Hello everyone,
I am following the instructions and using the FourCastNet model for weather forecasting to generate inferences. The model provides 6-hourly forecasts up to 10 days, which performs well in capturing large-scale atmospheric patterns. However, I need to estimate intermediate hourly values to enhance temporal resolution.
So far, I have tried linear and cubic interpolation methods, but they seem insufficient in accurately reconstructing finer details. I am looking for more advanced techniques or suggestions to improve the interpolation of missing hourly data between the available 6-hour intervals.
Any insights or recommendations would be greatly appreciated, thanks in advance :)
Hello everyone,
I am following the instructions and using the FourCastNet model for weather forecasting to generate inferences. The model provides 6-hourly forecasts up to 10 days, which performs well in capturing large-scale atmospheric patterns. However, I need to estimate intermediate hourly values to enhance temporal resolution.
So far, I have tried linear and cubic interpolation methods, but they seem insufficient in accurately reconstructing finer details. I am looking for more advanced techniques or suggestions to improve the interpolation of missing hourly data between the available 6-hour intervals.
Any insights or recommendations would be greatly appreciated, thanks in advance :)