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Evaluation of Precipitation from Weather Prediction Models, Satellites and Radars

Evaluation of Precipitation from Weather Prediction Models, Satellites and Radars. Charles Lin Department of Atmospheric and Oceanic Sciences McGill University, Montreal, Quebec, Canada. Collaborators. Slavko Vasić Isztar Zawadzki Barry Turner. Objective.

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Evaluation of Precipitation from Weather Prediction Models, Satellites and Radars

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  1. Evaluation of Precipitation from Weather Prediction Models, Satellites and Radars Charles Lin Department of Atmospheric and Oceanic Sciences McGill University, Montreal, Quebec, Canada

  2. Collaborators • Slavko Vasić • Isztar Zawadzki • Barry Turner

  3. Objective • To characterize and inter-compare precipitation retrieved from models, radars and satellites using spectral analysis and frequency distribution

  4. Methodology • Spectral domain • Scale decomposition using Fourier modes and wavelets • Frequency domain • Frequency distribution

  5. Domain of analysis • Central and eastern US: 2,160 km × 2,160 km • Weather prediction models • Canadian GEM/HIMAP (10 km resolution) • US ETA, NCEP (12 km) • Radar data • US composite (5 km resolution) • Satellite • GOES (5 km resolution)

  6. 1-hour accumulated precipitation 21 days in 2003, 200413 days with satellite data • 2003 • September 12, 13, 18, 27 • October 14, 17, 25, 26, 28 • 2004 • May 21, 22, 30, 31 • June 11, 24 • July 3, 4, 5, 6 • August 19, 20

  7. Scale decomposition analysis

  8. Accumulated precipitation for 11h-12h- October 26, 2003 - 2,160 km 2,160 km

  9. Scale decomposition: Fourier and wavelet

  10. Spectral amplitudes at different wavenumbers (kH = 2n)

  11. Lag correlation analysis, 1-h accumulation (R = radar; G = GEM)

  12. Frequency Distribution Analysis • 21 days of 24 hourly accumulated precipitation → over 500 hourly samples • Domain of 2,160 km × 2,160 km at 12 km resolution → Total of 180 × 180 = 32,400 pixels • Bin hourly precipitation of each pixel, with bin width δ • Count frequency of precipitation for each bin → histogram

  13. Frequency distribution: Near power law dependence Radar: ±1 standard deviation

  14. Quartile analysis (3 days) Total frequency

  15. Compare skill of model forecasts and radar nowcasts

  16. Categorical skill scores for 24-h GEM/HIMAP and ETA model forecasts POD: Probability of detection FAR: False alarm rate CSI: Critical success index

  17. Skill of Precipitation Forecasts from NWP Models and Radar Nowcasts Theoretical limit of predictability Nowcasting methods Numerical weather prediction models Golding (1998), Austin et al. (1987), Wilson et al. (1998)

  18. Experimental Design Time [h] 6 9 15 18 21 24 12 24-hour GEM-HIMAP & ETA forecasts Five 9-hour radar nowcasts: 0300, 0600, 0900, 1200 & 1500 UTC

  19. 21-day averaged skill scores(1-hour accumulated precipitation, 2003-2004)- Threshold: 0.1 mm/h - Lin, Vasic, Kilambi, Turner and Zawadzki (2005)

  20. Summary • Scale decomposition and frequency analysis of model and radar precipitation • Enhanced falloff of power of model precipitation at high wavenumbers compared to radar • Significant loss in power of models at scales ≈ 10 Δx • Approximate power law dependence for frequency distribution • Skill of 24-h model forecast is relatively constant, and is better than radar nowcasts after a lead time of about 6-8 hours

  21. Thank you!

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