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Model Biases

Model Biases

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Model Biases

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  1. Model Biases • ECMWF • Compared to the other 2 operational models described above, the ECMWF does the best in predicting mid/upper tropospheric heights during the colder part of the year(such as October through April). The ECMWF tends to perform quite well in predicting amplitudes of planetary-scale regimes such as the Pacific/North American teleconnection (PNA). This model can also perform outstandingly during low to high planetary-scale wavenumber transition events, and northern hemispheric-scale regime transitions (Berry et al. 1996, CR TM 111). • Outperforms the other medium-range forecast models during shallow cold air situations. • Tends to overdevelop mid/upper cyclones across the southwestern U.S. Situations arise where this model will be too slow to predict the movement of cyclones from the southwest deserts. • Has a slight tendency to forecast mid/upper tropospheric heights and the resultant thickness calculations too high (i.e.; a warm bias). • Sometimes, especially during the warmer portion of the annual cycle, this model has too many closed lows. This bias may be related to its high resolution. • Tends to overamplify the long wave pattern, resulting in slower than observed progression of systems through the westerlies. This can result in overly weak and northward displaced short waves and associated surface features lifting into the long wave ridge position. • Found to have the smallest overall distance errors with springtime closed low forecasts during days four and five. • Westward forecast bias of closed cyclones (related to the issue described above) • Often too slow moving short wave features in deamplifying or zonal patterns • Of the medium range models, the ECMWF performs best with driving Arctic fronts down the east slopes of the Rockies. • The ECMWF too often incorrectly digs closed upper lows SWWD then WWD underneath strong upper ridges over the Eastern Pacific.

  2. Model Biases

  3. Bias by Subjective Data - GFS

  4. Bias by Subjective Data - GFS

  5. Bias by Subjective Data – GFES/SREF

  6. Real-time Verification

  7. Real-time Verification • http://www.meteo.psu.edu/~gadomski/MOSERR_CURRENT/framecur.html

  8. Model Bias – in Real Time • http://www.emc.ncep.noaa.gov/mmb/research/nearsfc/nearsfc.verf.html • http://www.emc.ncep.noaa.gov/mmb/mmbpll/eric.html • http://www.emc.ncep.noaa.gov/mmb/gplou/emchurr/nwprod/ • http://www.hpc.ncep.noaa.gov/mdlbias/biastext.shtml

  9. Model Biases • ECMWF – twice a day • GFS – four times a day Real-time assessment at: http://www.hpc.ncep.noaa.gov/mdlbias/

  10. 00Z Run – 72hr Forecast 500hPa Heights Top-NAM Below - GFS Top-CMC Bottom - ECMWF

  11. Model BiasesSummary • Blocking Patterns (particularly high latitude) are a weakness • Over-phasing of short waves in the 4-10 day period • Terrain related effects (Cold Air Damming) are handled poorly • CPS feedback is inconsistent