1 / 20

Stephanie Guedj Florence Rabier Vincent Guidard Benjamin Ménétrier

Observation error estimation in a convective-scale NWP system. Stephanie Guedj Florence Rabier Vincent Guidard Benjamin Ménétrier. Outline. Introduction 1. SEVIRI assimilation experiments (various observation densities) 2. Diagnosis of error correlations SEVIRI IASI

qamra
Download Presentation

Stephanie Guedj Florence Rabier Vincent Guidard Benjamin Ménétrier

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Observation error estimation in a convective-scale NWP system Stephanie Guedj Florence Rabier Vincent Guidard Benjamin Ménétrier

  2. Outline Introduction 1. SEVIRI assimilation experiments (various observation densities) 2. Diagnosis of error correlations SEVIRI IASI Conclusions and Future work

  3. Context Potential of MTG for Convective scale NWP models IRS : horizontal resolution of SEVERI and spectral resolution ~ IASI AROME WMED (Fourrié et al., 2014) • Aims to support HyMeX campaigns • to improve our understanding of the water cycle, with emphases on the predictability and evolution of intense events • Is inherited from the operational AROME/FRANCE model (Seity et al., 2011 and Brousseau et al., 2008) Resolutions : 60 vertical levels, Horizontal 2.5 km Assimilation : 3D-Var assimilation system used to produce 8 daily analysis using conventional data, reflectivity, radar Doppler, GEO winds, GEO/LEO radiances … AROME WMED domain

  4. 1. SEVIRI assimilation experiments Overview of SEVIRI assimilation in AROME-WMED Horizontal R. 4km • Thinning 70km Repeat cycle 15 min • Analysis every 3h Information Humidity (±400 hPa) SEVIRI WV 6.2 observations Assimilated vs Rejected 17/10/2011 - 0UTC

  5. 1. SEVIRI assimilation experiments High-density assimilation experiments : configurations (No-cycled) BACKGROUND (with all observations previously assimilated) Assimilation of SEVIRI WV only Thinning distances 5 km 10 km 20 km 40 km 70 km 100 km Current OPER Ana-5 Ana-10 Ana-20 Ana-40 Ana-70 Ana-100 F3h-5 F3h-10 F3h-20 F3h-40 F3h-70 F3h-100 Evaluation : • Analysis Increments • Forecast verification using independent observations (IASI, radiosondes …)

  6. 1. SEVIRI assimilation experiments Analysis increments moisture moisture 70 km Analysis – Background specific humidity (630 hPa) 17/10/2011, 0UTC • Increments show similar but sharper structures in EXP10 than EXP70. 10 km

  7. 1. SEVIRI assimilation experiments Analysis increments moisture moisture 70 km Analysis – Background specific humidity (Cross-section) 17/10/2011, 0UTC • Increments show similar but sharper structures in EXP10 than EXP70. • Wrong propagation toward the surface ? 10 km

  8. 1. SEVIRI assimilation experiments Forecast Verification F3h vs IASI radiances Fg-departures (8 days) (from 17/10 – 0h to 24/10 – 21h 2011) • The bias in FGd to IASI high-peaking WV channels is significantly improved.

  9. 1. SEVIRI assimilation experiments Forecast Verification F3h vs IASI radiances Fg-departures (8 days) (from 17/10 – 0h to 24/10 – 21h 2011) Scores for 2 WV IASI channels Fg-departures as a function of thinning distances for SEVIRI assimilation STD RMS The RMS indicates a degradation of the F3h if SEVIRI is assimilated at very high density (5 and 10 km)

  10. 1. SEVIRI assimilation experiments Forecast Verification F3h vs radiosondes Fg-departures (8 days) (from 17/10 – 0h to 24/10 – 21h 2011) • Scores for the fit to IASI observations : NEGATIVE >> POSITIVE • Bias reduction in FGd to radiosonde humidity • But, large degradations close to the surface. Seem to confirm the wrong propagation of humidity increments toward the surface ?

  11. 1. SEVIRI assimilation experiments Comments • Increasing the observation density : • produce sharper analysis increments structures • Main results over the first-guess : • Large impacts over the humidity fields (radiosondes & IASI WV channels) • Indication of a bias in the model ? • The First-guess fit to independent observations can be slightly improved when SEVIRI WV observations are assimilated every 20 km.

  12. 2. Diagnosis of error correlations Motivations Liu and Rabier (2002) and Desroziers (2011) : • For observations with spatially uncorrelated error, increasing the observation density always significantly improve the analysis accuracy. • The analysis quality decreases, if the density of the observational data set is too large and error correlations are neglected. Current approach : • data thinning • Reduce the amount of used obs • inflated diagonal R matrix • Reduce the weigth of obs in the analysis (Dando et al., 2007; Collard and McNally, 2009) Uncorrelated Sub-optimal optimal Separation distance (km)

  13. 2. Diagnosis of error correlations Data & Methods Error sources : Measurement, Forward model, Representativeness, Quality control error For each data type, observation error are determined from random Gaussian distribution that may be horizontally, vertically or channel-correlated or uncorrelated. DATA : First-Guess or analysis departures from pair of SEVIRI WV6.2 observations Binning interval =20 km Period : 30 October (8 cycles – 32000 radiances) METHODS : A priori • Hollingsworth/Lönnberg (1986) • Background ensemble method (Bormann and Bauer, 2010) A posteriori Desroziers diagnostic (Desroziers et al., 2003)

  14. 2. Diagnosis of error correlations Estimate of observation errors Hollingsworth/Lönnberg Assumption : errors in the observations are spatially uncorrelated and the spatially correlated part of the background departures (FGd) is due to errors in FG. Cov(FGd) = HBHT+R Sigma O² = 0.13 Sigma B² = 1.32 Separation distance (km)

  15. 2. Diagnosis of error correlations Estimate of observation errors Desroziers diagnostic Assumption: since DA follows linear estimation theory, the weigth given to the observations in the analysis is in agreement with true error covariances Sigma O² = 0.10 Sigma B² = 1.33 Separation distance (km)

  16. 2. Diagnosis of error correlations Estimate of observation errors Hollingsworth/Lönnberg and Desroziers diagnostic Obs. error estimates : PROBLEM : Radiometric error estimate = 0.75K • H/L limitation : « The presence of any spatially correlated observation error will lead to an underestimation of the observation error, as such spatial correlation are neglected. » (Bormann and Bauer, 2010) 2) Desroziers limitation : « The method have the capability of retrieving error structures as long as the true background error and the true observation error have sufficiently different correlation structures » (Desroziers, personal communication)

  17. Estimation from IASI observations Observation error amplitude (sigma O) DATA: IASI clear radiances 15 days (01/09-15/09) Domain: AROME-WMED 55 T channels 96 + 20 Q channels • Good agreement between the 2 methods for T channels but large differences for Q channels. • Estimated errors usually close to instrument noise (Desroziers Method) • Estimated errors lower than errors IASI spec system

  18. Estimation from IASI observations Inter-channel observation error correlations Desroziers Desroziers Q channels T channels • Several elements in the first off-diagonal are correlated due to opodisation effects • Tropospheric sounding humidity channels exhibit blocs of strong inter-channel error correlations

  19. Estimation from SEVIRI observations Horizontal observation error correlations DATA: SEVIRI clear radiances (full resolution) 15 days (01/09-15/09) Domain: AROME-WMED Surface Temperature Humidity L0.2Humidity ~ 25 km

  20. Conclusion & Future work • Taking observation error correlations into account in the assimilation system is an area of active research at Météo-France and at various NWP centres. • SEVIRI WV6.2 observations were assimiled at several density • Thinning distance from 70km to full resolution (5km) No significant impacts were shown on 3h-forecast skills (except humidity bias) • Estimation of observation errors and their correlation for SEVIRI/IASI data (with 3 methods) : • Following Bormann and Bauer (2010), observation error and their correlations have been estimated. • Desroziers diagnostic demonstrated misleading results for these data (obs error lower than the instrumental noise, low horizontal correlation° • Realistic observation error correlations were estimated using the background error method. • No/small inter-channel error correlations for temperature sounding channels • Strong inter-channel error correlations for tropospheric humidity sounding channels • No horizontal error correlation are considered because they appear small and are otherwise difficult to tune in conjonction with the channel correlation. • Focus on channel correlation (to be implemented in AROME)

More Related