Recent developments at ECMWF
Working Group in Diagnostic: structure and role
1. Over-sight of collaborative projects
• Diagnosis of existing assimilation or
forecasting problems
• Diagnosis of major new model cycles
2. Strategic coordination of diagnostic
developments
• Highlighting opportunities for new
diagnostic tools of common interest
• Making existing diagnostic tools more
widely usable
• Ensuring sufficient computing and
storage resources for diagnostics
3. Across-section communication of
information and results
• Using a central diagnostics web page
• By coordination with the special topics
of the OD/RD meeting
• With seminars on tools and
collaborative projects
Carla Cardinali
DAOS –TORPEX meeting
Exeter June 2011
Satellite
Data
Data
Assimilatio
n
Ocean
WGD
Predictabil
ity
Joint Projects
Common Tools
Reanalysis
Communicatio
n
Met.Ops
Numerics
Physics
1
Trouble shooting: Spring scores investigation
•
Diagnostics and investigation are underway to address the issue of a number “of”
busts in Spring.
pilot
FORECAST SENSITIVITY TO OBSERVATION [] (Used)
Data Period = 2011-04-01 09 - 2011-04-15 09
EXP = 0052, Level = 0.00 - 400.00 hPa
Min: -13.5776
Max: 81.6133
Mean: -0.252669
pilot
150°W
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
airep
150°E
150°W
60°N
60°N
30°N
30°N
0°N
0°N
30°S
30°S
60°S
60°S
150°W
120°W
temp
150°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
150°E
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
0°N
30°S
30°S
60°S
60°S
0°E
30°E
60°E
90°E
120°E
0°E
30°E
60°E
90°E
120°E
150°E
30°N
30°N
0°N
0°N
30°S
30°S
60°S
60°S
150°W
150°W
0°N
30°W
30°W
60°N
150°E
30°N
60°W
60°W
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
79.41
4.29
3.82
3.34
2.86
2.39
1.91
1.43
0.95
0.48
0.00
-0.00
-0.48
-0.95
-1.43
-1.91
-2.39
-2.86
-3.34
-3.82
-265.13
150°E
amv
30°N
90°W
90°W
Statistics for uspeed from 999
FORECAST SENSITIVITY TO OBSERVATION [] (Used)
Data Period = 2011-04-01 09 - 2011-04-15 09
EXP = 0052, Level = 0.00 - 400.00 hPa
Min: -74.3849
Max: 133.062
Mean: -0.315936
60°N
120°W
120°W
60°N
Statistics for uspeed from 99/2099
FORECAST SENSITIVITY TO OBSERVATION [] (Used)
Data Period = 2011-04-01 09 - 2011-04-15 09
EXP = 0052, Level = 0.00 - 400.00 hPa
Min: -41.22
Max: 17.0553
Mean: -0.328265
60°N
150°W
81.61
2.85
2.53
2.21
1.90
1.58
1.26
0.95
0.63
0.32
0.00
-0.00
-0.32
-0.63
-0.95
-1.26
-1.58
-1.90
-2.21
-2.53
-13.58
Statistics for uspeed from 99/2099
FORECAST SENSITIVITY TO OBSERVATION [] (Used)
Data Period = 2011-04-01 09 - 2011-04-15 09
EXP = 0052, Level = 0.00 - 400.00 hPa
Min: -265.13
Max: 79.4122
Mean: -0.274434
150°E
17.06
2.91
2.58
2.26
1.94
1.62
1.29
0.97
0.65
0.32
0.00
-0.00
-0.32
-0.65
-0.97
-1.29
-1.62
-1.94
-2.26
-2.58
-41.22
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
150°E
60°N
60°N
30°N
30°N
0°N
0°N
30°S
30°S
60°S
60°S
150°W
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
150°E
133.06
2.35
2.09
1.83
1.57
1.31
1.05
0.78
0.52
0.26
0.00
-0.00
-0.26
-0.52
-0.78
-1.05
-1.31
-1.57
-1.83
-2.09
-74.38
pilot
MEAN FIRST GUESS DEPARTURE (OBS-FG) [m/s] (Used)
Data Period = 2011-04-01 09 - 2011-04-15 09
EXP = 0052, Level = 0.00 - 400.00 hPa
Min: -16.1924
Max: 9.62379
Mean: -0.156835
pilot
150°W
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
airep
150°E
150°W
60°N
60°N
30°N
30°N
0°N
0°N
30°S
30°S
60°S
60°S
150°W
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
150°E
9.62
1.00
0.89
0.78
0.67
0.56
0.45
0.33
0.22
0.11
0.00
-0.00
-0.11
-0.22
-0.33
-0.45
-0.56
-0.67
-0.78
-0.89
-16.19
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
30°N
30°N
0°N
0°N
30°S
30°S
60°S
60°S
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
30°W
0°E
30°E
60°E
90°E
120°E
150°E
30°N
30°N
0°N
0°N
30°S
30°S
60°S
60°S
150°W
150°W
60°N
120°W
60°W
60°N
amv
150°E
60°N
150°W
90°W
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
150°E
12.50
1.18
1.05
0.92
0.79
0.66
0.52
0.39
0.26
0.13
0.00
-0.00
-0.13
-0.26
-0.39
-0.52
-0.66
-0.79
-0.92
-1.05
-10.75
Statistics for uspeed from 999
MEAN FIRST GUESS DEPARTURE (OBS-FG) [m/s] (Used)
Data Period = 2011-04-01 09 - 2011-04-15 09
EXP = 0052, Level = 0.00 - 400.00 hPa
Min: -10.09
Max: 8.40628
Mean: 0.0249993
temp
120°W
120°W
60°N
Statistics for uspeed from 99/2099
MEAN FIRST GUESS DEPARTURE (OBS-FG) [m/s] (Used)
Data Period = 2011-04-01 09 - 2011-04-15 09
EXP = 0052, Level = 0.00 - 400.00 hPa
Min: -2.94923
Max: 3.94669
Mean: 0.0509037
150°W
Statistics for uspeed from 99/2099
MEAN FIRST GUESS DEPARTURE (OBS-FG) [m/s] (Used)
Data Period = 2011-04-01 09 - 2011-04-15 09
EXP = 0052, Level = 0.00 - 400.00 hPa
Min: -10.7474
Max: 12.4996
Mean: 0.148039
150°E
3.95
1.07
0.95
0.83
0.71
0.59
0.47
0.36
0.24
0.12
0.00
-0.00
-0.12
-0.24
-0.36
-0.47
-0.59
-0.71
-0.83
-0.95
-2.95
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
150°E
60°N
60°N
30°N
30°N
0°N
0°N
30°S
30°S
60°S
60°S
150°W
120°W
90°W
60°W
30°W
0°E
30°E
60°E
90°E
120°E
150°E
8.41
1.43
1.27
1.11
0.95
0.79
0.64
0.48
0.32
0.16
0.00
-0.00
-0.16
-0.32
-0.48
-0.64
-0.79
-0.95
-1.11
-1.27
-10.09
Statistics for RADIANCES from AMSUA
Channel =5, Used data [ time step = 12 hours ]
Area: lon_w= 140.0, lon_e= 240.0, lat_s= 20.0, lat_n= 75.0 (over All_surfaces)
EXP = 0052
Statistics for RADIANCES from AMSUA
Channel = 5 [ time step = 12 hours ]
FORECAST SENSITIVITY TO OBSERVATION [j/kg], Used
EXP = 0052, AREA = 30N - 60N
Min: -12.2017
Max: 10.4202
Mean: -0.174648
FSO
15
0.00
[K ]
-0.12
13
-0.25
-0.37
-0.49
11
1
3
5
7
9
11
13
15
9
11
13
15
9
11
13
15
2011
OBS-FG
10.42
3.83
9
3.40
0.0658
2.55
2.13
0.0470
[K ]
Time
2.98
0.0282
1.70
7
1.28
0.0094
0.85
0.43
-0.0094
0.00
5
1
3
5
7
-0.00
2011
-0.43
n_used
-0.85
9000
-1.28
-1.70
3
7200
-2.13
-2.55
-2.98
-3.40
Number
Apr
OBS-AN
5400
3600
-12.20
1
-160
-80
0
Longitudes
80
160
1800
0
1
The largest degradation follow
the amplification of the ridge
3
5
7
2011
Land surface data assimilation evolution
Patricia de Rosnay et al
1999
2004
2009
2010/2011
Optimum Interpolation (OI)
Revised snow analysis
Structure Surface Analysis:
Screen Level Analysis
Drusch et al. (2004)
•
Douville et al. (2000)
Cressman snow depth analysis
using SYNOP data improved
•
by using NOAA / NSEDIS Snow
cover extend data
Mahfouf et al. (2000)
Soil moisture analysis based
on Temperature and relative
humidity analysis
OI snow analysis and high
resolution NESDIS data (4km)
SEKF Soil Moisture analysis
Simplified Extended Kalman Filter
METOP-ASCAT
SMOS
De Rosnay et al., ECMWF NewsLetter, 2011
Sabater et al., ECMWF NewsLetter, 2011
Recent developments/implementations:
• SEKF surface analysis
• Use of active microwave data: ASCAT soil moisture product
• Use of passive microwave SMOS Brightness Temperature product
• New snow analysis and use of NOAA/NESDIS 4km snow cover product
EKF soil moisture analysis
For each grid point, Analysed soil moisture state vector θa:
θa(t) = θb(t) + K (y(t)-H θb(t))
θb Background soil moisture state vector
Dimension Nx (=3, for the top three layers analysed),
y Observation vector ,
Dimension Ny (=2 when T2m and Rh2m are used)
H Jacobian matrix of the observation operator
Estimated in finite differences (perturbed simulations)
Dimension Nx raws, Ny columns
K Kalman gain matrix, fn of H and covariance matrix of
background Bg (Nx . Nx) and observation R (Ny.Ny) errors.
Relevant Observations:
• Used in operations: Conventional observations (T2m,
RH2m)
• Used in research: ASCAT Soil Moisture
• Under development: SMOS Brightness temperature
EKF corrects the trajectory
of the
Land Surface Model
ECMWF Soil Moisture Analysis verification
Albergel Clement
-
Validated for several sites across Europe (Italy, France, Spain, Belgium)
Validation results in France Dec 2008- Dec 2009
Verification of ECMWF SM over the SMOSMANIA Network
Snow analysis
Snow analysis uses SYNOP snow depth data and
NOAA/NESDIS IMS snow cover
2010
implementation:
- New Snow analysis based on the Optimum
Interpolation with Brasnett 1999 structure functions
-A new IMS 4km snow cover product to replace the
24km product
-Improved QC (monitoring, Blacklisting)
2011:
- Assimilate additional snow data
From Sweden (New Report Type)
SYNOPdata
cm
New Surface data
Direct 4D-Var assimilation of NCEP Stage IV rain data
(Lopez 2011, MWR, in press)
Ingredients:
• Data: NCEP Stage IV radar + gauge precipitation product (4-km resol.).
• Data are averaged to model resolution prior to assimilation.
• Domain: eastern USA.
• 6-hourly accumulations are assimilated  smoother & more linear.
• Ln(RR6h[mm/h]+1) transform (background departures closer to Gaussian).
Screening:
• Obs rejected in regions with steep orography, surface snowfall or ducting.
• Only points that are rainy in both background and obs are assimilated.
• Fixed observation error: o=0.2 (in log-space).
• Variational bias correction implemented.
ECMWF 2011
Direct 4D-Var assimilation of NCEP Stage IV rain data
Short-range precipitation forecast is significantly improved..
Equitable Threat Score
Equitable Threat Score
False Alarm Rate
False Alarm Rate
ECMWF 2011
12h-accumulated precipitation FC 00Z+12h (T511 L91)
Sept-Oct 2009
April-May 2009
Impact of NCEP Stage IV assimilation
on 12h forecasts of precipitation.
Sept-Oct 2009 average
(CY35R2; T511 L91)
NCEP Stage IV obs (mm/day)
CTRL – NCEP Stage IV
 Mean bias and RMS error are reduced
NEW – NCEP Stage IV
ECMWF 2011
ECMWF 2011
Direct 4D-Var assimilation of NCEP Stage IV rain data
Impact on forecast scores for other parameters (Z, T, wind, RH):
- neutral or slightly positive impact on the global scale.
- some hint of positive impact over Europe (days 4-5) and Asia (days 8-10).
RMSE North. Hemis. 500hPa wind
RMSE Europe 500hPa temperature
good Forecast Root Mean Square Error changes due to
direct 4D-Var assimilation of NCEP Stage IV rain data
1 April – 6 June 2010,
T1279 (~15 km global) L91
RMSE South. Hemis. 500hPa wind
RMSE Asia 850hPa Temperature
EDA
• The EDA system is designed to provide estimates of
analysis and background uncertainty
• This has intrinsic value as an estimate of the quality of
the deterministic analysis (i.e., Re-analysis applications,
synoptic evaluation,…)
• It improves the representation of initial uncertainties in
the Ensemble Prediction System
• It is used to estimate state-dependent background error
variances in the deterministic 4D-Var