evaluation of precipitation from weather prediction models, satellites and radars
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Evaluation of Precipitation from Weather Prediction Models, Satellites and Radars
Charles Lin
Department of Atmospheric and Oceanic SciencesMcGill University, Montreal, Quebec, Canada
Collaborators
• Slavko Vasić
• Isztar Zawadzki
• Barry Turner
Objective
• To characterize and inter-compare precipitation retrieved from models, radars and satellites using spectral analysis and frequency distribution
Methodology
• Spectral domain– Scale decomposition using Fourier modes and
wavelets
• Frequency domain– Frequency distribution
• 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)
1-hour accumulated precipitation 21 days in 2003, 2004
13 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
Scale decomposition analysis
Accumulated precipitation for 11h-12h- October 26, 2003 -
2,160 km
2,1
60
km
Scale decomposition: Fourier and wavelet
Spectral amplitudes at different wavenumbers (kH = 2n)
Lag correlation analysis, 1-h accumulation (R = radar; G = GEM)
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
Frequency distribution: Near power law dependence
Radar: ±1 standard deviation
Quartile analysis (3 days)
Total frequency
Compare skill of model forecasts and radar nowcasts
Categorical skill scores for 24-h GEM/HIMAP and ETA model forecasts
POD: Probability of detection
FAR: False alarm rate
CSI: Critical success index
Skill of Precipitation Forecasts from NWP Models and Radar Nowcasts
Golding (1998), Austin et al. (1987), Wilson et al. (1998)
Theoretical limit of predictability
Nowcasting methods
Numerical weather prediction models
Experimental Design
6 9 12 15 18 21 24
Five 9-hour radar nowcasts:0300, 0600, 0900, 1200 & 1500 UTC
24-hour GEM-HIMAP & ETA forecasts
Time [h]
21-day averaged skill scores(1-hour accumulated precipitation, 2003-2004)
- Threshold: 0.1 mm/h -
Lin, Vasic, Kilambi, Turner and Zawadzki (2005)
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
Thank you!