Is Including Pressure in a Kalman Filter for Wind Forecast Causing High Error?

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SUMMARY

The discussion centers on the challenges of incorporating pressure as a predictor in a Kalman Filter for wind forecasting. The user reports significant errors, reaching up to 1000 Pa, when pressure is included in the model. Despite attempts to convert pressure from Pa to hPa and adjusting values, the high error persists, indicating potential limitations of the Kalman Filter when applied to this specific scenario. The user seeks insights on whether pressure is an unsuitable predictor for this application.

PREREQUISITES
  • Understanding of Kalman Filter algorithms
  • Familiarity with numerical weather prediction models
  • Knowledge of pressure units (Pa and hPa)
  • Experience with error analysis in forecasting models
NEXT STEPS
  • Research the limitations of Kalman Filters in meteorological applications
  • Explore alternative predictors for wind forecasting
  • Learn about data preprocessing techniques for pressure data
  • Investigate error reduction strategies in numerical weather prediction
USEFUL FOR

Meteorologists, data scientists working with weather models, and researchers focused on improving forecasting accuracy will benefit from this discussion.

maggie_pui
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Hi,
I am now using multiple parameters kalman filter to remove the systematic error of the output from the numerical weather prediction model. when applying kalman filter to the wind forecast, there is high error if pressure is included to be the predictors. I would like to know if there any limitation for kalman filter or pressure is not suitable to included into kalman filter to do postprocessing?

p.s. pressure is in Pa
I tried to convert them to hPa and also subtract them by 1000 but still the error is so large, up to 1000Pa.
 
Last edited:
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can anyone help me??
 

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