Air temperature

Sensitivity to the forecast hours

The model performance typically decreases with increasing forecast hours. The accuracy of the 2 m air temperature by choosing the meteoblue learning multimodel (mLM) is within 1.2 K for the 24h forecast and within 2.0 K for the 6 day forecast. This implies that the 24h forecast of the mLM is as good as the 6 day forecast of the stand-alone numerical weather forecast models.   

MAE [K] as a function of the forecast hours for the mLM for single analysis days and the average (black). The 24h forecast error for MOS (blue) and the raw models (red) is additionally shown. 

A quick introduction to the meteoblue learning multimodel can be downloaded below: 

mlm_leaflet.pdf (6.96 MB)

24 hour forecast of historical and forecast data

Our results show that the meteoblue learning multimodel (mLM), which is used in the operational forecast, performs significantly better than MOS simulations and the historical reanalysis model ERA5 (MAE = 1.2 K vs. MAE = 1.5 K). The accuracy of stand-alone numerical weather forecast models (e.g. NEMS, GFS) is significantly worse than MOS and mLM in particular. 

Over 90 % of all meteorological stations have an accuracy better than 2 K by using the meteoblue learning multimodel (mLM). This number is reduced to 85 % by using the reanalysis model ERA5 and to 50 % (36%) by using the stand-alone numerical weather forecast model NEMS (GFS).

Continental regions and regions in high elevation are typically simulated worse than maritime and low elevated regions. The errors in Europe and North America are typically lower than on the Southern Hemisphere. Air temperatures are typically worse simulated in Northern Hemispheric winter than in summertime.

Model performance of the 24h forecast of the mLM (top panel), the reanalysis model ERA5 (bottom left) and the numerical weather forecast model GFS (bottom right) for September – October 2018. 

Temperature

NEMS Global 25km - verification of temperature

Temperature simulations with MOS:

  • Corrects most errors
  • 92% of stations with MAE* < 2.0°C
  • Improvement vs. RAW = 0.8°C
  • 85% of all hourly errors < 2.0°C

Used in all meteoblue forecasts.

meteoblue predicts more than 70% of all temperatures with less than 2°C difference from measured temperature - 3 days (72 hours) in advance. For 12 hours ahead, more than 80% of all temperature forecasts are less than 2°C different from measurement - on an hourly basis. The RMSE (Root Mean Square Error) of the hourly forecast is less than 2.5°C for up to 3 days forecasts, and around 2°C for 1 day forecast (these data are valid for Europe and North America).
What does that mean?

Personally: If you assume the you can distinguish temperature differences of more than 2°C (by feeling), then 2/3 of all meteoblue hourly temperature forecasts are already correct 3 days in advance! It means that when you look at a meteoblue forecast for the next 72 hours, you will experience during at least 54 hours the same temperature as meteoblue forecasts.
Technically: If you interpolate temperature measurements from a weather station made every 3 hours into hourly data and compare them to the actual hourly measurements, your RMSE will be 1.5-2.0°C. The meteoblue forecast error is on average 2.2°C. This means that the temperature forecast is as good as a measurement every 6 hours.

MAE*: Mean absolute error

Example of use

In the following example the probability of a frost event is shown.

Hypothetical example: You are interested in temperatures below 0°C, frost, because you are a farmer and you are worried about your crop. The error of the model is +/- 2°C in 85% of the cases. This means with a risk of 15% the temperature can vary more than 2°C. But you are only interested in decreasing temperatures, so the risk of temperature variations in negative direction is only half: 7.5%. Precise, at a temperature of 2°C you have a probability of 7.5% that the temperature falls below 0°C. If the critical temperature for your crop is -1°C, the risk is even lower: 3.75%; as well as for a critical temperature of -2°C: 1.875%.

The same thought can be made with an error of 1°C in 85% of the cases. There is a decrease in probability that the temperature falls below 0°C; the risk is 3.75% at 0°C. 

If the forecast is only valid for 60% of all cases the risk looks as follows: At a temperature of 2°C you have a probability of 20% that the temperature falls below 0°C; 10% for a temperature below -1°C. 

All these considerations assume that you are estimating the risk of temperatures below 0°C at an actual temperature of 2°C. If this calculations are made at for example 1°C, the risk changes. 

In the figure, these facts are shown in a visual way. 

Visualization of the risk, that the temperature drops below 0°C.