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Dr. Ronald M. Errico Goddard Earth Sciences and Technology Center (UMBC)

Pertinent Issues and Open Questions Regarding the Use of Ensembles for Weather Analysis and Prediction. Dr. Ronald M. Errico Goddard Earth Sciences and Technology Center (UMBC) Global Modeling and Assimilation Office (NASA). Outline. 1. Determination of analysis error

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Dr. Ronald M. Errico Goddard Earth Sciences and Technology Center (UMBC)

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  1. Pertinent Issues and Open Questions Regarding the Use of Ensembles forWeather Analysis and Prediction Dr. Ronald M. Errico Goddard Earth Sciences and Technology Center (UMBC) Global Modeling and Assimilation Office (NASA)

  2. Outline 1. Determination of analysis error 2. Some basic aspects of atmospheric predictability 3. Relationship between SVs and BGVs 4. Sampling issues 5. Some outstanding questions

  3. A Kalman filter for a single norm mode

  4. Error growth as a function of model resolution (from D. Baumhefner) Variance of z at 500 hPa Forecast Day

  5. Use of an OSSE Analysis error standard deviations: u on eta=0.5 surface From Errico et al., 2007

  6. Power spectra of forecast differences From Lorenz 1969 Variance at indicated scale Horizontal length scale (km)

  7. Predictability experiments with the NCAR CCM Variance (m2) From Tribbia and Baumhefner MWR 2004 Wave number

  8. Predictability Experiments with a NCAR/PSU MM3 rms T diff (deg K) rms q diff (g/Kg) From Anthes et al. 1985 Forecast time (hours)

  9. Predictability Experiments with a NCAR/PSU MM3 500 hPa h diff (2 m) From Errico & Baumhefner MWR 1987

  10. Predictability Experiments with a NCAR/PSU MM3 500 hPa h diff (2 m) From Errico & Baumhefner MWR 1987

  11. Mesoscale Predictability with MM5 1-hour accumulated precipitation Exp 1 Exp 2 From Nuss & Miller 2001 Precipitation contour interval 1mm; topography shade interval 250 m

  12. Predictability Experiments with NCAR CCM3 500 hPa h Initial Control Perturbed From unpbl. work with D. Baumhefner

  13. Predictability Experiments with NCAR CCM3 500 hPa h diff (20 m) Day 0 Day 1 Day 2 Day 3

  14. Predictability Experiments with NCAR CCM3 500 hPa h Day 5 Control Perturbed

  15. Example of Model Error: Errico et al. QJRMS 2001 6-hour accumulated precip. With 3 versions of MM5 Contour interval 1/3 cm Kain - Fritsch Betts - Miller Grell

  16. Gelaro et al. MWR 2000

  17. Bred Modes (LVs) And SVs Results for Leading 10 SVs Gelaro et al. QJRMS 2002

  18. Statistics from 10-member ensemble Variance 0., 2. Mean -.75, 1 True Covar. 0., 1. Sample Covar. -.8, 1.4

  19. Statistics from 100-member ensemble Variance .75, 1.35 Mean -.3, .3 True Covar. 0., 1. Sample Covar. -.3, 1.2

  20. Relationship between SVs and predictability

  21. How Many SVs are Growing Ones? Truncated R-norm SM Summer Moist Model SD Summer Dry Model WM Winter Moist Model WD Winter Dry Model Singular Value Squared Errico et al. Tellus 2001 Mode Index

  22. Tellus 1999

  23. The Skill of Quantitative Precipitation Forecasts as described by a US national program Acceleration of progress Extrapolation into the future

  24. Some outstanding questions • 1. What are the characteristics of model error in the best current models? • 2. What are the characteristics of analysis error in current DASs? • 3. What are the relative influences of model versus initial condition error on • the errors produced by the best current forecast systems? • How predictable are various aspects of weather (and climate)? • How do error doubling times depend on spatial scale? • 6. Is Lorenz’s argument for finite predictability true? • 7. What are reasonable goals for improving quantitative precipitation forecasts? • 8. How can we apply our understanding of the limits of predictability to • more appropriately utilize the information content of forecasts? • What are the implications of very rapid, non-modal error growth? • Do mountains enhance or diminish predictability? • What are the implications of average growth rates varying with resolution? • How many ensemble members are needed for a given purpose? • How can perturbations be initiated on the attractor?

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