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DAOS-WG: Data Assimilation and Observing Systems Working Group Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal.

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Presentation on theme: "DAOS-WG: Data Assimilation and Observing Systems Working Group Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal."— Presentation transcript:

1 DAOS-WG: Data Assimilation and Observing Systems Working Group Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal

2 Merge of OS with DAOS Work of the Observing Systems working groups already covered through OPAG-IOS Best to combine with DAOS (Observing Systems) One co-chair from OS group (R. Saunders) and F. Rabier resigns as co- chair of the DAOS-WG. Composition of the working group (16 members) Pierre Gauthier (UQAM, Canada) and Roger Saunders (UK Met Office), co-chairs * Bertrand Calpini (MeteoSwiss, Switzerland), Carla Cardinali (ECMWF) * Jochen Dibbern (EUMETNET/DWD), Ron Gelaro (NASA, US) * Tom Hamill (NOAA/ESRL), Tom Keenan (CAWCR, Australia) * Ko Koizumi (JMA), Rolf Langland (NRL), Andrew Lorenc (UK Met Office) * Tetsuo Nakazawa (JMA, Japan), Florence Rabier (Météo-France) * Peter Steinle (CAWCR, Australia) * Michael Tsyroulnikov (Hydromet Research Centre, Russia) * Chris Velden (University of Wisconsin, US),, Germany),

3 Data Assimilation and Observing Systems mission The DAOS WG ensures that THORPEX contributes to the optimisation of the use of the current WMO Global Observing System. It contributes to the development of a well-founded strategy for the evolution of the Global Observing System to support NWP (primarily 1-14 days).

4 Data Assimilation and Observing Systems WG strategy It addresses issues in DA and improved understanding of the sources and growth of errors in analyses and forecasts It promotes research activities that lead to a better use of observations and the understanding of their value It provides input and guidance for THORPEX regional campaigns for the deployment of observations to achieve their objectives This will be done in collaboration with the CBS OPAG-IOS

5 Approaches to measuring the impact of assimilated observations Information content * based on the relative accuracy of observations and the background state Observing System Experiments * Data denials * Global view of the impact of observations on the quality of the forecasts Observation impact on the quality of the forecasts * Sensitivities with respect to observations based on adjoint methods (Baker and Daley, 2000; Langland and Baker, 2004) * Ensemble methods

6 Information content Ratio of the analysis error covariance to B The information gained from assimilating a given set of observations is represented by the second term, where N is the dimension of the model space … and in observation space with M being the number of observations

7 We assumed that the complete set of observations can be split in observation subsets with independent errors (R is block-diagonal); Regions : HN, HS, TROPICS; Obs_types : AI, GO, PR, SF, SW, AMSU-A, AMSU-B, RAOB; Computation of DFS in MSCs 3D-Var and 4D-Var systems (Lupu, 2009) DFS for each type of observations

8 Observation impact per observation in each region Lupu, 2009

9 OSEs experiments: 3D-Var and 4D-Var, North America DFS values per obstype normalized by the number of observations. NO_RAOB: DFS per single observation notably increases, especially for AMSU-B and GO; NO_AIRCRAFT: DFS per single observation notably increases, especially for RAOB, SF and PR; For other observations (GO, SW and AMSU-B) DFS per obs also increases slightly.

10 Observations move the model state from the background trajectory to the new analysis trajectory The difference in forecast error norms,, is due to the combined impact of all observations assimilated at 00UTC Observation Impact Methodology ( Langland and Baker, 2004) OBSERVATIONS ASSIMILATED 00UTC + 24h

11 Evaluation of the impact of observations At initial time where : observation departure from the background state Computation of the Observation Impact: One can obtain w by slightly adapting the assimilation to solve

12 Removal of AMSUA results in large increase in AIRS (and other) impacts Removal of AIRS results in significant increase in AMSUA impact Removal of raobs results in significant increase in AMSUA, aircraft and other impacts (but not AIRS) Combined Use of ADJ and OSEs (Gelaro et al., 2008) …ADJ applied to various OSE members to examine how the mix of observations influences their impacts

13 Observing System Simulation Experiments Effort carried out at NASA and NOAA with the collaboration of ECMWF, JCSDA and KNMI * High resolution nature run: Free-running T511 L91 w. 3-hourly dumps May through June (13 month long) T799 L91 levels, one-hourly dump Periods of 35 days * Generation of synthetic observations for all observations * Objective: to add synthetic observations for upcoming new satellite instruments and assess their impact in the observing environment that should prevail when the instrument is launched Validation * Realism of the nature run (e.g., cloud distribution and properties) * Impact of observations of existing instruments should compare to that of their synthetic counterpart

14 WMO Workshop on impact studies Geneva, May 2008 Organized by the OPAG-IOS Participation of the DAOS * Cardinali, Gauthier, Gelaro, Koizumi, Langland, Rabier, Steinle * Preliminary results from the intercomparison experiment were presented by Cardinali, Gelaro and Langland DAOS-WG to provide input on the design of the global observing system * Recommendation at the workshop to use the adjoint based method to get a more detailed assessment of the impact of observations * Nice complement to OSEs

15 Intercomparison experiment Numerous differences exist between assimilation systems and influence the evaluation of the impact of observations * Observation coverage * Assimilation methodology * Forecast model * Metrics used to compute the forecast sensitivity Possibility to use more appropriate metrics for socio-economic applications * Baseline experiment provided a common context against which three different systems evaluated the impact of observations with the same tool * Differences nevertheless persist in terms of assimilation methodologies and models (e.g., 3D-Var and 4D-Var) * For each system, the total impact of observations evaluated with the LB04 method is consistent with results from OSEs. Further experimentation with different approaches * Ensemble methods

16 Value of targeted data (1) Value of extra-tropical targeted data has been found to be positive but small, on average * Observations taken in sensitive areas have more value than observations deployed randomly * Past experiments do not provide evidence of big impact obtained from just a few observations (when averaged over a large sample of cases) * There are limitations due to the current assimilation methodologies (not yet fully flow-dependent) * Sensitive areas characterization does not appear to be the first order problem Additional observations for tropical cyclones have proven to be useful.

17 Value of targeted data (3): Recommendations Additional benefit may be obtained from : * Optimization of existing operational resources * Adaptive processing and data selection of satellite data OSSEs would be useful to evaluate the impact of instruments and targeting strategies * Collaborative effort on OSSEs based on nature run from ECMWF * Calibration with respect to the impact of synthetic data Using OI adjoint based methods, synthetic data show a similar quantitative impact to real observations * Evaluation of the anticipated impact of future instruments need to be made in the context of the future observing network and future modeling and assimilation systems

18 REAL OBSOSSE impact count J/kg Impact of various observing systems on GEOS-5 24h forecast error OSSE calibration for Jan 2006 vs. Jan 2007 reference *First Results*

19 Value of targeted data (4) Targeting for longer range forecasts * Issues for targeting at shorter range remain and should be addressed before getting into longer range forecasts * Which flow regimes show lower predictability and what impact additional data may have. Cardinali et al. (2007) and Langland et al. (2008) have presented results on that. Can we make this distinction? * Evidence shows that removing or adding data does not lead to significant impact in the longer range Experiments from Kelly et al. (2007) show that removing data from the North Pacific does not have any impact on Europe at day-6

20 Day 1 Day 2 Day 5 Day 6 Impact of denying observations over the Pacific on forecasts of increasing length in 3D-VAR RMSE Denial – RMSE Control 500 hPa Height 00UTC Dec 2003-Feb 2004 Kelly et al. 2007, QJRMS …signal becomes global and diffused in the medium range

21 Day 1 Day 2 Day 5 Day 6 Impact of denying observations over the Pacific on forecasts of increasing length in 4D-VAR RMSE Denial – RMSE Control 500 hPa Height 00UTC Dec 2003-Feb 2004 Kelly et al. 2007, QJRMS …medium range signal is even weaker in 4D-VAR (but less diffused)

22 Conclusions Diagnosing the by-products of the assimilation * allows to estimate the information content brought by any given type of observations on the analysis * Same diagnostics provide information to recalibrate the error statistics used in the assimilation * Todling (2009): observation impact is obtained from lagged forecasts Impact of observations depend on the observing environment * When removing some observation type, remaining observations may compensate by having more impact. Value of targeted data * Preparation of a review paper to summarize the results from several studies * Optimization of existing operational resources * Experimentation on adaptive processing and selection of satellite data Observations * Quality of Indian radiosondes has significantly improved * Russian radiosondes network * Scatterometer data and MODIS winds * GPS radiooccultation


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