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

AI Thread Summary
Including pressure as a predictor in a Kalman filter for wind forecasting may lead to significant errors, with discrepancies reaching up to 1000 Pa. The user is seeking insights on potential limitations of the Kalman filter regarding pressure integration in postprocessing. Despite attempts to convert pressure units from Pa to hPa and adjusting values, the high error persists. The discussion highlights the need for further investigation into the suitability of pressure as a predictor in this context. Addressing these issues could enhance the accuracy of wind forecasts derived from numerical weather prediction models.
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.
 
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