The three-dimensional variational data assimilation (3D-VAR) scheme for ALADIN was first implemented in 1999 in Toulouse Siroka and Horanyi, 1999). Then in 2000 the scheme was successfully ported to Budapest (Horanyi, 2000) for the Origin 2000 machine of the Hungarian Meteorological Service (HMS). Recently AL13 code version of ALADIN is used for 3D-VAR taking into account classical NMC statistics for the background term and SYNOP and TEMP observations for the observational term of the cost function. It is mentioned that 3D-VAR is acting for the upper-air meteorological fields and optimal interpolation (CANARI) analysis is applied for the surface variables. Recently new background error statistics were computed based on the lagged constant coupling formulation of the NMC method (Horanyi and Boloni, 2001). Intensive sensitivity experiments were performed with the lagged method in order to see the effect of the different forecast differences and integration lengths on the computed background error covariances. All the ingredients mentioned above will serve as a basis for the work to be carried out in Budapest by Steluta.

Design and implementation of a 3D-VAR data assimilation system around the ALADIN/HU (Horanyi et al, 1996) mesoscale numerical weather prediction model. The initial version of the system should be not worse than the recently operational dynamical adaptation system and should contain several promising developmental areas for the further perfecting of the system. For the end of the work in Budapest the developed 3DVAR system should be robust and superior to the dynamical adaptation technique in terms of consecutive forecasts originating from the 3D-VAR provided initial conditions. The system should be capable for the easy and smooth transition towards 4D-VAR. The research and development areas needed for 4D-VAR should be clearly distinguished and possible solutions should be addressed.

Recently only SYNOP and TEMP observations are taken into account in the course of the 3D-VAR scheme in Budapest. New data sources should be investigated (e.g. satellite and/or radar data, AIREP data etc.) and impact studies should performed. Alternatively observation information from Toulouse can be taken and then only additional local information can be added to that file. The recent version using SYNOP and TEMP measurements is considered to be the reference and it should be carefully tested whether adding new data the overall quality of the forecasts starting from 3D-VAR analysis is improved. It means that this study should be performed in a successive way. A possible direction of attack is as follows:

- wind data from Doppler radars (probably it is easy to try),
- investigation and use of AIREP data,
- introduction of raw radiances in the control variable (LTOVSCV switch in the code),
- radar reflectivities (coordination with Toulouse activities)

It is noted that there are also several technical issues related to the observation handling. Roger Randriamampianina will be in charge of supporting Steluta in the technical problems related to observations.

Several sets of background error statistics are available for the ALADIN/HU domain. Choices should be done between classical (standard) statistics and lagged statistics. In the latter case the best variant of the lagged statistics should be applied. This part of the work will be carried out together with Gergely Boloni, who would complete the started work on the lagged statistics. It shouldn't be forgotten that there is a strong inter-relation between the choice of background errors and first guess (for instance in case of lagged statistics, when large scales are removed from the covariances the first guess should contain information coming from the large scales). See more details in the next point, where the different possibilities for the first guess is explained.

It is not obvious at all what first guess to use in the context of mesoscale data assimilation. It is rather clear for global models, because there only the 6h forecast from a previous model state can be applied. In mesoscale one of the following three possibilities might be chosen:

- Short range (6h) forecast from an earlier model run. It contains information on the small scales (the scales of the model), but due to the fact that it is a forecast it is not fully precise.
- Analysis of the global (regional) model. Only information is hold from larger scale, but on the other hand the analysis is rather a precise mirror of the atmosphere (so it is exact).
- Mixture of the previous two sources (blending), which is an optimal blend of the small scale and large scale information. Simple blending algorithm (spectral blending, where the small scales are taken from the ALADIN forecasts and the large scales are taken from the ARPEGE analysis) should be tuned and tried for obtaining first guesses for 3D-VAR. In case of combining blending and 3D-VAR the order of blending and 3D-VAR is also to be studied (see the work of Maria Siroka in Prague).

The first tests with initialisation and 3D-VAR was performed by Adam Dziedzic (Dziedzic, 2000) in Toulouse. Adam found that DFI in its classical formulation might destroy some useful signal of the 3D-VAR increments. At that time it was decided to try to use the incremental version of DFI and see if it is a more appropriate filter. Based on this work the following questions should be addressed:

- Is there a need for initialisation after the execution of the 3D-VAR scheme?
- If yes, what are the possible initialisation methods?
- In case of application of digital filtering what is the best version of it to be used (incremental DFI?)?
- What is the precise relation between the choice of first guess, background errors and initialisation methods?

It is of scientific interest to study the effect of double nesting in the 3D-VAR data assimilation scheme. Experiments can be performed with the systematic comparison of 3D-VAR ALADIN, which is coupled directly from ARPEGE and coupled from ALADIN/LACE. Sensitivity tests should be carried out with respect to the domain size, resolution and coupling frequency.

It is of great importance to create an effective validation framework on the testing of all the different options tried for ALADIN 3D-VAR. The following main tools are proposed:

- Subjective evaluation: chagal and/or echkevo plots. It can be used mostly for case studies.
- Objective scores (RMS, bias, etc.). This can be used for evaluating case studies, and more importantly double suites.
- Posteriori evaluation tools: Jmin test, ratio of Jo and Jb (Sadiki, 2000, Talagrand, 1999), generalised cross validation (Wahba et al., 1995), etc.

At the end of the work on 3D-VAR some outlook should be given on the tasks to be carried out for the 4D-VAR implementation.

Hereafter some schedule is planned to be drawn with special emphasis and details on the first stay of Steluta.

- First adaptation in Budapest (administrative matters, email, users, etc.), 1 week
- Study of the existing informatic environment with the existing and working scripts with special emphasis on the 3D-VAR scripts (including mandalay, obsort, screening, 3D-VAR job - conf=131, surface canari, full-pos) and the available evaluation tools, 4 weeks
- Creation of reference experiments with the existing tools, 2 weeks
- Creation and tuning of simple blending framework, 4 weeks
- Cycling experimentation with the different background error statistics, first guesses, initialisation methods, together with their monitoring and posteriori evaluation, 8 weeks
- Re-tuning of the version judged to be the best, 4 weeks
- Final implementation, double suites, 4 weeks

Outcome: Replacement of dynamical adaptation with a scientifically sound 3D-VAR scheme.

- Double nesting versus simple nesting (blending parameters, comparison of the different background error statistics: ARPEGE, ALADIN/LACE and ALADIN/HU).
- Improvement of the 3D-VAR scheme through observations: new observations (radar, satellite, AIREP), better use of the already existing observations, new observation operators and their adjoint, introduction of new observations in the course of minimisation, etc.
- New formulation of the Jb term, relaxing homogeneity and isotropy (co-operation with Simona Stefanescu, who supposed to work in that topic).

- Left others for 3DVAR
- Frequency of 3D-VAR, alternative algorithms toward 4D-VAR (potential usage of asynoptic data)
- March towards 4D-VAR (initial experimentation)
- Redaction of a scientific paper (thesis)

The work will be conducted by Andras Horanyi.

Strong cooperation with the local NWP team of HMS, especially with Gergely Boloni for background error statistics related questions and Roger Randriamampianina with observation handling related matters. Sandor Kertesz will give some support on screening questions and Gabor Radnoti on blending and code related issues. Afterall it is expected that a strong team (with Steluta, Gergely and Roger in its core) will be out together and this team will be capable to carry out together all the scientific and technical work described above.

It is highly expected that frequent e-mail discussions should take part with other ALADIN colleagues working on 3D-VAR. Hereafter the main subjects and persons to discuss with will be summarised:

Subject |
Person |
---|---|

All matters |
Claude Fischer, Maria Siroka, Andras Horanyi |

Strategic decisions |
Jean-Francois Geleyn |

Jb |
Loik Berre, Gergely Boloni, Simona Stefanescu |

Blending |
Gabor Radnoti, Radmila Brozkova |

Observations |
Patrick Moll, Roger Randriamampianina |

Screening |
Parick Moll, Sandor Kertesz |

Posteriori validation |
Wafaa Sadiki |

Code related matters |
Gabor Radnoti |

- Spring, 2002: Mid-term review of the ALATNET project, Brussels.
- Mini-workshop on 3D-VAR in Budapest (anticipated for May, 2002)
- October, 2002: EWGLAM meeting, the Netherlands

- Dziedzic, A., 2000: Geometrie et Initialisation dans le 3D-VAR/ALADIN. In French. ALADIN internal note.
- Horanyi, A., 2000: 3DVAR workshop in Budapest. ALATNET Newsletter No. 1, 36-40.
- Horanyi, A and G. Boloni, 2001: Lagged constant coupling background error statistics: preliminary results for the ALADIN model. Submitted to the Proceedings of the 10th ALADIN workshop held in Toulouse (7-8 June, 2001).
- Horanyi, A., I. Ihasz and G. Radnoti, 1996: ARPEGE/ALADIN: A numerical weather prediction model for Central-Europe with the participation of the Hungarian Meteorological Service. Idojaras, 100, 277-301.
- Sadiki, W., 2000: Reglage des statistiques "lagged NMC" dans 3D-VAR ALADIN. In French. ALADIN internal note.
- Siroka, M. and A. Horanyi, 1999: The development of three-dimensional variational data assimilation (3DVAR) scheme for ALADIN. ALADIN internal report.
- Talagrand, O., 1998: A posteriori evaluation and verification of analysis and assimilation algorithms. Proceedings of the seminar on "Diagnosis of Data Assimilation systems", pp 17-28. Available from ECMWF.
- Wahba, G., D. R. Johnson, F. Gao and J. Gong, 1995: Adaptive tuning of numerical weather prediction models: randomised GCV in three- and four-dimensional data assimilation. Monthly Weather Review, 123, 3358-3369.

- Jean Pailleux: Variational assimilation. Prepared for a summer school on data assimilation in Toulouse, 1993.
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- Hollingsworth and Lonnberg, 1986: The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: the wind field. Tellus 38A, 111-136.
- Hollingsworth and Lonnberg, 1986: The statistical structure of short-range forecast errors as determined from radiosonde data. Part II: the covariance of height and wind errors. Tellus 38A, 137-161.
- Sadiki, Fischer, Geleyn, 2000: Mesoscale Background error covariances: recent results obtained with the limited-area model ALADIN over Morocco, MWR, 123, 3927-3935.