3 rd THORPEX DAOS Working Group meeting

1. 3rd THORPEX DAOS Working Group meeting Université du Québec à Montréal8-9 July 2010Montréal (Québec) CANADA

2. A few general considerations • Objective • Review our view on the issue of adaptive observations • Results from T-PARC (winter phase and TCS-08) • Update on other campaigns and activities • Review paper on the value of targeted data to be published in peer-review literature (BAMS) • Presentations available at • http://web.sca.uqam.ca/~wgne/DAOS/DAOS3_meeting/

3. DAOS-WG recommendations forTargeted Observing Field Programs • Impact of targeted observations from previous field programs (esp. WSRP, ATreC-2003TPARC) • Expensive observation campaigns should not be justified based only on previous methods of targeting • Rabier et al. (2008) • Carefully consider data assimilation issues (impacts of small vs. large sets of observations, frequency of special observations, etc. ) • Develop and test new adaptive strategies • Adaptive selection and assimilation of satellite observations (less than 10% of available data currently used)

4. 700hPa Case study – Global sensitivity function Initial temperature corrections for the 12 UTC January 27, 2003 analysis Corrections responsible for the forecast improvement of the Canadian Maritimes system and cross section of initial temperature correction made along the arrow.

5. Global-GEM operational forecast Global-GEM adapted forecast Energy (J/Kg) Global-GEM sensitivity forecast Forecast hour Case study –Forecast improvement Energy (total) of the forecast error average over Northern Hemisphere Extra-tropics (25N - 90N)

6. Observability of a structure function(Lupu and Gauthier, 2010) • Correlation between the innovations and a structure function v • This defines the observability of a structure functions • Can the observations detect a given structure function?

7. Observability of different structure functions based on key analyses

8. Observability of a pseudo-inverse obtained from a finite number of singular vectors (Mahidjiba et al., 2007) • Leading singular vectors are the structures that will grow the most rapidly over a finite period of time • Leading 60 SVs were computed based on a total dry energy norm at a lead time of 48-h • The forecast error is projected onto those SVs at the final time which allows to express the error at initial time that explains that forecast error (pseudo-inverse) • Experiments • 18 cases were considered in December 2007 • Are those structures observable from available observations? • Observability of SV1, the leading singular vectors • Observability of the pseudo-inverse

9. Observability of the leading singular vector and pseudo-inverse

10. The intercomparison experiment on the impact of observations • A goal of THORPEX is to improve our understanding of the ‘value’ of observations provided by the current global network • optimize the use of current observations • inform the design/deployment of new obs systems • In 2007, DAOS-WG proposed a comparison of observation impacts in several forecast systems, facilitated by the emergence of new (adjoint-based) techniques • Experiments for a baseline observation set were designed by DAOS members from NRL, GMAO, EC, ECMWF, Météo-France • …so far, results obtained for 3 systems: NRL, EC, GMAO

11. Summary of Current Results • Comparison experiments for NRL, GMAO and EC systems completed (published) for baseline set of observations • Despite differences in DA algorithms, RTMs and data handling, overall quantitative results similar for all systems • Largest overall impact provided by AMSU-A, but also raobs, satwinds and aircraft • Details of impact differ between forecast systems: impacts per-ob, -channel, -region • Only a small majority of assimilated observations improve the forecast • Much improvement comes from a large number of observations with small individual impacts

12.             Daily average observation impacts Global domain: 00+06 UTC assimilations Jan 2007 NRL NOGAPS GMAO GEOS-5 EC GDPS  AMSU-A, Raob, Satwind and Aircraft have largest impact in all systems

13. (17) Impacts per-observation Global domain: 00+06 UTC assimilations Jan 2007 NRL NOGAPS GMAO GEOS-5 EC GDPS Impact per-ob varies significantly between forecast systems GEOS-5 has smallest impact per-ob …2-3x more obs assimilated, other factors?

14. Singular vector-based thinning of satellite data Peter Bauer, Roberto Buizza, Carla Cardinali and Jean-Noel Thepaut European Centre for Medium-Range Weather Forecasts

15. 3. Experiments list EXP-HI This plot shows the density of AMSU-A channel 9 data for the case of 2008/12/14@00UTC for the different experiments: • EXP-HI: global thinning to 0.625o • EXP: global thinning to 1.25o (i.e. ope) • EXP-SV: EXP but with SV thinning 0.625o • EXP-CLI:EXP but with SVcli thinning 0.625o • EXP-RND: EXP but with random thinning 0.625o Target areas occupy same fraction (15%) of the Southern hemisphere. The SV-based climatology was derived from the mean 2007 SV-areas Experiments have been run for JAS08 and D08JF09 Forecasts from these experiments have been verified against EXP-HI analyses (~83 cases per season, i.e. without first 7 days to avoid spin-up) EXP EXP-SV EXP-CLI EXP-RND

16. 3. Degree of Freedom for Signal (DFS) The DFS measures the amount of information extracted from the observations during the assimilation process (Cardinali et al 2004). DFS depends on the observation and the background accuracies and model used as space/time propagator This figure shows the average DFS in JAS08 Cold S for satellite radiances EXP-HI 70% more DFS than EXP 33% IASI-AMSU-A 21% AIRS 7% HIRS 6% Others

17. 3. DFS and Nobs In the 2007 studies, we found that DFS was always higher for EXP-SV compared to RD Observed structures in SV-areas analyzed (c) In D08JF09DFS(RND)<DFS(SV) in agreement with the previous studies By contrast, in JAS08 DFS(RND)>DFS(SV) DA system was NOT able to extract information from the obs located in the SV-area. • B static Flow- dependent 2) Observation error larger than signal JAS08 D08JF09 (c) JAS08 D08JF09

18. Left: AMV (IR-only) field produced from routinely available hourly sequence of MTSAT-1 images during Typhoon Sinlaku Bottom Left: Same as above, but using a 15-min rapid scan sequence from MTSAT-2 (better AMV coverage and coherence) Bottom Right: Same as above, but using a 4-min rapid scan sequence (improved coverage/detail of typhoon flow fields) Example of AMVs from MTSAT-2 Rapid Scan images

19. TC Diagnostic Studies using High-Res. Rapid-Scan AMVs Example: Typhoon Sinlaku during TPARC 150 hPa Divergence analyzed from upper level R/S AMVs 150 hPa Vorticity analyzed from upper level R/S AMVs

20. 500 hPa analyses in the Mid-Lats during TC Sinlaku Hourly MTSAT AMVs have positive impact, particularly during the period of large NOGAPS track forecast errors (NOAMV exp.) for Sinlaku

21. Results from Winter Storms Reconnaissance Program (WSRP) • Summary of main results (Y. Song) • Future plans on the reanalysis of the results obtained from WSRP • Discussions as to how to quantify the forecast improvement. • The current system is based on comparisons with radio-sondes within a 1000km radius of the initial target area which changes from one case to another. This can pose problems if there are only few observations in the verification domain. • Suggestions • larger fixed domains and using more relevant fields such as total energy norm and/or precipitation • impact of the observations be judged against the general rate of improvements due to advances in assimilation systems etc., increase of the volume of data

22. Review of the impact of targeted data • Community paper being written • Lead author is SharanMajumdar with contributions from the DAOS-WG and scientists involved in targeting campaigns (including Y. Song and Z. Toth) • Reconcile seemingly opposing views on the impact of targeted data • Summary of results obtained so far • Identify issues that need to be addressed to improve the use of observations that impact weather forecasts (e.g., metrics, assimilation methods, sampling of precursors to dynamic instability)

23. Conclusion • THORPEX data assimilation and observing systems working group has reached a mature stage • Focus on fundamental issues associated with data assimilation to improve forecasts • Links with CBS, satellite agencies and NWP Centres • WMO does not have a specific working group on data assimilation • Important for model validation and reanalysis