compare to values from 24 and 48 hours ago
Did they go UP, DOWN, or stay SAME?
Match pattern to the Station 2 cheat sheet
Write your prediction based on the pattern
📝 COMPLETE THE STATION 3 FORM BELOW👇🙋 Need Hints? — CER Scaffold + Sentence Starters 👇 Design hints and sentence starters.
Forecast Building Hints:
Start simple: Write what the weather is RIGHT NOW (temp, clouds, wind)
Use your data: Cite at least 2 specific numbers (e.g., "pressure dropped from 1018 to 1003 mb")
Pick a confidence level and explain WHY — do all your data points agree?
Sentence Starters:
"Current conditions show _____, and over the past 48 hours, _____ has been _____"
"I predict _____ in the next 24 hours because the data shows _____"
"My confidence is _____ because (all indicators agree / some data conflicts)"
Word Bank: barometric pressure, humidity, temperature trend, warm front, cold front, confidence level, forecast, evidence-based, reliable, uncertain
23 Points | ~15 Minutes
Demonstrate mastery by integrating concepts from Weeks 1-2.
👩🎓 Show What You
Learned
Question Types:
2 NEW - Data analysis and pattern
recognition (this week)
2 SPIRAL - Week 1 review (air masses,
fronts)
1 INTEGRATION - Connect data patterns to
predictions
1 SEP-4 - Analyze data and draw conclusions
👇👩🎓 AUTONOMY SUPPORT: How to
Ace the Exit Ticket (23 pts)
👇Quick review of Week 1 and Week 2 concepts you'll need.
The Exit Ticket tests INTEGRATION - connecting ideas across
weeks. Here's how to prepare:
Quick Review Before You Start:
Week 1 (Air Masses/Fronts): Warm and cold
fronts bring distinct, predictable changes in weather.
Week 2 (Data Collection): You map those
fronts using temperature, pressure, and humidity trends.
KEY DISTINCTION: Single point data (it is
70 degrees) vs trend data (temperature rose 10 degrees in
1 hour).
Integration question tip: The best answers
connect the data points to the physics of fronts. Example:
"The falling pressure indicates a front is arriving, and the
rising temperature shows it is a warm front."
👇✍️ COMPLETE THE EXIT TICKET
👇
This is your final assessment for Week 2. Take your time!
👇✍️ Complete Your Worksheet
👇
Complete the "DAY 2 EXIT TICKET" and "SCIENCE CIRCLE"
sections:
Q1 (NEW): Data analysis and pattern
recognition
Q2 (SPIRAL): Air masses and fronts
Q3 (INTEGRATION): Connect data patterns to
predictions
Science Circle: Your "Aha!" moment and
remaining question
🎯 Bonus: +5 points for Day 2
Exit Ticket, +3 points for Science Circle!
Dr. Marshall Shepherd is a leading meteorologist, former NASA
researcher, and past president of the American Meteorological
Society. He is known for his research on urban weather effects and
how cities change local precipitation patterns. He is also a
passionate advocate for science literacy and weather preparedness
in underserved communities. His research shows how weather
forecasting saves lives — and how prediction tools must
reach all communities equally.
⚖️ Environmental Justice: Unequal
Access to Forecasts
Severe weather impacts on unhoused populations in St. Louis
highlight how weather prediction is an equity issue. Even with
accurate forecasts, people without shelter, reliable
communication, or transportation face disproportionate danger
during storms, tornadoes, and extreme heat events. The data
collection and pattern recognition skills you practiced this week
seem purely scientific. But there's a justice issue hiding in
those numbers: not all communities have equal access to accurate
forecasts or adequate warning time.
