Transcript MSG data assimilation to evaluate the potential of MTG

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
Conclusions and Future work
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
AROME WMED domain
•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 …
1. SEVIRI assimilation experiments
Overview of SEVIRI assimilation in AROME-WMED
SEVIRI WV 6.2 observations
Assimilated vs Rejected
17/10/2011 - 0UTC
Horizontal R. 4km
Thinning 70km
Repeat cycle 15
min
Analysis every 3h
Information
Humidity
(±400 hPa)
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
Ana-5
Ana-10
Ana-20
Ana-40
Ana-70
Ana-100
F3h-5
F3h-10
F3h-20
F3h-40
F3h-70
F3h-100
Current OPER
Evaluation :
1) Analysis Increments
2) Forecast verification using independent observations (IASI, radiosondes …)
1. SEVIRI assimilation experiments
Analysis increments
Analysis – Background
specific humidity (630 hPa)
70 km
17/10/2011, 0UTC
moisture
moisture
• Increments show similar but
sharper structures in EXP10 than
EXP70.
10 km
1. SEVIRI assimilation experiments
Analysis increments
Analysis – Background
specific humidity (Cross-section)
70 km
17/10/2011, 0UTC
moisture
moisture
• Increments show similar but
sharper structures in EXP10 than
EXP70.
• Wrong propagation toward the
surface ?
10 km
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
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
RMS
STD
The RMS indicates a degradation of the F3h if SEVIRI is assimilated at very high density (5 and 10 km)
1. SEVIRI assimilation experiments
Forecast Verification
F3h vs radiosondes
Fg-departures (8 days)
• Scores for the fit to IASI observations :
NEGATIVE >> POSITIVE
(from 17/10 – 0h to 24/10 – 21h 2011)
• 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.
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 :
1)data thinning
Reduce the amount of used obs
1)inflated diagonal R matrix
Reduce the weigth of obs in the analysis
Uncorrelated
Sub-optimal
optimal
(Dando et al., 2007; Collard and McNally, 2009)
Separation distance (km)
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)
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
Separation distance (km)
Sigma O² = 0.13
Sigma B² = 1.32
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)
2. Diagnosis of error correlations
Estimate of observation errors
Hollingsworth/Lönnberg and Desroziers diagnostic
Obs. error estimates :
H/L
Desroziers
Sigma O
0.36 K
0.31 K
Sigma B
1.14 K
1.15 K
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)
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
Estimation from IASI observations
Inter-channel observation error correlations
T channels
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
Q channels
Desroziers
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
L0.2 Humidity ~ 25 km
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)