LAM EPS and TIGGE LAM. Tiziana Paccagnella ARPA-SIMC

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1 DRIHMS_meeting Genova 14 October 2010 Tiziana Paccagnella ARPA-SIMC

2 Ensemble Prediction Ensemble prediction is based on the knowledge of the chaotic behaviour of the atmosphere and on the awareness of the limitation (errors, approximations) in our Forecasting Systems (analysis/assimilation & models). These limitations induce uncertainty in our forecasts. Ensemble prediction is aimed to quantify this uncertainty by producing a sample of alternative/possible future atmospheric states obtained by mimic our possible errors. Uncertainty derives from errors both in the analysed initial conditions (analysis errors) and in the forecast evolution (model errors).

3 Ensemble Prediction Deterministic thinking To obtain EPS products which are better than deterministic products (e.g. 500 hpa ensemble mean) To obtain a forecast of the forecast skill Probabilistic thinking To have alternative evolution scenarios To have probabilities associated to the occurrence of events

4 Ensemble Prediction Deterministic thinking To obtain EPS products which are better than deterministic products (e.g. 500 hpa ensemble mean) To obtain a forecast of the forecast skill Probabilistic thinking To have alternative evolution scenarios To have probabilities associated to the occurrence of specific events

5 Ensemble System Characteristics

6 Ensemble System Characteristics Ensemble SPREAD Ensemble spread

7 Ensemble System Characteristics Ensemble Mean

8 Ensemble System Characteristics Ensemble ERROR Ensemble error

9 True state Ensemble Mean Ensemble ERROR Ensemble SPREAD Climatology

10 Model Errors Accounting for uncertainties in Numerical forecast Analysis Errors

11 Model Errors Perturbations/changes Model parameters Stochastic Phisics Changes of model modules Multi Model Accounting for uncertainties in Numerical forecast Perturbations on the Initial Conditions: SVs Breeding EnKF ETKF Multi-Analysis Analysis Errors

12 LAM EPS Ics & BCs Global EPS

13 GCM (1) (higher GCM (2) res. from deterministic (higher GCM (3) res. suite) from deterministic (higher GCM (4) res. suite) from deterministic (higher.. res. suite) from deterministic.. suite) GCM (n) Global EPS Ics & BCs LAM EPS

14 Scientific issues Added value with respect to a simple larger scale downscaling? LAM/ Local perturbation on the Initial Conditions and consistency with larger scale perturbations transmitted by the driving larger scale system Breeding Singular Vectors ETKF Blending of large and local perturbations Model perturbations Multi-physics Change of parameter settings Stochastic Physics Multi-Model Surface state perturbations Multi-Model, Multi.. Advantages from different genetics? Going toward the convection permitting scale

15 Scientific issues Calibration Verification / Verification of rare events Link with data assimilation Link with application (hydrological modelling)

16 TIGGE A key component of THORPEX: a World Weather Research Programme to accelerate the improvements in the accuracy of 1-day to 2- week high-impact weather forecasts for the benefit of humanity

17 TIGGE The TIGGE project has developed a database of global ensemble forecasts collected in near real-time. Objectives: Enhance collaboration on ensemble prediction, both internationally and between operational centres & universities. Facilitate research on ensemble prediction methods, especially methods to combine ensembles and to correct systematic errors Enable evolution towards a prototype operational system, the Global Interactive Forecast System For more about TIGGE, see

18 TIGGE LAM A TIGGE-LAM Panel was established to coordinate the LAM EPS contribution to TIGGE and to the GIFS system. TIGGE LAM activity is and will be developed by: providing guidelines coordinating activities fostering research

19 TIGGE LAM Scientific aspects Implementation / technical Aspects Output variable/fields Output format Data exchange Archiving Interoperability Aspects Data policy...

20 LAM EPS systems

21

22

23 What has been done/ is being doing Output parameter list defined GRIB2 coding adopted following TIGGE directives GRIB2 specifics defined Archiving of LAM EPS Products Sub-set of HP parameters defined for verification/research/end users (i.e. hydrological applications) HP parameters will be interpolated on a standard geographical lat/lon grid at 0,1 resolution HP parameters should be archived at the three TIGGE Archiving Centres, NCAR, ECMWF and CMA As regards the data access, the same policy adopted by TIGGE will be proposed with a reduction of the delay from 48 hours to 24 hours.

24 Archiving of LAM EPS Products What has been done/ is being doing SRNWP Interoperability Project Interoperability: content and format of IC/BC TIGGE LAM Parameters and coding specifics taken as starting point Link : participation to the meetings and by Transmission of information fror comments and feedbacks

25 Thanks to the experience gained during the first period of activity, it is clear that TIGGE LAM cannot coordinate on a global scale. Specific initiatives and applications must be organized at regional levels with strong links with the THORPEX Regional Committees.

26 Some about what is going on TIGGE LAM Panel reorganization in regional sub-groups: better coordination with the Thorpex Regional Committee Better link regional initiatives Better focus on scientific issues and actions/activities Set-up of cross-working group discussion and cooperation programmes (WG Mesoscale Research, WGNE, Verification, SERA,...) TIGGE LAM Plan: under writing

27 Panel

28 Some concepts Models and EPS systems are constantly changing and evolving. The main added value from LAM EPS in the future will be probably in the representation of phenomena at the convective-permitting scale TIGGE LAM should contribute to the definition of strategies and methodologies to improve the predictability of High Impact Weather events by using LAM EPS.

29 Some concepts Going from global to LAM EPS, the process to represent forecast uncertainty is more complicated since larger scale perturbations interact with perturbations generated to represent smaller scale errors typical of LAM systems. Many possibilities to generate and couple these perturbations are tractable and it is almost impossible to evaluate a-priori the best configurations of such complex systems. This is an important reason to support coordinated scientific actions to facilitate the achievement of conclusive results.

30 TIGGE Plan: Scientific Issues Mesoscale predictability Mesoscale model inadequacy Interactions between larger scale perturbations given by the driving global systems and local perturbations generated specifically for the LAM EPS. Domain size and local perturbations on initial conditions Perturbations associated to soil/surface description Cycling short-range uncertainty for initial perturbations o o Multi Model EPS Quantify the additional benefit of Multi-LAM EPS Evaluate the performance of convective permitting EPS systems and their benefit over restricted integration domains versus the combination of lower resolution ensemble systems Predictability at the convection-resolving scale to support the design of the future LAM EPS systems. Biases in deterministic models and calibration Verification methods Assess the probability of rare meteorological events Probabilistic forecasts for other modelling applications LAM EPS and Data Assimilation: which are the key characteristics of a LAM EPS to best represent short-range forecast error co variances for use in DA?

31 TIGGE Plan: Actions / Activities Action 0: Reorganization of the TIGGE LAM Panel Action 1: Set-up of cross-working group discussion between TIGGE-LAM, the MWFR working group and the WGNE. Action 2: Set-up of cross-working group discussion with the Verification Working Group and SERA. Action 3: Definition of the key requirements for regional ensemble forecasting. Action 4: Identification of possible Funding opportunities to support the development and implementation of the Regional activities. Action 5: Activate link with major mesoscale applications (hydrology). Action 5: Set up of the TIGGE-LAM Database Action 6: TIGGE LAM Data Policy Action 7: Implementation of regional observational dataset for objective verification of Mesoscale Deterministic and Ensemble forecasting. Action 8: Definition of further HIW related specific products. Action 9: Set up of specific projects and collaborations addressed to the specific scientific issues (e.g. EU projects, HYMEX, ) Action 10: Coordinate the participation of LAM EPS groups to RDPs and FDPs. Action 11: Define standards to exchange meteorological fields required as Initial and Boundary conditions Interoperability Action 12: Provide LAM EPS products in format compliant with GIFS-TIGGE directives

32 Thank You

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