# Presentation: 124582

## Ensemble Prediction at ECMWF

***Roberto Buizza******1******, Martin Leutbecher******1******, Tim Palmer******1****** and Glenn Shutts******1,2***
***Contributions from Jean Bidlot, Graham Holt, Martin Miller, Mark Rodwell, Adrian Simmons and Nils Wedi to the development of VAREPS are acknowledged.***
***1******: European Centre for Medium-Range Weather Forecasts (***_**[www.](http://www.ecmwf.int/)**__**[ecmwf](http://www.ecmwf.int/)**__**[.](http://www.ecmwf.int/)**__**[int](http://www.ecmwf.int/)**_***)***
***2******: Met Office (***_**[www.met-office.](http://www.met-office.gov.uk/)**__**[gov](http://www.met-office.gov.uk/)**__**[.](http://www.met-office.gov.uk/)**__**[uk](http://www.met-office.gov.uk/)**_***)***

## The three key messages of this talk

-  The ECMWF Ensemble Prediction System (EPS) has been continuously improving. Results indicate a ~2-3 days/decade gain in predictability for probabilistic products.
-  Changes implemented in September 2004 have improved the reliability of tropical cyclones’ track prediction. Future changes in the singular vectors are expected to improve the accuracy of EPS forecasts, especially in the earlier forecast range. The future implementation of the VAriable Resolution EPS is expected to improve the EPS accuracy in the early/medium-range, and will extend the EPS forecast length to 14 (or 15) days.
-  ECMWF is very supportive of the TIGGE concept. ECMWF will be hosting the 1st TIGGE Workshop during the week 1-4 March 2005. The WS will give the opportunity to academic institutions and meteorological operational centers to identify the key scientific questions that TIGGE should approach, and to define the TIGGE infrastructure.

## Outline

** ****Performance of the ECMWF EPS from May 1994 to date**
** ****Developments in the simulation of initial uncertainties**
** ****Developments in the simulation of model imperfections**
** ****Developments in the EPS configuration: **
- VAriable Resolution EPS (VAREPS)
- Use of Ensemble Data Assimilation (EDA) in VAREPS
** ****THORPEX/TIGGE **

## The ECMWF Ensemble Prediction System

- The Ensemble Prediction System (EPS) consists of 51 10-day forecasts run at resolution TL255L40 (~80km, 40 levels) *[5,7,8,13]*.
- The EPS is run twice a-day, at 00 and 12 UTC (products are disseminated at ~07 and 19 UTC).
- Initial uncertainties are simulated by perturbing the unperturbed analyses with a combination of T42L40 singular vectors, computed to optimize total energy growth over a 48h time interval (OTI).
- Model uncertainties are simulated by adding stochastic perturbations to the tendencies due to parameterized physical processes.

## The EPS performance has been continuously increasing

- The continuous improvement of the EPS accuracy is shown, e.g., by the time evolution of three accuracy measures, ROCA[f>c], BSS[f>c] and RPPS.

## Over NH, Z500 EPS predictability has increased by ~2d/dec

- Results indicate that considering Z500 d+5 and d+7 forecasts over NH:
-  The EPS control has improved by ~ 1 day/decade
-  The EPS ens-mean has improved by ~ 1.5 day/decade
-  The EPS probabilistic products have improved by ~2-3 day/decade

## Outline

** ****Performance of the ECMWF EPS from May 1994 to date**
** ****Developments in the simulation of initial uncertainties**
** ****Developments in the simulation of model imperfections**
** ****Developments in the EPS configuration: **
- VARiable Resolution EPS (VAREPS)
- Use of Ensemble Data Assimilation (EDA) in VAREPS
** ****THORPEX/TIGGE **

## Initial uncertainties: why changing TC’ areas and sampling

- The old (pre-September 2004) EPS had two known weaknesses:
-  TR-SVs’ target areas - in the old EPS *[1,15]*:
- TR-SVs were computed inside areas with northern boundary with 25°N: this was causing an artificial ensemble-spread reduction when tropical cyclones were crossing 25°N
- TR-SVs were computed only if WMO cl-2 TC were detected between 25°S-25°N
- Up to 4 tropical areas were considered
-  EPS initial perturbations: the distribution of coefficients  j and j was un-prescribed and un-known
- The introduction of model cycle 28R3 on 28 September 2004 addressed these issues. Parallel experimentation showed that this change improved the EPS performance.

## The Sep ’04 change in the definition of TR-SVs’ target areas

- On 28 Sep, one major change was introduced in the EPS. In the new system:
-  Target areas are computed considering TCs’ predictions
-  Areas are allowed to extend north of 30oN
-  Up to 6 areas can now be targeted
-  Tropical depression (WMO cl1) detected between 40°S-40°N are targeted
-  SVs are computed using a new ortho-normalization procedure

## Impact of the Sep ’04 change in the TR-SVs’ target areas

- Results based on 44 cases (from 3 Aug to 15 Sep 2004) indicate that the implemented changes in the computation of the tropical areas have a positive impact on the reliability diagram of strike probability.

## The Sep ’04 change in the SVs’ sampling

- The EPS ICs are defined by adding a perturbation to the unperturbed analysis *e**0**(0):*
- After the implementation of Gaussian sampling:
-  The distribution of coefficients j,k and  j,k is set to be Gaussian *[11]*
-  The 50 EPS initial perturbations are not any more symmetric
-  It is technically easier to set NSV independently from NENS
- Results have indicated a neutral impact of this change on the EPS.

## Initial uncertainties – Why should the SVs be changed?

- In the current EPS:
-  SVs are computed at T42L40 resolution over a 48h time optimization interval
-  Extra-tropical SVs are still computed with a tangent dry physics *[3]*
-  Tropical SVs are computed with a tangent moist physics *[1,12,15],* but with the state vector still defined in terms of [V,D,T,ln(sp)] only (ie without humidity)
- To better capture perturbations’ growth, especially in cases of intense, small-scale cyclonic developments, it is thought that a tangent moist physics should be used. Recent results *[10]* have indicated that when moist processes are considered, a T63 truncation would be better than a T42, and a 24h OTI is more suitable than the 48h OTI used for dry SVs.
- The plan is to investigate the use of 24h, T63 or TL95 SVs computed with the new moist tangent physics.

## Impact of moist processes on T63L31-24h SVs for French storm

- 27 Dec ‘99 00Z: French storm Martin.
- The top panels *[10]* show a weighted geographical distribution of the first 10 T63L31-24h dry SVs at initial and final time (ci x50 at final time).
- The bottom panels show the weighted distribution of the first 10 T63L31-24h full-physics SVs, superimposed on the basic state total column water content.
- In the moist experiment, SVs evolve along the upstream side of the tongue of moisture into the storm region.

## Outline

** ****Performance of the ECMWF EPS from May 1994 to date**
** ****Developments in the simulation of initial uncertainties**
** ****Developments in the simulation of model imperfections**
** ****Developments in the EPS configuration: **
- VARiable Resolution EPS (VAREPS)
- Use of Ensemble Data Assimilation (EDA) in VAREPS
** ****THORPEX/TIGGE**

## Model imperfections – Should the approach be changed?

- In the current EPS:
-  Model imperfections are simulated using ‘stochastic physics’, a simple scheme designed to simulate the random errors in parameterized forcing that are coherent among the different parameterization schemes (moist-processes, turbulence, ...).
-  Coherence with respect to parameterization schemes has been achieved by applying the stochastic forcing on total tendencies. Space and time coherence has been obtained by imposing space-time correlation on the random numbers.
- The scheme has been shown *[14]* to have a positive impact on the EPS, especially on the accuracy of probabilistic precipitation prediction. But diagnostics and recent studies *[17]* have indicated that the scheme has from some weaknesses, eg:
-  In the lower levels, it seems to generate too large spread and too intense rainfall
-  In the upper levels its impact on the ensemble spread is rather limited (~5%)
-  Random numbers have a very crude spatial and temporal correlations
-  It is controlled by parameters that have been tuned in a rather ‘ad-hoc’ manner

## Cellular Automaton Stochastic Backscatter Scheme

- The new Cellular Automaton Stochastic Backscatter Scheme *[17]* (CASBS):
-  CASBS is based on the physical argument that kinetic energy sources that counteract energy drain occurring in the near-grid scale can improve the performance of numerical models.
-  Kinetic energy is backscattered by introducing vorticity perturbations into the flow with a magnitude proportional to the square root of the total dissipation rate.
-  The spatial form of vorticity perturbations is derived from an exotic pattern generator (cellular automaton) that crudely represents the spatial/temporal correlations of the atmospheric meso-scale
- TL159L40 EPS experiments for 10 cases have indicated that:
-  CASBS reduces the excessive heavy rainfall events
-  It is more effective at generating model spread
-  It generates a better meso-scale energy spectrum

## CASBS’ positive impact on heavy precipitation events

- Experiments based on TL159L40 EPS forecasts for 10 cases indicate that:
-  The operational stochastic physics scheme (dashed blue) generates too many cases of heavy precipitation
-  CASBS (dash green) performs more in agreement with observed statistics (black solid)

## CASBS’ positive impact on EPS spread

- Experiments based on TL159L40 EPS forecasts for 10 cases indicate that:
-  CASBS (red solid) induces more divergence among the ensemble members than the operational scheme (blue dashed)
-  CASBS’ ensemble-spread around the control is closer to the average error of the control forecast (black chain-dashed)

## Outline

** ****Performance of the ECMWF EPS from May 1994 to date**
** ****Developments in the simulation of initial uncertainties**
** ****Developments in the simulation of model imperfections**
** ****Developments in the EPS configuration: **
- VARiable Resolution EPS (VAREPS)
- Use of Ensemble Data Assimilation (EDA) in VAREPS
** ****THORPEX/TIGGE**

## VAREPS: definition, and planned implementation schedule

-  VAREPS configuration:
**D0-7: T****L****399L40, dt=1800s**
**D7-14(15): T****L****255L40, dt=2700s**
-  Rationale: predictability of small scales is lost relatively earlier in the forecast range. Therefore, while forecasts benefit from a resolution increase in the early forecast range, they do not suffer so much from a resolution reduction in the long range.
-  Implementation: Q3-Q4 2004

## VAREPS: preliminary results

- Results (CY28R3, 51-members, 4 cases) indicate that the benefit of VAREPS can be detected well beyond the truncation time (t+168h), both in the accuracy of the ensemble-mean (left) and of probabilistic forecasts (right).

## EDA: towards a probabilistic analysis & forecast system?

- Ensemble Data Assimilation *[6]* may be used in the future to generate the EPS initial perturbations. A future EPS configuration could include:
-  N-member EDA
-  N*M member EDA-SV EPS, TL399(d0:7)+TL255(d7:14)
-  ICs from each perturbed members and/or the EDA ensemble-mean

## Outline

** ****Performance of the ECMWF EPS from May 1994 to date**
** ****Developments in the simulation of initial uncertainties**
** ****Developments in the simulation of model imperfections**
** ****The future: **
- VAriable Resolution EPS (VAREPS)
- Use of Ensemble Data Assimilation (EDA) in VAREPS
** ****THORPEX/TIGGE **

## TIGGE and ECMWF

- ECMWF is very supportive of TIGGE (the THORPEX Interactive Grand Global Ensemble), and has been asked by some member states to build the necessary infrastructure to operate the experiment.
- TIGGE will provide a framework for international collaboration on the development of ensemble prediction for NWP, create a multi-model ensemble database as a resource for THORPEX researchers, and constitute a facility to test the idea of a possible “future global interactive multi-model ensemble forecast system, which would generate numerical probabilistic products, available to all WMO Members including developing countries."
- TIGGE will give the opportunity to address a set of open questions in ensemble prediction, to define which could be the benefits of a multi-model ensemble system, and to assess the technical challenges that building such a structure could involve.

## 1st Workshop of TIGGE, 1-4 March 2005, ECMWF

- ECMWF will be hosting the 1stTIGGE WS, from the 1st to the 4th of March 2005.
**The purpose of the 1****st**** TIGGE WS is to collect views on what TIGGE science aims should be, what the requirements are for use of the TIGGE data and what are the infrastructure requirements. Based on the input from the WS, the TIGGE Working Group will initiate the planning and development of TIGGE facility and associated research projects.**
-  The WS has been initiated by the WMO-THORPEX project
-  ECMWF and the Met Office will act as co-sponsors of the workshop.

## 1st Workshop of TIGGE, 1-4 March 2005, ECMWF

- The TIGGE WS will have 1.5 days of invited talks and 1.5 days of working group and plenary discussions. Issues that will be discussed at the WS include:
-  methods to simulate initial uncertainties, including observation errors
-  methods to take into account model imperfections
-  methods to combine and calibrate different ensemble systems
-  product generations and users requirements
-  applications, including flooding and severe weather prediction
-  technical infrastructure requirements
- Working groups will be asked to make recommendations on issues including the science of TIGGE, applications and infrastructure needs.

## Some key scientific questions that TIGGE could address

- Some of the key questions that can be addressed under TIGGE are the following:
-  Which is the best way to combine ensembles with different characteristics?
-  How should products based on a multi-model ensemble be constructed?
-  Which could be the best (in terms of cost/benefits, given users’ demands and computer/telecommunication constraints) multi-model ensemble configuration in terms of resolution/size/frequency?
- Addressing these questions is essential to advance probabilistic numerical weather prediction. Let us not forget that the current operational ensemble systems have rather different characteristics *[2,9]*.

## ECMWF, MSC and NCEP performance for 3 month (JJA02)

- Considering the ECMWF, MSC and NCEP systems, it has been concluded *[9]* that the ECMWF EPS can be considered the most accurate single-model ensemble system.
- This is shown, e.g., by the comparison of the EV* of 10-member ensembles based on the ECMWF, MSC (Meteorological Service of Canada) and NCEP (National Centers for Environmental Predictions) EPSs *[9] *(Z500 over NH).
- * EV, the potential economic value, is the reduction of the mean expenses with respect to the reduction that can be achieved by using a perfect forecast *[4,16]*.

## ECMWF, MSC and NCEP performance for 3 month (JJA02)

- The ECMWF leading performance *[9]*, estimated to be equivalent to a gain of ~1 day of predictability, has been linked to:
-  A better analysis
-  A better model
-  A better estimation of the PDF of forecast states.
- This latest point can be seen, e.g., by comparing the ensemble spread and the ensemble-mean forecast error of 10-member ensembles based on the NCEP, MSC and ECMWF EPSs (Z500 over NH).

## Conclusions

- The forthcoming years will hopefully witness further improvements of the EPS, and its transformation into the first building block of a seamless ensemble prediction system that will provide users with probabilistic forecast from day 0 to day 180.
- The success of the ECMWF EPS is the result of the continuous work of many ECMWF staff, consultants and visitors, and the documented gains in predictability reflects  the improvements of the ECMWF model, analysis, diagnostic and technical systems. The work of all contributors, in particular of former ECMWF staff (**Jan Barkmeijer, Franco Molteni, Robert Mureau, Anders Persson, Thomas Petroliagis, David Richardson, Stefano Tibaldi**), visitors and consultants is acknowledged.

## References

- [1] Barkmeijer, J., Buizza, R., Palmer, T. N., Puri, K., & Mahfouf, J.-F., 2001: **Tropical singular vectors computed with linearized diabatic physics**. *Q. J. R. Meteorol. Soc*., 127, 685-708.
- [2] Bourke, W., Buizza, R., & Naughton, M., 2004: **Performance of the ECMWF and the BoM Ensemble Systems in the Southern Hemisphere**. *Mon. Wea. Rev*., in press.
- [3] Buizza, R., 1994: **Sensitivity of Optimal Unstable Structures.** *Q. J. R. Meteorol. Soc.,* 120, 429-451.
- [4] Buizza, R., 2001: **Accuracy and economic value of categorical and probabilistic forecasts of discrete events**. *Mon. Wea. Rev*., 129, 2329-2345.
- [5] Buizza, R., & Palmer, T. N., 1995: **The singular vector structure of the atmospheric general circulation**. *J. Atmos. Sci*., 52, 1434-1456.
- [6] Buizza, R., & Palmer, T. N., 1999: **Ensemble Data Assimilation.  ***Proceedings of the AMS 13**th** Conference on Numerical Weather Prediction, 13-17 Sep 1999, *published by AMS, 231-234.
- [7] Buizza, R., Miller, M., & Palmer, T. N., 1999: **Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System**. *Q. J. R. Meteorol. Soc*., 125, 2887-2908.
- [8] Buizza, R., Richardson, D. S., & Palmer, T. N., 2003: **Benefits of increased resolution in the ECMWF ensemble system and comparison with poor-man's ensembles**. *Q. J. R. Meteorol. Soc.,*129, 1269-1288.

## References (cont.)

- [9] Buizza, R., Houtekamer, P. L., Toth, Z., Pellerin, G., Wei, M., & Zhu, Y., 2004: **A comparison of the ECMWF, MSC and NCEP Global Ensemble Prediction Systems**. *Mon. Wea. Rev.*, in press.
- [10] Coutinho, M. M., Hoskins, B. J., & Buizza, R., 2004: **The influence of physical processes on extra-tropical singular vectors**. *J. Atmos. Sci*., 61, 195-209.
- [11] Ehrendorfer, M., & Beck, A., 2003: **Singular vector-based multivariate sampling in ensemble prediction **ECMWF *Technical Memorandum n. 416* (available from ECMWF).
- [12] Mahfouf, J.-F., 1999: **Influence of physical processes on the tangent linear approximation.** *Tellus*, 51A, 147-166.
- [13] Molteni, F., Buizza, R., Palmer, T. N., & Petroliagis, T., 1996: **The new ECMWF ensemble prediction system: methodology and validation**. *Q. J. R. Meteorol. Soc.*, 122, 73-119.
- [14] Mullen, S., & Buizza, R., 2001: **Quantitative precipitation forecasts over the United States by the ECMWF Ensemble Prediction System**. *Mon. Wea. Rev.,*129, 638-663.
- [15] Puri, K., Barkmeijer, J., & Palmer, T. N., 2001: **Ensemble prediction of tropical cyclones using targeted diabatic singular vectors.** *Q. J. R. Meteorol. Soc.*, 127, 709-731.
- [16] Richardson, D. S., 2000: **Skill and relative economic value of the ECMWF Ensemble Prediction System**. *Q. J. R. Meteorol. Soc.,* 127, 2473-2489.
- [17] Shutts, G., 2004: **A stochastic kinetic energy backscatter algorithm for use in ensemble prediction systems. **ECMWF *Technical Memorandum n. 449* (available from ECMWF).