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Overview of the new MESCAN precipitation analysis system

Overview of the new MESCAN precipitation analysis system. C. Soci, E. Bazile, J-F. Mahfouf with contributions from F. Besson and M. Lafaysse. Outline. Analysis of precipitation Validation Issues Further plans. Analysis of accumulated precipitation in MESCAN. Principle :

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Overview of the new MESCAN precipitation analysis system

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  1. Overview of the new MESCAN precipitation analysis system C. Soci, E. Bazile, J-F. Mahfouf with contributions from F. Besson and M. Lafaysse

  2. Outline • Analysis of precipitation • Validation • Issues • Further plans

  3. Analysis of accumulated precipitation in MESCAN • Principle: • Based on the OI code used also for the analysis of T2m and RH2m • Background field: accumulated precipitation forecasted by a numerical model (ARPEGE, AROME, ALADIN, HIRLAM). • Observations: cumulated precipitation from raingauge measurements (at this stage) • Possibility of producing 24-h, 12-h, 6-h, 3-h or 1-h accumulated precipitation analysis, the only limiting factor is that the background and the observations must span the same period. • The analysis is performed at the background horizontal resolution

  4. Analysis of accumulated precipitation in MESCAN (2) • Two options are available: • MESCAN-Log: analysis in log-space on a transformed variable ln(RR+1) • RR ~ LN(0, 12)  ln(RR) ~ N (0, 22) • Preliminary error statistics: o=0.6 et b =0.71, L=43km • [after Mahfouf et al., 2007 for CAnadian Precipitation Analysis Project (CAPA)] • MESCAN-RR: analysis in physical space on RR ~ N (0, 2) • Preliminary error statistics:o =5 et b =13, L=35km (as in SAFRAN) • Correlation function:

  5. Preliminary validation over France at 5.5 km grid • Test period : October 2009 – June 2010 • First guess: ALADIN operational downscaled with fullpos • Indirect validation of the MESCAN analysis system by forcing with its output (analyses of T2m, RH2m, RR24) the surface scheme ISBA and the hydrological module MODCOU. Evaluation of the impact of changing the surface forcing variables (T2m, RH2m, RR24, 10-m wind speed, downward SW and LW radiation) on: • River flow, and • Snow Depth The reference system used for validation is the operational system called SIM (SAFRAN-ISBA-MODCOU)

  6. Validation of MESCAN precipitation analysis vs SAFRAN over France Heidke Skill Score Mar 2010Jun 2010 Heidke Skill Score Nov 2009Feb 2010 Heidke Skill Score Nov 2009Jun 2010 ~ 4200 obs for computing scores ~ 1700 obs for analyses • MESCAN-RR compared to SAFRAN and MESCAN-Log for spring (Mar-Jun): • improves the HSS for RR > 5mm/day, but • deteriorates HSS for RR < 2mm/day

  7. SAFRAN MAX=309mm MESCAN-Log MAX=277mm Validation on extreme event: Var, 2010 (Grid 0.1°, RR24h) OBS MAX=397mm ALADIN RR24 MAX=77mm

  8. SAFRAN MAX=309mm MESCAN-Log MAX=277mm Validation on extreme event: Var, 2010 (Grid 0.1°, RR24h) OBS MAX=397mm MESCAN-RR MAX=265mm

  9. Validation of MESCAN precipitation analysis vs SAFRAN over France Accumulated daily RR for 1/10/2009 to 31/07/2010 over France [mm] ~50mm Obs (~4200) MESCAN-RR MESCAN-Log SAFRAN • Bias correction needed for MESCAN-log

  10. Coupling the 2D surface analysis and a Surface Scheme for Indirect Validation and Hydrological purposes 2D Analysis at ~5 km: T2m, Rh2m, RR Forecast model: SW, LW, Wind, Ps SH, LH, snow depth, snow cover, soil moisture, Ts_lake SURFEX (V7): Surface Externalized (www.cnrm.meteo.fr/surfex) with a sophisticated snow scheme (Vionet et al. submitted) and the lake model: Flake (Mironov, 2008) Hydrological Model

  11. Indirect validation of MESCAN precipitation analysis using the MODCOU hydrological model (F. Besson) Map based on Nash scores • MESCAN-RR (physical space) and SAFRAN are of equal quality with regard to Nash criterion. • SAFRAN is better over the western part of France, i.e. flat terrain!

  12. Daily river discharge for the Seine river at Paris Plots provided by F. Besson Impact of different types of forcing on the river flow (Oct09-Jun10) Forcing by ALADIN precipitation Forcing by MESCAN-RR • The forcing provided by the MESCAN-RR precipitation analysis is significantly improved in comparison to the one generated by the ALADIN precipitation.

  13. Daily river discharge for the Rhone river at Beaucaire Provided by F. Besson Impact of precipitation on the river flow (Oct09-Jun10) - in June, the different behaviour of the river flow forced by MESCAN-RR is probably due to a lower accumulation of snowfall during the winter throughout the Rhone watershed. - Compared with SAFRAN, this underestimation is very likely due to the misleading information provided by the non-heated rain gauges in the mountains during the winter season.

  14. FLAINE (1640m) Bellecote Nivose (3000 m) Validation of snow depth at observation location (M. Lafaysse) • Observations • SAFRAN • ALADIN-forecast • MESCAN v0 • MESCAN v1 • MESCAN v2 • Generally, the underestimation of the snow depth forcing by MESCAN RR is probably due to the misleading information provided by the non-heated rain gauges or to the under catch due to wind.

  15. Issues • Observations: • high level of uncertainty in using precipitation data particularly in winter (heated/ non-heated rain gauges, ‘Zero’ precip reports when it snows etc) • rain gauge measurements not corrected for the wind • Background(downscaled HIRLAM fields from 22km to 5.5km): • What guess to use: fc00+30-fc00+06, fc06+24, fc00+18-fc00+06 + fc12+18 - fc12+06? The quality of the background fields is still under investigation. • revised statistics for background and observation errors

  16. Further plans • At ECMWF for the 2007-2010 (2011 ??) period: • running and monitoring the production of the background files for both the precipitation and screen-level (T2m/RH2m) analyses; • running and monitoring the production of the reanalysis: • T2m/RH2m every 6 hours (4 analysis per day) • RR24 (one analysis per day) • Validation of re-analyses • monthly mean, RMSE of 6-hourly T2m • computation of some categorical scores for RR24

  17. EURO4M - reanalysis domain Thank you for your attention! Acknowledgements The research leading to these results has received funding from the European Union, Seventh Framework Programme (FP/2007-2013) under grant agreement no 242093.

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