Diagnosis of very short range forecast errors with the ALADIN limited area model
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Diagnosis of very short range forecast errors with the ALADIN limited area model Gergely Bölöni Hungarian Meteorological Service. The aim of the study. To diagnose the sources of errors in LAM short range forecasts (3 to 6 hours). Model uncertainty. LBC uncertainty. Analysis uncertainty.

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The aim of the study

Diagnosis of very short range forecast errors with the ALADIN limited area modelGergely BölöniHungarian Meteorological Service


The aim of the study

The aim of the study

To diagnose the sources of errors in LAM short range forecasts (3 to 6 hours)

Model uncertainty

LBC uncertainty

Analysis uncertainty

  • How to diagnose (represent) these different uncertainties?

  • What are the relative importance of the different uncertainties?

  • What can we learn from that?  What developments in NWP models are most welcome in order to improve small-scale, short-range forecasts? (Nowcasting…)


Error representation methodology

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Error representation methodology

  • Ensemble Data Assimilation (EDA) experiments

  • (ALADIN model, dx=8km, 5 members, 1 months period)

  • perturbation of the initial state  representation of analysis error

  • LBC perturbations  representation of the LBC errors

  • Physics perturbations  representation of model errors


Error representation methodology1

Error representation methodology

Experiments done:

(error simulations from a 5 member EDA system accumulated over July 2007)

PLBC („Perfect LBC framework”): representation of the analysis errors alone  initial perturbations applied but same LBCs used for all members

PIC („Perfect Initial Condition framework”): representation of the LBC errors alone  LBC perturbations from a global EDA system (Isaksen et al., 07/2007, 4DVAR T255/L91) but initial conditions are the same for all members

LAM EDA: representation of analysis + LBC errors  initial and LBC perturbations (like above)

LAM EDA +Q: representation of model errors (in addition to analysis and LBC errors)  perturbation of physical parametrizations (physics of the control modified: microphysics, convection)


Diagnostics used

Diagnostics used

  • Error variance spectra: variance of the simulated error in spectral (Fourier) space  diagnoses how is the variance distribution according to spatial scales (Bouttier et al. 1997, Berre 2000)

  • Spectral PEACA: (Perturbation vs. Error Amplitude Correlation Analysis)  diagnoses how the simulated error amplitude compares with the „real” error amplitude (Wei and Tóth, 2003):

simulated forecast error

„real” forecast error ( )


Diagnostic results

Error variance spectra of 6h forecasts ~ 500 hPa

Diagnostic results


Diagnostic results1

Error variance spectra of 6h forecasts ~ 850 hPa

Diagnostic results


The aim of the study

Error variance spectra: 6h vs 3h forecasts ~ 1000 hPa

6h forecasts

3h forecasts


Summary error variance spectra

Summary: Error variance spectra

  • Free atmosphere: analysis error is the most important counterpart of forecast errors. Model and LBC errors are of less importance

  • PBL: analysis, LBC and model errors are all important. Analysis errors dominate intermediate scales (scales of the observing network used), LBC errors dominate large scales, model errors dominate small scales

  • Decreasing the forecast range makes analysis errors more dominant: For 3 hour forecasts, analysis error makes a larger contribution to the full forecast error thank for 6 hour forecasts


Diagnostic results2

Spectral PEACA of 6h forecasts ~ 500 hPa

Diagnostic results


Diagnostic results3

Spectral PEACA of 6h forecasts ~ 850 hPa

Diagnostic results


Summary spectral peaca

Summary: Spectral PEACA

  • Free atmosphere: analysis error dominate and LBC error has also an important contribution to the full forecast error. Model error has no contribution (at least in the way it is represented here). This is true for all scales.

  • PBL: analysis error has the most important contribution to the full forecast error even on the smallest scales. LBC and model errors has similarly large contribution as well.


Practical application

Practical application

Use the simulated errors in 3DVAR background

constraint (assimilation + forecast exp July 2007):

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RH

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Conclusions

Conclusions

  • The contribution of different error sources to the full forecast error has been diagnosed using the EDA technique as basis.

  • Small-scale, short-range forecast errors might mostly originate fromanalysis deficiences and model errors such as physical parametrization deficiences (and others not tested here…). LBC errors are of a bit less importance here..  data assimilation and physics to improve as going towards nowcasting applications

  • Beware that the 2 different diagnostics used show slightly different contributions of the error sources to the full forecast error (different distribution over spatial scales)

  • Besides the diagnostics the simulated errors were used as background error constraint in the ALADIN 3DVAR system in Hungary


Thank you for your attention

Thank you for your attention!

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