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Temperature Forecast Techniques and Models
Temperature Forecast Techniques and Models
Understanding how to forecast temperature is crucial for meteorologists and students studying meteorology. Various techniques and models are used to predict the daytime high and overnight low temperatures for the next day. Here we will explain each of the following methods: climatology, persistence, NAM, GFS, NAM MOS, and GFS MOS.
Note: It is important to use Universal Time (UTC) for consistency. The overnight period is from 7 p.m. (0000 UTC) to 7 a.m. (1200 UTC) the day of your forecast, and the daytime period is from 7 a.m. (1200 UTC) to 7 p.m. (0000 UTC) this would be considered the next day for in CDT.
Definition: Climatology forecasting is based on the average weather conditions for a specific location over a long period (typically 30 years). This method assumes that future weather will be similar to past weather patterns.
How to Use:
Access Historical Data: Gather historical temperature data (link above) for the specific date and location.
Forecast: Use these averages as the forecasted temperatures. Be sure to look at the next day.
Example: The average daytime high temperature for June 13th in Algona is 79°F and the average overnight low is 57°F, these values are used as the forecast for June 13th.
Question: Why might climatology not always be accurate for short-term forecasts?
The image below shows climate data for Algona.
Daytime High - 79 F
Overnight Low - 57 F
Definition: Persistence forecasting assumes that the current weather conditions will continue into the near future. This method works well in stable weather patterns but is less accurate during changing conditions.
How to Use:
Access Persistence Data: Gather the data for Algona's Station (KAXA) link above.
Be sure to enter in Algona's Station "KAXA"
Forecast: Identify the Overnight Low (0000 UTC - 1200 UTC) and the Daytime High (1200 UTC - 0000 UTC)
Example: If yesterday's Overnight Low was 54 and the Daytime High was 76. Then the forecasted low would be 54F and the high would be 76 F.
Question: In what situations might persistence forecasting be most effective?
Persistence Data: Waterloo
North American Mesoscale (NAM) Model: NAM (KAXA)
Definition: The NAM model is a numerical weather prediction model used primarily for short-term weather forecasts. It provides detailed information on temperature, precipitation, and other weather variables.
How to Use:
Access NAM Data: Obtain the latest NAM model output from the link above.
Analyze Temperature Predictions: Look at the forecasted overnight low (0000 UTC - 1200 UTC) and daytime high (1200 UTC - 0000 UTC) temperatures for the next day.
Use the crosshair to locate these values
Locate the value identified by the crosshair at the top of the page
Be sure you are on the "Hourly Temperature Forecast"
Example: The NAM model predicts a daytime high of 72°F and an overnight low of 50°F for tomorrow. Use these values in your temperature forecast.
Question: What advantages does the NAM model have for short-term weather forecasting?
Example Data Below: NAM Algona
Global Forecast System (GFS) Model: GFS (KAXA)
Definition: The GFS model is a global numerical weather prediction model that forecasts weather up to 5 days in advance.
How to Use:
Access GFS Data: Obtain the latest GFS model output from the link above
Analyze Temperature Predictions: Look at the forecasted overnight low (0000 UTC - 1200 UTC) and daytime high (1200 UTC - 0000 UTC) temperatures for the next day.
Use the crosshair to locate these values
Locate the value identified by the crosshair at the top of the page
Be sure you are on the "Hourly Temperature Forecast"
Example: The GFS model predicts a daytime high of 74°F and an overnight low of 52°F for tomorrow. Use these values in your temperature forecast.
Question: How does the GFS model differ from the NAM model in terms of coverage?
Example Data Below: GFS Algona
NAM MOS: NAM MOS (KAXA)
Definition: NAM MOS is a statistical post-processing technique applied to NAM model output to refine temperature forecasts.
How to Use:
Access NAM MOS Data: Obtain NAM MOS output from the link above.
Analyze Temperature Predictions: Look at the forecasted overnight low (0000 UTC - 1200 UTC) and daytime high (1200 UTC - 0000 UTC) temperatures for the next day.
You will want to use a 1200 UTC run or 1800 UTC run for the best results. This can be located near the top right of the model.
Example: NAM MOS indicates a daytime high of 71°F and an overnight low of 49°F for tomorrow. Use these values in your temperature forecast.
Question: Why might NAM MOS provide more accurate forecasts than the raw NAM model output?
Example Data Below: NAM MOS Algona
GFS MOS: GFS MOS (KAXA)
Definition: GFS MOS is a statistical post-processing technique applied to GFS model output to refine temperature forecasts.
How to Use:
Access GFS MOS Data: Obtain GFS MOS output from the link above.
Analyze Temperature Predictions: Look at the forecasted overnight low (0000 UTC - 1200 UTC) and daytime high (1200 UTC - 0000 UTC) temperatures for the next day.
You will want to use a 1200 UTC run or 1800 UTC run for the best results. This can be located near the top right of the model.
Example: GFS MOS indicates a daytime high of 73°F and an overnight low of 51°F for tomorrow. Use these values in your temperature forecast.
Example Data Below: GFS MOS Algona
Fill out the Google Form to give a temperature prediction: Google Form Prediction Link
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