Ensemble assimilation & prediction at Mto-France Lok Berre
& Laurent Descamps Slide 2 Outline 1. Ensemble assimilation (L.
Berre) to provide flow-dependent B. 2. Ensemble prediction (L.
Descamps) for probabilistic forecasting. Slide 3 Part 1: Ensemble
assimilation Lok Berre, Grald Desroziers, Laure Raynaud, Olivier
Pannekoucke, Bernard Chapnik, Simona Stefanescu, Benedikt Strajnar,
Rachida El Ouaraini, Pierre Brousseau, Rmi Montroty Slide 4
Reference (unperturbed) assimilation cycle and the cycling of its
errors (e.g. Ehrendorfer 2006 ; Berre et al 2006) ea = (I-KH) eb +
K eo eb = M ea + em Idea: simulate the error cycling of this
reference system with an ensemble of perturbed assimilations ? K =
reference gain matrix (possibly hybrid) Slide 5 Observation
perturbations are explicit, while background perturbations are
implicit (but effective). (Houtekamer et al 1996; Fisher 2003 ;
Ehrendorfer 2006 ; Berre et al 2006) An ensemble of perturbed
assimilations : to simulate the error evolution (of a reference
(unperturbed) assimilation cycle) Flow-dependent B Slide 6
Strategies to model/filter ensemble B and link with ensemble size
Two usual extreme approaches for modelling B in Var/EnKF: Var:
correlations are often globally averaged (spatially). robust with a
very small ensemble, but lacks heterogeneity completely. EnKF:
correlations and variances are often purely local. a lot of
geographical variations potentially, but requires a rather large
ensemble, & it ignores the spatial coherence (structure) of
local covariances. An attractive compromise is to calculate local
spatial averages of covariances, to obtain a robust flow-dependence
with a small ensemble, and to account for the spatial coherence of
local covariances. Slide 7 A real time assimilation ensemble 6
global members T359 L60 with 3D-Fgat (Arpge). Spatial filtering of
error variances, to further increase the sample size and robustness
(~90%). A double suite uses these bs of the day in 4D-Var.
operational within 2008. Coupling with six LAM members during two
seasons of two weeks, with both Aladin (10 km) and Arome (2.5 km).
Slide 8 RAW b ENS #1 Large scale structures look similar & well
connected to the flow ! Optimize further the estimation, by
accounting for spatial structures (of signal & noise). ONE
EXAMPLE OF RAW b MAPS (Vor, 500 hPa) FROM TWO INDEPENDENT 3-MEMBER
ENSEMBLES RAW b ENS #2 Slide 9 INCREASE OF SAMPLE SIZE BY LOCAL
SPATIAL AVERAGING: CONCEPT Idea: MULTIPLY(!) the ensemble size Ne
by a number Ng of gridpoint samples. If Ne=6, then the total sample
size is Ne x Ng = 54. The 6-member filtered estimate is as accurate
as a 54-member raw estimate, under a local homogeneity asumption.
Ng=9 latitude longitude Slide 10 INCREASE OF SAMPLE SIZE BY LOCAL
SPATIAL AVERAGING: OPTIMAL ESTIMATE FORMALISM & IMPLEMENTATION
b * ~ b with = signal / (signal+noise) Apply the classical BLUE
optimal equation (as in data assim), with a filter accounting for
spatial structures of signal and noise: is a low-pass filter (as K
in data assim). ( see Laure Raynauds talk also ! ) Slide 11 RESULTS
OF THE FILTERING b ENS 2 RAW b ENS 2 FILTERED b ENS 1 FILTERED b
ENS 1 RAW Slide 12 Ensemble dispersion: large sigmab over France
Mean sea level pressure : storm over France Connection between
large sigmab and intense weather ( 08/12/2006, 03-06UTC ) Slide 13
Colours: sigmab field Purple isolines : mean sea level pressure
Connection between large sigmab and intense weather ( 15/02/2008,
12UTC ) Large sigmab near the tropical cyclone Slide 14 Validation
of ensemble sigmabs of the day HIRS 7 (28/08/2006 00h) Ensemble
sigmabs Observed sigmabs cov( H dx, dy ) ~ H B H T (Desroziers et
al 2005) => model error estimation. Slide 15 REDUCTION OF
NORTHERN AMERICA AVERAGE GEOPOTENTIAL RMSE WHEN USING SIGMABs OF
THE DAY Height (hPa) Forecast range (hours) _____ = NOV 2006 - JAN
2007 (3 months)FEB - MARCH 2008 (1 month) SEPT - OCT 2007 (1 month)
Slide 16 +24h 500 hPa WIND RMSE over EUROPE ( bs of the day versus
climatological bs ) Reduction of RMSE peaks (intense weather
systems) Slide 17 Wavelet filtering of correlations of the day Raw
length-scales (6 members) (Pannekoucke, Berre and Desroziers, 2007
; Deckmyn and Berre 2005) Wavelet length-scales (6 members) ( see
Olivier Pannekouckes talk also ! ) Slide 18 LAM ensemble (Arome) :
seasonal dependence of correlations _____ anticyclonic winter - - -
- - convective summer (Desroziers et al, 2007) Slide 19 Conclusions
of Part 1 A 6-member ensemble assimilation in real time (double
suite). flow-dependent sigmabs of the day . flow-dependent sigmabs
of the day . operational within 2008. operational within 2008.
Spatial filtering of sigmabs strengthens their robustness. later
extension to correlations of the day (spectral/wavelet), later
extension to correlations of the day (spectral/wavelet), and to
high-resolution LAMs. Comparisons with innovation diagnostics and
impact experiments are encouraging. Use innovations to estimate
model errors. Applications for ensemble prediction too: see PART 2
by Laurent Descamps.
