This suggests that the Jua model is more effective at reducing consistent forecasting errors. This is a crucial discovery since these systematic errors can impact the long-term reliability of weather predictions.
Remarkably, up to the 30-hour mark, the RMSE is even lower by 1 Kelvin. This represents a substantial 13% improvement over the IFS model. So, in short-term forecasts, it's clear that the Vilhelm model is more precise in predicting air temperatures.
This shows that Vilhelm excels at accurately reflecting the statistical patterns and relationships between the actual observations and the forecasted values.
Our analysis indicates that the Vilhelm model has an edge over the IFS model by ECMWF. Specifically, when it comes to accurately and reliably predicting air temperatures 2 meters above the ground in the short-term forecasts, Vilhelm consistently delivers superior performance.
Jua's Vilhelm delivers the same accuracy at 54 hours that the IFS delivers at 12 hours.
Comparison between Vilhelm and IFS Total Precipitation forecasts - 2018
The analysis reveals that Jua's precipitation model consistently outperforms the IFS model in several areas. It has notably superior performance in predicting total rainfall more than two days in advance and excels in nowcasting, or very short-term forecasting. Both models, do show their best performance when forecasting weather conditions over land surfaces.
suggesting that it is better at eliminating systematic errors in the forecasts. This is an important finding, as systematic errors can affect the reliability of weather forecasts over time.
with only 1 m/s error in wind speed and direction. The RMSE values for both models match until the 36th hour, indicating that they have similar accuracy in predicting wind conditions until then.
indicating that it is better at reproducing statistical moments and correlations between the observed and forecasted values.
Our analysis shows that the Vilhelm model, has a slight advantage over the IFS model by ECMWF. When it comes to accurately and reliably predicting wind speed and direction 10 meters above the ground in short-term forecasts, the Vilhelm model comes out a bit ahead.
This suggests that Jua is more efficient in reducing consistent forecasting errors. This finding is significant as these systematic errors can impact the long-term dependability of weather predictions.
Impressively, for the initial 30 hours, Vilhelm's RMSE is even lower by 1 Kelvin - a significant 13% improvement over the IFS model. This suggests that, at least in the short term, Vilhelm is more precise in predicting air temperatures.
Interestingly, both models have matching Root Mean Square Error (RMSE) values up to the 36-hour mark. This suggests that when it comes to predicting wind conditions, both models offer similar accuracy up to that point.
This suggests that Vilhelm is superior in accurately reflecting the statistical patterns and the relationship between what was actually observed and what was forecasted.
Vilhelm model slightly outperforms the IFS model by ECMWF. Specifically, when it comes to short-term forecasts of wind speed and direction 10 meters above the ground, the Vilhelm model proves to be a bit more accurate and reliable.