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Enhancing Radar Data Assimilation in AROME Model at 1.3 km Resolution

Explore the impact and benefits of higher-density radar assimilation in the operational AROME model, enhancing forecast accuracy and resolution. Learn about experiments, diagnostics, and future prospects.

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Enhancing Radar Data Assimilation in AROME Model at 1.3 km Resolution

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  1. Higher density radar assimilation in the operational AROME model at 1.3 km horizontal resolution Eric Wattrelot, Thibaut Montmerle and Jean-François Mahfouf CNRM-GAME (Météo-France/CNRS) 6th WMO Workshop on the Impact of Various Observing Systems on NWP Shanghaï (China) – May 2016 1

  2. Outline • Introduction • Radar assimilation in the 3D-Var Arome • Preliminary experiments of increased density of the radar data assimilated 2.What can we learn from a posteriori diagnostics? 3. Assimilation experiments of increased density 4. Conclusion and prospect

  3. AROME • Simulation of reflectivity and radial wind at the observation location • 1D Bayesian inversion of Z into relative humidity profiles OBS  AROME • Computations of geometrical caracteristics • radar data filters • Radar observations considered as profiles in the model Minimisation Guess Analysis Forecast t0+1h Current radar assimilation: summary C Band • All elevations (cartesian/polar) in BUFR gathered for each radar S Band X Band Wattrelot et al. 2014, Montmerle et Faccani, 2009, et Caumont et al. 2011

  4. Data usage in the AROME 3D-Var system Monthly averaged number of data used in AROME RADAR RADIAL WIND RADAR RELATIVE HUMIDITY • November 2008 : AROME becomes operational including radar radial winds • Spring 2010 : Operational assimilation of radar reflectivities • Autumn 2010 : Improved assimilation of « no rain » information from reflectivities • Spring 2015: High horizontal resolution model (1.3 km AROME) • Autumn 2015: High density of radar observations (8th December 2015)

  5. Data usage in the AROME 3D-Var system After 8December 2015 - Number of assimilated observations Large variability according to rainy or little rainy situations RADARS (Vr) RADARS (Z) RADARS (Z) RADARS (Vr)

  6. Better representativity of the Arome model at 1.3 • Resolution 1.3 km (against 2.5 km before) and 90 vertical levels ( against 60): regular increase of resolution with altitude Statistics on convective cells (number and size, 41 dBZ threshold) Convective cells in 1.3 km are more realistic (closer to the radar) 1.3 km: increase of the small cells and decrease of larger cells

  7. Various horizontal thinning in Arome at 2.5 km: consistency with departures ___ « 8 km » radar density ___ « 15 km » radar density After two months of cycling: systematic degradation of the « 8 km » experiment

  8. Radar 8 km Radar 8 km Radar 8 km 09 UTC 12 UTC 15 UTC 3h forecast 3h forecast Various thinning in Arome at 1.3 km (3-hour cycling): consistency with departures July 2013 ** Radar 15 km Radar 15 km Radar 15 km 09 UTC 12 UTC 15 UTC « 15 km » radar density 3h forecast 3h forecast « 15 km » radar density with first-guess from cycled « 8 km » +3% increase Tuning of sigmao with Desroziers diagnostic : 0.67

  9. Application at the hourly cycling Same tuning of sigmao than for the 3-hour cycling (although empirical tuning of sigmab) Long-term forecast scores: neutral over 3 months (summer 2014) for wind gusts and RR 6h Brier Skill Scores The more the score is close to 1, better is the score Regional average of « Brier-Skill » scores (Amodei et al. 2015) for wind gusts and 6-hour cumulated precipitations. The score is an average of 4 forecast ranges between 6 and 24h for the thresholds 0.5, 2 and 5 mm for the rain rates and 40 km/h for the 1-hour wind gusts. ___ « 8 km » radar density ___ « 15 km » radar density

  10. Hourly cycing: better at short-term Improvement up to 12h forecast range, slight degradation after… Forecast range 18h 24h ___ « 8 km » radar density ___ « 15 km » radar density 6h 12h Threshold Threshold

  11. 1-hour cycle – 1.3 km Arome forecasts valid at 09h TU for the 2014/09/19: reflectivity field at 700 hpa for both Arome model images (left and middle) 8 km 15 km Radar 1-hour cycle –(1.3 km) – 6-hour cumulated precipitations Rain Gauges 8 km 15 km Cumulated rain over 100 mm on the Lozère and Ardèche: much better forecasted with high-density radar data

  12. J = ½ (x– xb)T.B-1.(x– xb) + ½ (y - H (xb))T. R-1.(y - H (xb)) • Observ. error e0 = y – H(xt) = (y – yt) + ( yt – H(xt) ) v v v y = obs and xt model truth at model resolution Instrumental error • Obs. operator • Model resolution • Quality control… Obs. error R matrix: definition and effects • R = E(e0. e0T) describes errors in observations as well as the forward model (representativity error) Role of observation error covariances • Could the obs. error be better specified? Knowing that the analysis can be worse than the background if specified sigmaotoo small • What (useful) information can provide a posteriori estimates of observation error and error spatial correlations (limitation of density or sigmao inflation or correlation specification needed ?

  13. Estimates of obs. error correlations • Research of reliable estimate of HBHT by Ensemble-based data assimilation (90 members): the background-error method 2. Direct estimate of R by Desroziers method Horizontal correlations Along-beam correlations

  14. Eij => ~ H Btrue HT R~ E(db , dbT) -Eij =>R~E(da , dbT) Estimates of (along-beam) obs. error correlations Estimates of spatial correlations in AROME 2.5 km resolution Eij = H(xk,ib - xib ) (H(xk,jb - xjb ))T E(da , dbT) = (I-HK)E(db , dbT) = R(R + H B HT )-1(Rtrue + H Btrue HT) (Reliable method if the system is optimally specified)

  15. Estimates of (along-beam) obs. error correlations All correlations estimates (by both methods) decrease between 2.5 km resolution and 1.3 km resolution : representativity error • Both methods give similar estimates of HBHT (green) • Large different estimates of obs. error correlations between the methods (red) • Very few obs. error correlations (estimated by Desroziers) • Iterative method to estimate obs. error standard deviation doesn’t converge (not shown)

  16. Comparison between along-beam and horizontal obs. error correlations No large differences between horizontal and along-beam obs. error correlations (by Desroziers method) Comparison with similar computings at Met Office: • Estimates quite similar to those computed by Waller et al. 2015, for along-beam obs. error correlations • But quite different estimates for horizontal obs. error correlations)

  17. Estimates of an additional diagnostic : lag-innovation correlation To computeE[dn+1dnT](lag-innovations correlation): We know that the innovations used for the analysis n : dn = yon- Hn (xbn) = yon- Hn (xtruen) - Hk (xbn - xtruen) ~ eon- Hnebn = dn and dn+1 = yon+1– Hn+1 Mn+1 (xan ) = yon+1– Hn+1 (Mn+1 (xbn + Kndn )) dn+1 ~ eon+1 - Hn+1Mn+1(ebn+ Kndn) if model error is neglected E[dn+1dnT] ~ Hn+1Mn+1( Btruen HnT - Kn E[dndnT] ) if E[ eon+1 eonT]= E[ eon ebnT ]=0 ~ Hn+1Mn+1( Btruen HnT E[dndnT] –1 - Kn) E[dndnT] E[dn+1dnT] ~ HkMk( Ktruen- Kn) E[dndnT] E[dn+1dnT] = 0: additional diagnostic of optimality (Daley 1992, Ménard 2015).

  18. Estimates of an additional diagnostic : lag-innovation correlation Non-zero lag-innovations correlations => The Kalman gain is not optimal: Ktruen # Kn (Desroziers’s method is likely not reliable)

  19. Lo = 41.7 km Lo = 25 km Estimates of obs. error correlations Estimation of the lengthscale of obs. correlations use of (bigger) estimate by background-error method • Following Liu and Rabier, 2002, 8/10 km of range thinning (~ Lo/2) appears to be a strong limit… • to keep an inflation to account for the negative impact of obs. error correlations • consistent with negative impact of still reducing sigmao

  20. Assimilation experiments of increased density New experimental design: additional tuning (of the 1D) based on the analysis of very close analyses increments in case of asymmetric departures (if background doesn’t produce rain: difficulty to create precipitation): no value-added of high density Analyzed reflectivity Observation Experimental revised set-up (possible under new parallel computing facilities): • HD_RAD_TUN : High radar density + revised set-up + tuned sigmao • LD_RAD_TUN : Low radar density + revised set-up + tuned sigmao

  21. Effect on long term forecast scores Much better scores over long period (with better QPF at longer term up to 24h) Significant BSS for the high threshold 10 mm in 6 hours (computed against Quantitative Precipitation Estimation**) ** QPE computed by a blended radar and raingauges data ___ HD_RAD_TUN ___ Operational AROME (before December 2015) ___ HD_RAD_TUN ___ LD_RAD_TUN

  22. Triggering of the convection in the 1-hour forecasts… radar LD_RAD_TUN P1 – r6 P1 – r7 P1 – r8 Triggering of the convective system in HD_RAD_TUN P1 – r6 P1 – r7 P1 – r8

  23. Illustration on a flash flood event 03 October 2015

  24. Flash flood event: 1-hour QPF (12-hour lead time forecast)/ QPE 106 mm in one hour 175 in two hours Much better hourly rainrate in HD_RAD_TUN and better stability between successive cycles

  25. Conclusion A posteriori diagnostics indicate: • from 2.5 km at 1.3 km, representativity error decreases the length of obs. correlation at 1.3 km • But estimates still between 10 and 25 km of obs. error correlation length (No evidence of reliable Des. Method: the Kalman gain K not optimally specified) • Limitations to increase horizontal thinning up to 8 km (sigmao inflation needed) Experiments of increased density • Use of a revised set-up of radar assimilation: mainly QPF scores improved (up to 24 hour lead time forecast) and better forecast of convective cases • Introduction in the latest suite of AROME (December 2015): better capabiblity to forecast heavy precipitating events, less variability between hourly cycles

  26. Prospect Further: • Better understanding of reliability of a posteriori diagnostics • Implementation and evaluate the impact of specifying obs. error correlations Other radar activities: • Introduction of the precipitating hydrometeors in the control variable of the variational assimilation: useful to directly assimilate both reflectivity and observations from dual-pol radars • revision of the backscattering of reflectivity observator (« look-up » tables: in particular to better use of reflectiviy from X-band radar and DPOL variables) • Very soon use of EUMETNET/OPERA radar data… a big challenge because of different preprocessing and quality of raw radar data: the last OPERA4 phase is providing a good step to allow radar data to be used by NWP

  27. Thank you for attention… 6th WMO Workshop on the Impact of Various Observing Systems on NWP Shanghaï (China) – May 2016

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