Assimilation of IASI
reconstructed radiances
from Principal
Components in AROME
model
J. Andrey, V. Guidard, N. Fourrie
and J.-F. Mahfouf
CNRM-GAME (Météo-France and CNRS)
October 30th , 2015
Outline
1
Background
2
Methodology
3
Results
Differences between PCs and RAD IASI products
Assimilation of RAD and PCs in AROME model
4
Conclusions and future works
RR assimilation in AROME model | Andrey et al.
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Background
RR assimilation in AROME model | Andrey et al.
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Background
RR assimilation in AROME model | Andrey et al.
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Background
Why Principal Components Analysis (PCA) compression?
I
Use PCs instead of Radiances (RAD) => Use more information from
each observation
I
Data from future Infrared sounders will be disseminated in this format
I
Reduction of data dissemination volume by a factor around 50 (IASI
case)
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Basic ideas about PCA theory
I
PCA allows the reduction of the dimensionality of a problem by
examining the linear relationship between all the variables contained in a
multivariate dataset
I
The original set of correlated variables, y obs , is replaced a smaller
number of uncorrelated variables called principal components, x pcs . A
corresponds with the eigenvectors matrix:
x pcs = A ∗ y obs
I
To return to the original space it is only need to make the following
multiplication:
y pcs = AT ∗ x pcs
I
These new variables retain most of the information contained in the
original dataset (most of the gaussian noise is filtered):
y obs = A ∗ x pcs + residuals = y pcs + residuals
RR assimilation in AROME model | Andrey et al.
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Météo France AROME model
AROME is the operational convective-scale limited area Numerical Weather Prediction
(NWP) model used at Météo France
RR assimilation in AROME model | Andrey et al.
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Météo France AROME model
AROME is the operational convective-scale limited area Numerical Weather Prediction
(NWP) model used at Météo France
Geographical domain
RR assimilation in AROME model | Andrey et al.
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Météo France AROME model
AROME is the operational convective-scale limited area Numerical Weather Prediction
(NWP) model used at Météo France
Geographical domain
Up to 04/2015
Levels
60
Mesh grid
2.5 km
Model top
1 hPa
Assim. cycle
3h
RR assimilation in AROME model | Andrey et al.
4 / 13
Météo France AROME model
AROME is the operational convective-scale limited area Numerical Weather Prediction
(NWP) model used at Météo France
Geographical domain
⇒ In April 2015, a new version became
operational:
AROME
AROME-HR
Mesh grid
2.5 km
1.3 km
Assim. cycle
3h
1h
Levels
60
90
Model top
1 hPa
10 hPa
Up to 04/2015
Levels
60
Mesh grid
2.5 km
Model top
1 hPa
Assim. cycle
3h
RR assimilation in AROME model | Andrey et al.
4 / 13
Outline
1
Background
2
Methodology
3
Results
Differences between PCs and RAD IASI products
Assimilation of RAD and PCs in AROME model
4
Conclusions and future works
RR assimilation in AROME model | Andrey et al.
5 / 13
Standard deviation of PCs-RAD radiances differences
RR assimilation in AROME model | Andrey et al.
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Outline
1
Background
2
Methodology
3
Results
Differences between PCs and RAD IASI products
Assimilation of RAD and PCs in AROME model
4
Conclusions and future works
RR assimilation in AROME model | Andrey et al.
7 / 13
Impact of PCs assimilation: experiments description
AROME
Operational configuration of AROME model
1. B58N: Reference, assimilation of radiances
2. B5AJ: Experiment, assimilation of RR
Assimilation cycle: 3 hr, mesh grid: 2.5 km,
levels: 60
Time window: 20141108 to 201411208
RR assimilation in AROME model | Andrey et al.
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Impact of PCs assimilation: experiments description
AROME
Operational configuration of AROME model
1. B58N: Reference, assimilation of radiances
2. B5AJ: Experiment, assimilation of RR
Assimilation cycle: 3 hr, mesh grid: 2.5 km,
levels: 60
Time window: 20141108 to 201411208
AROME-HR reduced version
Operational configuration of AROME-HR model
1. B59V: Reference, IASI rad. from RAD
2. B5CH: Experiment, IASI rad. from PCs
Assimilation cycle: 3 hr, mesh grid: 1.3 km,
levels: 90
Time window: 20150616 to 20150716
RR assimilation in AROME model | Andrey et al.
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AROME Obs. assim. statistics: T from radiosoundings
5.0
10.0
20.0
30.0
50.0
70.0
100.0
150.0
200.0
250.0
300.0
400.0
500.0
700.0
850.0
1000.0
0
1
2
3
Std.Dev.
RR assimilation in AROME model | Andrey et al.
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5
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exp-ref
nobsexp
-1
0
0
0
0
0
0
0
0
1
0
0
0
0
4
-2
1261
4746
4542
4941
4991
5247
5726
6101
7062
6646
8008
9140
12121
12391
13477
4513
ref FG departure
exp FG departure
ref AN departure
exp AN departure
5.0
10.0
20.0
30.0
50.0
70.0
100.0
150.0
200.0
250.0
300.0
400.0
500.0
700.0
850.0
1000.0
-1 -0.5 0 0.5 1 1.5 2
Bias
Pressure [hPa]
Pressure [hPa]
B5AJ (exp) vs B58N (ref), 2014110803-2014120821 (03)
TEMP-T N.Hemis
Used T
AROME-HR Obs. assim. statistics: T from radiosoundings
5.0
10.0
20.0
30.0
50.0
70.0
100.0
150.0
200.0
250.0
300.0
400.0
500.0
700.0
850.0
1000.0
0
1
2
3
Std.Dev.
RR assimilation in AROME model | Andrey et al.
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5
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exp-ref
nobsexp
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
152
207
201
115
85
129
106
86
45
79
91
136
47
24
4
ref FG departure
exp FG departure
ref AN departure
exp AN departure
5.0
10.0
20.0
30.0
50.0
70.0
100.0
150.0
200.0
250.0
300.0
400.0
500.0
700.0
850.0
1000.0
-3
-2
-1
0 1
Bias
2
3
Pressure [hPa]
Pressure [hPa]
B5CH (exp) vs B59V (ref), 2015061603-2015063021 (03)
TEMP-T N.Hemis
Used T
AROME Differences in forecast fields - T at 800 hPa, RH at 2 m
20141117 21:00 + 00h, forecast analysis fields
RR assimilation in AROME model | Andrey et al.
11 / 13
AROME Differences in forecast fields - T at 800 hPa, RH at 2 m
20141117 21:00 + 00h, forecast analysis fields
RR assimilation in AROME model | Andrey et al.
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Conclusions and open questions
1. PCs-RAD radiance differences
I
Small PCs-RAD differences in the radiance space and first two bands in the
BT space. Differences up to 4 K in Band 3
I
Differences in BT space: High and very high clouds, O3 B1 and CO2 B3 (not
shown), IASI band 3. Not a problem for the moment
2. Assimilation of PCs in precedent AROME model
I
I
Neutral impact on other (not IASI) observation assimilation statistics
Neutral impact on forecast fields
3. Some open questions
I
Finish quantification the impact of using RR in AROME-HR, with a lower
model top (Experiments currently running)
Can we improve NWP forecasts using RR from IASI band 3?
I
Direct assimilation of PCs instead of RR (RTTOV version 11.3)
I
RR assimilation in AROME model | Andrey et al.
12 / 13
Conclusions and open questions
1. PCs-RAD radiance differences
I
Small PCs-RAD differences in the radiance space and first two bands in the
BT space. Differences up to 4 K in Band 3
I
Differences in BT space: High and very high clouds, O3 B1 and CO2 B3 (not
shown), IASI band 3. Not a problem for the moment
2. Assimilation of PCs in precedent AROME model
I
I
Neutral impact on other (not IASI) observation assimilation statistics
Neutral impact on forecast fields
3. Some open questions
I
Finish quantification the impact of using RR in AROME-HR, with a lower
model top (Experiments currently running)
Can we improve NWP forecasts using RR from IASI band 3?
I
Direct assimilation of PCs instead of RR (RTTOV version 11.3)
I
RR assimilation in AROME model | Andrey et al.
12 / 13
Conclusions and open questions
1. PCs-RAD radiance differences
I
Small PCs-RAD differences in the radiance space and first two bands in the
BT space. Differences up to 4 K in Band 3
I
Differences in BT space: High and very high clouds, O3 B1 and CO2 B3 (not
shown), IASI band 3. Not a problem for the moment
2. Assimilation of PCs in precedent AROME model
I
I
Neutral impact on other (not IASI) observation assimilation statistics
Neutral impact on forecast fields
3. Some open questions
I
Finish quantification the impact of using RR in AROME-HR, with a lower
model top (Experiments currently running)
Can we improve NWP forecasts using RR from IASI band 3?
I
Direct assimilation of PCs instead of RR (RTTOV version 11.3)
I
RR assimilation in AROME model | Andrey et al.
12 / 13
Thank you for your attention