In the previous lesson, you created a map of real-time weather data collected from satellites, radars, and weather stations around the world. You learned a little bit about how the data was collected and who collected it, but in this lesson, you'll go on a deeper exploration of your data and use what you learn to predict weather across time and space.
First, you'll look for trends in your data. Accurate weather forecasting is dependent on seeing what patterns are happening now. You'll start by examining your temperature layer.
- If necessary, open your Real-Time Weather Map in ArcGIS Online (or ArcGIS Enterprise).
- Turn on the NOAA METAR Temperature layer. Click the layer to view its sublayers.
Previously, you symbolized this layer so that every point had the same symbol. Now, you'll change the symbology so that warmer and colder temperatures have a different color.
- Point to Stations and click Change Style. For Choose an attribute to show, choose Air Temperature.
The default style for this attribute is Counts and Amounts (Size), which symbolizes higher temperatures with larger symbols.
- For Counts and Amounts (Color), click Select.
The symbols on the map change. Now, higher temperatures have a darker color. The default color scheme is from light blue to dark blue, which might not be the best for styling temperature.
- For Counts and Amounts (Color), click Options. Click Symbols.
A window appears with style options for your symbols.
- Click the Fill tab. Scroll to the bottom of the list of color ramps and choose the blue-to-red color ramp.
By default, high values are blue, while low values are red. Usually, high temperatures are associated with red and low temperatures are associated with blue, so you'll invert the color ramp.
- Click the Invert color ramp button.
- Click OK. In the Change Style pane, click OK and Done.
- Symbolize the Buoys sublayer the same way: Air Temperature attribute, Counts and Amounts (Color) drawing style, and inverted blue-to-red color ramp.
To see the temperature ranges each symbol represents, open the Legend pane.
In the example image, temperatures seem to be strongly affected by latitude (your temperatures may vary). At least in the United States, the hottest temperatures are in the south, with the coldest temperatures in the north. However, the correlation between temperature and latitude is not exact, and some states on the same latitude have much different temperatures.
You'll explore worldwide to see what other patterns you can find.
- Navigate to the West Europe bookmark.
Most of western Europe is at the same latitude as Canada and the northern United States. For instance, London is at a similar latitude as Vancouver. Do the latitudinal trends that were apparent in the United States still apply to Europe?
Although your map may vary, Europe is generally warmer than the United States at similar latitudes. Why might this be? Are there any other trends you see when comparing the two continents?
- Navigate to the East Asia bookmark.
In eastern Asia, there are far fewer weather stations and buoys than in the United States or Europe. Although this dataset does span the world, it's important to keep in mind that coverage is not uniform. Forecasts for areas with less weather data are likely to be less accurate than areas with more.
- Navigate to the Australia bookmark.
Australia is in the Southern Hemisphere, so its seasons are different than those in the Northern Hemisphere. For instance, when it's summer in the United States, it's winter in Australia, and vice versa. How might seasonal variations influence temperature?
- Navigate to the Colorado, United States bookmark. Turn off the World Light Gray Base layer and change the basemap to Terrain with Labels.
Colorado is on the border of the Rocky Mountains. The western half of the state is mountainous, while the eastern half is flat plains. Does altitude have a pronounced effect on temperature?
When zoomed in, labels may appear for the points. Because this layer was originally used to show wind speed, the labels are relevant to that attribute. You can turn off labels by clicking the layer's More Options button, choosing Manage Labels, and unchecking Label Features.
- Navigate around the world using your bookmarks and the pan and zoom tools. Change the basemap as necessary. Answer the following questions:
- Which areas have the most data? Which areas have the least?
- Name two areas where latitude is the most likely explanation for observed temperature.
- Name two areas that will likely experience much different temperatures three months from now. Name one area that will likely experience the same temperatures three months from now.
- Are there any areas where altitude is causing temperatures that aren't explained by latitude or seasonal variation?
- What effect might oceans and large water bodies have on temperature? How do temperatures tend to differ between coastal and inland areas at the same latitude?
How does the temperature where you live compare to the temperature in the surrounding area? Does the recorded temperature match the temperature you're currently experiencing? If not, what might have caused the difference?
Next, you'll calculate statistics to find out the range of temperatures around the world.
- In the Contents pane, point to the NOAA METAR Temperature Stations sublayer and click Show Table.
- In the table, click the Air Temperature field and choose Statistics.
The Statistics window appears.
Your numbers will vary, but the range of temperatures will usually span over 100 degrees Fahrenheit, given seasonal and latitudinal variations around the world. Next, you'll locate the hottest and coldest areas.
- Close the Statistics window. In the table, click the Air Temperature field and choose Sort Descending.
The table sorts so that the highest temperature is shown first.
- Click the first row of the table to select it. Click the Options button and choose Center on Selection.
The map navigates to the selected station, which is currently the hottest in the world.
- Where is this station located?
- Why is this station so hot? Is it primarily hot because of latitude, elevation, or season?
- Click the Air Temperature field and choose Sort Ascending. Navigate to the coldest temperature.
When you sort the row in ascending order, the first few rows may have no value, meaning the station did not record a temperature the last time the data updated. You may need to scroll down the table until you find the coldest temperature.
- Where is this station located?
- Why is this station so cold? Is it primarily cold because of latitude, elevation, or season?
- Close the table.
Predict rain with wind and pressure
Next, you'll take a closer look at your precipitation and pressure data. In conjunction with your other data layers, you'll predict where rainfall might occur in the near future.
First, you'll compare current rainfall to wind patterns to see where the wind might cause rain clouds to travel in the near future. Then, you'll learn how pressure can affect precipitation and determine areas with high and low pressure systems.
- Navigate to the Continental United States bookmark. Turn off the NOAA METAR Temperature layer and turn on the NEXRAD Precipitation and GOES Satellite Imagery Transparent layers.
- Which areas are experiencing rainfall?
- What patterns do you see in the location of rainfall?
- How closely correlated are the clouds in the satellite imagery and the rain captured by radar?
- Turn off the GOES Satellite Imagery Transparent layer and turn on the GOES Satellite Imagery layer.
Does precipitation tend to occur where the clouds are bright white (cold) or dark gray (warm)?
In general, warm clouds are lower to the ground and absorb moisture through evaporation. As they rise, they become colder, and the water vapor condenses into liquid droplets. This makes the cloud heavier, so it drops and releases the liquid in the form of precipitation.
- Turn off the GOES Satellite Imagery layer. Turn on the NOAA METAR Wind Speed and Direction layer and change the basemap to Topographic.
- Does wind speed have any relationship to current precipitation? If so, what is it?
- Name two spatial patterns that you observe about the current wind speed and direction.
- Zoom to an area of heavy precipitation (dark green, yellow, orange, or red).
In the example image, southern Louisiana and Mississippi are experiencing a lot of rainfall. The labels for the wind speed features indicate the speed of the wind in kilometers per hour. While not all the wind speed arrows point in the same direction, the overall wind pattern is toward the north and east. If this wind persists, the city of Alexandria might experience rain soon. But how soon?
- Find a city that is currently dry but, based on wind direction, might experience rain soon. On the ribbon, click Measure, choose Distance, and set the units to Kilometers.
- Click a wind speed arrow near precipitation that is pointing toward the city you found. Then, double-click the city.
In the example image, a northeastern arrow with a wind speed of 17 kilometers per hour is about 180 kilometers away from Alexandria. At this rate, it would take over 10 hours for rain to reach the city. Additionally, other stations in the area record either no wind, slower wind, or wind that is more easterly. It's possible the precipitation will pass south of the city altogether.
- How far away is rainfall from the city you found?
- How long would it take rainfall to reach the city given the wind speed and direction?
- Are there other winds that might cause the rainfall to avoid your city?
Overall, how likely would you say it is that your city receives rain?
Wind is not the only factor that influences precipitation. Pressure is also important. Low pressure causes air to rise, cool, and condense into rain clouds. High pressure causes air to flow down and heat up. In the Northern Hemisphere, air tends to move counterclockwise around a low pressure system and clockwise around a high pressure system (this trend is reversed in the Southern Hemisphere).
Based on your satellite imagery, precipitation, and wind speed layers, you'll predict where pressure is high and low.
- Close the Measure window. Navigate to the Continental United States bookmark and turn on the GOES Satellite Imagery Transparent layer.
Based on what you just learned about air pressure, where in the United States do you think air pressure is highest? Lowest?
You'll add map notes to track your predictions.
- On the ribbon, click Add and choose Add Map Notes.
- In the Add Map Notes window, for Name, type Pressure Predictions and add today's date. Click Create.
It's important to add today's date, because while your weather data updates in real time, your map notes layer does not.
- In the Add Features pane, for Pressure Predictions - Text, click Text.
- Click a location of the map where you think pressure is high. Type H and press Enter.
- Click the map note you added. Drag its handles to make it larger and reposition it as necessary.
In the example image, central New Mexico was chosen as a possible area of high pressure due to its lack of rainfall, low-lying (dark-colored) clouds, and generally clockwise winds (although the wind does not move in a uniformly clockwise pattern).
- In the Add Features pane, click Text. Click the map where you think the pressure is low, type L, and press Enter. Make the map note larger.
In the example image, Louisiana was chosen as a possible area of low pressure. Not only does it have high precipitation and high (white) clouds, but winds also move generally counterclockwise around it.
- Add two more map notes, one for another area of predicted high pressure and one for another area of predicted low pressure.
Next, you'll symbolize your pressure layer to see if your predictions were accurate.
- On the ribbon, click Details. Turn off all layers except Pressure Predictions and turn on the NOAA METAR Pressure layer.
Like the precipitation layer, you'll symbolize this layer based on an attribute, with different colors for different levels of pressure.
- Open the Change Style pane for the NOAA METAR Pressure layer Stations sublayer. For Choose an attribute to show, choose Altimeter Pressure (Millibars) and for Counts and Amounts (Color), click Select.
The map updates with the new symbology. The default color scheme of light blue to dark blue is fine, but the distribution of the data into each symbol class is skewed by the way the data was reported.
Air pressure is usually between 1,000 and 1,030 millibars, but stations that did not report any data are listed as 0. You'll change the data classification to the quantile method, which will sort the recorded pressures into even groups that won't be skewed by a few outliers in the dataset.
- For Counts and Amounts (Color), click Options. Under Classify Data, for Using, choose Quantile.
- Click OK and Done. Symbolize the NOAA METAR Pressure Buoys sublayer the same way, but using the Sea Level Pressure (Millibars) attribute instead.
- How accurate were your predictions?
- Were there other areas of high or low pressure that you did not predict?
- Are there any areas of high or low pressure where you wouldn't expect them to be?
- Which areas may soon experience rainfall based on air pressure?
- Save the map.
Predict rain with temperature
You've predicted precipitation based on existing rainfall, wind direction, and air pressure. But there are more factors that influence rainfall, including heat and humidity.
The amount of water vapor that air can hold depends on its temperature (hotter air holds more). When air holds the maximum amount of water vapor possible, it becomes saturated. The water vapor begins to condense into tiny water droplets to remove it from the air. This condensation can lead to precipitation. Saturation can occur when hot air holding a lot of moisture cools suddenly.
Your temperature data contains a field called Dew Point Temperature. The dew point temperature is the temperature to which the air would have to cool to become saturated. Thus, the dew point temperature is a measure of how much moisture is in the air. If the dew point temperature is close to the air temperature, the air has a high relative humidity and may soon become saturated. When there is a large difference between the dew point and air temperatures, then the air is dry.
To determine where the dew point and air temperature is close, you'll create an Arcade expression that changes the style. You can't create Arcade expressions for sublayers, so you'll first have to add a new version of the original wind speed and direction layer that only shows stations, not buoys.
- Turn off the Pressure Predictions and NOAA METAR Pressure layers. On the ribbon, click the Add button and choose Add Layer from Web.
- Add a layer using the following URL:
The NOAA METAR current wind speed direction - Stations layer is added to the map. This layer is similar to your NOAA METAR Wind Speed and Direction layer, but it doesn't include sublayers (and only shows stations).
- Rename the NOAA METAR current wind speed direction - Stations layer to NOAA METAR Dew Point Temperature Difference.
- Open the Change Style pane for the NOAA METAR Dew Point Temperature Difference layer. For Choose an attribute to show, choose New Expression.
A window to create a new Arcade expression appears. The expression you create will be simple. It will subtract the dew point temperature from the air temperature to find the difference between the two values. Then, it will determine whether that difference is greater or less than 4 degrees Fahrenheit.
If the difference is less than 4, saturation—and possibly precipitation—is close. If the difference is greater than 4, saturation is less likely to occur.
- Next to Custom, click Edit. Change the name of the expression to Dew Point Temperature Difference and click Save.
- Under Globals, for Field: Air Temperature, click $feature.TEMP (you may need to scroll down).
$feature.TEMP is added to the Expression box. In Arcade notation, it refers to the Air Temperaturefield.
- In the Expression box, after $feature.TEMP, press the spacebar. Type - (the minus sign) and press the spacebar again.
- Under Globals, scroll through the list of fields and click $feature["DEW_POINT"].
Your expression now subtracts the dew point temperature from the air temperature. Next, you'll adjust the expression to determine whether the difference is less than 4.
- In the Expression box, add parentheses around the existing expression. At the end of the expression, press the spacebar and type < 4.
The full expression reads ($feature.TEMP - $feature["DEW_POINT"]) < 4.
- Click OK.
The expression is saved and the map is automatically styled based on it.
The map has two types of symbols: false (red) and true (blue). The expression you created is false when the difference between air and dew point temperature is greater than 4. It is true when the difference is less than 4. Blue points show areas that are close to saturation.
The number 4 was chosen because it was small, but if the difference is 6 or even 10 degrees, an area might still be at risk of saturation. For an optional challenge, in the Change Style pane, click the Edit Expression button. Change your expression to find values where the difference is less than 6 or 10 degrees and compare your results to when the difference is less than 4 degrees.
- What patterns do you find?
- What differences exist between the various expressions, and what similarities?
- In the Change Style pane, click Done. Turn on the NEXRAD Precipitation layer.
- Is your dew point temperature difference expression a good predictor of precipitation?
- If you completed the optional challenge (see the note in the previous step), which expression seems to be the best predictor of precipitation?
Based on the dew point temperature difference, which areas aren't currently experiencing precipitation but might soon?
It's difficult to compare both the dew point temperature difference and the air temperature at the same time, because the layers mostly overlap. Another way to compare them is to label features. You'll label your dew point temperature difference layer with the air temperature at each point.
- In the Contents pane, point to the NOAA METAR Dew Point Temperature Difference layer, click More Options, and choose Create Labels.
The Label Features pane appears. The default label shows the wind speed.
- For Text, choose Air Temperature.
At your current extent, the labels may not be visible.
- For Visible Range, drag the slider so that the labels are visible at all extents.
The labels are added. However, by default, every temperature value includes two decimal places. The added decimals clutter the map and don't add much information. You'll create an expression to remove the decimal places.
- For Text, choose New Expression. In the window for the new expression, change the expression name to Air Temperature (No Decimals).
- For Expression, build (or copy and paste) the following expression:
This expression will round the Air Temperature field to 0 decimal places.
- Click OK.
The decimals are removed from the labels on the map.
In areas where many points are clustered (such as the area around Phoenix, Arizona, in the example image), some of the labels may not appear. If you zoom in, you'll be able to see all the labels.
- In the Label Features pane, click OK. Explore the map to answer the following questions:
- Does air that is close to being saturated tend to be warmer or colder? Or is there no correlation?
- Does precipitation tend to occur more near cold air that is close to saturation or warm air?
Hurricanes are large storms that tend to form over the ocean. They can make landfall and cause property damage and loss of life, and often have strong winds, precipitation, and low pressure. Next, you'll examine your hurricanes layer and see what connection you can find between hurricanes and some of your other data layers.
Depending on the time of year you take this lesson, there may not be any active hurricanes. If so, you can skip this section.
- Turn off the NOAA METAR Dew Point Temperature Difference and NEXRAD Precipitation layers. Turn on the Active Hurricanes layer.
- Navigate around the map until you locate an active hurricane.
The example image shows Hurricane Alcide near the northern tip of Madagascar. Each orange point represents the hurricane's observed position on each day of its existence.
- Access the legend.
- Based on the Observed Track, what kind of storm did this hurricane begin as? Has it gotten stronger or weaker over time? (In the legend, the symbols are ranked from weakest to strongest, with Hurricane5 being the strongest.)
Based on the Forecast Position, what kind of hurricane will this storm become in the next few days? Will it become stronger or weaker than it currently is?
Hurricane Alcide started as a relatively weak tropical storm but eventually became a Category 2 Hurricane. In the next few days, it is forecasted to become weaker, eventually becoming a tropical storm again and not a hurricane.
- Use the Measure tool to measure how far the hurricane has moved each day in kilometers.
- How fast is the hurricane moving in kilometers per hour? Has it sped up or slowed down over time?
- Return to the Contents pane. Turn on the GOES Satellite Imagery and World Light Gray Base layers.
- Can you see the hurricane in the imagery? How big is it compared to the width of the hurricane track line?
- What is the shape of the cloud cover around the hurricane?
- If this hurricane makes landfall, about how much area will it cover?
- Return to the Continental United States bookmark. Turn off the Active Hurricanes and GOES Satellite Imagery Transparent layers.
So far, the weather predictions you've made have been about determining what weather will be like in the future. However, sometimes it's important to make predictions about what the weather is now in areas where no data is available.
Your temperature, pressure, and wind speed data all comes from weather stations around the world. But there isn't a weather station covering every location worldwide. How can you know what the weather is in an area with no station? One way is to interpolate a surface. Interpolation estimates unknown values across space based on their proximity to known values. Basically, it uses the data you have to make guesses about the data you don't.
You'll interpolate the temperature data for a defined geographic area, one with enough data that you can feel confident that your interpolation will be accurate but with enough gaps to make interpolation useful. For this exercise, you'll choose the state of California, in the United States. First, you'll filter your States layer to show only California.
- Turn on the States layer. Point to the layer and click the Filter button.
The Filter window appears. You'll create a simple expression so that only features with the name California are shown.
- Click the first drop-down menu and choose STATE_NAME. Leave the second drop-down menu unchanged. For the third drop-down menu, click Unique and choose California.
Your expression reads STATE_NAME is California.
- Click Apply Filter and Zoom To.
You navigate to the state of California, which is now the only state shown in the States layer.
- Turn on the NOAA METAR Temperature layer. If necessary, turn off its labels (click the More Options button for the Stations sublayer, choose Manage Labels, uncheck Label Features, and click OK).
California has a lot of stations along the southern coast (where Los Angeles and San Diego are located) and near the San Francisco Bay. In general, the number of stations tends to correspond to centers of population. But the eastern and northern parts of the state often have fewer stations. Using interpolation, you can estimate the temperatures in these areas.
- On the ribbon, click Analysis.
- In the Perform Analysis pane, click Analyze Patterns and choose Interpolate Points.
The Interpolate Points pane appears. This pane contains options for how you want to interpolate.
- For Choose point layer containing locations with known values, choose NOAA METAR Temperature-Stations. For Choose field to interpolate, choose Air Temperature.
You can also decide whether you want to optimize the interpolation process for speed or accuracy. By default, the tool does not prioritize one over the other, which is fine for your purposes. However, you do want to change an option so that the interpolation is filtered to the state of California.
- Under Optimize for, click Options. For Clip output to, choose States.
Because you filtered the layer to show only California, this will clip the interpolated surface to California too.
- For Result Layer Name, type California Temperature Interpolation and add the current date.
- Uncheck Use current map extent and click Run Analysis.
The tool runs and the layer is added to the map. Your result will look different than the example image.
- Is there a relationship between temperature and proximity to the ocean? If so, what is it?
- California has some of the highest and lowest elevations in the United States. Is there a relationship between temperature and elevation? Try using a basemap that shows topographic features such as mountains to help answer the question.
- How well does your interpolated surface match the station temperature data?
- The Interpolate Points tool uses a statistical interpolation method called empirical Bayesian kriging. Based on the method's documentation, how confident are you in the accuracy of your interpolated surface?
- For an optional challenge, try creating an interpolated surface for atmospheric pressure or wind speed in California (you can do so by changing the Choose field to interpolate parameter of the Interpolate Points tool). What patterns do you see in the results? How do these patterns differ from the patterns you see in the interpolated temperature surface?
- Zoom to the northeastern corner of California.
- How many weather stations are in this area?
- How confident are you in the predicted temperature surface here compared to the San Francisco Bay area? Why?
- Do you think the interpolated surface for this area would be much more accurate if your interpolated surface included data from the weather stations in Oregon and Nevada? Which area or areas in California might have more accurate interpolated surfaces if you included weather stations from neighboring states?
- For an optional challenge, try creating an interpolated temperature surface for the states of California, Nevada, Oregon, and Arizona (you can do so by adding expressions to the States layer filter). What differences do you notice?
- Navigate to the North Africa bookmark.
- If you were to create an interpolated surface of the country of Algeria, how confident would you be in it compared to the interpolated surface you created for California?
- Which areas around the world do you think would have the most accurate interpolated surfaces? Which would have the least?
- For an optional challenge, try creating an interpolated temperature surface for the country of Algeria. What layer would you need to add to your map to create this interpolated surface? Where can you find this layer?
- Navigate to the California, United States bookmark. Turn on the Counties layer.
- Counties are subdivisions of states and generally much smaller. About how many weather stations are there per county?
- Would you be confident in an interpolated surface that was created for a single county?
- Navigate to the Continental United States bookmark. Turn off the States, Counties, and California Temperature Interpolation layers.
- Save the map.
You've now predicted the weather not only across time, but also space. Throughout this lesson, you've learned how temperature, precipitation, pressure, and wind combine to create the temperature we experience. You answered questions about your data and performed statistical analysis on it to derive new insight.
You can apply many of the concepts you learned in this lesson to any weather-related GIS workflow—or even create your own workflow. What questions can you answer using your real-time data that weren't asked in this lesson?
You can find more lessons in the Learn ArcGIS Lesson Gallery.