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

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

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 … Context Potential of MTG for Convective scale NWP models AROME WMED domain

SEVIRI WV 6.2 observations Assimilated vs Rejected 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) 17/10/ UTC

1. SEVIRI assimilation experiments High-density assimilation experiments : configurations (No-cycled) BACKGROUND (with all observations previously assimilated) 70 km40 km20 km10 km5 km100 km Assimilation of SEVIRI WV only Thinning distances Evaluation : 1)Analysis Increments 2)Forecast verification using independent observations (IASI, radiosondes …) Ana-70Ana-40Ana-20Ana-10Ana-5Ana-100 F3h-70F3h-40F3h-20F3h-10F3h-5F3h-100 Current OPER

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

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

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.

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

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 ?

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.

Liu and Rabier (2002) and Desroziers (2011) : Separation distance (km)  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 : 1)data thinning  Reduce the amount of used obs 1)inflated diagonal R matrix  Reduce the weigth of obs in the analysis (Dando et al., 2007; Collard and McNally, 2009) Uncorrelated Sub-optimal optimal 2. Diagnosis of error correlations Motivations

DATA : First-Guess or analysis departures from pair of SEVIRI WV6.2 observations Binning interval =20 km Period : 30 October (8 cycles – radiances) METHODS : A priori Hollingsworth/Lönnberg (1986) Background ensemble method (Bormann and Bauer, 2010) A posteriori Desroziers diagnostic (Desroziers et al., 2003) 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.

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. Sigma O² = 0.13 Sigma B² = 1.32 Cov(FGd) = HBH T +R Separation distance (km) 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 Separation distance (km) 2. Diagnosis of error correlations Estimate of observation errors Sigma O² = 0.10 Sigma B² = 1.33

Hollingsworth/Lönnberg and Desroziers diagnostic Obs. error estimates : PROBLEM : Radiometric error estimate = 0.75K 1)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) 2. Diagnosis of error correlations Estimate of observation errors H/LDesroziers Sigma O0.36 K0.31 K Sigma B1.14 K1.15 K

Estimation from IASI observations Observation error amplitude (sigma O) 55 T channels 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 DATA: IASI clear radiances 15 days (01/09-15/09) Domain: AROME-WMED

Estimation from IASI observations T channels Desroziers Q channels Inter-channel observation error correlations Desroziers 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

Temperature Surface Humidity L 0.2 Humidity ~ 25 km Estimation from SEVIRI observations Horizontal observation error correlations DATA: SEVIRI clear radiances (full resolution) 15 days (01/09-15/09) Domain: AROME-WMED

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)