issues regarding the assimilation of precipitation observations
DESCRIPTION
Issues Regarding the Assimilation of Precipitation Observations Dr. Ronald M. Errico Goddard Earth Sciences Technology and Research Center (Morgan State University, Maryland) Global Modeling and Assimilation Office (NASA). Outline - PowerPoint PPT PresentationTRANSCRIPT
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Issues Regarding the Assimilation of Precipitation Observations
Dr. Ronald M. Errico
Goddard Earth Sciences Technology and Research Center (Morgan State University, Maryland) Global Modeling and Assimilation Office (NASA)
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Outline
1. Bayesian considerations2. Issues peculiar to precipitation assimilation3. Examples of several issues4. Summary and recommendations
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Bayesian Considerations
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Implications of the Bayesian Approach
1. We see that both instrument and observation operator errors should be considered.
2. Results may depend on shapes of distributions, not only their means and variances.
3. Multi-modality of the PDF can occur, particularly due to non-linearity of the forward observation operator4. We see that selection of a “best” analysis can be somewhat
ambiguous.
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Some issues peculiar to precipitation assimilation (Examples to follow)
1. Distribution of precipitation errors is likely non-Gaussian. 2. Precipitation forward model error is likely large! 3. Multi-modal cost functions are likely. 4. Minimization of cost function may be poor objective. 5. Results may be very sensitive to prior statistics. 6. Straight-forward adjoint models of convection may be useless. 7. Common descent algorithms may be useless. 8. Results may be sensitive to precipitation type. 9. Possible incompatibility with gravity wave constraints.
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The distributionof precipitation
Kedem andChiu 1986
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FromGeorgeHuffman
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Special treatment of 0From Errico et al. 2000 QJRMS
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Note that special treatment of 0 values are required.
A possible problem with use of logs in the cost function
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Betts - Miller
Kain - Fritsch
Grell
Example of Model Error:Errico et al. QJRMS 2001
6-hour accumulated precip.With 3 versions of MM5Contour interval 1/3 cm(0 contour omitted)
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Errico et al.2001 QJRMS
NormalizedPDFs ofModel “errors”
Grell vs.Kain-Fritschschemes
PDF of differences
PDF of logof ratios
Gaussianreference
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RAS scheme
ECMWF scheme
BM scheme
Fillion and Mahfouf 1999 MWR
Jacobians of Precipitation
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Validation of a Tangent Linear Model
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Tangent linear vs. nonlinear model solutions
Errico andRaeder 1999QJRMS
TLM
60x small pert NLM
NLM
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r
q- r=0
q=qs
|
Statistically-Based Sub-Grid Parameterization
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Dependence on precipitation typeFillion and Mahfouf 1999 MWR
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Errico, Raeder and Fillion, 2003 Tellus
Adjoint-derived, optimal perturbations
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Errico et al.2003 Tellus
Impacts for adjoint-derived optimalperturbations forforecasts startingindicated hours in the past.
Vort optimized
Rc optimzed Rn Optimzed
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Posterior (analysis) PDF of 1DVAR of Convection
T-1 (standard deviations)
T-2
0-4-4
4
4
0
Conditional PDF
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Full Field Gravitational-mode field
6-hour forecast
Contour interval 0.0025 mm/K
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Review of state-of-the-art at ECMWF
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The apparent neglect of many fundamentals
Few statistical considerations background estimates ignored background error correlations ignored observations considered too accurate (and Gaussian) representativeness (forward model) error ignoredFew balance considerations univariate error statistics unbalanced reference statesLimited evaluation limited cases limited measuresSome strange results ultra rapid convergence rates little decrease of norm of J-gradient
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Tsuyuki 1996
Navon 1999
ECMWF 2002
ECMWF 2002
Convergence of 4DVAR
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Summary
1. I am confused!2. How can so many apparently fundamental aspects of the problem be neglected, yet such good results be reported?3. Only in rare cases is enough information provided to help explain question 2.4. Since only an improvement over some baseline is required, it is not necessary that “correct” procedures are used, just “useful” ones.5. Successes may reveal more about the baseline results than about the correctness of a new assimilation procedure.6. With both observation and forward model errors likely very large, what should be a realistic expectation of the usefulness of precipitation information and how can this be realized?
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Recommendations
1. Be skeptical.2. Ask lots of questions.3. Consider Bayesian implications.4. Determine reasonable error estimates.5. Estimate what issues are generally important.6. Explain results.7. Encourage research at research institutions. 8. Entrain some interested experts.