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CONSENS Priority Project Status report COSMO year 2008/2009

CONSENS Priority Project Status report COSMO year 2008/2009. Involved scientists: Chiara Marsigli, Andrea Montani, Tiziana Paccagnella, Tommaso Diomede (ARPA-SIMC) Flora Gofa, Petroula Louka (HNMS) Felix Fundel (MeteoSwiss). Overview. Task 1: Running of the COSMO-SREPS suite

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CONSENS Priority Project Status report COSMO year 2008/2009

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  1. CONSENS Priority ProjectStatus report COSMO year 2008/2009 Involved scientists: Chiara Marsigli, Andrea Montani, Tiziana Paccagnella, Tommaso Diomede (ARPA-SIMC) Flora Gofa, Petroula Louka (HNMS) Felix Fundel (MeteoSwiss)

  2. Overview • Task 1: Running of the COSMO-SREPS suite • suite maintenance • implementation of the back-up suite • Task 2: Model perturbations • perturbation of physics parameters • perturbation of soil fields • Task 3: Ensemble merging • Multi-clustering • Task 4: Calibration

  3. The COSMO-SREPS ensemble • COSMO-SREPS has been developed within the SREPS PP, aiming at the development of a Short-Range Ensemble Prediction System • 3 days forecast range, 10 km of horizontal resolution • COSMO-SREPS provides boundary conditions for COSMO-DE-EPS, the 2.8 km ensemble system under development at DWD • Application: test of the use of COSMO-SREPS to estimate a flow-dependent B matrix in a 1D-Var DA of satellite data

  4. IFS – ECMWF global COSMO at 25 km on IFS P1: control P2: physics pert p2 P3: physics pert p3 P4: physics pert p4 … COSMO-SREPS GME – DWD global COSMO at 25 km on GME by AEMET Spain UM – UKMO global COSMO at 25 km on UM • COSMO (v 4.7) • 00 UTC and12 UTC • 10 km • 40 levels • 16 members • 72 h GFS – NCEP global COSMO at 25 km on GFS

  5. 1. Running of the COSMO-SREPS suiteARPA-SIMC • Maintenance of the COSMO-SREPS suite at ECMWF • Adaptation of the data output for COSMO-DE-EPS • Implementation of a 12 UTC run (beside the 00 UTC one) • Implementation of the back-up suite: delayed (9 months) • The work involves also DWD, even if implicitly! • AEMET has provided the int2lm code adapted for the NCEP and UKMO models • An agreement with UKMO has been signed, in order to receive regularly the boundary conditions from the UM

  6. Suite availability

  7. 2.1 Model perturbations: parametersCSPERT test suiteARPA-SIMC - HNMS • In order to study new parameter perturbations, a test suite (CSPERT) was already implemented at ECMWF, by ARPA-SIMC, during the SREPS PP. Results for SON 2007 can be found in the SREPS final report • According to the outcome of the SREPS PP, it was decided to analyse the impact of these perturbations on a dry season as well • New runs of the CSPERT suite were performed in autumn 2008, for the JJA 2008 period • Analysis of the results completed in May 2009

  8. IFS – ECMWF global The CSPERT suite P1: control (ope) P2: conv. scheme (KF) P3: parameter 1 P4: parameter 2 P5: … 16 LM runs at 10 km SON 07 + JJA 08

  9. BIAS MAE JJA 2008 – IT T2m Td2m

  10. BIAS RMSE JJA 2008 – GR T2m Td2m

  11. = - -- + ++ Summary of the perturbation impact

  12. Remarks from the CSPERT suite • The effect of perturbing each physics parameter on improving or worsening the statistical values of the results in comparison to the corresponding control was investigated • Based on these results, the next step was to explore the importance and the effect of selected physical perturbations further • It seems that the particular parameter perturbations do not influence greatly the mean horizontal wind apart from a few exceptions. Possibly looking at the vertical wind component would make the effects more apparent for some parameters

  13. Remarks (cont) • Looking separately at each parameter perturbation compared to the control run: • scaling factors related to the laminar layer (rlam_heat, rat_sea), turbulent length scale (tur_len) and evapotranspiration (crsmin), all associated with the development of the turbulent surface layer, are the physical parameters on which the main focus is given

  14. 2.1 Model perturbations: parametersthe new COSMO-SREPS configurationARPA-SIMC - HNMS • On the basis of the analysis of these results, a new configuration of the COSMO-SREPS suite has been implemented in May 2009 • An analysis of its performance over summer 2009 (JJA) has been carried out: • in terms of 2m temperature only over the Alpine area • In term of the continuous parameters (T, U and Td) over Greece • Precipitation has not been considered up to now mainly due to the summer season

  15. convection scheme: 0 Tiedtke 1 Kain-Fritsch maximal turbulent length scale length scale of thermal surface patterns scaling factor of the laminar layer depth ratio of laminar scaling factors for heat over sea minimal stomata resistance COSMO-SREPS new configuration (from the 5th of May 2009)

  16. Tiedtke Kain-Fritsch Tiedtke Kain-Fritsch rlam_heat < IFS tur_len > pat_len > tur_len < crsmin > rat_sea < GME rat_sea < pat_len > rlam_heat < crsmin > NCEP rlam_heat > rlam_heat > tur_len > tur_len < UM rat_sea > rat_sea > crsmin < crsmin <

  17. Relationship between error and spread t2m JJA09 Small sample, 30 days only SYNOP over the MAP D-PHASE domain SYNOP over the whole domain Nearest grid point

  18. Relationship between error and spread JJA09 t2m 12 UTC 00 UTC SYNOP over the whole domain - Nearest grid point

  19. 2.0 3.0 1.5 2.8 1.0 2.6 0.5 2.4 0.0 2.2 -0.5 2.0 -1.0 1.8 ecmwf gme ncep ukmo JJA09 2m T – deterministic scoresglobal model BIAS MAE SYNOP over the MAP D-PHASE domain Nearest grid point

  20. 2.0 3.0 1.5 2.8 1.0 2.6 0.5 2.4 0.0 2.2 -0.5 2.0 -1.0 1.8 Tiedtke Kain-Fritsch JJA09 2m T – deterministic scoresconvection scheme BIAS MAE SYNOP over the MAP D-PHASE domain Nearest grid point

  21. 2.0 3.0 1.5 2.8 1.0 2.6 0.5 2.4 0.0 2.2 -0.5 2.0 -1.0 1.8 tur_len=150 – ecmwf T tur_len=1000 – ecmwf KF tur_len=1000 – ncep T tur_len=150 – ncep KF JJA09 2m T – deterministic scorestur_len BIAS MAE SYNOP over the MAP D-PHASE domain Nearest grid point

  22. tur_len: maximal turbulent length scale

  23. 2.0 3.0 1.5 2.8 1.0 2.6 0.5 2.4 0.0 2.2 -0.5 2.0 -1.0 1.8 pat_len=10000 – ecmwf KF pat_len=10000 – gme T JJA09 2m T – deterministic scorespat_len BIAS MAE SYNOP over the MAP D-PHASE domain Nearest grid point

  24. 2.0 3.0 1.5 2.8 1.0 2.6 0.5 2.4 0.0 2.2 -0.5 2.0 -1.0 1.8 rlam_heat=0.1 crsmin=200 – ecmwf T rlam_heat=0.1 – gme KF rlam_heat=10 – ncep T rlam_heat=10 – ncep KF JJA09 2m T – deterministic scoresrlam_heat BIAS MAE SYNOP over the MAP D-PHASE domain Nearest grid point

  25. rlam_heat: scaling factor of the laminar layer depth

  26. rat_sea: ratio of laminar scaling factor for heat over sea

  27. 2.0 3.0 1.5 2.8 1.0 2.6 0.5 2.4 0.0 2.2 -0.5 2.0 -1.0 1.8 rlam_heat=1 crsmin=200 – ecmwf T rat_sea=1 crsmin=200 – gme T crsmin=50 – ukmo T crsmin=50 – ukmo KF JJA09 2m T – deterministic scorescrsmin BIAS MAE SYNOP over the MAP D-PHASE domain Nearest grid point

  28. crsmin: minimal stomata resistance

  29. Remarks • Some of the perturbations produce common effects on both regions (e.g. rlam_heat, crsmin, tur_len) • However, the impact of some of the physical perturbations (e.g. rat_sea) depends on the geographical characteristics of the region • Large values of rlam_heat produce an increase in the error, implying that, theoretically, a deeper laminar layer suppresses the vertical fluxes • The value of pat_len will be decreased in the new implementation to be more consistent • A paper about the SREPS outcomes is in preparation!

  30. Test of new parameter perturbations(new CSPERT suite) 15: ctrl T Nov 08 - MAMJ 09 16: ctrl KF

  31. 2.2 Model perturbations: Developing perturbations for the lower boundaryHNMS Aim Implement a technique for perturbing soil moisture conditions and explore its impacts Reasoning The lack of spread is typically worse near the surface rather than higher in the troposphere. Also, soil moisture is of primary importance in determining the partition of energy between surface heat fluxes, thus affecting surface temperature forecasts

  32. Soil Perturbation method Based on the method proposed by Sutton and Hamill (2004) • Select a period that provides variability in soil moisture e.g. spring • Use of data from a land–surface model analysis for the defined period for a few years in order to create some “climatology” (DWD SMA) • Implement the EOF (Empirical Orthogonal Function – Principal Component Analysis) to the data in order to generate random perturbations while retaining the spatial structure of the field • Define the number of perturbations that will be initially used • Test the impact of the perturbation within the COSMO-SREPS suite

  33. initial conditions by EPS initial conditions by MOGREPS 3. Ensemble merging: development of the COSMO-LEPS clustering(A. Montani, A. Corigliano) • A dynamical downscaling where driving members for COSMO are taken from different global ensembles is under testing • The cluster analysis is applied on a large set of members coming from different global ensembles • Up to now, ECMWF EPS and UKMO MOGREPS have been considered

  34. Issues • Consider both ECMWF EPS and UKMO MOGREPS and study the properties of the cluster analysis on multi-ensemble: • How many times do the 2 ensembles mix? • Where do the RMs come from? How to they score depending on their “origin”? • Is there added value with respect to single-model ensemble: • BEFORE dowscaling • AFTER downscaling

  35. data from TIGGE-PORTAL (everything in GRIB2) • 90 days (MAM09) of ECMWF-EPS and UKMO-MOGREPS run at 00 and 12 UTC • use Z500 at fc+96h as clustering variable; • for verifying analysis (at 00 and 12 UTC), consider Z500: • “consensus analysis” (average of UKMO and ECMWF high-res analyses), • independent analysis (e.g. from NCEP); • generate the following global ensembles: • EPS (50+1): 51 members • MOGREPS (23+1): 24 members • MINI-MIX (EPS24 + MOGREPS24): 48 members • MEGA-MIX (EPS51 +MOGREPS24): 75 members Forecast and analysis datasets

  36. perform cluster analysis with 16 clusters and select RMs (like in operations); • generate 16-member global ensembles (EPS_REDU, MOGREPS_REDU, MINI_REDU, MEGA_REDU). • How do “REDUs” ensembles rank with respect to EPS, MOGREPS, MINI-MIX, MEGA-MIX? • Where do the best (and the worst) elements of REDU ensembles come from? • How do they score depending on their “origin”? • BEFORE DOWNSCALING: is there added value with respect to single-model ensemble? Strategy

  37. Future plans finish by March 2010! Future future plans • Implement dynamical downscaling: nest COSMO model in the selected RMs and generate “hybrid” COSMO-LEPS using boundaries from members of different global ensembles. • For a number of case, compare operational COSMO-LEPS and “hybrid” COSMO-LEPS.

  38. Summary results • The availability of the COSMO-SREPS suite has been around 90% during this year, but the system is complete only about 50-60% of the times -> back-up suite! • The analysis of the parameter perturbations introduced in the SREPS PP has been completed in Spring, and new selected perturbations have been introduced in the COSMO-SREPS suite in May • There is a good impact of the new perturbations on the spread of the system • A new set of perturbations, also for the microphysics scheme, is currently under testing • A methodology for soil moisture perturbation has been selected and is being implemented at HNMS • The work on multi-clustering has started, using the GRIB2 fields of the TIGGE-PORTAL

  39. 4. CalibrationARPA-SIMC - MeteoSwiss • At MeteoSwiss (F. Fundel, Sep 08-Feb 09): • Sensitivity tests • Documentation/paper • At ARPA-SIMC (T. Diomede): • Data collection: • observations • MeteoSwiss reforecast • COSMO-LEPS forecasts • Choice of the methods • Code implementation • Evaluation Preparatory step: visit of Tom Hamill and Felix Fundel at ARPA-SIMC, June 2008

  40. Calibration Method 30 years COSMO-LEPS reforecasts (1971-2000) Observations (stations, gridded fields) CDF (for one grid point) Observations Reforecasts Reforecasts Return Period Return Period x x

  41. Verification Results I raw forecasts are overconfident calibrated forecasts nearly perfect reliable strong improvements during winter summer forecast already are reliable, only little improvement possible

  42. Sensitivity Study (precip) rel. improvement in RPS over 16 Member CLEPS DMO • Cost for 1 member is ~equal to 2 reforecasts • 15% improvement (over 16 member CLEPS DMO) using 11-12 members and calibrate with 8-10 years reforecasts • Depending on season: • more improvement during winter • less improvement during summer current setup (18% improvement) best, cheap setup (15% improvement)

  43. 158 raingauges 281 COSMO-LEPS grid points Calibration – data collection [m] Emilia-Romagna Region (22000 km2) • Observations • Emilia-Romagna Region • 24-h precipitation (08-08 UTC), 1970-2007 • COSMO-LEPS reforecasts (done by MeteoSwiss) • 30 years: 1971-2000 • 1 member, nested on ERA40, COSMO v4.0 • 1 run every third day (+90h) • COSMO-LEPS QPFs operational • 5 years: 2003-2007

  44. Calibration – choice of the methods • choice of methodologies which enable a calibration of the quantitative precipitation forecasts, not only of the probabilities of exceeding a threshold • aim: • improve COSMO-LEPS output (QPF) • hydrological applications • chosen methods up to now: • Cumulative Distribution Function (CDF) based • Linear regression • Analogues, based on the similarity of forecast fields: • precipitation • geopotential height

  45. CDF-based corrections Calibration methodologies • For each model grid point: • blue line  CDF of COSMO-LEPS reforecasts • red line  CDF of historical observations • “raw forecast”  each member of the operational COSMO-LEPS Ref: Zhu and Toth, 2005 AMS Annual Conf., and many others

  46. Linear Regression Calibration methodologies For each model grid point: x-axis: COSMO-LEPS reforecasts y-axis: historical observations Ref: any applied statistics textbook

  47. Calibration methodologies Analogues For each ensemble member’s forecast and 24-h forecast period (+ 20-44h , 44-68h , 68-92h , 92-116h): - the analog search is performed in terms of 24-h rainfall pattern over the Emilia-Romagna Region - the root-mean-square (rms) difference between the current forecast and each reforecast is computed, over all the grid points of the Emilia-Romagna Region - the historical date with the smallest rms difference is chosen as the date of the analog, then the past raingauge recordings are used as the calibrated forecast 1 analog date for the whole Emilia-Romagna Region and for each 24-h forecast period

  48. Calibration – analogues domain used for the analogue search example on the methodology used for the analogue search in terms of geopotential at 700 hPa

  49. Method comparison +20-44h autumn threshold: 5 mm/24 h threshold: 20 mm/24 h

  50. Method comparison +68-92h autumn threshold: 5 mm/24 h threshold: 20 mm/24 h

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