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Enhanced Temperature Forecast Verification using Quantile-to-Quantile Correction Method

This study aims to improve temperature forecast accuracy for 105 stations by correcting systematic biases in ECMWF Ensemble Prediction System data. Utilizing quantile-to-quantile correction technique based on historical forecasts and observations, the new MOS method significantly enhances forecast skill and reduces errors, as demonstrated through box plots, root mean square error analysis, and correlation coefficients. Regional and national results confirm the effectiveness of the new approach in capturing temperature distribution and improving overall forecast quality.

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Enhanced Temperature Forecast Verification using Quantile-to-Quantile Correction Method

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  1. Model Output Statistics (MOS) Temperature Forecast VerificationJJA 2011 Benjamin Campbell April 24 ,2012 EAS 4480

  2. Goal and Motivation • Our goal - to provide accurate temperature forecast for 105 stations by removing systematic biases associated with temperature forecast data from the European Centre for Medium Range Forecast (ECMWF) Ensemble Prediction System (EPS)

  3. Model Data • ECMWF Monthly Hindcast Temperature Data • Reforecast • ECMWF EPS Temperature Data • Both have horizontal resolution of 0.25 deg x 0.25 deg

  4. Observed Data • Temperature DataObservations from the NOAA Automated Surface Observing System (ASOS) network. http://www.crh.noaa.gov

  5. Interpolation of Model Data Longitude (0.25 o) Latitude (0.25 o)

  6. Q to Q Correction • To correct for the systematic model biases, the quantile to quantile technique was applied using historical forecast (18 years) and observations • Quantile-to-Quantile Map • Hindcast Data and Observations • ECMWF Fcst = 50 deg F • Fcstquant = 50 deg F • Observed quant = 48 deg F • New Fcst = 48 deg F

  7. MOS Forecast Verification Methods • Box Plots (individual sites only) • Forecast Skill: • Root mean square error • Anomaly Correlation Coefficient • Matlab: corrcoef(fcst-climo,obs-climo) • Uncertainty: Standard deviation of bootstrap • Correlation Coefficient • Matlab: corrcoef(fcst,obs) • Uncertainty: Standard deviation of bootstrap

  8. KTPA Results: Box Plots

  9. KTPA Results: RMSE

  10. KTPA Results: ACC and CC

  11. KTPA Results Skill

  12. S.E. Regional Results

  13. National Results: 105 Cities

  14. National Results: Skill

  15. Conclusions • KTPA • Box Plots show that New MOS may capture Observations distribution better • RMSE shows vast improvement from Old MOS  New MOS • Skill Plots show vast improvement from Old MOS  New MOS • Anomaly Corr. Coef. And CorrCoef. Show improvement at for T min forecast but worse for T max • For all sites and regional • Anomaly Corr. Coef. And CorrCoef. Results are similar • RMSE shows vast improvement from Old MOS  New MOS • National Skill Plot points to overall improvement • Implementation of New MOS • Run both • For each site and lead time, Final Forecast = MOS with lowest 21day running RMSE

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