1. Higher density radar assimilation in the operational AROME model at 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 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
Followed by results from pragmatic experiments which have been performed to progressively increase the density of assimilated radar data.
correlations pour le Vr et non le Hu

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

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) 4

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

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)
1.3 km: increase of the small cells and decrease of larger cells
Convective cells in 1.3 km are more realistic (closer to the radar)

7. Various horizontal thinning in Arome at 2
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. Various thinning in Arome at 1
Various thinning in Arome at 1.3 km (3-hour cycling): consistency with departures
09 UTC
12 UTC
15 UTC
3h forecast
Radar 8 km
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 ) 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. Obs. error R matrix: definition and effects
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) )
y = obs and xt model truth at model resolution
Instrumental error
Obs. operator
Model resolution
Quality control… v
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 sigmao too 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 ?
The lack of marked improvements can be linked to the role of obs. error correlations

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. 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
=> ~
H Btrue HT
Eij
R ~ E(db , dbT) - Eij
E(da , dbT) = (I-HK) E(db , dbT)
= R (R + H B HT )-1(Rtrue + H Btrue HT)
=> R ~ E(da , dbT)
(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)
All estimates decrease with resolution (attention to scale).

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)
All estimates decrease with resolution (attention to scale).

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

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. 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
Lo = 25 km
Lo = 41.7 km

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
Observation
Analyzed reflectivity
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 convective system in HD_RAD_TUN
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