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Short-Range Ensemble Forecasts of Precipitation Type

Short-Range Ensemble Forecasts of Precipitation Type. Matt Wandishin John Cortinas Steve Mullen Mike Baldwin University of Arizona OU/CIMMS. The Algorithms.

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Short-Range Ensemble Forecasts of Precipitation Type

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  1. Short-Range Ensemble Forecasts of Precipitation Type Matt Wandishin John Cortinas Steve Mullen Mike Baldwin University of Arizona OU/CIMMS

  2. The Algorithms • Ramer (1993)—ice fraction, I, determined by phase at generation level and subsequent melting or freezing based on Twand p as hydrometeor falls • Baldwin (et al. 1994)—compares warm and cold layers within column (area between Tw and 0 ºC) • Bourgouin (2000)—similar to Baldwin, but computes melting and freezing energies from tephigram

  3. The Algorithms • Czys (et al. 1996)—computes ratio of time an ice sphere remains in a warm layer to time required to completely melt the sphere—uses fixed sphere size and bulk properties of warm layer • Cortinas (2001)—similar to Czys but computes melting rate of ice sphere at each vertical level

  4. The Ensemble • Short-range ensemble from NCEP • 10 members 5 Eta, 5 RSM (Regional Spectral Model) • Regional bred modes • 48 km horizontal grid spacing • 3 hourly output to 48 hours

  5. The Experiment • Precipitation-type algorithms applied to ensemble runs from January and February 2002—43 days • Only conditional statistics computed Some type of precipitation must be both forecast and observed • Surface temperature must be less than 5 ºC • Time window +/– 0 h • Unless otherwise noted statistics computed for all forecast hours lumped together

  6. Full Ensemble 0.663 -0.033 -1.069 0.629

  7. Eta Ensemble 0.574 0.096 -0.502 0.523

  8. RSM Ensemble 0.581 0.122 -0.281 0.604

  9. Fixed-model/variable-algorithm--Eta 0.556 0.025 -0.541 0.522

  10. Fixed-model/variable-algorithm--RSM 0.527 -0.036 -0.366 0.556

  11. Fixed-algorithm/variable-model 0.593:0.461 0.145:-0.095 0.007:-7.279 0.598-0.484

  12. Full Ensemble

  13. Spread Ratio

  14. Full Ensemble 100 % 80 % 50 %

  15. RN-etan2 RN-rsmp1 RN-alg1 RN-alg5

  16. ZR-etac0 ZR-rsmn2 Correspondence Ratio Correspondence Ratio ZR-alg1 ZR-alg5 Correspondence Ratio Correspondence Ratio

  17. Conclusions • Rain and snow forecasted very well • ZR, IP forecasts have low reliability (overforecast), and low skill but still discriminate well and provide some value • Skill of ZR, IP forecast actually decreases for the larger ensemble • Forecast spread is much greater for IP, ZR than for R, SN • Different models provide more spread than different algorithms

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